Notebooks/Jupyter/Comprehension-AI-Aids-for-Can-AI-Solve-Science.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd4cc0a3",
   "metadata": {},
   "source": [
    "# Comprehension AI Aids for “Can AI Solve Science?”\n",
    "\n",
    "Anton Antonov   \n",
    "[MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com)   \n",
    "[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction)   \n",
    "March 2024  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21b18846",
   "metadata": {},
   "source": [
    "-------\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this notebook we use the Large Language Model (LLM) prompts[”ThemeTableJSON”](https://www.wolframcloud.com/obj/antononcube/DeployedResources/Prompt/ThemeTableJSON/), [\"ExtractArticleWisdom\"](https://www.wolframcloud.com/obj/antononcube/DeployedResources/Prompt/ExtractArticleWisdom/), [\"FindHiddenMessage\"](https://www.wolframcloud.com/obj/antononcube/DeployedResources/Prompt/FindHiddenMessage/), and [\"FindPropagandaMessage\"](https://www.wolframcloud.com/obj/antononcube/DeployedResources/Prompt/FindPropagandaMessage/) to facilitate the reading and comprehension of Stephen Wolfram’s article [\"Can AI Solve Science?\"](https://writings.stephenwolfram.com/2024/03/can-ai-solve-science/), [SW1].\n",
    "\n",
    "**Remark:** We use “simple” text processing, but since the article has lots of images multi-modal models would be more appropriate.\n",
    "\n",
    "Here is an image of [article's start](\"https://writings.stephenwolfram.com/2024/03/can-ai-solve-science/\"):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21dd817c",
   "metadata": {},
   "source": [
    "![](https://www.wolframcloud.com/files/a23d407c-b07e-4f20-84fb-9b5213e994a4/htmlcaches/images/f3258ba5bd7944cbf10c30a1516b75841ce226a68acf3b886dced11f5fe865fa)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61c0a86",
   "metadata": {},
   "source": [
    "The computations are done with Wolfram Language (WL) chatbook. The LLM functions used in the workflows are explained and demonstrated in [SW2, AA1, AA2, AAn1÷ AAn4]. The workflows are done with OpenAI's models. Currently the models of Google's (PaLM) and MistralAI cannot be used with the workflows below because their input token limits are too low."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aea7d46",
   "metadata": {},
   "source": [
    "\n",
    "### Structure\n",
    "\n",
    "The structure of the notebook is as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d998001",
   "metadata": {},
   "source": [
    "\n",
    "| No | Part | Content |\n",
    "| -- | --- | ------- |\n",
    "| 1  | **Getting the article's text and setup** | Standard ingestion and setup. |\n",
    "| 2  | **Article's structure** | TL;DR via a table of themes. |\n",
    "| 3  | **Flowcharts** | Get flowcharts relating article's concepts. |\n",
    "| 4  | **Extract article wisdom** | Get a summary and extract ideas, quotes, references, etc. |\n",
    "| 5  | **Hidden messages and propaganda** | Reading it with a conspiracy theorist hat on. |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b972995f",
   "metadata": {},
   "source": [
    "-------\n",
    "\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82baf230",
   "metadata": {},
   "source": [
    "Here we load a view packages and define ingestion functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48c97665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "&strip-html"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use HTTP::Tiny;\n",
    "use JSON::Fast;\n",
    "use Data::Reshapers;\n",
    "\n",
    "sub text-stats(Str:D $txt) { <chars words lines> Z=> [$txt.chars, $txt.words.elems, $txt.lines.elems] };\n",
    "\n",
    "sub strip-html(Str $html) returns Str {\n",
    "\n",
    "    my $res = $html\n",
    "    .subst(/'<style'.*?'</style>'/, :g)\n",
    "    .subst(/'<script'.*?'</script>'/, :g)\n",
    "    .subst(/'<'.*?'>'/, :g)\n",
    "    .subst(/'&lt;'.*?'&gt;'/, :g)\n",
    "    .subst(/'&nbsp;'/, ' ', :g)\n",
    "    .subst(/[\\v\\s*] ** 2..*/, \"\\n\\n\", :g);\n",
    "\n",
    "    return $res;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f603059",
   "metadata": {},
   "source": [
    "### Ingest text\n",
    "\n",
    "Here we get the plain text of the article:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad40e47d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(chars => 218931 words => 20218 lines => 1433)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $htmlArticleOrig = HTTP::Tiny.get(\"https://writings.stephenwolfram.com/2024/03/can-ai-solve-science/\")<content>.decode;\n",
    "\n",
    "text-stats($htmlArticleOrig);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "850c687b",
   "metadata": {},
   "source": [
    "Here we strip the HTML code from the article:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a3e49ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(chars => 102693 words => 16434 lines => 479)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $txtArticleOrig = strip-html($htmlArticleOrig);\n",
    "\n",
    "text-stats($txtArticleOrig);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c805110c",
   "metadata": {},
   "source": [
    "Here we clean article’s text :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cf8f071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(chars => 98011 words => 15840 lines => 389)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $txtArticle = $txtArticleOrig.substr(^$txtArticleOrig.index(\"Posted in:\"));\n",
    "\n",
    "text-stats($txtArticle);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a18f672",
   "metadata": {},
   "source": [
    "### LLM access configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa45197d",
   "metadata": {},
   "source": [
    "Here we configure LLM access -- we use OpenAI's model \"gpt-4-turbo-preview\" since it allows inputs with 128K tokens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf66c482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $conf = llm-configuration('ChatGPT', model => 'gpt-4-turbo-preview', max-tokens => 4096, temperature => 0.7);\n",
    "\n",
    "$conf.Hash.elems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6bdeb03",
   "metadata": {},
   "source": [
    "------\n",
    "\n",
    "## Themes\n",
    "\n",
