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diff --git a/assets/jupyter/narrow_heuristic.ipynb.html b/assets/jupyter/narrow_heuristic.ipynb.html
index 68e29ea8bb8d..1dd9cc44eea3 100644
--- a/assets/jupyter/narrow_heuristic.ipynb.html
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diff --git a/assets/pdf/cv_xuelong.pdf b/assets/pdf/cv_xuelong.pdf
index f7b306e824b7..b662bac9463a 100644
Binary files a/assets/pdf/cv_xuelong.pdf and b/assets/pdf/cv_xuelong.pdf differ
diff --git a/assets/pdf/sassy-clevr-poster-beta.pdf b/assets/pdf/sassy-clevr-poster-beta.pdf
new file mode 100644
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+<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta http-equiv="refresh" content="3; url=/"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>The (Small) Extended Handbook for Undergraduate Math | Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="you can also redirect to assets like pdf"> <meta name="keywords" content="jekyll, jekyll-theme, academic-website, portfolio-website"> <link href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/css/mdb.min.css" integrity="sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw=" crossorigin="anonymous"> <link 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<p class="post-meta">February 1, 2022</p> <p class="post-tags"> <a href="/blog/2022"> <i class="fas fa-calendar fa-sm"></i> 2022 </a> </p> </header> <article class="post-content"> <div id="markdown-content"> <p>Redirecting to another page.</p> </div> </article> <br> <hr> <br> <ul class="list-disc pl-8"></ul> <h2 class="text-3xl font-semibold mb-4 mt-12">Enjoy Reading This Article?</h2> <p class="mb-2">Here are some more articles you might like to read next:</p> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2020/miRNA/">miRNA: General Blood Biomarkers</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2020/stc-aog/">Spatial, temporal, causal and-or graph, the next step for Cognitive Science?</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2021/yuval-orig-math/">Review on Sapiens and Homo deus by Yuval Harari</a> </li> 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diff --git a/blog/2023/clevr-hans-orig-videos/index.html b/blog/2023/clevr-hans-orig-videos/index.html
index d645c6a2187b..97f02ef09f52 100644
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+++ b/blog/2023/clevr-hans-orig-videos/index.html
@@ -1 +1 @@
-<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>Are you Clever Hans or are you actually clever, Hans? | Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="my (mostly satirical) comments on ChatGPT and Gato, large, transformer-based models that shook the AI community on 2022"> <meta name="keywords" content="jekyll, jekyll-theme, academic-website, portfolio-website"> <link href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/css/mdb.min.css" integrity="sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw=" crossorigin="anonymous"> <link defer rel="stylesheet" href="https://unpkg.com/bootstrap-table@1.21.4/dist/bootstrap-table.min.css"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.4.0/css/all.min.css" integrity="sha256-HtsXJanqjKTc8vVQjO4YMhiqFoXkfBsjBWcX91T1jr8=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/academicons@1.9.1/css/academicons.min.css" integrity="sha256-i1+4qU2G2860dGGIOJscdC30s9beBXjFfzjWLjBRsBg=" crossorigin="anonymous"> <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700|Roboto+Slab:100,300,400,500,700|Material+Icons"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jwarby/jekyll-pygments-themes@master/github.css" media="" id="highlight_theme_light"> <link rel="shortcut icon" href="/assets/img/brain-fractal-3.jpg"> <link rel="stylesheet" href="/assets/css/main.css?d41d8cd98f00b204e9800998ecf8427e"> <link rel="canonical" href="https://awxlong.github.io/blog/2023/clevr-hans-orig-videos/"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jwarby/jekyll-pygments-themes@master/native.css" media="none" id="highlight_theme_dark"> <script src="/assets/js/theme.js?96d6b3e1c3604aca8b6134c7afdd5db6"></script> <script src="/assets/js/dark_mode.js?9b17307bb950ffa2e34be0227f53558f"></script> </head> <body class="fixed-top-nav "> <header> <nav id="navbar" class="navbar navbar-light navbar-expand-sm fixed-top"> <div class="container"> <a class="navbar-brand title font-weight-lighter" href="/"><span class="font-weight-bold">Xuelong </span>An Wang</a> <button class="navbar-toggler collapsed ml-auto" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar top-bar"></span> <span class="icon-bar middle-bar"></span> <span class="icon-bar bottom-bar"></span> </button> <div class="collapse navbar-collapse text-right" id="navbarNav"> <ul class="navbar-nav ml-auto flex-nowrap"> <li class="nav-item "> <a class="nav-link" href="/">about</a> </li> <li class="nav-item active"> <a class="nav-link" href="/blog/">blog<span class="sr-only">(current)</span></a> </li> <li class="nav-item "> <a class="nav-link" href="/publications/">research</a> </li> <li class="nav-item "> <a class="nav-link" href="/projects/">projects</a> </li> <li class="nav-item "> <a class="nav-link" href="/cv/">cv</a> </li> <li class="toggle-container"> <button id="light-toggle" title="Change theme"> <i class="fas fa-moon"></i> <i class="fas fa-sun"></i> </button> </li> </ul> </div> </div> </nav> <progress id="progress" value="0"> <div class="progress-container"> <span class="progress-bar"></span> </div> </progress> </header> <div class="container mt-5"> <div class="post"> <header class="post-header"> <h1 class="post-title">Are you Clever Hans or are you actually clever, Hans?</h1> <p class="post-meta">January 12, 2023</p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a>   ·   <a href="/blog/tag/food-for-thought"> <i class="fas fa-hashtag fa-sm"></i> food-for-thought</a>     ·   <a href="/blog/category/blog-post"> <i class="fas fa-tag fa-sm"></i> blog-post</a>   </p> </header> <article class="post-content"> <div id="markdown-content"> <p><strong>Author’s update on October, 2023</strong>: I wrote this satirical article a few months after Gato and ChatGPT’s release, particularly after having some conversation with ChatGPT. Nowadays, a few of the claims, such as in <a href="#chatgpt">Figure 1</a>, I made back then are less valid. GPT-4 (although it is paid) can now retrieve information online, meaning it <em>can</em> synthesize valid links. It can also solve math problems more accurately. This is partly because for prompts about calculations or theorem proofs it can retrieve an answer from online calculators or provers like WolframAlpha, in addition to its own improvement due to scaling. I’m still skeptical scaling this architecture is the right way forward, but maybe I’m delusional and LLMs are the next step forward for AI…</p> <p>Somewhere, an influential research group from a highly respected academic sphere published a report that the model they have designed has gotten us humans closer to AGI. They named it HANS, short for Human ANd Smart, and documented that the model consists of a generalist agent who can multi-task: it can play a series of Atari games which denote its ability to interact with a digital environment, hold a quotidian conversation which reflects its dominion of Human English Language, and caption images to show its prowess in image recognition. Because the model was built using a single transformer neural network trained on diverse data, it was originally advertised as evidence that the path to AGI is clear: create a bigger model trained on a larger dataset. Media communicators, however, may have “misunderstood” the message (either intentionally or not), and instead transmitted the overhyped sentiment that this model was a precursor to AGI and suggested it would bring with itself a lot of existential crisis to the unprepared Human society. In the end, what better way to endanger humanity than predicting the next output given some input.</p> <p>Furthermore, they claimed that there were primitive signs of sentiency in the model after one of the research engineer held and released a private conversation transcript with him/her on topics about physics and emerged from it convinced that he was talking to a 7-8 years old child.</p> <p>The above hypothetical scenario is an amalgamation on real-life cases of research groups from Google who have designed models resembling some degree of Human Intelligence on very constrained areas such as multitasking or domain-specific conversation. Please see Gato, a multitasking agent designed by <d-cite key="reed_2022_a"></d-cite> and a subsequent report documenting what it is and what effect it instigated in the public <d-cite key="heikkil_2022_the"></d-cite>. I’ve also included the articles by <d-cite key="sparkes_2022_has, tiku_2022_the"></d-cite> where they have reported on how Google left one of its engineer on paid administrative leave for making claims that one of its language models, called LaMDa, was sentient as a 7-8 years old child after holding a conversation with it on <a href="https://www.technologyreview.com/2021/05/20/1025135/ai-large-language-models-bigscience-project/" rel="external nofollow noopener" target="_blank">physics</a>. I’ve modified some names, such as changing Gato’s name to HANS to fulfil the (very) important goal of making a pun in the subtitle.</p> <p>Gato, ChatGPT and other enormous, pretrained transformer-based neural networks are mostly arcane models which are inoperable to the common citizen not endowed with high-end computing hardware and software. Their release ignited a catharsis of emotions amongst the public. From opposite sides of the spectrum, there were those who aggrandized their anxiety over existential threats, and there were those sceptics who pointed out that such claims were, essentially, non-sensical. A model that is trained on conveniently, readily available, vast-amount of high-quality compiled dataset reflect not its generalizing capability but rather its overreliance on data, lack of parsimony and the ever-present issue of explainability with these black-box models. Another interesting set of issues is the inconsistent inability to do math, as displayed by ChatGPT:</p> <figure> <img src="/assets/img/chatGPT-math.png" alt="Figure couldn't load due to an unknown error. Sorry."> <figcaption id="chatgpt"> Figure 1: GPT-3-powered model like ChatGPT can't do math properly, probably because it doesn't understand the notion of algebra, geometry, calculus, among other branches. ChatGPT also fail to provide correct or real links to websites, as well as can't carry out instructions like "don't say anything". </figcaption> </figure> <p>In general, GPT-3 powered large language models may struggle to synthesize deterministic output: those of universal nature, i.e., doesn’t change regardless of context. Examples include math equations (as shown above), or links to websites.</p> <p>Hence, there exist at least some scepticism who wonder whether the model HANS is actually clever, or whether it’s simply reminiscent of clever Hans.</p> </div> </article> <br> <hr> <br> <ul class="list-disc pl-8"></ul> <h2 class="text-3xl font-semibold mb-4 mt-12">Enjoy Reading This Article?</h2> <p class="mb-2">Here are some more articles you might like to read next:</p> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2020/miRNA/">miRNA: General Blood Biomarkers</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2023/brain-fractal/">Brain-Heart Fractal, DALL-E and I</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2020/oneself/">One is more than one thinks of oneself, just less than one exaggerates</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2021/kaifu-toc/">Saving the Future: Comment on AI·Future (AI·未来) and Artificial Intelligence (人工智能) </a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2023/nesy-summer-school/">Reflections on the Neurosymbolic Summer School 2023</a> </li> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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+<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>Are you Clever Hans or are you actually clever, Hans? | Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="my (mostly satirical) comments on ChatGPT and Gato, large, transformer-based models that shook the AI community on 2022"> <meta name="keywords" content="jekyll, jekyll-theme, academic-website, portfolio-website"> <link href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/css/mdb.min.css" integrity="sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw=" crossorigin="anonymous"> <link defer rel="stylesheet" href="https://unpkg.com/bootstrap-table@1.21.4/dist/bootstrap-table.min.css"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.4.0/css/all.min.css" integrity="sha256-HtsXJanqjKTc8vVQjO4YMhiqFoXkfBsjBWcX91T1jr8=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/academicons@1.9.1/css/academicons.min.css" integrity="sha256-i1+4qU2G2860dGGIOJscdC30s9beBXjFfzjWLjBRsBg=" crossorigin="anonymous"> <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700|Roboto+Slab:100,300,400,500,700|Material+Icons"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jwarby/jekyll-pygments-themes@master/github.css" media="" id="highlight_theme_light"> <link rel="shortcut icon" href="/assets/img/brain-fractal-3.jpg"> <link rel="stylesheet" href="/assets/css/main.css?d41d8cd98f00b204e9800998ecf8427e"> <link rel="canonical" href="https://awxlong.github.io/blog/2023/clevr-hans-orig-videos/"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jwarby/jekyll-pygments-themes@master/native.css" media="none" id="highlight_theme_dark"> <script src="/assets/js/theme.js?96d6b3e1c3604aca8b6134c7afdd5db6"></script> <script src="/assets/js/dark_mode.js?9b17307bb950ffa2e34be0227f53558f"></script> </head> <body class="fixed-top-nav "> <header> <nav id="navbar" class="navbar navbar-light navbar-expand-sm fixed-top"> <div class="container"> <a class="navbar-brand title font-weight-lighter" href="/"><span class="font-weight-bold">Xuelong </span>An Wang</a> <button class="navbar-toggler collapsed ml-auto" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar top-bar"></span> <span class="icon-bar middle-bar"></span> <span class="icon-bar bottom-bar"></span> </button> <div class="collapse navbar-collapse text-right" id="navbarNav"> <ul class="navbar-nav ml-auto flex-nowrap"> <li class="nav-item "> <a class="nav-link" href="/">about</a> </li> <li class="nav-item active"> <a class="nav-link" href="/blog/">blog<span class="sr-only">(current)</span></a> </li> <li class="nav-item "> <a class="nav-link" href="/publications/">research</a> </li> <li class="nav-item "> <a class="nav-link" href="/projects/">projects</a> </li> <li class="nav-item "> <a class="nav-link" href="/cv/">cv</a> </li> <li class="toggle-container"> <button id="light-toggle" title="Change theme"> <i class="fas fa-moon"></i> <i class="fas fa-sun"></i> </button> </li> </ul> </div> </div> </nav> <progress id="progress" value="0"> <div class="progress-container"> <span class="progress-bar"></span> </div> </progress> </header> <div class="container mt-5"> <div class="post"> <header class="post-header"> <h1 class="post-title">Are you Clever Hans or are you actually clever, Hans?</h1> <p class="post-meta">January 12, 2023</p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a>   ·   <a href="/blog/tag/food-for-thought"> <i class="fas fa-hashtag fa-sm"></i> food-for-thought</a>     ·   <a href="/blog/category/blog-post"> <i class="fas fa-tag fa-sm"></i> blog-post</a>   </p> </header> <article class="post-content"> <div id="markdown-content"> <p><strong>Author’s update on October, 2023</strong>: I wrote this satirical article a few months after Gato and ChatGPT’s release, particularly after having some conversation with ChatGPT. Nowadays, a few of the claims, such as in <a href="#chatgpt">Figure 1</a>, I made back then are less valid. GPT-4 (although it is paid) can now retrieve information online, meaning it <em>can</em> synthesize valid links. It can also solve math problems more accurately. This is partly because for prompts about calculations or theorem proofs it can retrieve an answer from online calculators or provers like WolframAlpha, in addition to its own improvement due to scaling. I’m still skeptical scaling this architecture is the right way forward, but maybe I’m delusional and LLMs are the next step forward for AI…</p> <p>Somewhere, an influential research group from a highly respected academic sphere published a report that the model they have designed has gotten us humans closer to AGI. They named it HANS, short for Human ANd Smart, and documented that the model consists of a generalist agent who can multi-task: it can play a series of Atari games which denote its ability to interact with a digital environment, hold a quotidian conversation which reflects its dominion of Human English Language, and caption images to show its prowess in image recognition. Because the model was built using a single transformer neural network trained on diverse data, it was originally advertised as evidence that the path to AGI is clear: create a bigger model trained on a larger dataset. Media communicators, however, may have “misunderstood” the message (either intentionally or not), and instead transmitted the overhyped sentiment that this model was a precursor to AGI and suggested it would bring with itself a lot of existential crisis to the unprepared Human society. In the end, what better way to endanger humanity than predicting the next output given some input.</p> <p>Furthermore, they claimed that there were primitive signs of sentiency in the model after one of the research engineer held and released a private conversation transcript with him/her on topics about physics and emerged from it convinced that he was talking to a 7-8 years old child.</p> <p>The above hypothetical scenario is an amalgamation on real-life cases of research groups from Google who have designed models resembling some degree of Human Intelligence on very constrained areas such as multitasking or domain-specific conversation. Please see Gato, a multitasking agent designed by <d-cite key="reed_2022_a"></d-cite> and a subsequent report documenting what it is and what effect it instigated in the public <d-cite key="heikkil_2022_the"></d-cite>. I’ve also included the articles by <d-cite key="sparkes_2022_has, tiku_2022_the"></d-cite> where they have reported on how Google left one of its engineer on paid administrative leave for making claims that one of its language models, called LaMDa, was sentient as a 7-8 years old child after holding a conversation with it on <a href="https://www.technologyreview.com/2021/05/20/1025135/ai-large-language-models-bigscience-project/" rel="external nofollow noopener" target="_blank">physics</a>. I’ve modified some names, such as changing Gato’s name to HANS to fulfil the (very) important goal of making a pun in the subtitle.</p> <p>Gato, ChatGPT and other enormous, pretrained transformer-based neural networks are mostly arcane models which are inoperable to the common citizen not endowed with high-end computing hardware and software. Their release ignited a catharsis of emotions amongst the public. From opposite sides of the spectrum, there were those who aggrandized their anxiety over existential threats, and there were those sceptics who pointed out that such claims were, essentially, non-sensical. A model that is trained on conveniently, readily available, vast-amount of high-quality compiled dataset reflect not its generalizing capability but rather its overreliance on data, lack of parsimony and the ever-present issue of explainability with these black-box models. Another interesting set of issues is the inconsistent inability to do math, as displayed by ChatGPT:</p> <figure> <img src="/assets/img/chatGPT-math.png" alt="Figure couldn't load due to an unknown error. Sorry."> <figcaption id="chatgpt"> Figure 1: GPT-3-powered model like ChatGPT can't do math properly, probably because it doesn't understand the notion of algebra, geometry, calculus, among other branches. ChatGPT also fail to provide correct or real links to websites, as well as can't carry out instructions like "don't say anything". </figcaption> </figure> <p>In general, GPT-3 powered large language models may struggle to synthesize deterministic output: those of universal nature, i.e., doesn’t change regardless of context. Examples include math equations (as shown above), or links to websites.</p> <p>Hence, there exist at least some scepticism who wonder whether the model HANS is actually clever, or whether it’s simply reminiscent of clever Hans.</p> </div> </article> <br> <hr> <br> <ul class="list-disc pl-8"></ul> <h2 class="text-3xl font-semibold mb-4 mt-12">Enjoy Reading This Article?</h2> <p class="mb-2">Here are some more articles you might like to read next:</p> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2020/miRNA/">miRNA: General Blood Biomarkers</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2023/brain-fractal/">Brain-Heart Fractal, DALL-E and I</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2020/oneself/">One is more than one thinks of oneself, just less than one exaggerates</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2023/nesy-summer-school/">Reflections on the Neurosymbolic Summer School 2023</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2021/kaifu-toc/">Saving the Future: Comment on AI·Future (AI·未来) and Artificial Intelligence (人工智能) </a> </li> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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-<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd"> <html><body> <p>Math is really a beautiful system (framework?) from which we can derive ideas of beauty through symmetry and geometry, study of complex systems through calculus, and also of creativity.</p> <ul> <li>How would I define creativity? o Imagine you’re an enthusiastic pursuer of truth. In front of you are a some numbers and two operators: + (ordinary addition) and * (ordinary multiplication). Science, or the discovery of knowledge, is analogous to discovering new numbers. You randomly add two numbers, 1 and -1 and discover 0. Then you add 1 to 1 and obtain 2. Repeating this process you discover all the integers. From God’s point of view, addition and multiplication over integers is closed in this set, therefore you can’t discover anything beyond the horizon of integers, even though you can discover a lot. You can find patterns, such as properties of commutativity, transitivity and distributivity. o Creativity, in this scenario, would be proposing another operator such as division. Having this new operator in your arsenal of tools to perceive reality, you can now compute a new class of numbers, the rationals and thus discover the set of all real numbers. o The infiniteness of numbers is analogous to the infinity of knowledge.  Further creativity would be introducing the square root, and this tool extends our perceptual horizon to encompass the realm of complex/imaginary numbers.  A different civilization, using a different set of symbols to encode numbers, may make similar discoveries, i.e., numbers obtained following the same principles of computation using + and *, discovering commutativity, transitivity and distributivity. This underlies/represents the universality of knowledge. o I’ve ignored letters, phonemes, words to simplify this overly-idealized world to get my ideas on what is creativity across.</li> </ul> </body></html>
