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Edited
Nov 21, 2024
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exec_magic_iframe(
`https://platform.openai.com/docs/guides/reasoning#how-reasoning-works`
)
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exec_magic_iframe("https://openai.com/index/learning-to-reason-with-llms/")
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dumbledore = html`<iframe width="560" height="315" src="https://www.youtube.com/embed/PaCmpygFfXo?si=pamD56WkBAsJBEPF" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>`
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// exec_magic_iframe("https://setosa.io/conditional/")
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// exec_magic_iframe(
// `https://seeing-theory.brown.edu/bayesian-inference/index.html`
// )
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// exec_magic_iframe("https://www.redblobgames.com/pathfinding/all-pairs/")
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import { exec_magic_iframe } from "@roboticsuniversity/dynamicland"
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// paper_ = pdfView("https://arxiv.org/pdf/1706.03762")
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pdfView("Probabilistic Robotics.pdf")
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top_priority_for_10_days = `


connect twitch chat to robotic artifact via


https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/`
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FileAttachment("Screenshot 2024-10-19 at 9.52.26 PM.png").image()
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xerox = FileAttachment("GQxi-X-WwAEG7Fe.jpeg").image()
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gqxiXWwaeg7fe = FileAttachment("GQxi-X-WwAEG7Fe.jpeg").image()
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StoryBoard_Annotation = html`
vonnegut
<ul>
<li>d3 + threejs svg renderer
<li>llm
<li>https://amansanger.com/
<li>list 5 observable improvemnts
<li>1. cursor mode - zed plugin
<li>2. click thing edit - learn keybinds
<li>3. bun cell - import new Bun(url) - tailscale
<li>4. deno webgpu serverside - simple-gpu
<li>5. pdf rendering
<li> mopve all code from repo into observable - under 7 notebooks
`
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Twitch_chat = html`<h2 className="text-xl font-bold mb-2">Twitch Plays Pokemon</h2>
<p className="mb-4">6d 17h 46m 27s</p>
<div className="flex mb-4">
<div className="w-1/2 pr-2">
<div className="bg-gray-700 h-4 rounded-full">
<div className="bg-red-500 h-full rounded-full" style={{width: '70%'}}></div>
</div>
<p className="text-center">Anarchy</p>
</div>
<div className="w-1/2 pl-2">
<div className="bg-gray-700 h-4 rounded-full">
<div className="bg-blue-500 h-full rounded-full" style={{width: '30%'}}></div>
</div>
<p className="text-center">Democracy</p>
</div>
</div>
<div className="space-y-1">
${users.map((user, index) => {
return html`<div className="flex justify-between">
<span className="truncate">${user.name}</span>
<span>${user.vote}</span>
</div>`;
})}
</div>
</div>
`
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users = [
{ name: "Mstrbojangl3s", vote: "b" },
{ name: "Kyddz", vote: "anarchy" },
{ name: "Bws41", vote: "b" }
// { name: 'Faith', vote: 'anarchy' },
// { name: 'Touptar', vote: 'b' },
// { name: 'Sverioramoebe', vote: 'left' },
// { name: 'Harblngr', vote: 'democracy' },
// { name: 'Downwiththesi', vote: 'right' },
// { name: 'Towerunb', vote: 'up' },
// { name: 'Odrquy132', vote: 'democracy' },
// { name: 'Darkjesal', vote: 'r' },
// { name: 'Bluediegl', vote: 'democracy' },
// { name: 'Capo1dg', vote: 'r' },
// { name: 'Rushifiedba', vote: 'anarchy' },
// { name: 'Ulcyuubt', vote: 'r' },
// { name: 'B2badare', vote: 'democracy' },
// { name: 'Go3sauer', vote: 'r' },
]
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import { pdfView } from "@saneef/pdf-view"
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https://observablehq.com/@hubgit/pdf-text-extraction
https://academic.oup.com/jrsssa/article/182/2/389/7070184
//# make a python module and have 3 agents predict each other -> do that in real world with a camera