    "Here we extract the themes found in the article and tabulate them (using the prompt [\"ThemeTableJSON\"](https://www.wolframcloud.com/obj/antononcube/DeployedResources/Prompt/ThemeTableJSON/)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2e7db02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $tblThemes = llm-synthesize(llm-prompt(\"ThemeTableJSON\")($txtArticle, \"article\", 50), e => $conf, form => sub-parser('JSON'):drop);\n",
    "\n",
    "$tblThemes.&dimensions;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dab36a3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\"><thead><tr><th>theme</th><th>content</th></tr></thead><tbody><tr><td>Introduction to AI in Science</td><td>Discusses the potential of AI in solving scientific questions and the belief in AI&#39;s eventual capability to do everything, including science.</td></tr><tr><td>AI&#39;s Role and Limitations</td><td>Explores deeper questions about AI in science, its role as a practical tool or a fundamentally new method, and its limitations due to computational irreducibility.</td></tr><tr><td>AI Predictive Capabilities</td><td>Examines AI&#39;s ability to predict outcomes and its reliance on machine learning and neural networks, highlighting limitations in predicting computational processes.</td></tr><tr><td>AI in Identifying Computational Reducibility</td><td>Discusses how AI can assist in identifying pockets of computational reducibility within the broader context of computational irreducibility.</td></tr><tr><td>AI&#39;s Application Beyond Human Tasks</td><td>Considers if AI can understand and predict natural processes directly, beyond emulating human intelligence or tasks.</td></tr><tr><td>Solving Equations with AI</td><td>Explores the potential of AI in solving equations, particularly in areas where traditional methods are impractical or insufficient.</td></tr><tr><td>AI for Multicomputation</td><td>Discusses AI&#39;s ability to navigate multiway systems and its potential in finding paths or solutions in complex computational spaces.</td></tr><tr><td>Exploring Systems with AI</td><td>Looks at how AI can assist in exploring spaces of systems, identifying rules or systems that exhibit specific desired characteristics.</td></tr><tr><td>Science as Narrative</td><td>Explores the idea of science providing a human-accessible narrative for natural phenomena and how AI might generate or contribute to scientific narratives.</td></tr><tr><td>Finding What&#39;s Interesting</td><td>Discusses the challenge of determining what&#39;s interesting in science and how AI might assist in identifying interesting phenomena or directions.</td></tr><tr><td>Beyond Exact Sciences</td><td>Explores the potential of AI in extending the domain of exact sciences to include more subjective or less formalized areas of knowledge.</td></tr><tr><td>Conclusion</td><td>Summarizes the potential and limitations of AI in science, emphasizing the combination of AI with computational paradigms for advancing science.</td></tr></tbody></table>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% html\n",
    "$tblThemes ==> data-translation(field-names=><theme content>)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5647143",
   "metadata": {},
   "source": [
    "**Remark:** A fair amount of LLMs give their code results within Markdown code block delimiters (like \"```\".) Hence, (1) the (WL-specified) prompt [\"ThemeTableJSON\"](https://www.wolframcloud.com/obj/antononcube/DeployedResources/Prompt/ThemeTableJSON/) does not use `Interpreter[\"JSON\"]`, but `Interpreter[\"String\"]`, and (2) we use above the sub-parser 'JSON' with dropping of non-JSON strings in order to convert the LLM output into a Raku data structure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708d8537",
   "metadata": {},
   "source": [
    "### Related definitions\n",
    "\n",
    "After reading the breakdown in the table above we might want to get a few clarifications and definitions of certain concepts. For those, we might (i) just use general knowledge of the LLM or (ii) articles's text. Here are a few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d892ef81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "According to Stephen Wolfram, computational irreducibility refers to situations where the only way to determine the behavior of a system is to explicitly simulate each step of its evolution. In other words, there is no shortcut or algorithm that can predict the outcome of the system without actually running the simulation.\n",
       "\n",
       "On the other hand, computational reducibility refers to situations where the behavior of a system can be predicted or understood through the use of simpler or more efficient computational methods. In these cases, it is possible to analyze the system using algorithms or mathematical models that can reduce the complexity of the problem."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% markdown \n",
    "llm-synthesize('What are computational irreducibility and computational reducibility according to Stephen Wolfram?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc248b7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Computational irreducibility and computational reducibility are foundational concepts introduced by Stephen Wolfram that play a crucial role in understanding the limits and potentials of computation, including what AI can achieve in the realm of science.\n",
       "\n",
       "### Computational Irreducibility\n",
       "\n",
       "Computational irreducibility is the idea that for some processes or systems, there's no shortcut to predicting their future state other than essentially simulating each step of the process. This implies that for such systems, no matter how much we know about their initial condition and the rules governing their evolution, we cannot predict their outcome in a more efficient manner than running the entire computation. This has profound implications, suggesting that there are inherent limits to prediction and understanding in science. It indicates that for certain systems, we can't \"jump ahead\" to the end without going through all the intermediate steps, making them inherently complex and unpredictable in detail.\n",
       "\n",
       "### Computational Reducibility\n",
       "\n",
