Let me start by saying a few things that seem obvious,” Geoffrey Hinton, “Godfather” of deep learning, and one of the most celebrated scientists of our time, told a leading AI conference in Toronto in 2016.

 THE BAD NEWS is waiting for us on the first page of the first chapter of Octavia E. Butler’s 1998 science fiction novel Parable of the Talents.

In the 35 years since John Carpenter‘s first big studio movie, 1982’s The Thing, which he made for Universal, was released, it’s metamorphosed from flop to underground classic in the horror genre.

Whatever future comes, one thing is fairly certain, excepting total American collapse—the goods will continue to flow. By human, by vehicle, by automaton, or by some combination of the three,stuff will be distributed, sold, and speculated upon across the land.

And a chatbot is not a human. And a linguist named Emily M. Bender is very worried what will happen when we forget this. This article was featured in One Great Story, New York’s reading recommendation newsletter. Sign up here to get it nightly. Nobody likes an I-told-you-so.

This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What they are, where they are headed, comparisons and analogies to present-day operating systems, and some of the security-related challenges of this new computing paradigm. As of November 2023 (this field moves fast!).

Context: This video is based on the slides of a talk I gave recently at the AI Security Summit. The talk was not recorded but a lot of people came to me after and told me they liked it. Seeing as I had already put in one long weekend of work to make the slides, I decided to just tune them a bit, record this round 2 of the talk and upload it here on YouTube. Pardon the random background, that's my hotel room during the thanksgiving break.

• Slides as PDF: https://drive.google.com/file/d/1pxx_ZI7O-Nwl7ZLNk5hI3WzAsTLwvNU7/view?usp=share_link (42MB)
• Slides. as Keynote: https://drive.google.com/file/d/1FPUpFMiCkMRKPFjhi9MAhby68MHVqe8u/view?usp=share_link (140MB)

Few things I wish I said (I'll add items here as they come up): - The dreams and hallucinations do not get fixed with finetuning. Finetuning just "directs" the dreams into "helpful assistant dreams". Always be careful with what LLMs tell you, especially if they are telling you something from memory alone. That said, similar to a human, if the LLM used browsing or retrieval and the answer made its way into the "working memory" of its context window, you can trust the LLM a bit more to process that information into the final answer. But TLDR right now, do not trust what LLMs say or do. For example, in the tools section, I'd always recommend double-checking the math/code the LLM did. - How does the LLM use a tool like the browser? It emits special words, e.g. |BROWSER|. When the code "above" that is inferencing the LLM detects these words it captures the output that follows, sends it off to a tool, comes back with the result and continues the generation. How does the LLM know to emit these special words? Finetuning datasets teach it how and when to browse, by example. And/or the instructions for tool use can also be automatically placed in the context window (in the “system message”). - You might also enjoy my 2015 blog post "Unreasonable Effectiveness of Recurrent Neural Networks". The way we obtain base models today is pretty much identical on a high level, except the RNN is swapped for a Transformer. http://karpathy.github.io/2015/05/21/rnn-effectiveness/ - What is in the run.c file? A bit more full-featured 1000-line version hre: https://github.com/karpathy/llama2.c/blob/master/run.c

Chapters: Part 1: LLMs 00:00:00 Intro: Large Language Model (LLM) talk 00:00:20 LLM Inference 00:04:17 LLM Training 00:08:58 LLM dreams 00:11:22 How do they work? 00:14:14 Finetuning into an Assistant 00:17:52 Summary so far 00:21:05 Appendix: Comparisons, Labeling docs, RLHF, Synthetic data, Leaderboard Part 2: Future of LLMs 00:25:43 LLM Scaling Laws 00:27:43 Tool Use (Browser, Calculator, Interpreter, DALL-E) 00:33:32 Multimodality (Vision, Audio) 00:35:00 Thinking, System 1/2 00:38:02 Self-improvement, LLM AlphaGo 00:40:45 LLM Customization, GPTs store 00:42:15 LLM OS Part 3: LLM Security 00:45:43 LLM Security Intro 00:46:14 Jailbreaks 00:51:30 Prompt Injection 00:56:23 Data poisoning 00:58:37 LLM Security conclusions End 00:59:23 Outro

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About this video essay: We've all felt it: the movies have changed. But how so exactly, and why? And what can be done about it? In this extensive critique, I try to capture the decline of modern cinema in one unifying theory.