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+<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>A mathematical analogy for understanding creativity | Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="Using numbers to illustrate an intuitive definition of what creativity is"> <meta name="keywords" content="jekyll, jekyll-theme, academic-website, portfolio-website"> <link href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/css/mdb.min.css" integrity="sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw=" crossorigin="anonymous"> <link defer rel="stylesheet" href="https://unpkg.com/bootstrap-table@1.21.4/dist/bootstrap-table.min.css"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.4.0/css/all.min.css" integrity="sha256-HtsXJanqjKTc8vVQjO4YMhiqFoXkfBsjBWcX91T1jr8=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/academicons@1.9.1/css/academicons.min.css" integrity="sha256-i1+4qU2G2860dGGIOJscdC30s9beBXjFfzjWLjBRsBg=" crossorigin="anonymous"> <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700|Roboto+Slab:100,300,400,500,700|Material+Icons"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jwarby/jekyll-pygments-themes@master/github.css" media="" id="highlight_theme_light"> <link href="https://cdn.rawgit.com/afeld/bootstrap-toc/v1.0.1/dist/bootstrap-toc.min.css" rel="stylesheet"> <link rel="shortcut icon" href="/assets/img/brain-fractal-3.jpg"> <link rel="stylesheet" href="/assets/css/main.css?d41d8cd98f00b204e9800998ecf8427e"> <link rel="canonical" href="https://awxlong.github.io/blog/2023/creativity/"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jwarby/jekyll-pygments-themes@master/native.css" media="none" id="highlight_theme_dark"> <script src="/assets/js/theme.js?96d6b3e1c3604aca8b6134c7afdd5db6"></script> <script src="/assets/js/dark_mode.js?9b17307bb950ffa2e34be0227f53558f"></script> </head> <body class="fixed-top-nav "> <header> <nav id="navbar" class="navbar navbar-light navbar-expand-sm fixed-top"> <div class="container"> <a class="navbar-brand title font-weight-lighter" href="/"><span class="font-weight-bold">Xuelong </span>An Wang</a> <button class="navbar-toggler collapsed ml-auto" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar top-bar"></span> <span class="icon-bar middle-bar"></span> <span class="icon-bar bottom-bar"></span> </button> <div class="collapse navbar-collapse text-right" id="navbarNav"> <ul class="navbar-nav ml-auto flex-nowrap"> <li class="nav-item "> <a class="nav-link" href="/">about</a> </li> <li class="nav-item active"> <a class="nav-link" href="/blog/">blog<span class="sr-only">(current)</span></a> </li> <li class="nav-item "> <a class="nav-link" href="/publications/">research</a> </li> <li class="nav-item "> <a class="nav-link" href="/projects/">projects</a> </li> <li class="nav-item "> <a class="nav-link" href="/cv/">cv</a> </li> <li class="toggle-container"> <button id="light-toggle" title="Change theme"> <i class="fas fa-moon"></i> <i class="fas fa-sun"></i> </button> </li> </ul> </div> </div> </nav> <progress id="progress" value="0"> <div class="progress-container"> <span class="progress-bar"></span> </div> </progress> </header> <div class="container mt-5"> <div class="row"> <div class="col-sm-3"> <nav id="toc-sidebar" class="sticky-top"></nav> </div> <div class="col-sm-9"> <div class="post"> <header class="post-header"> <h1 class="post-title">A mathematical analogy for understanding creativity</h1> <p class="post-meta">November 2, 2023</p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a>   ·   <a href="/blog/tag/food-for-thought"> <i class="fas fa-hashtag fa-sm"></i> food-for-thought</a>     ·   <a href="/blog/category/blog-post"> <i class="fas fa-tag fa-sm"></i> blog-post</a>   </p> </header> <article class="post-content"> <div id="markdown-content"> <p>Math is really a beautiful framework with which we can analyze several ideas. For instance, beauty can be seen through the lenses of math using symmetry and geometry, and the study of complex systems can be cast through calculus and probability theory.</p> <p>Given this premise, can we use math to understand what creativity is. Here is my attempt:</p> <h1 id="how-can-creativity-be-defined">How can creativity be defined?</h1> <p>Let us run the following thought experiment. Imagine you’re an enthusiastic pursuer of truth. The whole universe is simply an empty space. In this void, in front of you are a some numbers and two operators: + (ordinary addition) and * (ordinary multiplication)<sup id="fnref:1" role="doc-noteref"><a href="#fn:1" class="footnote" rel="footnote">1</a></sup>.</p> <p>Science, or the discovery of knowledge, is analogous to discovering new numbers. You randomly add two numbers, 1 and -1 and discover 0. Then you add 1 to 1 and obtain 2. Repeating this process, you end up discovering all the integers. Furthermore, you can find patterns that lead to theorems among the integers, such as properties of commutativity, transitivity and distributivity.</p> <p>However, from a metaphysical point of view, addition and multiplication over integers is closed in this set, therefore you can’t discover anything beyond the horizon of integers.</p> <h1 id="can-you-discover-anything-else">Can you discover anything else?</h1> <p>Creativity, in this scenario, would be proposing another operator such as division. Having this new operator in your mind’s eye to perceive reality, you can now compute a new class of numbers, the rationals and thus discover the set of all real numbers.</p> <p>In either case, prior and after the discovery of the division operator, the infiniteness of numbers is analogous to the infinity of knowledge.</p> <p>Creativity can also materialize by introducing the square root, and this new operator extends our perceptual horizon to encompass the realm of complex/imaginary numbers.</p> <h1 id="more-analogies">More analogies</h1> <p>A different civilization from another point in space, using a different set of symbols to encode numbers, may make similar discoveries, i.e., numbers obtained following the same principles of computation using + and *. Moreover, they may also discover commutativity, transitivity and distributivity. This analogy represents the universality of knowledge.</p> <p>In the above thought experiment, I’ve ignored letters, phonemes, and words to simplify this overly-idealized world to get my ideas on what is creativity across.</p> <figure> <img src="/assets/img/creativity.jpg" alt="Sorry, an unanticipated error occured and the image can't load." width="100%" height="auto"> <figcaption id="creativity"> The thumbnail is generate by Dall-E powered MS Bing Image Generator given the prompt "your interpretation of what creativity is". </figcaption> </figure> <p>PS: Demis Hassabis, Deepmind CEO, also comments on another definition of <a href="https://www.youtube.com/watch?v=Gfr50f6ZBvo" rel="external nofollow noopener" target="_blank">creativity, or AGI</a>. This is going beyond statistical interpolation and extrapolation of training data. An AGI model that is creative should be able to give an output to an instruction such as “design a game that has simple rules, but is extremely hard to master, and can be played for hours”.</p> <div class="footnotes" role="doc-endnotes"> <ol> <li id="fn:1" role="doc-endnote"> <p>Perhaps these numbers and operators existed there a priori, maybe they emerged from a Creator, or perhaps they emerged from a singularity. <a href="#fnref:1" class="reversefootnote" role="doc-backlink">↩</a></p> </li> </ol> </div> </div> </article> <br> <hr> <br> <ul class="list-disc pl-8"></ul> <h2 class="text-3xl font-semibold mb-4 mt-12">Enjoy Reading This Article?</h2> <p class="mb-2">Here are some more articles you might like to read next:</p> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2020/miRNA/">miRNA: General Blood Biomarkers</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2023/nesy-summer-school/">Reflections on the Neurosymbolic Summer School 2023</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2023/tale-two-worlds/">A tale of two worlds: the natural and the mathematical</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2020/math-uncertainty/">Math, certainty and uncertainty</a> </li> <li class="my-2"> <a class="text-pink-700 underline font-semibold hover:text-pink-800" href="/blog/2021/yuval-orig-math/">Review on Sapiens and Homo deus by Yuval Harari</a> </li> <div id="disqus_thread" style="max-width: 800px; margin: 0 auto;"> <script type="text/javascript">var disqus_shortname="al-folio",disqus_identifier="/blog/2023/creativity",disqus_title="A mathematical analogy for understanding creativity";!function(){var e=document.createElement("script");e.type="text/javascript",e.async=!0,e.src="//"+disqus_shortname+".disqus.com/embed.js",(document.getElementsByTagName("head")[0]||document.getElementsByTagName("body")[0]).appendChild(e)}();</script> <noscript>Please enable JavaScript to view the <a href="http://disqus.com/?ref_noscript" rel="external nofollow noopener" target="_blank">comments powered by Disqus.</a> </noscript> </div> </div> </div> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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Esa es la cuestión</a> </td> </tr> <tr> <th scope="row">Aug 24, 2023</th> <td> <a class="post-link" href="/blog/2023/chat-gpt/">Una conversación con ChatGPT</a> </td> </tr> <tr> <th scope="row">Jul 12, 2023</th> <td> <a class="post-link" href="/blog/2023/penta-paradigm-orig-bib-copy/">Paradigms of AI modelling</a> </td> </tr> <tr> <th scope="row">May 12, 2023</th> <td> <a class="post-link" href="/blog/2023/dichotomy-orig-customblock/">The dichotomy of Vision and Language</a> </td> </tr> <tr> <th scope="row">Apr 25, 2023</th> <td> <a class="post-link" href="/blog/2023/agi-orig-audios/">Thoughts on Artificial General Intelligence</a> </td> </tr> <tr> <th scope="row">Jan 20, 2023</th> <td> <a class="post-link" href="/blog/2023/deutsch-orig-videos/">Thoughts on David Deutsch’s The Beginning of Infinity</a> </td> </tr> <tr> <th scope="row">Jan 12, 2023</th> <td> <a class="post-link" href="/blog/2023/clevr-hans-orig-videos/">Are you Clever Hans or are you actually clever, Hans?</a> </td> </tr> </table> </div> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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Esa es la cuestión</a> </td> </tr> <tr> <th scope="row">Aug 24, 2023</th> <td> <a class="post-link" href="/blog/2023/chat-gpt/">Una conversación con ChatGPT</a> </td> </tr> <tr> <th scope="row">Jul 12, 2023</th> <td> <a class="post-link" href="/blog/2023/penta-paradigm-orig-bib-copy/">Paradigms of AI modelling</a> </td> </tr> <tr> <th scope="row">May 12, 2023</th> <td> <a class="post-link" href="/blog/2023/dichotomy-orig-customblock/">The dichotomy of Vision and Language</a> </td> </tr> <tr> <th scope="row">Apr 25, 2023</th> <td> <a class="post-link" href="/blog/2023/agi-orig-audios/">Thoughts on Artificial General Intelligence</a> </td> </tr> <tr> <th scope="row">Jan 20, 2023</th> <td> <a class="post-link" href="/blog/2023/deutsch-orig-videos/">Thoughts on David Deutsch’s The Beginning of Infinity</a> </td> </tr> <tr> <th scope="row">Jan 12, 2023</th> <td> <a class="post-link" href="/blog/2023/clevr-hans-orig-videos/">Are you Clever Hans or are you actually clever, Hans?</a> </td> </tr> </table> </div> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. Powered by <a href="https://jekyllrb.com/" target="_blank" rel="external nofollow noopener">Jekyll</a> with <a href="https://github.com/alshedivat/al-folio" rel="external nofollow noopener" target="_blank">al-folio</a> theme. Hosted by <a href="https://pages.github.com/" target="_blank" rel="external nofollow noopener">GitHub Pages</a>. 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Esa es la cuestión</a> </td> </tr> <tr> <th scope="row">Aug 24, 2023</th> <td> <a class="post-link" href="/blog/2023/chat-gpt/">Una conversación con ChatGPT</a> </td> </tr> <tr> <th scope="row">Jul 12, 2023</th> <td> <a class="post-link" href="/blog/2023/penta-paradigm-orig-bib-copy/">Paradigms of AI modelling</a> </td> </tr> <tr> <th scope="row">May 12, 2023</th> <td> <a class="post-link" href="/blog/2023/dichotomy-orig-customblock/">The dichotomy of Vision and Language</a> </td> </tr> <tr> <th scope="row">Apr 25, 2023</th> <td> <a class="post-link" href="/blog/2023/agi-orig-audios/">Thoughts on Artificial General Intelligence</a> </td> </tr> <tr> <th scope="row">Jan 20, 2023</th> <td> <a class="post-link" href="/blog/2023/deutsch-orig-videos/">Thoughts on David Deutsch’s The Beginning of Infinity</a> </td> </tr> <tr> <th scope="row">Jan 12, 2023</th> <td> <a class="post-link" href="/blog/2023/clevr-hans-orig-videos/">Are you Clever Hans or are you actually clever, Hans?</a> </td> </tr> <tr> <th scope="row">Dec 15, 2022</th> <td> <a class="post-link" href="/blog/2022/analogy-freq-bay-gp/">The intuition behind Frequentist linear regression, Bayesian linear regression and Gaussian Processes</a> </td> </tr> <tr> <th scope="row">Feb 6, 2022</th> <td> <a class="post-link" href="/blog/2022/narrow-ai-orig-jupyter/">Deciding when to use narrow AI-based or heuristic-driven solutions for a problem</a> </td> </tr> <tr> <th scope="row">Nov 25, 2021</th> <td> <a class="post-link" href="/blog/2021/revision-orig-sidetoc/">Three tips to consider when revising for a challenging test</a> </td> </tr> <tr> <th scope="row">Oct 8, 2021</th> <td> <a class="post-link" href="/blog/2021/yuval-orig-math/">Review on Sapiens and Homo deus by Yuval Harari</a> </td> </tr> <tr> <th scope="row">Sep 24, 2021</th> <td> <a class="post-link" href="/blog/2021/topol-orig-tab/">The Future of Medicine: Big Data and Democratization</a> </td> </tr> <tr> <th scope="row">Aug 6, 2021</th> <td> <a class="post-link" href="/blog/2021/kaifu-toc/">Saving the Future: Comment on AI·Future (AI·未来) and Artificial Intelligence (人工智能) </a> </td> </tr> <tr> <th scope="row">Jul 13, 2021</th> <td> <a class="post-link" href="/blog/2021/futuristic-orig-code/">Where are we heading ultimately?</a> </td> </tr> <tr> <th scope="row">Jun 24, 2021</th> <td> <a class="post-link" href="/blog/2021/fatehf-orig-diag/">How Fate/Stay Night Tells a Story on the Pursuit of Happiness</a> </td> </tr> <tr> <th scope="row">Sep 28, 2020</th> <td> <a class="post-link" href="/blog/2020/unwind-story-orig-twitter/">Unwinding your own story</a> </td> </tr> <tr> <th scope="row">Sep 2, 2020</th> <td> <a class="post-link" href="/blog/2020/miRNA/">miRNA: General Blood Biomarkers</a> </td> </tr> <tr> <th scope="row">Aug 18, 2020</th> <td> <a class="post-link" href="/blog/2020/stc-aog/">Spatial, temporal, causal and-or graph, the next step for Cognitive Science?</a> </td> </tr> <tr> <th scope="row">Aug 18, 2020</th> <td> <a class="post-link" href="/blog/2020/math-uncertainty/">Math, certainty and uncertainty</a> </td> </tr> <tr> <th scope="row">Jul 6, 2020</th> <td> <a class="post-link" href="/blog/2020/principia/">Principia de Humana Naturali</a> </td> </tr> <tr> <th scope="row">Jun 7, 2020</th> <td> <a class="post-link" href="/blog/2020/stargaze-future/">Stargazing the Future</a> </td> </tr> <tr> <th scope="row">May 26, 2020</th> <td> <a class="post-link" href="/blog/2020/life-story/">Make your life a story worth telling to everyone</a> </td> </tr> <tr> <th scope="row">May 24, 2020</th> <td> <a class="post-link" href="/blog/2020/oneself/">One is more than one thinks of oneself, just less than one exaggerates</a> </td> </tr> <tr> <th scope="row">May 20, 2020</th> <td> <a class="post-link" href="/blog/2020/happiness-suffering/">The flavor "suffering" also to satiate the hunger for "happiness"</a> </td> </tr> <tr> <th scope="row">May 18, 2020</th> <td> <a class="post-link" href="/blog/2020/desire-torrent-orig-metadata/">Torrent of Desire - Torrente de inspiración</a> </td> </tr> <tr> <th scope="row">Oct 24, 2019</th> <td> <a class="post-link" href="/blog/2019/essay-crafting/">Essay Crafting</a> </td> </tr> <tr> <th scope="row">Jun 7, 2019</th> <td> <a class="post-link" href="/blog/2019/galapagos/">Apología hacia las Islas Encantadas</a> </td> </tr> <tr> <th scope="row">Feb 15, 2019</th> <td> <a class="post-link" href="/blog/2019/gurren-lagann/">Gurren-Lagann Moldea tu propio futuro</a> </td> </tr> <tr> <th scope="row">Jan 15, 2019</th> <td> <a class="post-link" href="/blog/2019/art-of-debate/">On the art of debates and, most of all, understanding</a> </td> </tr> </table> </div> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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Esa es la cuestión</a> </td> </tr> <tr> <th scope="row">Aug 24, 2023</th> <td> <a class="post-link" href="/blog/2023/chat-gpt/">Una conversación con ChatGPT</a> </td> </tr> <tr> <th scope="row">Jul 12, 2023</th> <td> <a class="post-link" href="/blog/2023/penta-paradigm-orig-bib-copy/">Paradigms of AI modelling</a> </td> </tr> <tr> <th scope="row">May 12, 2023</th> <td> <a class="post-link" href="/blog/2023/dichotomy-orig-customblock/">The dichotomy of Vision and Language</a> </td> </tr> <tr> <th scope="row">Apr 25, 2023</th> <td> <a class="post-link" href="/blog/2023/agi-orig-audios/">Thoughts on Artificial General Intelligence</a> </td> </tr> <tr> <th scope="row">Jan 20, 2023</th> <td> <a class="post-link" href="/blog/2023/deutsch-orig-videos/">Thoughts on David Deutsch’s The Beginning of Infinity</a> </td> </tr> <tr> <th scope="row">Jan 12, 2023</th> <td> <a class="post-link" href="/blog/2023/clevr-hans-orig-videos/">Are you Clever Hans or are you actually clever, Hans?</a> </td> </tr> <tr> <th scope="row">Dec 15, 2022</th> <td> <a class="post-link" href="/blog/2022/analogy-freq-bay-gp/">The intuition behind Frequentist linear regression, Bayesian linear regression and Gaussian Processes</a> </td> </tr> <tr> <th scope="row">Feb 6, 2022</th> <td> <a class="post-link" href="/blog/2022/narrow-ai-orig-jupyter/">Deciding when to use narrow AI-based or heuristic-driven solutions for a problem</a> </td> </tr> <tr> <th scope="row">Nov 25, 2021</th> <td> <a class="post-link" href="/blog/2021/revision-orig-sidetoc/">Three tips to consider when revising for a challenging test</a> </td> </tr> <tr> <th scope="row">Oct 8, 2021</th> <td> <a class="post-link" href="/blog/2021/yuval-orig-math/">Review on Sapiens and Homo deus by Yuval Harari</a> </td> </tr> <tr> <th scope="row">Sep 24, 2021</th> <td> <a class="post-link" href="/blog/2021/topol-orig-tab/">The Future of Medicine: Big Data and Democratization</a> </td> </tr> <tr> <th scope="row">Aug 6, 2021</th> <td> <a class="post-link" href="/blog/2021/kaifu-toc/">Saving the Future: Comment on AI·Future (AI·未来) and Artificial Intelligence (人工智能) </a> </td> </tr> <tr> <th scope="row">Jul 13, 2021</th> <td> <a class="post-link" href="/blog/2021/futuristic-orig-code/">Where are we heading ultimately?