       "In contrast, computational reducibility refers to situations where, despite the underlying complexity of a system, certain properties or behaviors can be predicted or understood without simulating each step of the process. This suggests that for some systems or aspects of systems, there are shortcuts or simplified descriptions that allow us to understand or predict their behavior without fully computing each step. This concept underlies much of traditional science, where we seek general laws or principles that can efficiently describe or predict outcomes without needing to account for every detail of a system's state or its step-by-step evolution.\n",
       "\n",
       "### Implications for Science and AI\n",
       "\n",
       "The interplay between computational irreducibility and reducibility is central to the limits and capabilities of AI in advancing science. While AI can exploit pockets of computational reducibility to make predictions, help in understanding complex systems, or suggest new scientific directions, computational irreducibility imposes fundamental limits on prediction and understanding. It suggests that there will always be systems or aspects of systems where the only way to know the outcome is to simulate the process in full, step by step, with all the computational effort that entails.\n",
       "\n",
       "In the context of science, this means that while AI can significantly aid in exploring computationally reducible aspects of the universe, uncovering new laws, and automating scientific discovery, there will remain areas where the unpredictability and complexity inherent in computational irreducibility limit our ability to predict and understand. This duality highlights the richness of the computational universe and frames the ongoing dialogue between what is knowable and predictable and what remains fundamentally beyond our current reach, even with advanced AI technologies."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% markdown \n",
    "llm-synthesize([\n",
    "    \"What are computational irreducibility and computational reducibility according to the following text?\",\n",
    "    \"Give concise definitions.\\n\\n\", \n",
    "    $txtArticle\n",
    "], e=>$conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d391cd83",
   "metadata": {},
   "source": [
    "------\n",
    "\n",
    "## Flowcharts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d45483",
   "metadata": {},
   "source": [
    "\n",
    "In this section we LLMs to get [Mermaid-JS](https://mermaid.js.org) flowcharts that correspond to the content of [SW1].\n",
    "\n",
    "**Remark:** Below in order to display Mermaid-JS diagrams we use both the package [\"WWW::MermaidInk\"](https://raku.land/zef:antononcube/WWW::MermaidInk), [AAp7], and the dedicated *mermaid magic cell* of Raku Chatabook, [AA6].\n",
    "\n",
    "### Big picture concepts\n",
    "\n",
    "Here we generate Mermaid-JS flowchart for the “big picture” concepts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "100039d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "```mermaid\n",
       "graph TD\n",
       "    A[AI in Science] --> B[Computational Irreducibility]\n",
       "    A --> C[Pockets of Computational Reducibility]\n",
       "    A --> D[AI Measurements]\n",
       "    A --> E[AI Predictions]\n",
       "    B --> F[Limitations of AI in Science]\n",
       "    C --> G[Science Possible]\n",
       "    D --> H[New Exact Sciences]\n",
       "    E --> I[Practical Uses in Science]\n",
       "    G --> J[Discoveries and Formalizations]\n",
       "    H --> K[Formalizing Unstructured Data]\n",
       "    I --> L[Combining AI with Computational Paradigm]\n",
       "    J -.-> M[Computational Paradigm]\n",
       "    L --> M\n",
       "```"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $mmdBigPicture = \n",
    "  llm-synthesize([\n",
    "    \"Create a concise mermaid-js graph for the connections between the big concepts in the article:\\n\\n\", \n",
    "    $txtArticle, \n",
    "    llm-prompt(\"NothingElse\")(\"correct mermaid-js\")\n",
    "  ], e => $conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db192e1",
   "metadata": {},
   "source": [
    "Here we define “big picture” styling theme:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3bb3c830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "%%{init: {'theme': 'neutral'}}%%\n"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $mmdBigPictureTheme = q:to/END/;\n",
    "%%{init: {'theme': 'neutral'}}%%\n",
    "END"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9360e948",
   "metadata": {},
   "source": [
    "Here we create the flowchart from LLM’s specification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e39e543c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg id=\"mermaid-svg\" width=\"100%\" xmlns=\"http://www.w3.org/2000/svg\" style=\"max-width: 1205.04px; background-color: cornsilk;\" viewBox=\"-8 -8 1205.0390625 386\" role=\"graphics-document document\" aria-roledescription=\"flowchart-v2\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><style>#mermaid-svg{font-family:\"trebuchet ms\",verdana,arial,sans-serif;font-size:16px;fill:#000000;}#mermaid-svg .error-icon{fill:#552222;}#mermaid-svg .error-text{fill:#552222;stroke:#552222;}#mermaid-svg .edge-thickness-normal{stroke-width:2px;}#mermaid-svg .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg .marker{fill:#666;stroke:#666;}#mermaid-svg .marker.cross{stroke:#666;}#mermaid-svg svg{font-family:\"trebuchet ms\",verdana,arial,sans-serif;font-size:16px;}#mermaid-svg .label{font-family:\"trebuchet 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      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mermaid-ink($mmdBigPictureTheme.chomp ~ $mmdBigPicture.subst(/ '```mermaid' | '```'/, :g), background => 'Cornsilk', format => 'svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be4951b",
   "metadata": {},
   "source": [
    "We made several “big picture” flowchart generations. Here is the result of another attempt: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "23250c02",
   "metadata": {},
   "outputs": [
    {