//#0 goal = ally with bret victorw
//#1 goal = ally with karpathy - secrets of the cosmos - made freely avaiable
//#2 make an easy way to get ---- 800 studnets interested in robotics -
//# make robotics funny + fun - (ai = scary)
---
title: 3. Planning and Prediction
---


```

```



# Path Planning Algorithms
# Environment Mapping and Localization
# probabilistic robots and graph modeling
# chart possibilites - 20 seconds update - every 1 second
# just use (llama + openai + anothropic) + twitter grok


## LLM - explain - attention paper
## LLM - explain - transformer paper
## LLM - explain - GPT paper
## LLM - explain - attention paper
## LLM - explain - transformer paper
## LLM - explain - GPT paper
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exec_magic_iframe(`https://www.norvig.com/spell-correct.html`)
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Select a data source…
Type Table, then Shift-Enter. Ctrl-space for more options.

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https://x.com/chris_j_paxton/status/1853507754729648524
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{
return {
lessons: [
{
title: "Lesson 1: Introduction to Prediction in Robotics",
resources: {
research_paper: "https://arxiv.org/abs/1706.04329",
blog_post:
"https://robohub.org/the-importance-of-prediction-in-robotics/",
github_repo: "https://github.com/petercorke/robotics-toolbox-python"
}
},
{
title: "Lesson 2: Fundamentals of Large Language Models (LLMs)",
resources: {
blog_post: "http://jalammar.github.io/illustrated-transformer/",
research_paper: "https://arxiv.org/abs/1706.03762",
github_repo:
"https://github.com/jadore801120/attention-is-all-you-need-pytorch",
jupyter_notebook:
"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/text/transformer.ipynb"
}
},
{
title: "Lesson 3: Integrating LLMs into Robotic Systems",
resources: {
research_paper: "https://arxiv.org/abs/2201.07207",
github_repo: "https://github.com/vimalabs/vima",
blog_post:
"https://deepmind.com/blog/article/combining-robotics-and-language-models"
}
},
{
title: "Lesson 4: Predicting Behavior of Other Agents Using LLMs",
resources: {
research_paper: "https://arxiv.org/abs/1506.01919",
github_repo: "https://github.com/agrimgupta92/sgan",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 5: Modeling Human Behavior with LLMs",
resources: {
research_paper: "https://arxiv.org/abs/1803.10813",
blog_post:
"https://towardsdatascience.com/understanding-human-behavior-with-ai-1b5df846c6c0",
github_repo:
"https://github.com/healthai-lab/Human-Activity-Recognition"
}
},
{
title: "Lesson 6: LLMs for Path Prediction in Multi-Agent Systems",
resources: {
research_paper: "https://arxiv.org/abs/2001.03093",
github_repo: "https://github.com/StanfordASL/Trajectron-plus-plus",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 7: Social Navigation with LLM-based Prediction",
resources: {
research_paper: "https://arxiv.org/abs/1803.10892",
github_repo: "https://github.com/agrimgupta92/sgan",
blog_post:
"https://spectrum.ieee.org/robot-navigation-social-awareness"
}
},
{
title: "Lesson 8: LLMs in Predictive Maintenance",
resources: {
research_paper: "https://www.mdpi.com/1424-8220/18/10/3653",
github_repo:
"https://github.com/Azure/MachineLearningSamples-PredictiveMaintenance",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 9: Stock Market Prediction with LLMs",
resources: {
research_paper: "https://arxiv.org/abs/1907.00970",
github_repo: "https://github.com/huseinzol05/Stock-Prediction-Models",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 10: Time Series Analysis Using LLMs",
resources: {
research_paper: "https://arxiv.org/abs/1912.09363",
github_repo: "https://github.com/unit8co/darts",
jupyter_notebook: "https://unit8co.github.io/darts/examples/"
}
},
{
title: "Lesson 11: Environmental Change Prediction",