</a> </td> </tr> <tr> <th scope="row">Jun 24, 2021</th> <td> <a class="post-link" href="/blog/2021/fatehf-orig-diag/">How Fate/Stay Night Tells a Story on the Pursuit of Happiness</a> </td> </tr> <tr> <th scope="row">Sep 28, 2020</th> <td> <a class="post-link" href="/blog/2020/unwind-story-orig-twitter/">Unwinding your own story</a> </td> </tr> <tr> <th scope="row">Sep 2, 2020</th> <td> <a class="post-link" href="/blog/2020/miRNA/">miRNA: General Blood Biomarkers</a> </td> </tr> <tr> <th scope="row">Aug 18, 2020</th> <td> <a class="post-link" href="/blog/2020/stc-aog/">Spatial, temporal, causal and-or graph, the next step for Cognitive Science?</a> </td> </tr> <tr> <th scope="row">Aug 18, 2020</th> <td> <a class="post-link" href="/blog/2020/math-uncertainty/">Math, certainty and uncertainty</a> </td> </tr> <tr> <th scope="row">Jul 6, 2020</th> <td> <a class="post-link" href="/blog/2020/principia/">Principia de Humana Naturali</a> </td> </tr> <tr> <th scope="row">Jun 7, 2020</th> <td> <a class="post-link" href="/blog/2020/stargaze-future/">Stargazing the Future</a> </td> </tr> <tr> <th scope="row">May 26, 2020</th> <td> <a class="post-link" href="/blog/2020/life-story/">Make your life a story worth telling to everyone</a> </td> </tr> <tr> <th scope="row">May 24, 2020</th> <td> <a class="post-link" href="/blog/2020/oneself/">One is more than one thinks of oneself, just less than one exaggerates</a> </td> </tr> <tr> <th scope="row">May 20, 2020</th> <td> <a class="post-link" href="/blog/2020/happiness-suffering/">The flavor "suffering" also to satiate the hunger for "happiness"</a> </td> </tr> <tr> <th scope="row">May 18, 2020</th> <td> <a class="post-link" href="/blog/2020/desire-torrent-orig-metadata/">Torrent of Desire - Torrente de inspiración</a> </td> </tr> <tr> <th scope="row">Oct 24, 2019</th> <td> <a class="post-link" href="/blog/2019/essay-crafting/">Essay Crafting</a> </td> </tr> <tr> <th scope="row">Jun 7, 2019</th> <td> <a class="post-link" href="/blog/2019/galapagos/">Apología hacia las Islas Encantadas</a> </td> </tr> <tr> <th scope="row">Feb 15, 2019</th> <td> <a class="post-link" href="/blog/2019/gurren-lagann/">Gurren-Lagann Moldea tu propio futuro</a> </td> </tr> <tr> <th scope="row">Jan 15, 2019</th> <td> <a class="post-link" href="/blog/2019/art-of-debate/">On the art of debates and, most of all, understanding</a> </td> </tr> </table> </div> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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&middot; &nbsp; <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2023/ritmo-ia/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">El ritmo del progreso de Inteligencia Artificial: una experiencia personal</h3> <p class="card-text">notas y consejos útiles para quienes estén interesados en estudiar sobre modelos de aprendizaje mecánico.</p> <p class="post-meta"> 6 min read &nbsp; &middot; &nbsp; <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2021/topol-orig-tab/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">The Future of Medicine: Big Data and Democratization</h3> <p class="card-text">review on Deep Medicine and The Patient Will See You Now, two books authored by Dr. Eric Topol</p> <p class="post-meta"> 16 min read &nbsp; &middot; &nbsp; <a href="/blog/2021"> <i class="fas fa-calendar fa-sm"></i> 2021 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2020/miRNA/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">miRNA: General Blood Biomarkers</h3> <p class="card-text">Research on a blood biomarker that can be used for the prognosis of diseases like cancer and Alzheimer, and how it relates to artificial intelligence with medicine.</p> <p class="post-meta"> 69 min read &nbsp; &middot; &nbsp; <a href="/blog/2020"> <i class="fas fa-calendar fa-sm"></i> 2020 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2020/stargaze-future/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">Stargazing the Future</h3> <p class="card-text">thoughts on what the future holds for us in terms of artificial intelligence, bioengineering, socio-economics and philosophy</p> <p class="post-meta"> 13 min read &nbsp; &middot; &nbsp; <a href="/blog/2020"> <i class="fas fa-calendar fa-sm"></i> 2020 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2020/happiness-suffering/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">The flavor "suffering" also to satiate the hunger for "happiness"</h3> <p class="card-text">soliloquy on the complementarity of happiness and sadness.</p> <p class="post-meta"> 14 min read &nbsp; &middot; &nbsp; <a href="/blog/2020"> <i class="fas fa-calendar fa-sm"></i> 2020 </a> </p> </div> </div> </div> </div> </a> </div> </div> </div> <hr> <ul class="post-list"> <li><h3> <a class="post-title" href="/blog/2023/creativity/">Creativity</a> </h3> <p></p> <p class="post-meta"> 2 min read &nbsp; &middot; &nbsp; November 2, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> </p></li> <li><h3> <a class="post-title" href="/blog/2023/many-faces-ML/">The many interpretations of terms in artificial intelligence</a> </h3> <p>Some thoughts concerning how to interpret quotidian words appearing in AI research in a non-quotidian way.</p> <p class="post-meta"> 6 min read &nbsp; &middot; &nbsp; November 1, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/food-for-thought"> <i class="fas fa-hashtag fa-sm"></i> food-for-thought</a> &nbsp; &nbsp; &middot; &nbsp; <a href="/blog/category/blog-post"> <i class="fas fa-tag fa-sm"></i> blog-post</a> &nbsp; </p></li> <li><div class="row"> <div class="col-sm-9"><h3> <a class="post-title" href="/blog/2023/sgd/">Structured Gradient Descent</a> </h3> <p>Some thoughts on the concept of structured gradient descent and a piece of artwork associated to it</p> <p class="post-meta"> 5 min read &nbsp; &middot; &nbsp; October 31, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/food-for-thought"> <i class="fas fa-hashtag fa-sm"></i> food-for-thought</a> &nbsp; <a href="/blog/tag/creative-work"> <i class="fas fa-hashtag fa-sm"></i> creative-work</a> &nbsp; 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&middot; &nbsp; <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2023/ritmo-ia/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">El ritmo del progreso de Inteligencia Artificial: una experiencia personal</h3> <p class="card-text">notas y consejos útiles para quienes estén interesados en estudiar sobre modelos de aprendizaje mecánico.</p> <p class="post-meta"> 6 min read &nbsp; &middot; &nbsp; <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2021/topol-orig-tab/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">The Future of Medicine: Big Data and Democratization</h3> <p class="card-text">review on Deep Medicine and The Patient Will See You Now, two books authored by Dr. Eric Topol</p> <p class="post-meta"> 16 min read &nbsp; &middot; &nbsp; <a href="/blog/2021"> <i class="fas fa-calendar fa-sm"></i> 2021 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2020/miRNA/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">miRNA: General Blood Biomarkers</h3> <p class="card-text">Research on a blood biomarker that can be used for the prognosis of diseases like cancer and Alzheimer, and how it relates to artificial intelligence with medicine.</p> <p class="post-meta"> 69 min read &nbsp; &middot; &nbsp; <a href="/blog/2020"> <i class="fas fa-calendar fa-sm"></i> 2020 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2020/stargaze-future/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">Stargazing the Future</h3> <p class="card-text">thoughts on what the future holds for us in terms of artificial intelligence, bioengineering, socio-economics and philosophy</p> <p class="post-meta"> 13 min read &nbsp; &middot; &nbsp; <a href="/blog/2020"> <i class="fas fa-calendar fa-sm"></i> 2020 </a> </p> </div> </div> </div> </div> </a> </div> <div class="card-item col"> <a href="/blog/2020/happiness-suffering/"> <div class="card hoverable"> <div class="row g-0"> <div class="col-md-12"> <div class="card-body"> <div class="float-right"> <i class="fa-solid fa-thumbtack fa-xs"></i> </div> <h3 class="card-title text-lowercase">The flavor "suffering" also to satiate the hunger for "happiness"</h3> <p class="card-text">soliloquy on the complementarity of happiness and sadness.</p> <p class="post-meta"> 14 min read &nbsp; &middot; &nbsp; <a href="/blog/2020"> <i class="fas fa-calendar fa-sm"></i> 2020 </a> </p> </div> </div> </div> </div> </a> </div> </div> </div> <hr> <ul class="post-list"> <li><div class="row"> <div class="col-sm-9"><h3> <a class="post-title" href="/blog/2023/creativity/">A mathematical analogy for understanding creativity</a> </h3> <p>Using numbers to illustrate an intuitive definition of what creativity is</p> <p class="post-meta"> 3 min read &nbsp; &middot; &nbsp; November 2, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/food-for-thought"> <i class="fas fa-hashtag fa-sm"></i> food-for-thought</a> &nbsp; &nbsp; &middot; &nbsp; <a href="/blog/category/blog-post"> <i class="fas fa-tag fa-sm"></i> blog-post</a> &nbsp; </p></div> <div class="col-sm-3"> <img class="card-img" src="/assets/img/creativity.jpg" style="object-fit: cover; height: 90%" alt="image"> </div> </div></li> <li><h3> <a class="post-title" href="/blog/2023/many-faces-ML/">The many interpretations of terms in artificial intelligence</a> </h3> <p>Some thoughts concerning how to interpret quotidian words appearing in AI research in a non-quotidian way.</p> <p class="post-meta"> 6 min read &nbsp; &middot; &nbsp; November 1, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/food-for-thought"> <i class="fas fa-hashtag fa-sm"></i> food-for-thought</a> &nbsp; &nbsp; &middot; &nbsp; <a href="/blog/category/blog-post"> <i class="fas fa-tag fa-sm"></i> blog-post</a> &nbsp; </p></li> <li><div class="row"> <div class="col-sm-9"><h3> <a class="post-title" href="/blog/2023/sgd/">Structured Gradient Descent</a> </h3> <p>Some thoughts on the concept of structured gradient descent and a piece of artwork associated to it</p> <p class="post-meta"> 5 min read &nbsp; &middot; &nbsp; October 31, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/food-for-thought"> <i class="fas fa-hashtag fa-sm"></i> food-for-thought</a> &nbsp; <a href="/blog/tag/creative-work"> <i class="fas fa-hashtag fa-sm"></i> creative-work</a> &nbsp; &nbsp; &middot; &nbsp; <a href="/blog/category/blog-post"> <i class="fas fa-tag fa-sm"></i> blog-post</a> &nbsp; </p></div> <div class="col-sm-3"> <img class="card-img" src="/assets/img/sgd4life.png" style="object-fit: cover; height: 90%" alt="image"> </div> </div></li> <li><div class="row"> <div class="col-sm-9"><h3> <a class="post-title" href="/blog/2023/tale-two-worlds/">A tale of two worlds: the natural and the mathematical</a> </h3> <p>I argue about the complementarity of math and the natural world, about nativism and empiricism, and implications of rejecting either of them.</p> <p class="post-meta"> 14 min read &nbsp; &middot; &nbsp; October 14, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/food-for-thought"> <i class="fas fa-hashtag fa-sm"></i> food-for-thought</a> &nbsp; <a href="/blog/tag/research"> <i class="fas fa-hashtag fa-sm"></i> research</a> &nbsp; &nbsp; &middot; &nbsp; <a href="/blog/category/blog-post"> <i class="fas fa-tag fa-sm"></i> blog-post</a> &nbsp; </p></div> <div class="col-sm-3"> <img class="card-img" src="/assets/img/world-math.jpg" style="object-fit: cover; height: 90%" alt="image"> </div> </div></li> <li><div class="row"> <div class="col-sm-9"><h3> <a class="post-title" href="/blog/2023/brain-fractal/">Brain-Heart Fractal, DALL-E and I</a> </h3> <p>I share some sketches on the complementarity of mind and heart made by me and DALL-E</p> <p class="post-meta"> 8 min read &nbsp; &middot; &nbsp; September 15, 2023 </p> <p class="post-tags"> <a href="/blog/2023"> <i class="fas fa-calendar fa-sm"></i> 2023 </a> &nbsp; &middot; &nbsp; <a href="/blog/tag/creative-work"> <i class="fas fa-hashtag fa-sm"></i> creative-work</a> &nbsp; &nbsp; &middot; &nbsp; <a href="/blog/category/blog-post"> <i class="fas fa-tag fa-sm"></i> blog-post</a> &nbsp; </p></div> <div class="col-sm-3"> <img class="card-img" src="/assets/img/sketch.jpg" style="object-fit: cover; height: 90%" alt="image"> </div> </div></li> </ul> <nav aria-label="Blog page naviation"> <ul class="pagination pagination-lg justify-content-center"> <li class="page-item disabled"> <a class="page-link" href="" tabindex="-1" aria-disabled="">Newer</a> </li><li class="page-item active"><a class="page-link" href="/blog/index.html" title="blog">1</a></li> <li class="page-item "><a class="page-link" href="/blog/page/2/index.html" title="blog - page 2">2</a></li> <li class="page-item "><a class="page-link" href="/blog/page/3/index.html" title="blog - page 3">3</a></li> <li class="page-item "><a class="page-link" href="/blog/page/4/index.html" title="blog - page 4">4</a></li> <li class="page-item "><a class="page-link" href="/blog/page/5/index.html" title="blog - page 5">5</a></li> <li class="page-item "> <a class="page-link" href="/blog/page/2/">Older</a> </li> </ul> </nav> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> &copy; 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diff --git a/blog/tag/food-for-thought/index.html b/blog/tag/food-for-thought/index.html
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-<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>food-for-thought | Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. 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Esa es la cuestión</a> </td> </tr> <tr> <th scope="row">Aug 24, 2023</th> <td> <a class="post-link" href="/blog/2023/chat-gpt/">Una conversación con ChatGPT</a> </td> </tr> <tr> <th scope="row">Jul 12, 2023</th> <td> <a class="post-link" href="/blog/2023/penta-paradigm-orig-bib-copy/">Paradigms of AI modelling</a> </td> </tr> <tr> <th scope="row">May 12, 2023</th> <td> <a class="post-link" href="/blog/2023/dichotomy-orig-customblock/">The dichotomy of Vision and Language</a> </td> </tr> <tr> <th scope="row">Apr 25, 2023</th> <td> <a class="post-link" href="/blog/2023/agi-orig-audios/">Thoughts on Artificial General Intelligence</a> </td> </tr> <tr> <th scope="row">Jan 12, 2023</th> <td> <a class="post-link" href="/blog/2023/clevr-hans-orig-videos/">Are you Clever Hans or are you actually clever, Hans?</a> </td> </tr> <tr> <th scope="row">Aug 18, 2020</th> <td> <a class="post-link" href="/blog/2020/stc-aog/">Spatial, temporal, causal and-or graph, the next step for Cognitive Science?</a> </td> </tr> <tr> <th scope="row">Aug 18, 2020</th> <td> <a class="post-link" href="/blog/2020/math-uncertainty/">Math, certainty and uncertainty</a> </td> </tr> <tr> <th scope="row">Jul 6, 2020</th> <td> <a class="post-link" href="/blog/2020/principia/">Principia de Humana Naturali</a> </td> </tr> <tr> <th scope="row">Jun 7, 2020</th> <td> <a class="post-link" href="/blog/2020/stargaze-future/">Stargazing the Future</a> </td> </tr> <tr> <th scope="row">May 26, 2020</th> <td> <a class="post-link" href="/blog/2020/life-story/">Make your life a story worth telling to everyone</a> </td> </tr> <tr> <th scope="row">May 24, 2020</th> <td> <a class="post-link" href="/blog/2020/oneself/">One is more than one thinks of oneself, just less than one exaggerates</a> </td> </tr> <tr> <th scope="row">May 20, 2020</th> <td> <a class="post-link" href="/blog/2020/happiness-suffering/">The flavor "suffering" also to satiate the hunger for "happiness"</a> </td> </tr> </table> </div> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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+<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>food-for-thought | Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. 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Esa es la cuestión</a> </td> </tr> <tr> <th scope="row">Aug 24, 2023</th> <td> <a class="post-link" href="/blog/2023/chat-gpt/">Una conversación con ChatGPT</a> </td> </tr> <tr> <th scope="row">Jul 12, 2023</th> <td> <a class="post-link" href="/blog/2023/penta-paradigm-orig-bib-copy/">Paradigms of AI modelling</a> </td> </tr> <tr> <th scope="row">May 12, 2023</th> <td> <a class="post-link" href="/blog/2023/dichotomy-orig-customblock/">The dichotomy of Vision and Language</a> </td> </tr> <tr> <th scope="row">Apr 25, 2023</th> <td> <a class="post-link" href="/blog/2023/agi-orig-audios/">Thoughts on Artificial General Intelligence</a> </td> </tr> <tr> <th scope="row">Jan 12, 2023</th> <td> <a class="post-link" href="/blog/2023/clevr-hans-orig-videos/">Are you Clever Hans or are you actually clever, Hans?</a> </td> </tr> <tr> <th scope="row">Aug 18, 2020</th> <td> <a class="post-link" href="/blog/2020/stc-aog/">Spatial, temporal, causal and-or graph, the next step for Cognitive Science?</a> </td> </tr> <tr> <th scope="row">Aug 18, 2020</th> <td> <a class="post-link" href="/blog/2020/math-uncertainty/">Math, certainty and uncertainty</a> </td> </tr> <tr> <th scope="row">Jul 6, 2020</th> <td> <a class="post-link" href="/blog/2020/principia/">Principia de Humana Naturali</a> </td> </tr> <tr> <th scope="row">Jun 7, 2020</th> <td> <a class="post-link" href="/blog/2020/stargaze-future/">Stargazing the Future</a> </td> </tr> <tr> <th scope="row">May 26, 2020</th> <td> <a class="post-link" href="/blog/2020/life-story/">Make your life a story worth telling to everyone</a> </td> </tr> <tr> <th scope="row">May 24, 2020</th> <td> <a class="post-link" href="/blog/2020/oneself/">One is more than one thinks of oneself, just less than one exaggerates</a> </td> </tr> <tr> <th scope="row">May 20, 2020</th> <td> <a class="post-link" href="/blog/2020/happiness-suffering/">The flavor "suffering" also to satiate the hunger for "happiness"</a> </td> </tr> </table> </div> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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-<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>cv | Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="my curriculum vitae"> <meta name="keywords" content="jekyll, jekyll-theme, academic-website, portfolio-website"> <link href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/css/mdb.min.css" integrity="sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw=" crossorigin="anonymous"> <link defer rel="stylesheet" href="https://unpkg.com/bootstrap-table@1.21.4/dist/bootstrap-table.min.css"> <link 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</li> <li class="nav-item "> <a class="nav-link" href="/blog/">blog</a> </li> <li class="nav-item "> <a class="nav-link" href="/publications/">research</a> </li> <li class="nav-item "> <a class="nav-link" href="/projects/">projects</a> </li> <li class="nav-item active"> <a class="nav-link" href="/cv/">cv<span class="sr-only">(current)</span></a> </li> <li class="toggle-container"> <button id="light-toggle" title="Change theme"> <i class="fas fa-moon"></i> <i class="fas fa-sun"></i> </button> </li> </ul> </div> </div> </nav> <progress id="progress" value="0"> <div class="progress-container"> <span class="progress-bar"></span> </div> </progress> </header> <div class="container mt-5"> <div class="post"> <header class="post-header"> <h1 class="post-title">cv <a href="/assets/pdf/cv_xuelong.pdf" target="_blank" rel="noopener noreferrer" class="float-right"><i class="fas fa-file-pdf"></i></a> </h1> <p class="post-description">my curriculum vitae</p> </header> <h4>Table of contents</h4> <ul class="timeline"> <li><a href="#General%20Information"><span class="badge-toc">General Information</span></a></li> <li><a href="#Education"><span class="badge-toc">Education</span></a></li> <li><a href="#Experience"><span class="badge-toc">Experience</span></a></li> <li><a href="#Academic%20Interests"><span class="badge-toc">Academic Interests</span></a></li> <li><a href="#Other%20Interests"><span class="badge-toc">Other Interests</span></a></li> </ul> <article> <div class="cv"> <a class="anchor" id="General Information"></a> <div class="card mt-3 p-3"> <h3 class="card-title font-weight-medium">General Information</h3> <div> <table class="table table-cv table-sm table-borderless table-responsive"> <tr> <td class="p-1 pr-2 font-weight-bold"><b>Full Name</b></td> <td class="p-1 pl-2 font-weight-light text">An Wang Xuelong</td> </tr> <tr> <td class="p-1 pr-2 font-weight-bold"><b>Date of Birth</b></td> <td class="p-1 pl-2 font-weight-light text">27th of August, 2001</td> </tr> <tr> <td