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class=\"label\" transform=\"translate(0, 0)\"><foreignObject width=\"0\" height=\"0\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: inline-block; white-space: nowrap;\"><span class=\"edgeLabel\"></span></div></foreignObject></g></g><g class=\"edgeLabel\"><g class=\"label\" transform=\"translate(0, 0)\"><foreignObject width=\"0\" height=\"0\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: inline-block; white-space: nowrap;\"><span class=\"edgeLabel\"></span></div></foreignObject></g></g></g><g class=\"nodes\"><g class=\"node default default flowchart-label\" id=\"flowchart-AI-0\" transform=\"translate(559.734375, 17)\"><rect class=\"basic label-container\" style=\"\" rx=\"0\" ry=\"0\" x=\"-83.4453125\" y=\"-17\" width=\"166.890625\" height=\"34\"></rect><g class=\"label\" style=\"\" transform=\"translate(-75.9453125, -9.5)\"><rect></rect><foreignObject width=\"151.890625\" height=\"19\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: 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      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% mermaid\n",
    "graph TD;\n",
    "    AI[Artificial Intelligence] --> CompSci[Computational Science]\n",
    "    AI --> CompThink[Computational Thinking]\n",
    "    AI --> NewTech[New Technology]\n",
    "    CompSci --> Physics\n",
    "    CompSci --> Math[Mathematics]\n",
    "    CompSci --> Ruliology\n",
    "    CompThink --> SoftwareDesign[Software Design]\n",
    "    CompThink --> WolframLang[Wolfram Language]\n",
    "    NewTech --> WolframAlpha[\"Wolfram|Alpha\"]\n",
    "    Physics --> Philosophy\n",
    "    Math --> Philosophy\n",
    "    Ruliology --> Philosophy\n",
    "    SoftwareDesign --> Education\n",
    "    WolframLang --> Education\n",
    "    WolframAlpha --> Education\n",
    "\n",
    "    %% Styling\n",
    "    classDef default fill:#8B0000,stroke:#333,stroke-width:2px;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9176079a",
   "metadata": {},
   "source": [
    "### Fine grained\n",
    "\n",
    "Here we derive a flowchart that refers to more detailed, finer grained concepts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "49ea29a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "```mermaid\n",
       "graph TD\n",
       "    AI[AI in Science] --> Limitations[Limitations]\n",
       "    AI --> Applications[Applications]\n",
       "    AI --> Potential[Potential for Discovery]\n",
       "    Limitations --> CI[Computational Irreducibility]\n",
       "    Limitations --> Precision[Requires High Precision]\n",
       "    Applications --> Predictions[Predictions]\n",
       "    Applications --> FeatureExtraction[Feature Extraction]\n",
       "    Applications --> Pruning[Pruning Search Space]\n",
       "    Potential --> ComputationalParadigm[Computational Paradigm]\n",
       "    Potential --> IrreducibleComputations[Irreducible Computations]\n",
       "    CI --> Pockets[Pockets of Computational Reducibility]\n",
       "    CI --> Surprises[Unexpected Surprises]\n",
       "    Predictions --> ShallowComputation[Shallow Computation]\n",
       "    FeatureExtraction --> Measurements[AI Measurements]\n",
       "    Pruning --> MulticomputationalIrreducibility[Multicomputational Irreducibility]\n",
       "    ComputationalParadigm --> EnumerateSystems[Enumerate Possible Systems]\n",
       "    IrreducibleComputations --> FundamentalSurprises[Fundamental Surprises]\n",
       "    Measurements --> UnstructuredData[Unstructured Raw Data]\n",
       "    Measurements --> FormalizedScience[Formalized Science]\n",
       "    UnstructuredData --> MeaningfulFeatures[Extract Meaningful Features]\n",
       "    FormalizedScience --> ComputationalLanguage[Computational Language]\n",
       "```"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $mmdFineGrained = \n",
    "  llm-synthesize([\n",
    "    \"Create a mermaid-js flowchart with multiple blocks and multiple connections for the relationships between concepts in the article:\\n\\n\", \n",
    "    $txtArticle, \n",
    "    \"Use the concepts in the JSON table:\", \n",
    "    $tblThemes, \n",
    "    llm-prompt(\"NothingElse\")(\"correct mermaid-js\")\n",
    "  ], e => $conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0153be8",
   "metadata": {},
   "source": [
    "Here we define “fine grained” styling theme:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e2d932d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "%%{init: {'theme': 'base','themeVariables': {'backgroundColor': '#FFF'}}}%%\n"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $mmdFineGrainedTheme = q:to/END/;\n",
    "%%{init: {'theme': 'base','themeVariables': {'backgroundColor': '#FFF'}}}%%\n",
    "END"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61865443",
   "metadata": {},
   "source": [
    "Here we create the flowchart from LLM’s specification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "24495349",
   "metadata": {},
   "outputs": [
    {
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xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: inline-block; white-space: nowrap;\"><span class=\"nodeLabel\">Unstructured Raw Data</span></div></foreignObject></g></g><g class=\"node default default flowchart-label\" id=\"flowchart-FormalizedScience-37\" transform=\"translate(1078.80078125, 353)\"><rect class=\"basic label-container\" style=\"\" rx=\"0\" ry=\"0\" x=\"-76.765625\" y=\"-17\" width=\"153.53125\" height=\"34\"></rect><g class=\"label\" style=\"\" transform=\"translate(-69.265625, -9.5)\"><rect></rect><foreignObject width=\"138.53125\" height=\"19\"><div xmlns=\"http://www.w3.org/1999/xhtml\" style=\"display: inline-block; white-space: nowrap;\"><span class=\"nodeLabel\">Formalized Science</span></div></foreignObject></g></g><g class=\"node default default flowchart-label\" id=\"flowchart-MeaningfulFeatures-39\" transform=\"translate(824.47265625, 437)\"><rect class=\"basic label-container\" style=\"\" rx=\"0\" ry=\"0\" x=\"-108.21875\" y=\"-17\" 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url(\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css\");</style></svg>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mermaid-ink($mmdFineGrainedTheme.chomp ~ $mmdFineGrained.subst(/ '```mermaid' | '```'/, :g), format => 'svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e9b623",
   "metadata": {},
   "source": [
    "We made several “fine grained” flowchart generations. Here is the result of another attempt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "78e3f503",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% mermaid\n",