resources: {
research_paper:
"https://www.sciencedirect.com/science/article/pii/S0924271618300775",
github_repo:
"https://github.com/rcdaudt/fully_convolutional_change_detection",
blog_post:
"https://www.weforum.org/agenda/2020/01/ai-climate-change-environmental-impact/"
}
},
{
title: "Lesson 12: LLMs in Autonomous Driving Prediction",
resources: {
research_paper: "https://arxiv.org/abs/2003.00089",
github_repo: "https://github.com/xk-huang/VectorNet",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 13: Ethical Considerations in LLM-based Prediction",
resources: {
research_paper:
"https://link.springer.com/article/10.1007/s00146-017-0760-8",
blog_post:
"https://www.brookings.edu/research/how-artificial-intelligence-is-transforming-the-world/"
}
},
{
title: "Lesson 14: Integrating Reinforcement Learning and LLMs",
resources: {
research_paper: "https://arxiv.org/abs/2002.04105",
github_repo: "https://github.com/dennybritz/reinforcement-learning",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 15: Predicting Human Intentions with LLMs",
resources: {
research_paper: "https://ieeexplore.ieee.org/document/8462891",
github_repo:
"https://github.com/interaction-lab/Human-Intent-Recognition",
blog_post:
"https://towardsdatascience.com/predicting-human-intent-with-ai-5d3e1dbba8e0"
}
},
{
title: "Lesson 16: LLMs in Natural Language Processing for Prediction",
resources: {
research_paper: "https://arxiv.org/abs/2005.14165",
github_repo: "https://github.com/openai/gpt-3",
jupyter_notebook:
"https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb"
}
},
{
title: "Lesson 17: Predicting System Failures with LLMs",
resources: {
research_paper: "https://arxiv.org/abs/1607.00148",
github_repo: "https://github.com/khundman/telemanom",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 18: LLMs for Decision Making in Robotics",
resources: {
research_paper: "https://arxiv.org/abs/2106.01345",
github_repo: "https://github.com/kzl/decision-transformer",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 19: Multimodal Data Prediction with LLMs",
resources: {
research_paper: "https://arxiv.org/abs/1907.01166",
github_repo: "https://github.com/facebookresearch/mmbt",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 20: LLMs in Predictive Control Systems",
resources: {
research_paper: "https://arxiv.org/abs/1802.04206",
github_repo: "https://github.com/locuslab/mpc.pytorch",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 21: Future Trends in LLM-based Prediction",
resources: {
blog_post:
"https://spectrum.ieee.org/robotics/artificial-intelligence",
research_paper: "https://arxiv.org/abs/2106.00000"
}
},
{
title: "Lesson 22: Case Study—Behavior Prediction in Robotics",
resources: {
research_paper: "https://arxiv.org/abs/1909.03807",
github_repo: "https://github.com/vita-epfl/CrowdNav",
blog_post:
"https://news.stanford.edu/2018/12/14/robots-predicting-human-movement/"
}
},
{
title: "Lesson 23: Case Study—Stock Market Prediction Using LLMs",
resources: {
research_paper: "https://arxiv.org/abs/2002.06103",
github_repo: "https://github.com/ZhengyaoJiang/PGPortfolio",
jupyter_notebook: "Included in the GitHub repository"
}
},
{
title: "Lesson 24: Hands-on Project—Implementing LLM-based Prediction",
resources: {
github_repo: "https://github.com/huggingface/transformers",
jupyter_notebook:
"https://github.com/huggingface/transformers/blob/main/notebooks/01-training.ipynb",
blog_post:
"https://towardsdatascience.com/how-to-build-a-language-model-733b7b9c6e19"
}
},
{
title: "Lesson 25: Review and Assessment",
resources: {
resources:
"Utilize the materials from the previous lessons for review.",
assessment_tools:
"Create quizzes using Google Forms or interactive exercises using Jupyter Notebook."
}
}
]
};
}
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