class="p-1 pr-2 font-weight-bold"><b>Languages</b></td> <td class="p-1 pl-2 font-weight-light text">Spanish, English, Chinese</td> </tr> </table> </div> </div> <a class="anchor" id="Education"></a> <div class="card mt-3 p-3"> <h3 class="card-title font-weight-medium">Education</h3> <div> <ul class="card-text font-weight-light list-group list-group-flush"> <li class="list-group-item"> <div class="row"> <div class="col-xs-2 cl-sm-2 col-md-2 text-center" style="width: 75px;"> <table class="table-cv"> <tbody> <tr> <td> <span class="badge font-weight-bold danger-color-dark text-uppercase align-middle" style="min-width: 75px;">expected 2024</span> </td> </tr> </tbody> </table> </div> <div class="col-xs-10 cl-sm-10 col-md-10 mt-2 mt-md-0"> <h6 class="title font-weight-bold ml-1 ml-md-4">Master of Science in AI for Biomedicine and Healthcare</h6> <table class="table-cv ml-1 ml-md-4 institution"> <tbody> <tr> <td style="vertical-align: top; text-align: center;" class="institution"><i class="fas fa-university iconinstitution"></i></td> <td class="institution">University College London</td> </tr> </tbody> </table> </div> </div> </li> <li class="list-group-item"> <div class="row"> <div class="col-xs-2 cl-sm-2 col-md-2 text-center" style="width: 75px;"> <table class="table-cv"> <tbody> <tr> <td> <span class="badge font-weight-bold danger-color-dark text-uppercase align-middle" style="min-width: 75px;">2023</span> </td> </tr> </tbody> </table> </div> <div class="col-xs-10 cl-sm-10 col-md-10 mt-2 mt-md-0"> <h6 class="title font-weight-bold ml-1 ml-md-4">Master of Arts with Honours</h6> <table class="table-cv ml-1 ml-md-4 institution"> <tbody> <tr> <td style="vertical-align: top; text-align: center;" class="institution"><i class="fas fa-university iconinstitution"></i></td> <td class="institution">University of Edinburgh</td> </tr> </tbody> </table> <ul class="items"> <li> <span class="item">2.1 Classification in Cognitive Science (Humanities)</span> </li> <li> <span class="item">Dissertation on surveying neurosymbolic methods and benchmarks</span> </li> <li> <span class="item-title">Notable courses:</span> <ul class="subitems"> <li><span class="subitem">Informatics 1 - Cognitive Science</span></li> <li><span class="subitem">Machine Learning and Pattern Recognition</span></li> <li><span class="subitem">Probabilistic Modelling and Reasoning</span></li> <li><span class="subitem">Informatics 2 - Introduction to Algorithms and Data Structures</span></li> <li><span class="subitem">Reinforcement Learning</span></li> <li><span class="subitem">Topics in Mind and Cognition</span></li> </ul> </li> </ul> </div> </div> </li> <li class="list-group-item"> <div class="row"> <div class="col-xs-2 cl-sm-2 col-md-2 text-center" style="width: 75px;"> <table class="table-cv"> <tbody> <tr> <td> <span class="badge font-weight-bold danger-color-dark text-uppercase align-middle" style="min-width: 75px;">2019</span> </td> </tr> </tbody> </table> </div> <div class="col-xs-10 cl-sm-10 col-md-10 mt-2 mt-md-0"> <h6 class="title font-weight-bold ml-1 ml-md-4">Science Baccalaureate (Bachillerato de Ciencias)</h6> <table class="table-cv ml-1 ml-md-4 institution"> <tbody> <tr> <td style="vertical-align: top; text-align: center;" class="institution"><i class="fas fa-university iconinstitution"></i></td> <td class="institution">American School of Guayaquil</td> </tr> </tbody> </table> <ul class="items"> <li> <span class="item">9.86/10</span> </li> <li> <span class="item-title">38/45 in IB Diploma</span> <ul class="subitems"> <li><span class="subitem">Physics - Higher Level</span></li> <li><span class="subitem">Hystory - Higher Level</span></li> <li><span class="subitem">Spanish - Higher Level</span></li> <li><span class="subitem">English - Higher Level</span></li> <li><span class="subitem">Biology - Standard Level</span></li> <li><span class="subitem">Mathematics - Standard Level</span></li> <li><span class="subitem">Theory of Knowledge</span></li> </ul> </li> </ul> </div> </div> </li> </ul> </div> </div> <a class="anchor" id="Experience"></a> <div class="card mt-3 p-3"> <h3 class="card-title font-weight-medium">Experience</h3> <div> <ul class="card-text font-weight-light list-group list-group-flush"> <li class="list-group-item"> <div class="row"> <div class="col-xs-2 cl-sm-2 col-md-2 text-center" style="width: 75px;"> <table class="table-cv"> <tbody> <tr> <td> <span class="badge font-weight-bold danger-color-dark text-uppercase align-middle" style="min-width: 75px;">2018 - 2019</span> </td> </tr> </tbody> </table> </div> <div class="col-xs-10 cl-sm-10 col-md-10 mt-2 mt-md-0"> <h6 class="title font-weight-bold ml-1 ml-md-4">Volunteer teacher</h6> <table class="table-cv ml-1 ml-md-4 institution"> <tbody> <tr> <td style="vertical-align: top; text-align: center;" class="institution"><i class="fas fa-university iconinstitution"></i></td> <td class="institution">Robert Allan Reed Center for Adults with Unfinished Education at the American School of Guayaquil</td> </tr> </tbody> </table> <ul class="items"> <li> <span class="item">Taught adults ranging from 18 - 50+ years old every Saturday, teaching mainly in the fields of math.</span> </li> </ul> </div> </div> </li> </ul> </div> </div> <a class="anchor" id="Academic Interests"></a> <div class="card mt-3 p-3"> <h3 class="card-title font-weight-medium">Academic Interests</h3> <div> <ul class="card-text font-weight-light list-group list-group-flush"> <li class="list-group-item"> <h5 class="font-italic">Master Algorithm for Computational Biology</h5> <ul class="subitems"> <li><span class="subitem">Neurosymbolic AI and Reinforcement Learning</span></li> <li><span class="subitem">Cell science, systems biology, pharmacogenomics, predictive medicine</span></li> </ul> </li> <li class="list-group-item"> <h5 class="font-italic">Consilience of knowledge</h5> <ul class="subitems"> <li><span class="subitem">Artificial intelligence, artificial general intelligence and the pursuit of meaning in life.</span></li> </ul> </li> </ul> </div> </div> <a class="anchor" id="Other Interests"></a> <div class="card mt-3 p-3"> <h3 class="card-title font-weight-medium">Other Interests</h3> <div> <ul class="card-text font-weight-light list-group list-group-flush"> <li class="list-group-item"> <u>Hobbies:</u> anime, rowing, singing (when I hit the right notes)</li> </ul> </div> </div> </div> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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+<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>cv | Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="my curriculum vitae"> <meta name="keywords" content="jekyll, jekyll-theme, academic-website, portfolio-website"> <link href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/css/mdb.min.css" integrity="sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw=" crossorigin="anonymous"> <link defer rel="stylesheet" href="https://unpkg.com/bootstrap-table@1.21.4/dist/bootstrap-table.min.css"> <link 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</li> <li class="nav-item "> <a class="nav-link" href="/blog/">blog</a> </li> <li class="nav-item "> <a class="nav-link" href="/publications/">research</a> </li> <li class="nav-item "> <a class="nav-link" href="/projects/">projects</a> </li> <li class="nav-item active"> <a class="nav-link" href="/cv/">cv<span class="sr-only">(current)</span></a> </li> <li class="toggle-container"> <button id="light-toggle" title="Change theme"> <i class="fas fa-moon"></i> <i class="fas fa-sun"></i> </button> </li> </ul> </div> </div> </nav> <progress id="progress" value="0"> <div class="progress-container"> <span class="progress-bar"></span> </div> </progress> </header> <div class="container mt-5"> <div class="post"> <header class="post-header"> <h1 class="post-title">cv <a href="/assets/pdf/cv_xuelong.pdf" target="_blank" rel="noopener noreferrer" class="float-right"><i class="fas fa-file-pdf"></i></a> </h1> <p class="post-description">my curriculum vitae</p> </header> <h4>Table of contents</h4> <ul class="timeline"> <li><a href="#General%20Information"><span class="badge-toc">General Information</span></a></li> <li><a href="#Education"><span class="badge-toc">Education</span></a></li> <li><a href="#Experience"><span class="badge-toc">Experience</span></a></li> <li><a href="#Academic%20Interests"><span class="badge-toc">Academic Interests</span></a></li> <li><a href="#Other%20Interests"><span class="badge-toc">Other Interests</span></a></li> </ul> <article> <div class="cv"> <a class="anchor" id="General Information"></a> <div class="card mt-3 p-3"> <h3 class="card-title font-weight-medium">General Information</h3> <div> <table class="table table-cv table-sm table-borderless table-responsive"> <tr> <td class="p-1 pr-2 font-weight-bold"><b>Full Name</b></td> <td class="p-1 pl-2 font-weight-light text">An Wang Xuelong</td> </tr> <tr> <td class="p-1 pr-2 font-weight-bold"><b>Date of Birth</b></td> <td class="p-1 pl-2 font-weight-light text">27th of August, 2001</td> </tr> <tr> <td class="p-1 pr-2 font-weight-bold"><b>Languages</b></td> <td class="p-1 pl-2 font-weight-light text">Spanish, English, Chinese</td> </tr> </table> </div> </div> <a class="anchor" id="Education"></a> <div class="card mt-3 p-3"> <h3 class="card-title font-weight-medium">Education</h3> <div> <ul class="card-text font-weight-light list-group list-group-flush"> <li class="list-group-item"> <div class="row"> <div class="col-xs-2 cl-sm-2 col-md-2 text-center" style="width: 75px;"> <table class="table-cv"> <tbody> <tr> <td> <span class="badge font-weight-bold danger-color-dark text-uppercase align-middle" style="min-width: 75px;">expected 2024</span> </td> </tr> </tbody> </table> </div> <div class="col-xs-10 cl-sm-10 col-md-10 mt-2 mt-md-0"> <h6 class="title font-weight-bold ml-1 ml-md-4">Master of Science in AI for Biomedicine and Healthcare</h6> <table class="table-cv ml-1 ml-md-4 institution"> <tbody> <tr> <td style="vertical-align: top; text-align: center;" class="institution"><i class="fas fa-university iconinstitution"></i></td> <td class="institution">University College London</td> </tr> </tbody> </table> </div> </div> </li> <li class="list-group-item"> <div class="row"> <div class="col-xs-2 cl-sm-2 col-md-2 text-center" style="width: 75px;"> <table class="table-cv"> <tbody> <tr> <td> <span class="badge font-weight-bold danger-color-dark text-uppercase align-middle" style="min-width: 75px;">2023</span> </td> </tr> </tbody> </table> </div> <div class="col-xs-10 cl-sm-10 col-md-10 mt-2 mt-md-0"> <h6 class="title font-weight-bold ml-1 ml-md-4">Master of Arts with Honours</h6> <table class="table-cv ml-1 ml-md-4 institution"> <tbody> <tr> <td style="vertical-align: top; text-align: center;" class="institution"><i class="fas fa-university iconinstitution"></i></td> <td class="institution">University of Edinburgh</td> </tr> </tbody> </table> <ul class="items"> <li> <span class="item">2.1 Classification in Cognitive Science (Humanities)</span> </li> <li> <span class="item">Dissertation on surveying neurosymbolic methods and benchmarks</span> </li> <li> <span class="item-title">Notable courses:</span> <ul class="subitems"> <li><span class="subitem">Informatics 1 - Cognitive Science</span></li> <li><span class="subitem">Machine Learning and Pattern Recognition</span></li> <li><span class="subitem">Probabilistic Modelling and Reasoning</span></li> <li><span class="subitem">Informatics 2 - Introduction to Algorithms and Data Structures</span></li> <li><span class="subitem">Reinforcement Learning</span></li> <li><span class="subitem">Topics in Mind and Cognition</span></li> </ul> </li> </ul> </div> </div> </li> <li class="list-group-item"> <div class="row"> <div class="col-xs-2 cl-sm-2 col-md-2 text-center" style="width: 75px;"> <table class="table-cv"> <tbody> <tr> <td> <span class="badge font-weight-bold danger-color-dark text-uppercase align-middle" style="min-width: 75px;">2019</span> </td> </tr> </tbody> </table> </div> <div class="col-xs-10 cl-sm-10 col-md-10 mt-2 mt-md-0"> <h6 class="title font-weight-bold ml-1 ml-md-4">Science Baccalaureate (Bachillerato de Ciencias)</h6> <table class="table-cv ml-1 ml-md-4 institution"> <tbody> <tr> <td style="vertical-align: top; text-align: center;" class="institution"><i class="fas fa-university iconinstitution"></i></td> <td class="institution">American School of Guayaquil</td> </tr> </tbody> </table> <ul class="items"> <li> <span class="item">9.86/10</span> </li> <li> <span class="item-title">38/45 in IB Diploma</span> <ul class="subitems"> <li><span class="subitem">Physics - Higher Level</span></li> <li><span class="subitem">Hystory - Higher Level</span></li> <li><span class="subitem">Spanish - Higher Level</span></li> <li><span class="subitem">English - Higher Level</span></li> <li><span class="subitem">Biology - Standard Level</span></li> <li><span class="subitem">Mathematics - Standard Level</span></li> <li><span class="subitem">Theory of Knowledge</span></li> </ul> </li> </ul> </div> </div> </li> </ul> </div> </div> <a class="anchor" id="Experience"></a> <div class="card mt-3 p-3"> <h3 class="card-title font-weight-medium">Experience</h3> <div> <ul class="card-text font-weight-light list-group list-group-flush"> <li class="list-group-item"> <div class="row"> <div class="col-xs-2 cl-sm-2 col-md-2 text-center" style="width: 75px;"> <table class="table-cv"> <tbody> <tr> <td> <span class="badge font-weight-bold danger-color-dark text-uppercase align-middle" style="min-width: 75px;">2018 - 2019</span> </td> </tr> </tbody> </table> </div> <div class="col-xs-10 cl-sm-10 col-md-10 mt-2 mt-md-0"> <h6 class="title font-weight-bold ml-1 ml-md-4">Volunteer teacher</h6> <table class="table-cv ml-1 ml-md-4 institution"> <tbody> <tr> <td style="vertical-align: top; text-align: center;" class="institution"><i class="fas fa-university iconinstitution"></i></td> <td class="institution">Robert Allan Reed Center for Adults with Unfinished Education at the American School of Guayaquil</td> </tr> </tbody> </table> <ul class="items"> <li> <span class="item">Taught adults ranging from 18 - 50+ years old every Saturday, teaching mainly in the fields of math.</span> </li> </ul> </div> </div> </li> </ul> </div> </div> <a class="anchor" id="Academic Interests"></a> <div class="card mt-3 p-3"> <h3 class="card-title font-weight-medium">Academic Interests</h3> <div> <ul class="card-text font-weight-light list-group list-group-flush"> <li class="list-group-item"> <h5 class="font-italic">Master Algorithm for Computational Biology</h5> <ul class="subitems"> <li><span class="subitem">Neurosymbolic AI and Reinforcement Learning</span></li> <li><span class="subitem">Cell science, systems biology, pharmacogenomics, precision medicine</span></li> </ul> </li> <li class="list-group-item"> <h5 class="font-italic">Consilience of knowledge</h5> <ul class="subitems"> <li><span class="subitem">Artificial intelligence, artificial general intelligence and the pursuit of meaning in life.</span></li> </ul> </li> </ul> </div> </div> <a class="anchor" id="Other Interests"></a> <div class="card mt-3 p-3"> <h3 class="card-title font-weight-medium">Other Interests</h3> <div> <ul class="card-text font-weight-light list-group list-group-flush"> <li class="list-group-item"> <u>Hobbies:</u> anime, rowing, singing (when I hit the right notes)</li> </ul> </div> </div> </div> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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diff --git a/feed.xml b/feed.xml
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+++ b/feed.xml
@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.2">Jekyll</generator><link href="https://awxlong.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://awxlong.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2023-11-02T13:47:09+00:00</updated><id>https://awxlong.github.io/feed.xml</id><title type="html">blank</title><subtitle>A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. </subtitle><entry><title type="html">Creativity</title><link href="https://awxlong.github.io/blog/2023/creativity/" rel="alternate" type="text/html" title="Creativity"/><published>2023-11-02T00:00:00+00:00</published><updated>2023-11-02T00:00:00+00:00</updated><id>https://awxlong.github.io/blog/2023/creativity</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/creativity/"><![CDATA[<p>Math is really a beautiful system (framework?) from which we can derive ideas of beauty through symmetry and geometry, study of complex systems through calculus, and also of creativity.</p> <ul> <li>How would I define creativity? o Imagine you’re an enthusiastic pursuer of truth. In front of you are a some numbers and two operators: + (ordinary addition) and * (ordinary multiplication). Science, or the discovery of knowledge, is analogous to discovering new numbers. You randomly add two numbers, 1 and -1 and discover 0. Then you add 1 to 1 and obtain 2. Repeating this process you discover all the integers. From God’s point of view, addition and multiplication over integers is closed in this set, therefore you can’t discover anything beyond the horizon of integers, even though you can discover a lot. You can find patterns, such as properties of commutativity, transitivity and distributivity. o Creativity, in this scenario, would be proposing another operator such as division. Having this new operator in your arsenal of tools to perceive reality, you can now compute a new class of numbers, the rationals and thus discover the set of all real numbers. o The infiniteness of numbers is analogous to the infinity of knowledge.  Further creativity would be introducing the square root, and this tool extends our perceptual horizon to encompass the realm of complex/imaginary numbers.  A different civilization, using a different set of symbols to encode numbers, may make similar discoveries, i.e., numbers obtained following the same principles of computation using + and *, discovering commutativity, transitivity and distributivity. This underlies/represents the universality of knowledge. o I’ve ignored letters, phonemes, words to simplify this overly-idealized world to get my ideas on what is creativity across.</li> </ul>]]></content><author><name></name></author><summary type="html"><![CDATA[Math is really a beautiful system (framework?) from which we can derive ideas of beauty through symmetry and geometry, study of complex systems through calculus, and also of creativity. How would I define creativity? o Imagine you’re an enthusiastic pursuer of truth. In front of you are a some numbers and two operators: + (ordinary addition) and * (ordinary multiplication). Science, or the discovery of knowledge, is analogous to discovering new numbers. You randomly add two numbers, 1 and -1 and discover 0. Then you add 1 to 1 and obtain 2. Repeating this process you discover all the integers. From God’s point of view, addition and multiplication over integers is closed in this set, therefore you can’t discover anything beyond the horizon of integers, even though you can discover a lot. You can find patterns, such as properties of commutativity, transitivity and distributivity. o Creativity, in this scenario, would be proposing another operator such as division. Having this new operator in your arsenal of tools to perceive reality, you can now compute a new class of numbers, the rationals and thus discover the set of all real numbers. o The infiniteness of numbers is analogous to the infinity of knowledge.  Further creativity would be introducing the square root, and this tool extends our perceptual horizon to encompass the realm of complex/imaginary numbers.  A different civilization, using a different set of symbols to encode numbers, may make similar discoveries, i.e., numbers obtained following the same principles of computation using + and *, discovering commutativity, transitivity and distributivity. This underlies/represents the universality of knowledge. o I’ve ignored letters, phonemes, words to simplify this overly-idealized world to get my ideas on what is creativity across.]]></summary></entry><entry><title type="html">The many interpretations of terms in artificial intelligence</title><link href="https://awxlong.github.io/blog/2023/many-faces-ML/" rel="alternate" type="text/html" title="The many interpretations of terms in artificial intelligence"/><published>2023-11-01T13:42:00+00:00</published><updated>2023-11-01T13:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/many-faces-ML</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/many-faces-ML/"><![CDATA[<p>I bumped into this paper on <a href="https://twitter.com/ChristophMolnar">X (formerly Twitter)</a></p> <div class="jekyll-twitter-plugin"><blockquote class="twitter-tweet"><p lang="en" dir="ltr">How do researchers define interpretability and explainability?