    "graph TD\n",
    "\n",
    "    AI[\"AI\"] -->|Potential & Limitations| Science[\"Science\"]\n",
    "    AI -->|Leverages| CR[\"Computational Reducibility\"]\n",
    "    AI -->|Fails with| CI[\"Computational Irreducibility\"]\n",
    "    \n",
    "    Science -->|Domain of| PS[\"Physical Sciences\"]\n",
    "    Science -->|Expanding to| S[\"'Exact' Sciences Beyond Traditional Domains\"]\n",
    "    Science -.->|Foundational Role of| CR\n",
    "    Science -.->|Limited by| CI\n",
    "    \n",
    "    PS -->|Traditional Formalizations via| Math[\"Mathematics/Mathematical Formulas\"]\n",
    "    PS -->|Now Leveraging| AI_Measurements[\"AI Measurements\"]\n",
    "    \n",
    "    S -->|Formalizing with| CL[\"Computational Language\"]\n",
    "    S -->|Leverages| AI_Measurements\n",
    "    S -->|Future Frontiers with| AI\n",
    "    \n",
    "    AI_Measurements -->|Interpretation Challenge| BlackBox[\"'Black-Box' Nature\"]\n",
    "    AI_Measurements -->|Potential for| NewScience[\"New Science Discoveries\"]\n",
    "    \n",
    "    CL -->|Key to Structuring| AI_Results[\"AI Results\"]\n",
    "    CL -->|Enables Irreducible Computations for| Discovery[\"Discovery\"]\n",
    "    \n",
    "    Math -.->|Transitioning towards| CL\n",
    "    Math -->|Limits when needing 'Precision'| AI_Limits[\"AI's Limitations\"]\n",
    "    \n",
    "    Discovery -.->|Not directly achievable via| AI\n",
    "    \n",
    "    BlackBox -->|Requires Human| Interpretation[\"Interpretation\"]\n",
    "    \n",
    "    CR -->|Empowered by| AI & ML_Techniques[\"AI & Machine Learning Techniques\"]\n",
    "    CI -.->|Challenge for| AI & ML_Techniques\n",
    "   \n",
    "    PS --> Observations[\"New Observations/Measurements\"] --> NewDirections[\"New Scientific Directions\"]\n",
    "    Observations --> AI_InterpretedPredictions[\"AI-Interpreted Predictions\"]\n",
    "    NewDirections -.-> AI_Predictions[\"AI Predictions\"] -.-> CI\n",
    "    NewDirections --> AI_Discoveries[\"AI-Enabled Discoveries\"] -.-> CR\n",
    "\n",
    "    AI_Discoveries --> NewParadigms[\"New Paradigms/Concepts\"] -.-> S\n",
    "    AI_InterpretedPredictions -.-> AI_Measurements\n",
    "\n",
    "    %% Styling\n",
    "    classDef default fill:#f9f,stroke:#333,stroke-width:2px;\n",
    "    classDef highlight fill:#bbf,stroke:#006,stroke-width:4px;\n",
    "    classDef imp fill:#ffb,stroke:#330,stroke-width:4px;\n",
    "    class PS,CL highlight;\n",
    "    class AI_Discoveries,NewParadigms imp;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81977b05",
   "metadata": {},
   "source": [
    "-------\n",
    "\n",
    "## Summary and ideas\n",
    "\n",
    "Here we get a summary and extract ideas, quotes, and references from the article:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1715a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(chars => 7386 words => 1047 lines => 78)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $sumIdea =llm-synthesize(llm-prompt(\"ExtractArticleWisdom\")($txtArticle), e => $conf);\n",
    "\n",
    "text-stats($sumIdea)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "096929c6",
   "metadata": {},
   "source": [
    "The result is rendered below.\n",
    "\n",
    "<hr width=\"65%\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1779c58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### SUMMARY\n",
       "\n",
       "Stephen Wolfram's writings explore the capabilities and limitations of AI in the realm of science, discussing how AI can assist in scientific discovery and understanding but also highlighting its limitations due to computational irreducibility and the need for human interpretation and creativity.\n",
       "\n",
       "#### IDEAS:\n",
       "\n",
       "- AI has made surprising successes but cannot solve all scientific problems due to computational irreducibility.\n",
       "- Large Language Models (LLMs) provide a new kind of interface for scientific work, offering high-level autocomplete for scientific knowledge.\n",
       "- The transition to computational representation of the world is transforming science, with AI playing a significant role in accessing and exploring this computational realm.\n",
       "- AI, particularly through neural networks and machine learning, offers tools for predicting scientific outcomes, though its effectiveness is bounded by the complexity of the systems it attempts to model.\n",
       "- Computational irreducibility limits the ability of AI to predict or solve all scientific phenomena, ensuring that surprises and new discoveries remain a fundamental aspect of science.\n",
       "- Despite AI's limitations, it has potential in identifying pockets of computational reducibility, streamlining the discovery of scientific knowledge.\n",
       "- AI's success in areas like visual recognition and language generation suggests potential for contributing to scientific methodologies and understanding, though its ability to directly engage with raw natural processes is less certain.\n",
       "- AI techniques, including neural networks and machine learning, have shown promise in areas like solving equations and exploring multicomputational processes, but face challenges due to computational irreducibility.\n",
       "- The role of AI in generating human-understandable narratives for scientific phenomena is explored, highlighting the potential for AI to assist in identifying interesting and meaningful scientific inquiries.\n",
       "- The exploration of \"AI measurements\" opens up new possibilities for formalizing and quantifying aspects of science that have traditionally been qualitative or subjective, potentially expanding the domain of exact sciences.\n",
       "\n",
       "#### QUOTES:\n",
       "\n",
       "- \"AI has the potential to give us streamlined ways to find certain kinds of pockets of computational reducibility.\"\n",
       "- \"Computational irreducibility is what will prevent us from ever being able to completely 'solve science'.\"\n",
       "- \"The AI is doing 'shallow computation', but when there’s computational irreducibility one needs irreducible, deep computation to work out what will happen.\"\n",
       "- \"AI measurements are potentially a much richer source of formalizable material.\"\n",
       "- \"AI... is not something built to 'go out into the wilds of the ruliad', far from anything already connected to humans.\"\n",
       "- \"Despite AI's limitations, it has potential in identifying pockets of computational reducibility.\"\n",
       "- \"AI techniques... have shown promise in areas like solving equations and exploring multicomputational processes.\"\n",
       "- \"AI's success in areas like visual recognition and language generation suggests potential for contributing to scientific methodologies.\"\n",
       "- \"There’s no abstract notion of 'interestingness' that an AI or anything can 'go out and discover' ahead of our choices.\"\n",
       "- \"The whole story of things like trained neural nets that we’ve discussed here is a story of leveraging computational reducibility.\"\n",
       "\n",
       "#### HABITS:\n",
       "\n",
       "- Continuously exploring the capabilities and limitations of AI in scientific discovery.\n",
       "- Engaging in systematic experimentation to understand how AI tools can assist in scientific processes.\n",
       "- Seeking to identify and utilize pockets of computational reducibility where AI can be most effective.\n",
       "- Exploring the use of neural networks and machine learning for predicting and solving scientific problems.\n",
       "- Investigating the potential for AI to assist in creating human-understandable narratives for complex scientific phenomena.\n",
       "- Experimenting with \"AI measurements\" to quantify and formalize traditionally qualitative aspects of science.\n",
       "- Continuously refining and expanding computational language to better interface with AI capabilities.\n",
       "- Engaging with and contributing to discussions on the role of AI in the future of science and human understanding.\n",
       "- Actively seeking new methodologies and innovations in AI that could impact scientific discovery.\n",
       "- Evaluating the potential for AI to identify interesting and meaningful scientific inquiries through analysis of large datasets.\n",
       "\n",
       "#### FACTS:\n",
       "\n",
       "- Computational irreducibility ensures that surprises and new discoveries remain a fundamental aspect of science.\n",
       "- AI's effectiveness in scientific modeling is bounded by the complexity of the systems it attempts to model.\n",
       "- AI can identify pockets of computational reducibility, streamlining the discovery of scientific knowledge.\n",
       "- Neural networks and machine learning offer tools for predicting outcomes but face challenges due to computational irreducibility.\n",
       "- AI has shown promise in areas like solving equations and exploring multicomputational processes.\n",
       "- The potential of AI in generating human-understandable narratives for scientific phenomena is actively being explored.\n",
       "- \"AI measurements\" offer new possibilities for formalizing aspects of science that have been qualitative or subjective.\n",
       "- The transition to computational representation of the world is transforming science, with AI playing a significant role.\n",
       "- Machine learning techniques can be very useful for providing approximate answers in scientific inquiries.\n",
       "- AI's ability to directly engage with raw natural processes is less certain, despite successes in human-like tasks.\n",
       "\n",
       "#### REFERENCES:\n",
       "\n",
       "- Stephen Wolfram's writings on AI and science.\n",
       "- Large Language Models (LLMs) as tools for scientific work.\n",
       "- The concept of computational irreducibility and its implications for science.\n",
       "- Neural networks and machine learning techniques in scientific prediction and problem-solving.\n",
       "- The role of AI in creating human-understandable narratives for scientific phenomena.\n",
       "- The use of \"AI measurements\" in expanding the domain of exact sciences.\n",
       "- The potential for AI to assist in identifying interesting and meaningful scientific inquiries.\n",
       "\n",
       "#### RECOMMENDATIONS:\n",
       "\n",
       "- Explore the use of AI and neural networks for identifying pockets of computational reducibility in scientific research.\n",
       "- Investigate the potential of AI in generating human-understandable narratives for complex scientific phenomena.\n",
       "- Utilize \"AI measurements\" to formalize and quantify traditionally qualitative aspects of science.\n",
       "- Engage in systematic experimentation to understand the limitations and capabilities of AI in scientific discovery.\n",
       "- Consider the role of computational irreducibility in shaping the limitations of AI in science.\n",
       "- Explore the potential for AI to assist in identifying interesting and meaningful scientific inquiries.\n",
       "- Continuously refine and expand computational language to better interface with AI capabilities in scientific research.\n",
       "- Investigate new methodologies and innovations in AI that could impact scientific discovery.\n",
       "- Consider the implications of AI's successes in human-like tasks for its potential contributions to scientific methodologies.\n",
       "- Explore the use of machine learning techniques for providing approximate answers in scientific inquiries where precision is less critical."
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% markdown\n",
    "$sumIdea.subst(/ ^^ '#' /, '###', :g)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd15e57",
   "metadata": {},
   "source": [
    "-------\n",
    "\n",
    "## Hidden and propaganda messages\n",
    "\n",
    " In this section we convince ourselves that the article is apolitical and propaganda-free. \n",
    "\n",
    "**Remark:** We leave to the reader as an exercise to verify that both the overt and hidden messages found by the LLM below are explicitly stated in article.\n",
    "\n",
    "Here we find the hidden and “propaganda” messages in the article:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5c3e488f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(chars => 6193 words => 878 lines => 64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my $propMess =llm-synthesize([llm-prompt(\"FindPropagandaMessage\"), $txtArticle], e => $conf);\n",
    "\n",