<br/><br/>An overview:<a href="https://t.co/bNjKv8F44g">https://t.co/bNjKv8F44g</a> <br/><br/>Summary: No consensus<br/><br/>My opinion: It&#39;s already the Wild West, no one will stick to any definition. Most pragmatic is to treat them interchangeably. <a href="https://t.co/mEh6mUuk4C">pic.twitter.com/mEh6mUuk4C</a></p>&mdash; Christoph Molnar (@ChristophMolnar) <a href="https://twitter.com/ChristophMolnar/status/1718921189333045752?ref_src=twsrc%5Etfw">October 30, 2023</a></blockquote> <script async="" src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> </div> <p>In today’s post, I don’t want to discuss interpretability or explainability, but rather elaborate on the above idea that in the AI literature, different authors express completely different ideas despite using the same words, often words that appear in our daily parlance, yet acquire uncommon interpretations.</p> <p>If you have just set foot into the world of artificial intelligence, here are my thoughts and advices on navigating this exciting, but often messy landscape.</p> <h1 id="terms">Terms</h1> <h4 id="explanation-hypothesis-model"><strong>Explanation, hypothesis, model</strong></h4> <p>When I began studying AI, the word “model” elicited the mental imagery of a 3D scale model of something. If I wanted a “model” of an atom, I could pick some recycled styrofoam balls, stick them together and, bam (pun intended), an atom is synthesized.</p> <p>Nowadays it mainly refers to a equation or function with some parameters $\theta$, where a ‘good’ model is defined as having optimal parameters $\theta$ that can make predictions with low error on the training and (hopefully) unseen, test data, compared to a ‘bad’ model.</p> <p>In rare cases, such as in <a href="https://www.youtube.com/watch?v=NsID1iM8gRw">Prof. Joshua Tenembaum’s talk on Cognition</a>, a (physics-) model can refer a simulator implemented in Unity.</p> <h4 id="knowledge"><strong>Knowledge</strong></h4> <p>This word may cause some confusion to peers with a philosophy or psychology background. At least to me, prior to studying AI, ‘knowledge’ meant pieces of information encoded in natural language, e.g. “Mount Chimborazo is actually higher than Mount Everest” somewhere in the brain. I don’t know how, nor where, nor whether it is encoded in natural language.</p> <p>At least in the early days of AI, first order logic constraints such as <code class="language-plaintext highlighter-rouge">smoker(X) :- edema(X)</code>, read as “it is true that if X is a smoker, then X has edema”, does resemble ‘knowledge’ encoded in a way that seems familiar to us.</p> <p>However, this is not always the case. The word ‘knowledge’, such as in ‘prior knowledge’, mainly used in Bayesian modelling, has not much to do with logic predicates or natural language. It mainly refers to what you believe on the distribution of the parameters of your model come from before your model is trained on any data. If you think your model must be parametrized by weights that more often take extreme values, you can set a prior belief with distributions with heavier tails. Guidance on what prior distributions can be used to describe your parameters can be checked in this <a href="https://twitter.com/bindureddy/status/1708664380987220427">tweext</a></p> <p>Lastly, sometimes researchers may even say that ‘knowledge’ of the data is encoded in the parameters of the model, which leads us to the next section:</p> <h4 id="explainability-and-interpretability"><strong>Explainability and interpretability</strong></h4> <p>Explainability and interpretability are terms that can be used interchangeably, as shown in the above paper. I avoid making a distinction in this post, and just focus on sharing what is usually meant by either term.</p> <p>The simplest manifestation of explainability can be observed with simple linear models like linear or logistic regression. Here explainability simply refers to the presence of coefficients estimated per each input variable of the model. These coefficients’ magnitudes often denote the contribution of the variable to the overall prediction of the model. Other non-linear, machine learning models such as decision trees are by design explainable, and such quality mainly refers to the thresholds appearing at each junction in the decision trees.</p> <p>Concerning deep learning, particularly convolutional neural networks (CNN), explainability can refer to the computation of feature maps on the input space to see on average, what are the filters of the CNN observing when solving a task like classification given an image. Deep generative models (DGM) that leverage neural networks and learn latent representations of the data can also become ‘explainable’ if there is some post-hoc (i.e. after training) processing of embeddings, such as dimensionality reduction through principal component analysis, that can map the embeddings on 2D space. In such space, it is hoped that the DGM learns disentangled features, albeit how are they interpreted is mainly up to the researchers, not by some objective reason.</p> <p>Explainability in the eyes of some authors may also refer to the capability of the researcher to compute a numerical value of the likelihood of the parameters of the data, or at least an approximation of it, such as the evidence lower bound. This scalar value can be used to compare the performance of different models, and these models can thus be referred to as “provable”.</p> <p>Other authors may also define explainability when deep neural networks, despite being black boxes, can accept inputs or display its predictions through a user-friendly interface that allows them to interact with the model.</p> <h1 id="bottom-line">Bottom line</h1> <p>My key advice in this brief article is to convince you not to trust the “intuitive” interpretation of a word, especially if it is used on a quotidian basis. For example, in my personal view, to be “explainable” means to be able to justify its own answer. Just like a friend to whom I ask why didn’t they go to a concert by singer Aimer, to be “explainable” (at least when referring to the common meaning of this word in daily life) means that they can give a justification as to why didn’t they go (maybe they became sick, or admit they have poor taste in music). ‘Explainable’ in the machine learning literature has not much to do with this daily life interpretation.</p> <p>Finally, the above terms are simply the tip of the iceberg. In the wider research literature, authors can diverge in what they mean by on many terms such as “language”, the word “intelligence” itself, and more controversial terms like “artificial general intelligence”. Because I don’t know the precise meaning of them myself, I refrain from commenting furthermore.</p> <p>Happy learning!</p>]]></content><author><name></name></author><category term="blog-post"/><category term="food-for-thought"/><summary type="html"><![CDATA[Some thoughts concerning how to interpret quotidian words appearing in AI research in a non-quotidian way.]]></summary></entry><entry><title type="html">Structured Gradient Descent</title><link href="https://awxlong.github.io/blog/2023/sgd/" rel="alternate" type="text/html" title="Structured Gradient Descent"/><published>2023-10-31T13:42:00+00:00</published><updated>2023-10-31T13:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/sgd</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/sgd/"><![CDATA[<h1 id="a-tale-of-a-scientist">A tale of a scientist</h1> <p><em>Shirley wanted to become a scientist. She wanted to find about jumping behavioral patterns of <a href="https://en.wikipedia.org/wiki/Tree_cricket">tree crickets</a>, which are insects of the order Orthoptera. Specifically, she aimed in testing the hypothesis that tree crickets understood human-issued commands that ordered them “to jump”. She caught a bunch of crickets and started testing her idea by telling one group of crickets to jump, and leaving a control group of crickets telling them nothing. Most crickets in either group jumped, merrily and highly. To her, these results weakly suggest that crickets understood the order to jump and did so accordingly. Then for the same group of crickets in both conditions, she mercilessly cut their legs off. Again, she issued the command to jump for one group, and left the control without saying anything. None of the crickets jumped. What did Shirley the scientist conclude? “Crickets stop understanding human-language after having their legs chopped off”</em></p> <figure> <img src="/assets/img/cricket.jpg" alt="Sorry, an unanticipated error occured and the image can't load." width="100%" height="auto"/> <figcaption id="sgd"> Depiction of a cricket generated by Dall-E powered MS Bing Image Creator given the prompt "a beautiful, photo-realistic cricket" </figcaption> </figure> <h1 id="its-not-her-fault-the-parallel-with-deep-neural-networks-learning">It’s not her fault: The parallel with deep neural network’s learning</h1> <p>Perhaps you’ve heard of the above story in a course introducing the scientific method.</p> <p>This tale is interesting given the parallels there exists with current deep neural networks. These are analogous to Shirley, their parameters a metaphor to what she learns, and her crickets resemble the training data. No one has any control with regard to what she learns given the training data. When she concluded that “crickets can’t interpret English after having their legs cut-off”, that is not a “wrong” interpretation, per se. Rather, reaching this conclusion is a simple consequence of how she was designed and the coincidences of the training data (i.e. crickets did stop jumping after their legs were chopped off despite Shirley’s instructions to jump).</p> <p>With respect to deep neural networks, you can train it to classify images in CIFAR-10 or FashionMNIST, or more generally, train to generate the images. As per what sort of embeddings it learns, how it learns, among other aspects of learning, are out of our control. Exemplified in <a href="https://arxiv.org/pdf/2011.12854.pdf">Stammer et al., (2021)</a>, in a ColorMNIST dataset where a ResNet learns to classify digits, if by chance most digits nines appeared with a purple color, then this ResNet would classify purple color digits as 9 in the test set, even though the digits may be of different shape. In such a case, the model has learned that the concept of color defines the identity of a digit, since we have no control over what artifacts does the ResNet pick.</p> <p>The main powerhouse driving learning is stochastic gradient descent, and it is partly due to this stochasticity that deep neural networks are black-boxes.</p> <p>The stochasticity of gradient descent is mainly derived from the order and the semantics of the incoming training data. We thus have no control over what the model learns. The AI research community has proposed several methods, such as trying to learn disentangled representations of data, which can be obtained by having a more intricate labelling of the dataset. Consider the <a href="https://cs.stanford.edu/people/jcjohns/clevr/">CLEVR Diagnostic Dataset</a>, in which each object appearing in each training image is exquisitely labeled with its (x, y, z) coordinates, shape, color, size, material and positional relations among others, along with questions, answers and functional programs to answer each question. A variant of such dataset with real-life scenes is <a href="https://arxiv.org/abs/1902.09506">GQA</a>.</p> <h1 id="structured-gradient-descent">Structured gradient descent</h1> <p>Given these precedents, I wanted to depict my desire to see in the future a kind of “structured” gradient descent (which in English coincides with the same acronym as stochastic gradient descent):</p> <figure> <img src="/assets/img/sgd4life.png" alt="Sorry, an unanticipated error occured and the image can't load." width="100%" height="auto"/> <figcaption id="sgd"> Not an actual depiction of structured gradient descent, but nonetheless a cool logo. </figcaption> </figure> <p>Structured gradient descent, as its name suggest, would allow the training of a model where we have control over what kind of representation it obtains from the supplied data. Analogous to the tale above, it’d akin to explicitly telling Shirley that crickets can’t understand human language, and that her line of reasoning has to start from the motility of legs of tree crickets in giving them the ability to jump. Afterwards, the jumping behavior of crickets is up to her to explain.</p> <p>The unanswered questions that follow would include:</p> <ul> <li>How would structured gradient descent materialize in a learning algorithm? <ul> <li>Will a grammar, prior knowledge in the form of logic or external ontologies play a pivotal role in the design of such an algorithm?</li> </ul> </li> <li>Will structured gradient descent allow a model to learn a disentangled representation, along with benefits such as a lower demand for computational power, dataset size and model size? <ul> <li>What would be the demands for structured gradient descent to run? More intricate labeled data?</li> <li>Most importantly, would more intricately labeled data compensate for low data?</li> </ul> </li> </ul> <figure> <img src="/assets/img/structuredgd.png" alt="Sorry, an unanticipated error occured and the image can't load." width="100%" height="auto"/> <figcaption id="sgd"> A (hopefully) cool logo of SGD</figcaption> </figure>]]></content><author><name></name></author><category term="blog-post"/><category term="food-for-thought"/><category term="creative-work"/><summary type="html"><![CDATA[Some thoughts on the concept of structured gradient descent and a piece of artwork associated to it]]></summary></entry><entry><title type="html">A tale of two worlds: the natural and the mathematical</title><link href="https://awxlong.github.io/blog/2023/tale-two-worlds/" rel="alternate" type="text/html" title="A tale of two worlds: the natural and the mathematical"/><published>2023-10-14T13:42:00+00:00</published><updated>2023-10-14T13:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/tale-two-worlds</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/tale-two-worlds/"><![CDATA[<p>There seems to exist two worlds which exist in the universe.</p> <p>The real, natural world which can be accessible through empirical means, and the abstract, mathematical world which are the means themselves with which we observe the real, natural world. Here, the real, natural world is what we can perceive with our senses. The mathematical world is an umbrella term to refer to the ‘math’ in our common day parlance, as well as including logic in its many forms: propositional, first-order and higher-order.</p> <p>The real, natural world is subject to the laws of physics, which underlie chemistry and biology. In the abstract, mathematical world, such laws are not present: there we can think of breaking Newton’s Universal Laws of Gravitation, the overall decrease of entropy against the Second Law of Thermodynamics, or, in general, anything within our mental capabilities<sup id="fnref:3" role="doc-noteref"><a href="#fn:3" class="footnote" rel="footnote">1</a></sup>.</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/world-math.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> A depiction of the complementarity of the empirical, natural world and the abstract, mathematical world. The image is generated via Microsoft Bing's Image Creator with the prompt "on the left, a picture of the natural world. On the right, a picture of equations, geometry and calculus". I have tried several other prompts and chose the one I believed suits what I wanted to convey. As I have discussed [before](https://awxlong.github.io/blog/2023/brain-fractal/), as well as in this article, I've avoided abstract terms such as 'complementarity' or 'abstract' since I'm unconfident a large vision system can understand it. </div> <p>To recount this tale of two worlds, we can take a step back to ancient debate of <a href="https://plato.stanford.edu/entries/innateness-cognition/">nativism and empiricism</a><d-cite key="samet_2012_innateness"></d-cite>. Namely, nativism argues that knowledge emerges from axioms and inference rules applied to those axioms that we humans are endowed since birth. Elaborating, thanks to a random mutation that kickstarted the beginning of <em>Homo sapiens</em>, our species have grown a capacity to understand elements of the natural world in terms of its objectness, and its fluent properties, such as shape, numerosity and size. This capacity allow us to understand objects of the real world in terms of how they change, how they hierarchically relate to one another and how they causally relate to each other. This capacity contrasts to what empiricists argue, namely that we acquiere knowledge dependent on the experiencies we’ve been exposed, and determine (causal) relationships among objects of the real world based on the extrapolation of previous experiences.</p> <p>I argue this is a false dichotomy, and that underlying it there is the complementarity of nativism and empiricism. Nativism mostly deals in how the human mind navigates the internal, abstract, mathematical world. If we want to prove that there exists infinite primes, nativists would argue that such theory can be proved via universal inference procedures such as proof by contradiction, and manipulating symbols involving the definition of primeness of a number. In such an scenario, empiricism would say that we have to extrapolate from prior observations of prime numbers to conclude they’re infinite. However, who can live for infinite amount of time in order to witness the infinity of primes and assert the above theory! Another example would concern Goldbach’s Conjecture, which proposes that all even numbers greater than two are the sum of two primes <sup id="fnref:1" role="doc-noteref"><a href="#fn:1" class="footnote" rel="footnote">2</a></sup>. Can we prove it by observing all even numbers, as nativists would argue? I don’t believe so, we have to resort to the mathematical reasoning mechanisms we were endowed in our mind. One way is to prove it by <a href="https://en.wikipedia.org/wiki/Euclid%27s_theorem">contradiction</a>. Assume there aren’t infinite primes, multiply the finite primes, and add one. This new number is not divisible by any of the finite primes, and thus is a prime. Infinity is a concept easier to understand and grasp when it is defined, as it can’t be learnt.</p> <p>However, we <em>Homo sapiens</em> don’t only reason about the mathematical world. We <em>use math to understand the external world</em>, and understanding the external world recquire experiencing it, and with experience comes learning. Empiricist, thus, can explain how we are able to sort out the relationships of objects in the external world as a form of interpolating and extrapolating observed evidence. However, empiricists need nativists as much as the latter need the former. Empiricism need the structure of nativists to accommodate the perceptual stimuli of the external world in order to reason about their relationships and derive new ones. Nativists need to learn from external stimuli so that the structure can evolve and be able to describe more complicated relationships.</p> <p>The natural world needs math to be understood, and math needs the natural world to exist. From this complementarity, it is argued that the mind consists of a grammar or a domain-specific language with which to give <a href="https://plato.stanford.edu/entries/language-thought/">shape to thought</a><d-cite key="rescorla_2019_the"></d-cite>. This grammar would be the basis for parsing the world’s objects in terms of their objectness, fluent properties and causal relations. Additionally, this innate grammar should have stochastic properties as probability acts as the pivot to ground this grammar with the natural world, and be able to describe it in a coherent manner. For example, after observing past experiences of a ‘hammer’, we can probabilistically ground a high-level, symbol ‘hammer’ with the actual object in the real world. Accommodating ‘hammer’ within some structure allow us to reason about its relations, functions, and properties. For further reading of stochastic grammars for vision, you can check Prof. Zhu-Song Chun’s <a href="http://www.stat.ucla.edu/~sczhu/Books/Book_2_Parsing.pdf">textbook on this topic</a> or <d-cite key="zhu_2020_dark"></d-cite>, which I find extremely difficult to follow, but nonetheless an interesting manuscript to read.</p> <h1 id="implications-of-rejecting-either-nativism-and-empiricism">Implications of rejecting either nativism and empiricism</h1> <p>I advocate that there exists complementarity between the mathematical and natural world. However, I can’t stop others from opting for the dichotomy between them, thus not only pitting nativism against empiricism or the other way around, but also rejecting one at the cost of the other.