    "text-stats($propMess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "926dc309",
   "metadata": {},
   "outputs": [],
   "source": [
    "ingest-prompt-data('user'):a"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f1e258f",
   "metadata": {},
   "source": [
    "**Remark:** The prompt [\"FindPropagandaMessage\"](https://www.wolframcloud.com/obj/antononcube/DeployedResources/Prompt/FindPropagandaMessage/) has an explicit instruction to say that it is intentionally cynical. It is also, marked as being \"For fun.\"\n",
    "\n",
    "The LLM result is rendered below.\n",
    "\n",
    "<hr width=\"65%\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6b6ba27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### OVERT MESSAGE: \n",
       " Stephen Wolfram evaluates the potential and limitations of AI in advancing science.\n",
       "\n",
       "### HIDDEN MESSAGE: \n",
       " Despite AI's growth, irreducible complexity limits its scientific problem-solving capacity.\n",
       "\n",
       "### HIDDEN OPINIONS: \n",
       "\n",
       "- AI can leverage computational reducibility akin to human minds.\n",
       "- Traditional mathematical methods surpass AI in solving precise equations.\n",
       "- AI's \"shallow computation\" struggles with computational irreducibility.\n",
       "- AI can provide practical tools for science within its computational reducibility limits.\n",
       "- AI's effectiveness is contingent on approximate answers, failing at precise perfection.\n",
       "- AI introduces a new, human-like method to harness computational reducibility.\n",
       "- Fundamental discoveries are more likely through irreducible computations, not AI.\n",
       "- Combining AI with the computational paradigm offers the best science advancement path.\n",
       "- AI's potential in science is hyped beyond its actual foundational impact.\n",
       "- AI's role in science is more about aiding existing processes than groundbreaking discoveries.\n",
       "\n",
       "SUPPORTING ARGUMENTS and###  QUOTES: \n",
       "\n",
       "- \"AI is doing 'shallow computation', but when there's computational irreducibility one needs irreducible, deep computation to work out what will happen.\"\n",
       "- \"Typical AI approach to science doesn’t involve explicitly 'formalizing things'.\"\n",
       "- \"In terms of fundamental potential for discovery, AI pales in comparison to what we can build from the computational paradigm.\"\n",
       "- \"AI can be very useful if an approximate ('80%') answer is good enough.\"\n",
       "- \"AI measurements seem to have a certain immediate 'subjectivity'.\"\n",
       "- \"AI introduces a new—and rather human-like—way of leveraging computational reducibility.\"\n",
       "- \"AI's effectiveness is contingent on approximate answers, failing at precise perfection.\"\n",
       "- \"AI's role in science is more about aiding existing processes than groundbreaking discoveries.\"\n",
       "- \"Irreducible computations that we do offer greater potential for discovery than typical AI.\"\n",
       "\n",
       "### DESIRED AUDIENCE OPINION CHANGE: \n",
       "\n",
       "- Appreciate the limitations and strengths of AI in scientific exploration.\n",
       "- Recognize the irreplaceable value of human insight in science.\n",
       "- View AI as a tool, not a replacement, for traditional scientific methods.\n",
       "- Embrace computational irreducibility as a barrier and boon to discovery.\n",
       "- Acknowledge the need for combining AI with computational paradigms.\n",
       "- Understand that AI's role is to augment, not overshadow, human-led science.\n",
       "- Realize the necessity of approximate solutions in AI-driven science.\n",
       "- Foster realistic expectations from AI in making scientific breakthroughs.\n",
       "- Advocate for deeper computational approaches alongside AI in science.\n",
       "- Encourage interdisciplinary approaches combining AI with formal sciences.\n",
       "\n",
       "### DESIRED AUDIENCE ACTION CHANGE: \n",
       "\n",
       "- Support research combining AI with computational paradigms.\n",
       "- Encourage the use of AI for practical, approximate scientific solutions.\n",
       "- Advocate for AI's role as a supplementary tool in science.\n",
       "- Push for education that integrates AI with traditional scientific methods.\n",
       "- Promote the study of computational irreducibility alongside AI.\n",
       "- Emphasize AI's limitations in scientific discussions and funding.\n",
       "- Inspire new approaches to scientific exploration using AI.\n",
       "- Foster collaboration between AI researchers and traditional scientists.\n",
       "- Encourage critical evaluation of AI's potential in groundbreaking discoveries.\n",
       "- Support initiatives that aim to combine AI with human insight in science.\n",
       "\n",
       "### MESSAGES: \n",
       " Stephen Wolfram wants you to believe AI can advance science, but he's actually saying its foundational impact is limited by computational irreducibility.\n",
       "\n",
       "### PERCEPTIONS: \n",
       " Wolfram wants you to see him as optimistic about AI in science, but he's actually cautious about its ability to make fundamental breakthroughs.\n",
       "\n",
       "### ELLUL'S ANALYSIS: \n",
       " Jacques Ellul would likely interpret Wolfram's findings as a validation of the view that technological systems, including AI, are inherently limited by the complexity of human thought and the natural world. The presence of computational irreducibility underscores the unpredictability and uncontrollability that Ellul warned technology could not tame, suggesting that even advanced AI cannot fully solve or understand all scientific problems, thus maintaining a degree of human autonomy and unpredictability in the face of technological advancement.\n",
       "\n",
       "### BERNAYS' ANALYSIS: \n",
       " Edward Bernays might view Wolfram's exploration of AI in science through the lens of public perception and manipulation, arguing that while AI presents a new frontier for scientific exploration, its effectiveness and limitations must be communicated carefully to avoid both undue skepticism and unrealistic expectations. Bernays would likely emphasize the importance of shaping public opinion to support the use of AI as a tool that complements human capabilities rather than replacing them, ensuring that society remains engaged and supportive of AI's role in scientific advancement.\n",