</p> <p>If we ignore nativism, and assume past experiencies are all that is needed to understand how the mind deduces new knowledge, we can think of the following. We can’t give shape to the mathematical world. It doesn’t “exist” in the real world. Circles, polygons, numbers, operations, vectors, matrices, infinity, among others, all reside within our mind. A friend once pointed out that he can draw a line on a dining table<sup id="fnref:2" role="doc-noteref"><a href="#fn:2" class="footnote" rel="footnote">3</a></sup> with a marker, and that’s a geometrical line. I disputed: “that is ink placed on a surface, which in your head is representing a geometrical line. While the ink and wooden surface are tangible objects, the geometrical line isn’t. Miraculously, however, I can also see and understand what you’re thinking concerning geometrical lines, suggesting the universality of math in the minds of <em>Homo sapiens</em>” (I may have edited my response to sound wiser, as well as edited the whole story to make it more entertaining).</p> <p>That math doesn’t exist in the real world, and thus can’t be picked up with our perceptual systems can explain why large vision models like DALL-E or MidJourney struggle with generating images which contain letters or numbers, unless they are fed with prior knowledge on <a href="https://research.google/pubs/pub52496/">characters</a><d-cite key="liu_2023_characteraware"></d-cite>. This is partly because the vast amount of data in which they’re trained mainly correspond to images taken from the natural, tangible, external world, e.g., photos under different styles of animals, buildings, groups of people, or landscapes.</p> <p>We also can’t accept nativism at the expense of empiricism. The history of artificial intelligence is a good reference to observe that resorting to only expert systems consisting of universal inference rules like forward and backward chaining results in systems that are disconnected with real world dynamics. Whilst math can be independent of the real natural world, it is nonetheless intricately connected to the world in order to describe it.</p> <h1 id="implications-on-the-quest-for-artificial-intelligence">Implications on the quest for artificial intelligence</h1> <p>Deep learning ignited the summer of AI of the twenty-first century, and the transformer is one of the torches that keeps the summer shining. How should we keep this summer from ending?</p> <p>Deep learning is a mostly pure expression of empiricism. Deep neural networks don’t manipulate symbols, are inspired by the brain’s architecture, and are mainly learn via training from a dataset of past experiences. There are several empirical observations, and <a href="https://plato.stanford.edu/entries/scientific-reduction/">reductionist</a> <d-cite key="riel_2014_scientific"></d-cite> claims suggesting that mental activities such as intuition and reasoning are the result of the biochemical processes of the brain. There are the recurrent achievements of new state of the arts performance in benchmarks used to measure the human faculties of vision and language. One of the striking research papers that caught my attention involved decoding a person’s brain activity to their perceived image:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/decode.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> The model's architecture, which is a diffusion model (i.e. a probabilistic model consisting of deep neural networks). On the right, the upper row are images presented to a person whose brain activity is recorded. This brain activity is then decoded to the below row of images. Image extracted from https://sites.google.com/view/stablediffusion-with-brain/ </div> <p>That there is some association between brain activity and external stimuli capturable by a probabilistic model is fascinating and encouraging for empiricism. However, most if not all research related to the neuroscience behind mental activity <strong>lacks the explanation of whether brain activity yields any insight into mathematical theorem proving or deduction</strong>. Experiments to explore this can involve assessing whether brain activity can reconstruct a person’s steps into solving an equation, deriving a proof or just in general have some abstract thought involving numbers.</p> <p>Furthermore, taking empiricism to an extreme may stifle the capabilities of an AI model, since radical empiricism subjects the model to be an extrapolation from supplied past experiences. It ignores the nativist capability of generating new thought, often called as creativity.</p> <p>Another counterargument to empiricism is also as follows. Image there is a program running on a computer. You can observe your program running on the screen, as in, you can see what input matches the corresponding output displayed. You also know the underlying algorithm specifying how the input should be transformed to the output. Now, you disassemble your computer and expose its mainframe, now able to look at the CPU and RAM running, all the while your program keeps running. In other words, only the computer screen is gone, so any high-level display your program disappears. You invite an empiricist and tell her that she can make all sorts of measurements she wants on the hardware of the computer (CPU, RAM, motherboard, graphics card). Such measurements could include voltage, heat maps, temperature, and whatever she can think of. Of course, because the computer screen is gone, and she is prohibited from reassembling it back, she is restricted to measure the low-level components of the computer. Now comes the main question, can she be able to figure out from measurements made on the hardware the algorithm being executed? Here, the computer and its low level components correspond to the biological brain, and the software is analogous to mental activity.</p> <p>While low-level activity can reflect to some degree higher level processes, I’m skeptical we can understand and simulate higher-level cognition without assuming its pre-existence. Therefore, we can only describe high level mental activity with high level mental activity. In the thought experiment, this would entail the empiricist may have to ‘hypothesize’ in a separate computer different programs that could be running in the monitor-less computer, use feedback from the low-level measurements obtained from the monitor-less computer, and find which hypothesized programs matches the measurements from the monitor-less computer. Such iterative process of trial and error, which combines both the high-level cognitive process of creativity and low-level signal processing for feedback.</p> <p>As argued repeatedly, the brain’s architecture, neuronal activations and modifications in synaptic strengths all belong to the external world. Drawing inspiration from the brain is a powerful means to achieve AI. However, mental activity such as deduction, and emergence of new ideas through <a href="https://aeon.co/essays/how-close-are-we-to-creating-artificial-intelligence">conjecturing</a><d-cite key="deutsch_2012_how"></d-cite> is a property that has to be embedded in the system, as it can not be learned by picking cues from the environment.</p> <h1 id="final-comments">Final comments</h1> <p>When I just learned about the scientific method, my colleagues and I were told of the story of <em>“The Blind Scientists and the Elephant”</em>. Each one of the scientists had to rigorously examine a part of the elephant in front of them and conclude what they were looking at:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/b-scie-elep.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> While this caricature illustrates that all scientists are comically wrong, it still conveys a deep message: each scientist, assuming each is rigorous in their findings, is correct. Points of disagreements are perhaps not due to contradictory findings, but simply because, as depicted, they are looking at different parts of the elephant. </div> <p>Nativists and empiricists are analogous to the scientists, and the brain is represented by the elephant. It is not the case that both hold opposite views. Rather, they are providing explanations to different aspects of the mind: empiricists focus on learning, and explain that the brain’s neuronal activity-associated synaptic changes can encode knowledge from the environment. Nativists are not opposed to this, per se. They are just looking at other aspects of the mind: the abstract, intangible portion which deals with the manipulation of math. Math is perhaps a faculty that is native to <em>Homo sapiens</em> and is the basis of thought.</p> <div class="footnotes" role="doc-endnotes"> <ol> <li id="fn:3" role="doc-endnote"> <p>what I listed above mainly involves equations with real and maybe imaginary numbers. However, what if there is more to these types of numbers that we haven’t thought of, or we’re incapable to even comprehend? Because this question is off-topic, I left it as a footnote. <a href="#fnref:3" class="reversefootnote" role="doc-backlink">&#8617;</a></p> </li> <li id="fn:1" role="doc-endnote"> <p>the easier version proposed by the Tortoise from Gödel, Escher, Bach is easier to prove, which states that all even numbers less than or equal to two are the sum of two primes. <a href="#fnref:1" class="reversefootnote" role="doc-backlink">&#8617;</a></p> </li> <li id="fn:2" role="doc-endnote"> <p>he didn’t clean it afterwards <a href="#fnref:2" class="reversefootnote" role="doc-backlink">&#8617;</a></p> </li> </ol> </div>]]></content><author><name>An Xuelong</name></author><category term="blog-post"/><category term="food-for-thought"/><category term="research"/><summary type="html"><![CDATA[I argue about the complementarity of math and the natural world, about nativism and empiricism, and implications of rejecting either of them.]]></summary></entry><entry><title type="html">Brain-Heart Fractal, DALL-E and I</title><link href="https://awxlong.github.io/blog/2023/brain-fractal/" rel="alternate" type="text/html" title="Brain-Heart Fractal, DALL-E and I"/><published>2023-09-15T13:42:00+00:00</published><updated>2023-09-15T13:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/brain-fractal</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/brain-fractal/"><![CDATA[<h1 id="dall-e-and-generative-art">DALL-E and generative art</h1> <p>I have my share of skepticism of transformer-based models to generate art. My understanding of probability theory leads me to believe that a probabilistic model trained on a dataset can, at least interpolate, and, at most, extrapolate. Nonetheless, I don’t think creativity emerges from the averaging of past experiences or extrapolation of them. For example, consider a model like DALL-E which has learned from massive amounts of images what avocado and chair are. While it was impressive for it to generate an image when prompted with ‘avocado chair’ despite never being present, it is nowadays a ‘known unknown’. This is, such output can be readily explained by its capability to generate picture from the prompt, which we can think of as an extrapolation of both objects ‘avocado’ and ‘chair’ after seeing them for a large amount of times. Furthermore, both ‘avocado’ and ‘chair’ are real, natural objects, which empirically have been shown to be easier to learn than characters and numbers <sup id="fnref:1" role="doc-noteref"><a href="#fn:1" class="footnote" rel="footnote">1</a></sup>.</p> <p>To me, art is also a reflection of the artist. However, since DALL-E has no prior life anecdotes, struggles or joyful moments for it to express through art, I personally find its generated art, no matter how grandiose they are advertised as they are through various mediums, to be shallow. I am greatly skeptical of whether it can generate a piece of work that is impactful and meaningful given a prompt such as “your understanding of peace and coexistence”.</p> <h1 id="exploring-prompts-on-complementarity-of-brain-and-heart">Exploring prompts on complementarity of brain and heart</h1> <p>Nonetheless, if we disentangle DALL-E from the creation of art, and think of DALL-E as a tool for artists rather than fall for the frenzy discussion that AI is soon replacing artists and winning art competitions. In my view, that AI-generated art can win <a href="https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html">competitions</a> reflects not a situation where AI is going to overtake humans in art (since AI didn’t even participate in this competition, it was a human who did, and prompted AI to generate a picture given specifications), but rather that we can elevate the standards of current competitions. I mean, wouldn’t it be incomprehensible if we pit humans against driverless automobiles in a race? We instead make racers on cars compete against other racers on cars. Therefore, I believe future competitions of art should allow artists to explore their humane creativity with the aid of DALL-E.</p> <p>Given the above premise, I have indulged myself in exploring DALL-E powered image generators in the web, including <a href="https://www.bing.com/images/create">Microsoft Bing Image Creator</a>, <a href="https://www.craiyon.com/">Craiyon</a>, <a href="https://openai.com/dall-e-2">Open AI’s Dall-E</a> and <a href="https://deepai.org/machine-learning-model/text2img">DeepAI’s text-to-image generator</a>. I didn’t focus with objects of the real world, such as space cats, Van Gogh-styled buildings or cyberpunk-ish characters, but rather focus on abstract concepts.</p> <p>Given my interest in the consilience of knowledge and complementarity of seemingly opposing ideas, I wanted to instead try abstract ideas like “the complementarity of the brain and heart”. Before resorting to DALL-E, I gave a try myself in sketching the idea:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/sketch.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> What I wanted to convey is that pure, cold rationality embodied by the brain complements the warmth and love embodied by the heart. Because I also like fractals, as in complexity is the result of the composition of simple rules, I wanted to use them as a pivot to join both sides. </div> <p>The following are some images generated by the above models:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/deep-ai-complementarity.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/openai-complementarity.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/bing-complementarity.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> The images are from DeepAI, OpenAI, and MS Bing, respectively. Each model is able to generate a brain, sometimes along a heart, but I personally am not convinced about whether it can illustrate complementarity. </div> <p>I tried to be more specific, with prompts such as “brain and heart joined together harmoniously like the yin and yang”, “a depiction that the brain and heart work together harmoniously to make us human”, and “beautiful, intricate fractal shaped as the brain and heart innervated with one another, red, black and white”.</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/yinyang-heart.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/make-us-human.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/intricate-fractal.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> The images are generated from OpenAI without paying. Despite providing more specific prompts, I'm generally disastified with the generated images. The model sometimes generate specific elements mentioned in the prompt, such as the 'human' in '... makes us human', even though it doesn't have to. </div> <p>However, I do like some of the generations. I also share more images obtained from MS Bing’s Image Creator, which I mostly like. The prompts I used were “brain and heart interwoven made of fractals” and “brain and heart composed of fractals, high contrast red and black color”:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/interwoven-bing.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/high-contrast.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> The images are from MS Bing's Image Creator with free tokens. I really like these generated images. Notice how I avoid using "complementarity", since I argue that the model can't understand high-level concepts. Nonetheless, by specifying the properties of what I want, I'm able to generate images close to what I desire. </div> <p>I’m actually using one of the images as my website icon.</p> <p>There is a plethora of images I’m skipping since I generated too many with different prompts across platforms. And I may generate once in a while if I’m interested in checkpointing whether they have improved.</p> <p>One of them which I also really liked is from DeepAI. I like the overall heart-shape, the colors used, and the presence of fractals:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/brain-heart-deepai.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <p>I really don’t think AI can replace artists. In general, I support the idea that “AI won’t replace [insert job profession]”, but rather “those who use AI will replace those who don’t”. I’m confident that anytime soon, AI models won’t be able to grasp high-level concepts since they lack their machine language which I believe is a tool to develop <a href="https://www.youtube.com/watch?v=Q-B_ONJIEcE">their own cognition for understanding the world</a>. Even if they acquire machine language, they still need to evolve to grasp extremely complicated and intricate concepts like “complementarity”.</p> <div class="footnotes" role="doc-endnotes"> <ol> <li id="fn:1" role="doc-endnote"> <p>My own hypothesis for such behaviour is because the model lacks its own machine language for which it can understand the world. There are objective reasons for why an avocado and a chair are the way they are, either due to biology or human affordance. However, such objective reasons are not present in the presentation of concepts like numerosity or with letters. <a href="#fnref:1" class="reversefootnote" role="doc-backlink">&#8617;</a></p> </li> </ol> </div>]]></content><author><name></name></author><category term="blog-post"/><category term="creative-work"/><summary type="html"><![CDATA[I share some sketches on the complementarity of mind and heart made by me and DALL-E]]></summary></entry><entry><title type="html">Reflections on the Neurosymbolic Summer School 2023</title><link href="https://awxlong.github.io/blog/2023/nesy-summer-school/" rel="alternate" type="text/html" title="Reflections on the Neurosymbolic Summer School 2023"/><published>2023-09-01T04:42:00+00:00</published><updated>2023-09-01T04:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/nesy-summer-school</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/nesy-summer-school/"><![CDATA[<p>I attended the NeuroSymbolic (NeSy) Summer School 2023 held <a href="https://neurosymbolic.github.io/nsss2023/">virtually</a> between August 29-30. What follows are my thoughts and comments with regard to some talks. They can be watched thanks to the available <a href="https://ibm.github.io/neuro-symbolic-ai/events/ns-workshop2023/">recordings</a>, which provide context for the ideas below:</p> <h1 id="reflection-on-talks-by-professors-benjamin-grosof-fabrizio-and-fabio-and-luc-de-raedt">Reflection on talks by Professors Benjamin Grosof, Fabrizio and Fabio, and Luc de Raedt</h1> <p>They mostly talked about the use of logic, propositional or first-order, to shape a problem domain and drive neural networks to make explainable predictions, discussing the strenghts and drawbacks of this approach.</p> <p>I am personally skeptical of the use of logic to encode knowledge or describe relations. This is because there exists infinite knowledge, and thus this entails that in order to solve real-world problems that involve complex systems, e.g. predicting cell progression in a tissue, may involve writing infinite amount of rules encompassing cell physiology or pathogenesis. It is also disputable what are the putative rules to encode in order to solve problems, and rarely two experts on the same field may converge on what rules should be encoded into a model for solving a problem like cell trajectory analysis. On a tangent, I also liked the point raised by Prof. Benjamin Grosof on defeasible knowledge, referring to rules which are only true circumstantially. For example, for a rule like ‘every cell has a nucleus’, this is only true except when it doesn’t such as during anaphase of mitosis or if it’s a bacterium. One can relax the “absoluteness” of rules by encoding probabilistic knowledge, i.e., rules with probability distributions on their truth values attached to them. However, this only exacerbates the inherent combinatorial search space related to the search for rules that can answer a query.</p> <p>Let us illustrate with the following hypothetical logic program:</p> <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>friendOf(kerry,  natalia).