       "\n",
       "### LIPPMANN'S ANALYSIS: \n",
       " Walter Lippmann would likely focus on the implications of Wolfram's findings for the \"pictures in our heads,\" or public understanding of AI's capabilities and limitations in science. Lippmann might argue that the complexity of AI and computational irreducibility necessitates expert mediation to accurately convey these concepts to the public, ensuring that society's collective understanding of AI in science is based on informed, rather than simplistic or sensationalized, interpretations.\n",
       "\n",
       "### FRANKFURT'S ANALYSIS: \n",
       " Harry G. Frankfurt might critique the discourse around AI in science as being fraught with \"bullshit,\" where speakers and proponents of AI might not necessarily lie about its capabilities, but could fail to pay adequate attention to the truth of computational irreducibility and the limitations it imposes. Frankfurt would likely appreciate Wolfram's candid assessment of AI, seeing it as a necessary corrective to overly optimistic or vague claims about AI's potential to revolutionize science.\n",
       "\n",
       "**### NOTE: \n",
       " This AI is tuned specifically to be cynical and politically-minded. Don't take it as perfect. Run it multiple times and/or go consume the original input to get a second opinion.**"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% markdown\n",
    "$propMess.subst(/ ^^ '#' /, '###', :g).subst(/ (<[A..Z \\h \\']>+ ':') /, { \"### {$0.Str} \\n\"}, :g)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93c6d25",
   "metadata": {},
   "source": [
    "------\n",
    "\n",
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "765a1523",
   "metadata": {},
   "source": [
    "### Articles\n",
    "\n",
    "[AA1] Anton Antonov,\n",
    "[\"Workflows with LLM functions\"](https://rakuforprediction.wordpress.com/2023/08/01/workflows-with-llm-functions/),\n",
    "(2023),\n",
    "[RakuForPrediction at WordPress](https://rakuforprediction.wordpress.com).\n",
    "\n",
    "[AA2] Anton Antonov,\n",
    "[\"LLM aids for processing of the first Carlson-Putin interview\"](https://rakuforprediction.wordpress.com/2024/02/12/llm-aids-for-processing-of-the-first-carlson-putin-interview/),\n",
    "(2024),\n",
    "[RakuForPrediction at WordPress](https://rakuforprediction.wordpress.com).\n",
    "\n",
    "\n",
    "[SW1] Stephen Wolfram, [\"Can AI Solve Science?\"](https://writings.stephenwolfram.com/2024/03/can-ai-solve-science/), (2024), [Stephen Wolfram's writings](https://writings.stephenwolfram.com).\n",
    "\n",
    "[SW2] Stephen Wolfram, [\"The New World of LLM Functions: Integrating LLM Technology into the Wolfram Language\"](https://writings.stephenwolfram.com/2023/05/the-new-world-of-llm-functions-integrating-llm-technology-into-the-wolfram-language/), (2023), [Stephen Wolfram's writings](https://writings.stephenwolfram.com)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3475bcc8",
   "metadata": {},
   "source": [
    "\n",
    "### Notebooks\n",
    "\n",
    "[AAn1] Anton Antonov, [\"Workflows with LLM functions (in WL)\"](https://community.wolfram.com/groups/-/m/t/2983602), (2023), [Wolfram Community](https://community.wolfram.com).\n",
    "\n",
    "[AAn2] Anton Antonov, [\"LLM aids for processing of the first Carlson-Putin interview\"](https://community.wolfram.com/groups/-/m/t/3121333), (2024), [Wolfram Community](https://community.wolfram.com).\n",
    "\n",
    "[AAn3] Anton Antonov, [\"LLM aids for processing Putin's State-Of-The-Nation speech given on 2/29/2024\"](https://community.wolfram.com/groups/-/m/t/3133899), (2024), [Wolfram Community](https://community.wolfram.com).\n",
    "\n",
    "[AAn4] Anton Antonov, [\"LLM over Trump vs. Anderson: analysis of the slip opinion of the Supreme Court of the United States\"](https://community.wolfram.com/groups/-/m/t/3136094), (2024), [Wolfram Community](https://community.wolfram.com).\n",
    "\n",
    "[AAn5] Anton Antonov, [\"Markdown to Mathematica converter\"](https://community.wolfram.com/groups/-/m/t/2625639), (2023), [Wolfram Community](https://community.wolfram.com).\n",
    "\n",
    "[AAn6] Anton Antonov, [\"Monte-Carlo demo notebook conversion via LLMs\"](https://community.wolfram.com/groups/-/m/t/3125446), (2024), [Wolfram Community](https://community.wolfram.com)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "203f4f5f",
   "metadata": {},
   "source": [
    "### Packages, repositories\n",
    "\n",
    "[AAp1] Anton Antonov,\n",
    "[WWW::OpenAI](https://github.com/antononcube/Raku-WWW-OpenAI) Raku package,\n",
    "(2023-2024),\n",
    "[GitHub/antononcube](https://github.com/antononcube).\n",
    "\n",
    "[AAp2] Anton Antonov,\n",
    "[WWW::PaLM](https://github.com/antononcube/Raku-WWW-PaLM) Raku package,\n",
    "(2023-2024),\n",
    "[GitHub/antononcube](https://github.com/antononcube).\n",
    "\n",
    "[AAp3] Anton Antonov,\n",
    "[WWW::MistralAI](https://github.com/antononcube/Raku-WWW-MistralAI) Raku package,\n",
    "(2023-2024),\n",
    "[GitHub/antononcube](https://github.com/antononcube).\n",
    "\n",
    "[AAp4] Anton Antonov,\n",
    "[LLM::Functions](https://github.com/antononcube/Raku-LLM-Functions) Raku package,\n",
    "(2023-2024),\n",
    "[GitHub/antononcube](https://github.com/antononcube).\n",
    "\n",
    "[AAp5] Anton Antonov,\n",
    "[LLM::Prompts](https://github.com/antononcube/Raku-LLM-Prompts) Raku package,\n",
    "(2023-2024),\n",
    "[GitHub/antononcube](https://github.com/antononcube).\n",
    "\n",
    "[AAp6] Anton Antonov,\n",
    "[Jupyter::Chatbook](https://github.com/antononcube/Raku-Jupyter-Chatbook) Raku package,\n",
    "(2023-2024),\n",
    "[GitHub/antononcube](https://github.com/antononcube).\n",
    "\n",
    "[AAp7] Anton Antonov,\n",
    "[WWW:::MermaidInk](https://github.com/antononcube/Raku-WWW-MermaidInk) Raku package,\n",
    "(2023-2024),\n",
    "[GitHub/antononcube](https://github.com/antononcube).\n",
    "\n",
    "[DMr1] Daniel Miessler, [Fabric](https://github.com/danielmiessler/fabric), (2024), [GitHub/danielmiessler](https://github.com/danielmiessler)."
   ]
  }
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