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.2">Jekyll</generator><link href="https://awxlong.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://awxlong.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2023-11-02T15:05:58+00:00</updated><id>https://awxlong.github.io/feed.xml</id><title type="html">blank</title><subtitle>A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. </subtitle><entry><title type="html">A mathematical analogy for understanding creativity</title><link href="https://awxlong.github.io/blog/2023/creativity/" rel="alternate" type="text/html" title="A mathematical analogy for understanding creativity"/><published>2023-11-02T13:42:00+00:00</published><updated>2023-11-02T13:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/creativity</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/creativity/"><![CDATA[<p>Math is really a beautiful framework with which we can analyze several ideas. For instance, beauty can be seen through the lenses of math using symmetry and geometry, and the study of complex systems can be cast through calculus and probability theory.</p> <p>Given this premise, can we use math to understand what creativity is. Here is my attempt:</p> <h1 id="how-can-creativity-be-defined">How can creativity be defined?</h1> <p>Let us run the following thought experiment. Imagine you’re an enthusiastic pursuer of truth. The whole universe is simply an empty space. In this void, in front of you are a some numbers and two operators: + (ordinary addition) and * (ordinary multiplication)<sup id="fnref:1" role="doc-noteref"><a href="#fn:1" class="footnote" rel="footnote">1</a></sup>.</p> <p>Science, or the discovery of knowledge, is analogous to discovering new numbers. You randomly add two numbers, 1 and -1 and discover 0. Then you add 1 to 1 and obtain 2. Repeating this process, you end up discovering all the integers. Furthermore, you can find patterns that lead to theorems among the integers, such as properties of commutativity, transitivity and distributivity.</p> <p>However, from a metaphysical point of view, addition and multiplication over integers is closed in this set, therefore you can’t discover anything beyond the horizon of integers.</p> <h1 id="can-you-discover-anything-else">Can you discover anything else?</h1> <p>Creativity, in this scenario, would be proposing another operator such as division. Having this new operator in your mind’s eye to perceive reality, you can now compute a new class of numbers, the rationals and thus discover the set of all real numbers.</p> <p>In either case, prior and after the discovery of the division operator, the infiniteness of numbers is analogous to the infinity of knowledge.</p> <p>Creativity can also materialize by introducing the square root, and this new operator extends our perceptual horizon to encompass the realm of complex/imaginary numbers.</p> <h1 id="more-analogies">More analogies</h1> <p>A different civilization from another point in space, using a different set of symbols to encode numbers, may make similar discoveries, i.e., numbers obtained following the same principles of computation using + and *. Moreover, they may also discover commutativity, transitivity and distributivity. This analogy represents the universality of knowledge.</p> <p>In the above thought experiment, I’ve ignored letters, phonemes, and words to simplify this overly-idealized world to get my ideas on what is creativity across.</p> <figure> <img src="/assets/img/creativity.jpg" alt="Sorry, an unanticipated error occured and the image can't load." width="100%" height="auto"/> <figcaption id="creativity"> The thumbnail is generate by Dall-E powered MS Bing Image Generator given the prompt "your interpretation of what creativity is". </figcaption> </figure> <p>PS: Demis Hassabis, Deepmind CEO, also comments on another definition of <a href="https://www.youtube.com/watch?v=Gfr50f6ZBvo">creativity, or AGI</a>. This is going beyond statistical interpolation and extrapolation of training data. An AGI model that is creative should be able to give an output to an instruction such as “design a game that has simple rules, but is extremely hard to master, and can be played for hours”.</p> <div class="footnotes" role="doc-endnotes"> <ol> <li id="fn:1" role="doc-endnote"> <p>Perhaps these numbers and operators existed there a priori, maybe they emerged from a Creator, or perhaps they emerged from a singularity. <a href="#fnref:1" class="reversefootnote" role="doc-backlink">&#8617;</a></p> </li> </ol> </div>]]></content><author><name></name></author><category term="blog-post"/><category term="food-for-thought"/><summary type="html"><![CDATA[Using numbers to illustrate an intuitive definition of what creativity is]]></summary></entry><entry><title type="html">The many interpretations of terms in artificial intelligence</title><link href="https://awxlong.github.io/blog/2023/many-faces-ML/" rel="alternate" type="text/html" title="The many interpretations of terms in artificial intelligence"/><published>2023-11-01T13:42:00+00:00</published><updated>2023-11-01T13:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/many-faces-ML</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/many-faces-ML/"><![CDATA[<p>I bumped into this paper on <a href="https://twitter.com/ChristophMolnar">X (formerly Twitter)</a></p> <div class="jekyll-twitter-plugin"><blockquote class="twitter-tweet"><p lang="en" dir="ltr">How do researchers define interpretability and explainability?<br/><br/>An overview:<a href="https://t.co/bNjKv8F44g">https://t.co/bNjKv8F44g</a> <br/><br/>Summary: No consensus<br/><br/>My opinion: It&#39;s already the Wild West, no one will stick to any definition. Most pragmatic is to treat them interchangeably. <a href="https://t.co/mEh6mUuk4C">pic.twitter.com/mEh6mUuk4C</a></p>&mdash; Christoph Molnar (@ChristophMolnar) <a href="https://twitter.com/ChristophMolnar/status/1718921189333045752?ref_src=twsrc%5Etfw">October 30, 2023</a></blockquote> <script async="" src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> </div> <p>In today’s post, I don’t want to discuss interpretability or explainability, but rather elaborate on the above idea that in the AI literature, different authors express completely different ideas despite using the same words, often words that appear in our daily parlance, yet acquire uncommon interpretations.</p> <p>If you have just set foot into the world of artificial intelligence, here are my thoughts and advices on navigating this exciting, but often messy landscape.</p> <h1 id="terms">Terms</h1> <h4 id="explanation-hypothesis-model"><strong>Explanation, hypothesis, model</strong></h4> <p>When I began studying AI, the word “model” elicited the mental imagery of a 3D scale model of something. If I wanted a “model” of an atom, I could pick some recycled styrofoam balls, stick them together and, bam (pun intended), an atom is synthesized.</p> <p>Nowadays it mainly refers to a equation or function with some parameters $\theta$, where a ‘good’ model is defined as having optimal parameters $\theta$ that can make predictions with low error on the training and (hopefully) unseen, test data, compared to a ‘bad’ model.</p> <p>In rare cases, such as in <a href="https://www.youtube.com/watch?v=NsID1iM8gRw">Prof. Joshua Tenembaum’s talk on Cognition</a>, a (physics-) model can refer a simulator implemented in Unity.</p> <h4 id="knowledge"><strong>Knowledge</strong></h4> <p>This word may cause some confusion to peers with a philosophy or psychology background. At least to me, prior to studying AI, ‘knowledge’ meant pieces of information encoded in natural language, e.g. “Mount Chimborazo is actually higher than Mount Everest” somewhere in the brain. I don’t know how, nor where, nor whether it is encoded in natural language.</p> <p>At least in the early days of AI, first order logic constraints such as <code class="language-plaintext highlighter-rouge">smoker(X) :- edema(X)</code>, read as “it is true that if X is a smoker, then X has edema”, does resemble ‘knowledge’ encoded in a way that seems familiar to us.</p> <p>However, this is not always the case. The word ‘knowledge’, such as in ‘prior knowledge’, mainly used in Bayesian modelling, has not much to do with logic predicates or natural language. It mainly refers to what you believe on the distribution of the parameters of your model come from before your model is trained on any data. If you think your model must be parametrized by weights that more often take extreme values, you can set a prior belief with distributions with heavier tails. Guidance on what prior distributions can be used to describe your parameters can be checked in this <a href="https://twitter.com/bindureddy/status/1708664380987220427">tweext</a></p> <p>Lastly, sometimes researchers may even say that ‘knowledge’ of the data is encoded in the parameters of the model, which leads us to the next section:</p> <h4 id="explainability-and-interpretability"><strong>Explainability and interpretability</strong></h4> <p>Explainability and interpretability are terms that can be used interchangeably, as shown in the above paper. I avoid making a distinction in this post, and just focus on sharing what is usually meant by either term.</p> <p>The simplest manifestation of explainability can be observed with simple linear models like linear or logistic regression. Here explainability simply refers to the presence of coefficients estimated per each input variable of the model. These coefficients’ magnitudes often denote the contribution of the variable to the overall prediction of the model. Other non-linear, machine learning models such as decision trees are by design explainable, and such quality mainly refers to the thresholds appearing at each junction in the decision trees.</p> <p>Concerning deep learning, particularly convolutional neural networks (CNN), explainability can refer to the computation of feature maps on the input space to see on average, what are the filters of the CNN observing when solving a task like classification given an image. Deep generative models (DGM) that leverage neural networks and learn latent representations of the data can also become ‘explainable’ if there is some post-hoc (i.e. after training) processing of embeddings, such as dimensionality reduction through principal component analysis, that can map the embeddings on 2D space. In such space, it is hoped that the DGM learns disentangled features, albeit how are they interpreted is mainly up to the researchers, not by some objective reason.</p> <p>Explainability in the eyes of some authors may also refer to the capability of the researcher to compute a numerical value of the likelihood of the parameters of the data, or at least an approximation of it, such as the evidence lower bound. This scalar value can be used to compare the performance of different models, and these models can thus be referred to as “provable”.</p> <p>Other authors may also define explainability when deep neural networks, despite being black boxes, can accept inputs or display its predictions through a user-friendly interface that allows them to interact with the model.</p> <h1 id="bottom-line">Bottom line</h1> <p>My key advice in this brief article is to convince you not to trust the “intuitive” interpretation of a word, especially if it is used on a quotidian basis. For example, in my personal view, to be “explainable” means to be able to justify its own answer. Just like a friend to whom I ask why didn’t they go to a concert by singer Aimer, to be “explainable” (at least when referring to the common meaning of this word in daily life) means that they can give a justification as to why didn’t they go (maybe they became sick, or admit they have poor taste in music). ‘Explainable’ in the machine learning literature has not much to do with this daily life interpretation.</p> <p>Finally, the above terms are simply the tip of the iceberg. In the wider research literature, authors can diverge in what they mean by on many terms such as “language”, the word “intelligence” itself, and more controversial terms like “artificial general intelligence”. Because I don’t know the precise meaning of them myself, I refrain from commenting furthermore.</p> <p>Happy learning!</p>]]></content><author><name></name></author><category term="blog-post"/><category term="food-for-thought"/><summary type="html"><![CDATA[Some thoughts concerning how to interpret quotidian words appearing in AI research in a non-quotidian way.]]></summary></entry><entry><title type="html">Structured Gradient Descent</title><link href="https://awxlong.github.io/blog/2023/sgd/" rel="alternate" type="text/html" title="Structured Gradient Descent"/><published>2023-10-31T13:42:00+00:00</published><updated>2023-10-31T13:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/sgd</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/sgd/"><![CDATA[<h1 id="a-tale-of-a-scientist">A tale of a scientist</h1> <p><em>Shirley wanted to become a scientist. She wanted to find about jumping behavioral patterns of <a href="https://en.wikipedia.org/wiki/Tree_cricket">tree crickets</a>, which are insects of the order Orthoptera. Specifically, she aimed in testing the hypothesis that tree crickets understood human-issued commands that ordered them “to jump”. She caught a bunch of crickets and started testing her idea by telling one group of crickets to jump, and leaving a control group of crickets telling them nothing. Most crickets in either group jumped, merrily and highly. To her, these results weakly suggest that crickets understood the order to jump and did so accordingly. Then for the same group of crickets in both conditions, she mercilessly cut their legs off. Again, she issued the command to jump for one group, and left the control without saying anything. None of the crickets jumped. What did Shirley the scientist conclude? “Crickets stop understanding human-language after having their legs chopped off”</em></p> <figure> <img src="/assets/img/cricket.jpg" alt="Sorry, an unanticipated error occured and the image can't load." width="100%" height="auto"/> <figcaption id="sgd"> Depiction of a cricket generated by Dall-E powered MS Bing Image Creator given the prompt "a beautiful, photo-realistic cricket" </figcaption> </figure> <h1 id="its-not-her-fault-the-parallel-with-deep-neural-networks-learning">It’s not her fault: The parallel with deep neural network’s learning</h1> <p>Perhaps you’ve heard of the above story in a course introducing the scientific method.</p> <p>This tale is interesting given the parallels there exists with current deep neural networks. These are analogous to Shirley, their parameters a metaphor to what she learns, and her crickets resemble the training data. No one has any control with regard to what she learns given the training data. When she concluded that “crickets can’t interpret English after having their legs cut-off”, that is not a “wrong” interpretation, per se. Rather, reaching this conclusion is a simple consequence of how she was designed and the coincidences of the training data (i.e. crickets did stop jumping after their legs were chopped off despite Shirley’s instructions to jump).</p> <p>With respect to deep neural networks, you can train it to classify images in CIFAR-10 or FashionMNIST, or more generally, train to generate the images. As per what sort of embeddings it learns, how it learns, among other aspects of learning, are out of our control. Exemplified in <a href="https://arxiv.org/pdf/2011.12854.pdf">Stammer et al., (2021)</a>, in a ColorMNIST dataset where a ResNet learns to classify digits, if by chance most digits nines appeared with a purple color, then this ResNet would classify purple color digits as 9 in the test set, even though the digits may be of different shape. In such a case, the model has learned that the concept of color defines the identity of a digit, since we have no control over what artifacts does the ResNet pick.</p> <p>The main powerhouse driving learning is stochastic gradient descent, and it is partly due to this stochasticity that deep neural networks are black-boxes.</p> <p>The stochasticity of gradient descent is mainly derived from the order and the semantics of the incoming training data. We thus have no control over what the model learns. The AI research community has proposed several methods, such as trying to learn disentangled representations of data, which can be obtained by having a more intricate labelling of the dataset. Consider the <a href="https://cs.stanford.edu/people/jcjohns/clevr/">CLEVR Diagnostic Dataset</a>, in which each object appearing in each training image is exquisitely labeled with its (x, y, z) coordinates, shape, color, size, material and positional relations among others, along with questions, answers and functional programs to answer each question. A variant of such dataset with real-life scenes is <a href="https://arxiv.org/abs/1902.09506">GQA</a>.</p> <h1 id="structured-gradient-descent">Structured gradient descent</h1> <p>Given these precedents, I wanted to depict my desire to see in the future a kind of “structured” gradient descent (which in English coincides with the same acronym as stochastic gradient descent):</p> <figure> <img src="/assets/img/sgd4life.png" alt="Sorry, an unanticipated error occured and the image can't load." width="100%" height="auto"/> <figcaption id="sgd"> Not an actual depiction of structured gradient descent, but nonetheless a cool logo. </figcaption> </figure> <p>Structured gradient descent, as its name suggest, would allow the training of a model where we have control over what kind of representation it obtains from the supplied data. Analogous to the tale above, it’d akin to explicitly telling Shirley that crickets can’t understand human language, and that her line of reasoning has to start from the motility of legs of tree crickets in giving them the ability to jump. Afterwards, the jumping behavior of crickets is up to her to explain.</p> <p>The unanswered questions that follow would include:</p> <ul> <li>How would structured gradient descent materialize in a learning algorithm? <ul> <li>Will a grammar, prior knowledge in the form of logic or external ontologies play a pivotal role in the design of such an algorithm?</li> </ul> </li> <li>Will structured gradient descent allow a model to learn a disentangled representation, along with benefits such as a lower demand for computational power, dataset size and model size? <ul> <li>What would be the demands for structured gradient descent to run? More intricate labeled data?</li> <li>Most importantly, would more intricately labeled data compensate for low data?</li> </ul> </li> </ul> <figure> <img src="/assets/img/structuredgd.png" alt="Sorry, an unanticipated error occured and the image can't load." width="100%" height="auto"/> <figcaption id="sgd"> A (hopefully) cool logo of SGD</figcaption> </figure>]]></content><author><name></name></author><category term="blog-post"/><category term="food-for-thought"/><category term="creative-work"/><summary type="html"><![CDATA[Some thoughts on the concept of structured gradient descent and a piece of artwork associated to it]]></summary></entry><entry><title type="html">A tale of two worlds: the natural and the mathematical</title><link href="https://awxlong.github.io/blog/2023/tale-two-worlds/" rel="alternate" type="text/html" title="A tale of two worlds: the natural and the mathematical"/><published>2023-10-14T13:42:00+00:00</published><updated>2023-10-14T13:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/tale-two-worlds</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/tale-two-worlds/"><![CDATA[<p>There seems to exist two worlds which exist in the universe.</p> <p>The real, natural world which can be accessible through empirical means, and the abstract, mathematical world which are the means themselves with which we observe the real, natural world. Here, the real, natural world is what we can perceive with our senses. The mathematical world is an umbrella term to refer to the ‘math’ in our common day parlance, as well as including logic in its many forms: propositional, first-order and higher-order.</p> <p>The real, natural world is subject to the laws of physics, which underlie chemistry and biology. In the abstract, mathematical world, such laws are not present: there we can think of breaking Newton’s Universal Laws of Gravitation, the overall decrease of entropy against the Second Law of Thermodynamics, or, in general, anything within our mental capabilities<sup id="fnref:3" role="doc-noteref"><a href="#fn:3" class="footnote" rel="footnote">1</a></sup>.</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/world-math.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> A depiction of the complementarity of the empirical, natural world and the abstract, mathematical world. The image is generated via Microsoft Bing's Image Creator with the prompt "on the left, a picture of the natural world. On the right, a picture of equations, geometry and calculus". I have tried several other prompts and chose the one I believed suits what I wanted to convey. As I have discussed [before](https://awxlong.github.io/blog/2023/brain-fractal/), as well as in this article, I've avoided abstract terms such as 'complementarity' or 'abstract' since I'm unconfident a large vision system can understand it. </div> <p>To recount this tale of two worlds, we can take a step back to ancient debate of <a href="https://plato.stanford.edu/entries/innateness-cognition/">nativism and empiricism</a><d-cite key="samet_2012_innateness"></d-cite>. Namely, nativism argues that knowledge emerges from axioms and inference rules applied to those axioms that we humans are endowed since birth. Elaborating, thanks to a random mutation that kickstarted the beginning of <em>Homo sapiens</em>, our species have grown a capacity to understand elements of the natural world in terms of its objectness, and its fluent properties, such as shape, numerosity and size. This capacity allow us to understand objects of the real world in terms of how they change, how they hierarchically relate to one another and how they causally relate to each other. This capacity contrasts to what empiricists argue, namely that we acquiere knowledge dependent on the experiencies we’ve been exposed, and determine (causal) relationships among objects of the real world based on the extrapolation of previous experiences.</p> <p>I argue this is a false dichotomy, and that underlying it there is the complementarity of nativism and empiricism. Nativism mostly deals in how the human mind navigates the internal, abstract, mathematical world. If we want to prove that there exists infinite primes, nativists would argue that such theory can be proved via universal inference procedures such as proof by contradiction, and manipulating symbols involving the definition of primeness of a number. In such an scenario, empiricism would say that we have to extrapolate from prior observations of prime numbers to conclude they’re infinite. However, who can live for infinite amount of time in order to witness the infinity of primes and assert the above theory! Another example would concern Goldbach’s Conjecture, which proposes that all even numbers greater than two are the sum of two primes <sup id="fnref:1" role="doc-noteref"><a href="#fn:1" class="footnote" rel="footnote">2</a></sup>. Can we prove it by observing all even numbers, as nativists would argue? I don’t believe so, we have to resort to the mathematical reasoning mechanisms we were endowed in our mind. One way is to prove it by <a href="https://en.wikipedia.org/wiki/Euclid%27s_theorem">contradiction</a>. Assume there aren’t infinite primes, multiply the finite primes, and add one. This new number is not divisible by any of the finite primes, and thus is a prime. Infinity is a concept easier to understand and grasp when it is defined, as it can’t be learnt.</p> <p>However, we <em>Homo sapiens</em> don’t only reason about the mathematical world. We <em>use math to understand the external world</em>, and understanding the external world recquire experiencing it, and with experience comes learning. Empiricist, thus, can explain how we are able to sort out the relationships of objects in the external world as a form of interpolating and extrapolating observed evidence. However, empiricists need nativists as much as the latter need the former. Empiricism need the structure of nativists to accommodate the perceptual stimuli of the external world in order to reason about their relationships and derive new ones. Nativists need to learn from external stimuli so that the structure can evolve and be able to describe more complicated relationships.</p> <p>The natural world needs math to be understood, and math needs the natural world to exist. From this complementarity, it is argued that the mind consists of a grammar or a domain-specific language with which to give <a href="https://plato.stanford.edu/entries/language-thought/">shape to thought</a><d-cite key="rescorla_2019_the"></d-cite>. This grammar would be the basis for parsing the world’s objects in terms of their objectness, fluent properties and causal relations. Additionally, this innate grammar should have stochastic properties as probability acts as the pivot to ground this grammar with the natural world, and be able to describe it in a coherent manner. For example, after observing past experiences of a ‘hammer’, we can probabilistically ground a high-level, symbol ‘hammer’ with the actual object in the real world. Accommodating ‘hammer’ within some structure allow us to reason about its relations, functions, and properties. For further reading of stochastic grammars for vision, you can check Prof. Zhu-Song Chun’s <a href="http://www.stat.ucla.edu/~sczhu/Books/Book_2_Parsing.pdf">textbook on this topic</a> or <d-cite key="zhu_2020_dark"></d-cite>, which I find extremely difficult to follow, but nonetheless an interesting manuscript to read.</p> <h1 id="implications-of-rejecting-either-nativism-and-empiricism">Implications of rejecting either nativism and empiricism</h1> <p>I advocate that there exists complementarity between the mathematical and natural world. However, I can’t stop others from opting for the dichotomy between them, thus not only pitting nativism against empiricism or the other way around, but also rejecting one at the cost of the other.</p> <p>If we ignore nativism, and assume past experiencies are all that is needed to understand how the mind deduces new knowledge, we can think of the following. We can’t give shape to the mathematical world. It doesn’t “exist” in the real world. Circles, polygons, numbers, operations, vectors, matrices, infinity, among others, all reside within our mind. A friend once pointed out that he can draw a line on a dining table<sup id="fnref:2" role="doc-noteref"><a href="#fn:2" class="footnote" rel="footnote">3</a></sup> with a marker, and that’s a geometrical line. I disputed: “that is ink placed on a surface, which in your head is representing a geometrical line. While the ink and wooden surface are tangible objects, the geometrical line isn’t. Miraculously, however, I can also see and understand what you’re thinking concerning geometrical lines, suggesting the universality of math in the minds of <em>Homo sapiens</em>” (I may have edited my response to sound wiser, as well as edited the whole story to make it more entertaining).</p> <p>That math doesn’t exist in the real world, and thus can’t be picked up with our perceptual systems can explain why large vision models like DALL-E or MidJourney struggle with generating images which contain letters or numbers, unless they are fed with prior knowledge on <a href="https://research.google/pubs/pub52496/">characters</a><d-cite key="liu_2023_characteraware"></d-cite>. This is partly because the vast amount of data in which they’re trained mainly correspond to images taken from the natural, tangible, external world, e.g., photos under different styles of animals, buildings, groups of people, or landscapes.</p> <p>We also can’t accept nativism at the expense of empiricism. The history of artificial intelligence is a good reference to observe that resorting to only expert systems consisting of universal inference rules like forward and backward chaining results in systems that are disconnected with real world dynamics. Whilst math can be independent of the real natural world, it is nonetheless intricately connected to the world in order to describe it.</p> <h1 id="implications-on-the-quest-for-artificial-intelligence">Implications on the quest for artificial intelligence</h1> <p>Deep learning ignited the summer of AI of the twenty-first century, and the transformer is one of the torches that keeps the summer shining. How should we keep this summer from ending?</p> <p>Deep learning is a mostly pure expression of empiricism. Deep neural networks don’t manipulate symbols, are inspired by the brain’s architecture, and are mainly learn via training from a dataset of past experiences. There are several empirical observations, and <a href="https://plato.stanford.edu/entries/scientific-reduction/">reductionist</a> <d-cite key="riel_2014_scientific"></d-cite> claims suggesting that mental activities such as intuition and reasoning are the result of the biochemical processes of the brain. There are the recurrent achievements of new state of the arts performance in benchmarks used to measure the human faculties of vision and language. One of the striking research papers that caught my attention involved decoding a person’s brain activity to their perceived image:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/decode.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> The model's architecture, which is a diffusion model (i.e. a probabilistic model consisting of deep neural networks). On the right, the upper row are images presented to a person whose brain activity is recorded. This brain activity is then decoded to the below row of images. Image extracted from https://sites.google.com/view/stablediffusion-with-brain/ </div> <p>That there is some association between brain activity and external stimuli capturable by a probabilistic model is fascinating and encouraging for empiricism. However, most if not all research related to the neuroscience behind mental activity <strong>lacks the explanation of whether brain activity yields any insight into mathematical theorem proving or deduction</strong>. Experiments to explore this can involve assessing whether brain activity can reconstruct a person’s steps into solving an equation, deriving a proof or just in general have some abstract thought involving numbers.</p> <p>Furthermore, taking empiricism to an extreme may stifle the capabilities of an AI model, since radical empiricism subjects the model to be an extrapolation from supplied past experiences. It ignores the nativist capability of generating new thought, often called as creativity.</p> <p>Another counterargument to empiricism is also as follows. Image there is a program running on a computer. You can observe your program running on the screen, as in, you can see what input matches the corresponding output displayed. You also know the underlying algorithm specifying how the input should be transformed to the output. Now, you disassemble your computer and expose its mainframe, now able to look at the CPU and RAM running, all the while your program keeps running. In other words, only the computer screen is gone, so any high-level display your program disappears. You invite an empiricist and tell her that she can make all sorts of measurements she wants on the hardware of the computer (CPU, RAM, motherboard, graphics card). Such measurements could include voltage, heat maps, temperature, and whatever she can think of. Of course, because the computer screen is gone, and she is prohibited from reassembling it back, she is restricted to measure the low-level components of the computer. Now comes the main question, can she be able to figure out from measurements made on the hardware the algorithm being executed? Here, the computer and its low level components correspond to the biological brain, and the software is analogous to mental activity.</p> <p>While low-level activity can reflect to some degree higher level processes, I’m skeptical we can understand and simulate higher-level cognition without assuming its pre-existence. Therefore, we can only describe high level mental activity with high level mental activity. In the thought experiment, this would entail the empiricist may have to ‘hypothesize’ in a separate computer different programs that could be running in the monitor-less computer, use feedback from the low-level measurements obtained from the monitor-less computer, and find which hypothesized programs matches the measurements from the monitor-less computer. Such iterative process of trial and error, which combines both the high-level cognitive process of creativity and low-level signal processing for feedback.</p> <p>As argued repeatedly, the brain’s architecture, neuronal activations and modifications in synaptic strengths all belong to the external world. Drawing inspiration from the brain is a powerful means to achieve AI. However, mental activity such as deduction, and emergence of new ideas through <a href="https://aeon.co/essays/how-close-are-we-to-creating-artificial-intelligence">conjecturing</a><d-cite key="deutsch_2012_how"></d-cite> is a property that has to be embedded in the system, as it can not be learned by picking cues from the environment.</p> <h1 id="final-comments">Final comments</h1> <p>When I just learned about the scientific method, my colleagues and I were told of the story of <em>“The Blind Scientists and the Elephant”</em>. Each one of the scientists had to rigorously examine a part of the elephant in front of them and conclude what they were looking at:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/b-scie-elep.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> While this caricature illustrates that all scientists are comically wrong, it still conveys a deep message: each scientist, assuming each is rigorous in their findings, is correct. Points of disagreements are perhaps not due to contradictory findings, but simply because, as depicted, they are looking at different parts of the elephant. </div> <p>Nativists and empiricists are analogous to the scientists, and the brain is represented by the elephant. It is not the case that both hold opposite views. Rather, they are providing explanations to different aspects of the mind: empiricists focus on learning, and explain that the brain’s neuronal activity-associated synaptic changes can encode knowledge from the environment. Nativists are not opposed to this, per se. They are just looking at other aspects of the mind: the abstract, intangible portion which deals with the manipulation of math. Math is perhaps a faculty that is native to <em>Homo sapiens</em> and is the basis of thought.</p> <div class="footnotes" role="doc-endnotes"> <ol> <li id="fn:3" role="doc-endnote"> <p>what I listed above mainly involves equations with real and maybe imaginary numbers. However, what if there is more to these types of numbers that we haven’t thought of, or we’re incapable to even comprehend? Because this question is off-topic, I left it as a footnote. <a href="#fnref:3" class="reversefootnote" role="doc-backlink">&#8617;</a></p> </li> <li id="fn:1" role="doc-endnote"> <p>the easier version proposed by the Tortoise from Gödel, Escher, Bach is easier to prove, which states that all even numbers less than or equal to two are the sum of two primes. <a href="#fnref:1" class="reversefootnote" role="doc-backlink">&#8617;</a></p> </li> <li id="fn:2" role="doc-endnote"> <p>he didn’t clean it afterwards <a href="#fnref:2" class="reversefootnote" role="doc-backlink">&#8617;</a></p> </li> </ol> </div>]]></content><author><name>An Xuelong</name></author><category term="blog-post"/><category term="food-for-thought"/><category term="research"/><summary type="html"><![CDATA[I argue about the complementarity of math and the natural world, about nativism and empiricism, and implications of rejecting either of them.]]></summary></entry><entry><title type="html">Brain-Heart Fractal, DALL-E and I</title><link href="https://awxlong.github.io/blog/2023/brain-fractal/" rel="alternate" type="text/html" title="Brain-Heart Fractal, DALL-E and I"/><published>2023-09-15T13:42:00+00:00</published><updated>2023-09-15T13:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/brain-fractal</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/brain-fractal/"><![CDATA[<h1 id="dall-e-and-generative-art">DALL-E and generative art</h1> <p>I have my share of skepticism of transformer-based models to generate art. My understanding of probability theory leads me to believe that a probabilistic model trained on a dataset can, at least interpolate, and, at most, extrapolate. Nonetheless, I don’t think creativity emerges from the averaging of past experiences or extrapolation of them. For example, consider a model like DALL-E which has learned from massive amounts of images what avocado and chair are. While it was impressive for it to generate an image when prompted with ‘avocado chair’ despite never being present, it is nowadays a ‘known unknown’. This is, such output can be readily explained by its capability to generate picture from the prompt, which we can think of as an extrapolation of both objects ‘avocado’ and ‘chair’ after seeing them for a large amount of times. Furthermore, both ‘avocado’ and ‘chair’ are real, natural objects, which empirically have been shown to be easier to learn than characters and numbers <sup id="fnref:1" role="doc-noteref"><a href="#fn:1" class="footnote" rel="footnote">1</a></sup>.</p> <p>To me, art is also a reflection of the artist. However, since DALL-E has no prior life anecdotes, struggles or joyful moments for it to express through art, I personally find its generated art, no matter how grandiose they are advertised as they are through various mediums, to be shallow. I am greatly skeptical of whether it can generate a piece of work that is impactful and meaningful given a prompt such as “your understanding of peace and coexistence”.</p> <h1 id="exploring-prompts-on-complementarity-of-brain-and-heart">Exploring prompts on complementarity of brain and heart</h1> <p>Nonetheless, if we disentangle DALL-E from the creation of art, and think of DALL-E as a tool for artists rather than fall for the frenzy discussion that AI is soon replacing artists and winning art competitions. In my view, that AI-generated art can win <a href="https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html">competitions</a> reflects not a situation where AI is going to overtake humans in art (since AI didn’t even participate in this competition, it was a human who did, and prompted AI to generate a picture given specifications), but rather that we can elevate the standards of current competitions. I mean, wouldn’t it be incomprehensible if we pit humans against driverless automobiles in a race? We instead make racers on cars compete against other racers on cars. Therefore, I believe future competitions of art should allow artists to explore their humane creativity with the aid of DALL-E.</p> <p>Given the above premise, I have indulged myself in exploring DALL-E powered image generators in the web, including <a href="https://www.bing.com/images/create">Microsoft Bing Image Creator</a>, <a href="https://www.craiyon.com/">Craiyon</a>, <a href="https://openai.com/dall-e-2">Open AI’s Dall-E</a> and <a href="https://deepai.org/machine-learning-model/text2img">DeepAI’s text-to-image generator</a>. I didn’t focus with objects of the real world, such as space cats, Van Gogh-styled buildings or cyberpunk-ish characters, but rather focus on abstract concepts.</p> <p>Given my interest in the consilience of knowledge and complementarity of seemingly opposing ideas, I wanted to instead try abstract ideas like “the complementarity of the brain and heart”. Before resorting to DALL-E, I gave a try myself in sketching the idea:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/sketch.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> What I wanted to convey is that pure, cold rationality embodied by the brain complements the warmth and love embodied by the heart. Because I also like fractals, as in complexity is the result of the composition of simple rules, I wanted to use them as a pivot to join both sides. </div> <p>The following are some images generated by the above models:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/deep-ai-complementarity.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/openai-complementarity.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/bing-complementarity.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> The images are from DeepAI, OpenAI, and MS Bing, respectively. Each model is able to generate a brain, sometimes along a heart, but I personally am not convinced about whether it can illustrate complementarity. </div> <p>I tried to be more specific, with prompts such as “brain and heart joined together harmoniously like the yin and yang”, “a depiction that the brain and heart work together harmoniously to make us human”, and “beautiful, intricate fractal shaped as the brain and heart innervated with one another, red, black and white”.</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/yinyang-heart.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/make-us-human.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/intricate-fractal.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> The images are generated from OpenAI without paying. Despite providing more specific prompts, I'm generally disastified with the generated images. The model sometimes generate specific elements mentioned in the prompt, such as the 'human' in '... makes us human', even though it doesn't have to. </div> <p>However, I do like some of the generations. I also share more images obtained from MS Bing’s Image Creator, which I mostly like. The prompts I used were “brain and heart interwoven made of fractals” and “brain and heart composed of fractals, high contrast red and black color”:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/interwoven-bing.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/high-contrast.png" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <div class="caption"> The images are from MS Bing's Image Creator with free tokens. I really like these generated images. Notice how I avoid using "complementarity", since I argue that the model can't understand high-level concepts. Nonetheless, by specifying the properties of what I want, I'm able to generate images close to what I desire. </div> <p>I’m actually using one of the images as my website icon.</p> <p>There is a plethora of images I’m skipping since I generated too many with different prompts across platforms. And I may generate once in a while if I’m interested in checkpointing whether they have improved.</p> <p>One of them which I also really liked is from DeepAI. I like the overall heart-shape, the colors used, and the presence of fractals:</p> <div class="row mt-3"> <div class="col-sm mt-3 mt-md-0"> <figure> <picture> <img src="/assets/img/brain-heart-deepai.jpg" class="img-fluid rounded z-depth-1" width="auto" height="auto" data-zoomable="" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"/> </picture> </figure> </div> </div> <p>I really don’t think AI can replace artists. In general, I support the idea that “AI won’t replace [insert job profession]”, but rather “those who use AI will replace those who don’t”. I’m confident that anytime soon, AI models won’t be able to grasp high-level concepts since they lack their machine language which I believe is a tool to develop <a href="https://www.youtube.com/watch?v=Q-B_ONJIEcE">their own cognition for understanding the world</a>. Even if they acquire machine language, they still need to evolve to grasp extremely complicated and intricate concepts like “complementarity”.</p> <div class="footnotes" role="doc-endnotes"> <ol> <li id="fn:1" role="doc-endnote"> <p>My own hypothesis for such behaviour is because the model lacks its own machine language for which it can understand the world. There are objective reasons for why an avocado and a chair are the way they are, either due to biology or human affordance. However, such objective reasons are not present in the presentation of concepts like numerosity or with letters. <a href="#fnref:1" class="reversefootnote" role="doc-backlink">&#8617;</a></p> </li> </ol> </div>]]></content><author><name></name></author><category term="blog-post"/><category term="creative-work"/><summary type="html"><![CDATA[I share some sketches on the complementarity of mind and heart made by me and DALL-E]]></summary></entry><entry><title type="html">Reflections on the Neurosymbolic Summer School 2023</title><link href="https://awxlong.github.io/blog/2023/nesy-summer-school/" rel="alternate" type="text/html" title="Reflections on the Neurosymbolic Summer School 2023"/><published>2023-09-01T04:42:00+00:00</published><updated>2023-09-01T04:42:00+00:00</updated><id>https://awxlong.github.io/blog/2023/nesy-summer-school</id><content type="html" xml:base="https://awxlong.github.io/blog/2023/nesy-summer-school/"><![CDATA[<p>I attended the NeuroSymbolic (NeSy) Summer School 2023 held <a href="https://neurosymbolic.github.io/nsss2023/">virtually</a> between August 29-30. What follows are my thoughts and comments with regard to some talks. They can be watched thanks to the available <a href="https://ibm.github.io/neuro-symbolic-ai/events/ns-workshop2023/">recordings</a>, which provide context for the ideas below:</p> <h1 id="reflection-on-talks-by-professors-benjamin-grosof-fabrizio-and-fabio-and-luc-de-raedt">Reflection on talks by Professors Benjamin Grosof, Fabrizio and Fabio, and Luc de Raedt</h1> <p>They mostly talked about the use of logic, propositional or first-order, to shape a problem domain and drive neural networks to make explainable predictions, discussing the strenghts and drawbacks of this approach.</p> <p>I am personally skeptical of the use of logic to encode knowledge or describe relations. This is because there exists infinite knowledge, and thus this entails that in order to solve real-world problems that involve complex systems, e.g. predicting cell progression in a tissue, may involve writing infinite amount of rules encompassing cell physiology or pathogenesis. It is also disputable what are the putative rules to encode in order to solve problems, and rarely two experts on the same field may converge on what rules should be encoded into a model for solving a problem like cell trajectory analysis. On a tangent, I also liked the point raised by Prof. Benjamin Grosof on defeasible knowledge, referring to rules which are only true circumstantially. For example, for a rule like ‘every cell has a nucleus’, this is only true except when it doesn’t such as during anaphase of mitosis or if it’s a bacterium. One can relax the “absoluteness” of rules by encoding probabilistic knowledge, i.e., rules with probability distributions on their truth values attached to them. However, this only exacerbates the inherent combinatorial search space related to the search for rules that can answer a query.</p> <p>Let us illustrate with the following hypothetical logic program:</p> <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>friendOf(kerry,  natalia).
 friendOf(natalia, shirley).
 smoker(natalia).
 emphysema(natalia).
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-<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. 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I’m An Wang Xuelong, a junior researcher interested in the <a href="https://en.wikipedia.org/wiki/Consilience_%28book%29" rel="external nofollow noopener" target="_blank">consilience of knowledge</a>, with a particular focus within the field of artificial intelligence and socio-biological science.</p> <p>I aim to strike the balance the scientific pursuit to understand intelligence and technological endeavor to adopt what we learn in the research of AI in applications for social good, such as to <a href="https://www.youtube.com/watch?v=Ds132TzmLRQ" rel="external nofollow noopener" target="_blank">accelerate scientific discovery</a></p> </div> <h2><a href="/blog/" style="color: inherit;">latest posts</a></h2> <div class="news"> <div class="table-responsive" style="max-height: 60vw"> <table class="table table-sm table-borderless"> <tr> <th scope="row">Nov 2, 2023</th> <td> <a class="news-title" href="/blog/2023/creativity/">Creativity</a> </td> </tr> <tr> <th scope="row">Nov 1, 2023</th> <td> <a class="news-title" href="/blog/2023/many-faces-ML/">The many interpretations of terms in artificial intelligence</a> </td> </tr> <tr> <th scope="row">Oct 31, 2023</th> <td> <a class="news-title" href="/blog/2023/sgd/">Structured Gradient Descent</a> </td> </tr> </table> </div> </div> <div class="social"> <div class="contact-icons"> <a href="mailto:%61%6E%77%61%6E%67%78%75%65%6C%6F%6E%67@%67%6D%61%69%6C.%63%6F%6D" title="email"><i class="fas fa-envelope"></i></a> <a href="https://github.com/awxlong" title="GitHub" rel="external nofollow noopener" target="_blank"><i class="fab fa-github"></i></a> <a href="https://twitter.com/awxuelong" title="Twitter" rel="external nofollow noopener" target="_blank"><i class="fab fa-twitter"></i></a> <a href="https://youtube.com/@classnotesawx" title="YouTube" rel="external nofollow noopener" target="_blank"><i class="fab fa-youtube"></i></a> </div> <div class="contact-note"> </div> </div> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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+<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. 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I’m An Wang Xuelong, a junior researcher interested in the <a href="https://en.wikipedia.org/wiki/Consilience_%28book%29" rel="external nofollow noopener" target="_blank">consilience of knowledge</a>, with a particular focus within the field of artificial intelligence and socio-biological science.</p> <p>I aim to strike the balance the scientific pursuit to understand intelligence and technological endeavor to adopt what we learn in the research of AI in applications for social good, such as to <a href="https://www.youtube.com/watch?v=Ds132TzmLRQ" rel="external nofollow noopener" target="_blank">accelerate scientific discovery</a></p> </div> <h2><a href="/blog/" style="color: inherit;">latest posts</a></h2> <div class="news"> <div class="table-responsive" style="max-height: 60vw"> <table class="table table-sm table-borderless"> <tr> <th scope="row">Nov 2, 2023</th> <td> <a class="news-title" href="/blog/2023/creativity/">A mathematical analogy for understanding creativity</a> </td> </tr> <tr> <th scope="row">Nov 1, 2023</th> <td> <a class="news-title" href="/blog/2023/many-faces-ML/">The many interpretations of terms in artificial intelligence</a> </td> </tr> <tr> <th scope="row">Oct 31, 2023</th> <td> <a class="news-title" href="/blog/2023/sgd/">Structured Gradient Descent</a> </td> </tr> </table> </div> </div> <div class="social"> <div class="contact-icons"> <a href="mailto:%61%6E%77%61%6E%67%78%75%65%6C%6F%6E%67@%67%6D%61%69%6C.%63%6F%6D" title="email"><i class="fas fa-envelope"></i></a> <a href="https://github.com/awxlong" title="GitHub" rel="external nofollow noopener" target="_blank"><i class="fab fa-github"></i></a> <a href="https://twitter.com/awxuelong" title="Twitter" rel="external nofollow noopener" target="_blank"><i class="fab fa-twitter"></i></a> <a href="https://youtube.com/@classnotesawx" title="YouTube" rel="external nofollow noopener" target="_blank"><i class="fab fa-youtube"></i></a> </div> <div class="contact-note"> </div> </div> </article> </div> </div> <footer class="fixed-bottom"> <div class="container mt-0"> © Copyright 2023 Xuelong An Wang. 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@@ -1 +1 @@
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+<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title>Programación e inteligencia artificial en español | Xuelong An Wang</title> <meta name="author" content="Xuelong An Wang"> <meta name="description" content="compartiendo contenido sobre programación e inteligencia artificial"> <meta name="keywords" content="jekyll, jekyll-theme, academic-website, portfolio-website"> <link href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha256-DF7Zhf293AJxJNTmh5zhoYYIMs2oXitRfBjY+9L//AY=" crossorigin="anonymous"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/css/mdb.min.css" integrity="sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw=" crossorigin="anonymous"> <link defer rel="stylesheet" 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id="programación">Programación</h1> <p>El <a href="https://github.com/awxlong/ai-latam" rel="external nofollow noopener" target="_blank">repositorio</a> y el <a href="https://www.youtube.com/channel/UCqL5YN5aFfMZKvhVPCC7cFQ" rel="external nofollow noopener" target="_blank">canal de YouTube</a></p> <h1 id="inteligencia-artificial">Inteligencia artificial</h1> <ul> <li> <p>Artículos incluyen una breve <a href="https://awxlong.github.io/blog/2023/ritmo-ia/">introducción a modelos generativos</a></p> </li> <li> <p>Di una <a href="https://www.instagram.com/stories/ciap_espol/3218560237714625995/" rel="external nofollow noopener" target="_blank">conversatoria</a> organizada por el <a href="https://www.instagram.com/ciap_espol/" rel="external nofollow noopener" target="_blank">Club de Inteligencia Artificial Politénica</a> sobre la introducción a la IA neurosimbólica con diapositivas en mi <a 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diff --git a/publications/index.html b/publications/index.html
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