00:00 The Marvelization of Cinema 01:52 What is Storytelling Entropy? 06:26 Hollow Franchises 11:15 Meta-References to Nowhere 17:40 Corporate Passion 25:23 Breaking the Cycle 33:08 Meaningful Engagement

Further Reading: Like Stories of Old – The Complete Reading List: https://kit.co/likestoriesofold/reading-list 10 Books that changed my life: https://kit.co/likestoriesofold/10-books-that-changed-my-life 10 More books that inspired my thinking: https://kit.co/likestoriesofold/10-more-books-that-inspired-my-thinking

My Camera Gear: https://kit.co/likestoriesofold/my-travel-camera-gear

Media included: 12 Angry Men; 2001 A Space Odyssey; A Good Day to Die Hard; A Hidden Life; Aftersun; Ahsoka; Alien; Alien vs. Predator; Aliens; Ant-Man and the Wasp Quantumania; Asteroid City; Avengers Age of Ultron; Avengers Endgame; Avengers Infinity War; Babylon; Barbie; Batman Begins; Batman v Superman; Blade Runner 2049; Captain America Civil War; Captain America The First Avenger; Captain America The Winter Soldier; Citadel; Deadpool; Decision to Leave; Die Hard; Doctor Strange in the Multiverse of Madness; Dune; Dungeons and Dragons; Eternals; Foundation; Fast X; Free Guy; Game of Thrones; Ghostbusters Afterlife; Ghosted; Gladiator; Godzilla; Godzilla vs Kong; Goodfellas; Inception; Indiana Jones and the Dial of Destiny; Invasion of the Body Snatchers; Iron Man 3; Iron Man; John Wick Chapter 3 & 4; Jurassic Park; Jurassic World; Jurassic World Dominion; Lawrence of Arabia; Loki; Mission Impossible 1, 2, Fallout, Rogue Nation & Dead Reckoning; Moon Knight, Obi-Wan Kenobi; Ocean's Eleven; Once Upon a Time in Hollywood; Past Lives; Predator 1 & 2; Red Notice; Secret Invasion; Seven Samurai; Shang-Chi; She-Hulk; Spiderman 1 & 2; Spider-Man No Way Home; Star Wars A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, The Revenge of the Sith, The Force Awakens, The Last Jedi & the Rise of Skywalker; Synecdoche New York; Terminator 1, 2 & Dark Fate; The Dark Knight, The Amazing Spider-Man 2; The Avengers; The Fabelmans; The Falcon and the Winter Soldier; The Flash; The Godfather 1 & 2; The Gray Man; The Hobbit Trilogy; The Lord of the Rings Trilogy; The Rings of Power; The Matrix 1 & 4; The Mummy; The Prestige; The Thing; The Witcher; Thor Love and Thunder; Thor Ragnarok; Thor The Dark World; Top Gun; Top Gun Maverick; Transformers Rise of the Beasts; Uncharted

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Stephen is delighted to welcome one of his favorite directors, the Master of Horror himself, John Carpenter. Watch as they discuss his 1982 classic film, “The Thing,” and stick around for a peek into Carpenter’s directing process for his latest project, “Suburban Screams.”

Colbert #SuburbanScreams #JohnCarpenter

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The bartender pouring free drinks glances around with nothing to do as the raucous room full of startup founders and software developers gravitates toward its preferred drug: networking.

A video about neural networks, function approximation, machine learning, and mathematical building blocks. Dennis Nedry did nothing wrong. This is a submission for #SoME3

My Links Patreon: https://www.patreon.com/emergentgarden Discord: https://discord.gg/ZsrAAByEnr

Links and Content: On Mathematical Maturity, Thomas Garrity: https://www.youtube.com/watch?v=zHU1xH6Ogs4 Earth Rotation Loop: https://www.youtube.com/watch?v=aiQdLP2mBJE Modeling Shell Surfaces: https://www.geogebra.org/m/xtv7zpn5 Fourier Features Paper: https://arxiv.org/abs/2006.10739 Code for mandelbrot/image approximations: https://github.com/MaxRobinsonTheGreat/mandelbrotnn Code for line/surface approximations: https://github.com/MaxRobinsonTheGreat/ManimApproximations

Music: https://youtube.com/@acolyte-compositions

Timestamps (0:00) Functions Describe the World (3:15) Neural Architecture (5:35) Higher Dimensions (11:55) Taylor Series (15:20) Fourier Series (21:25) The Real World (24:32) An Open Challenge

Subreddit to discuss about ChatGPT and AI. Not affiliated with OpenAI.

When we talk about artificial intelligence, we rely on metaphor, as we always do when dealing with something new and unfamiliar. Metaphors are, by their nature, imperfect, but we still need to choose them carefully, because bad ones can lead us astray.

People assume that adjusting to climate change means giving up stuff we want. But what if it means giving up things we’re well rid of?

The incessant hype over AI tools like ChatGPT is inspiring lots of bad opinions from people who have no idea what they’re talking about.

I won’t watch House of the Dragon, despite its high ratings. The HBO show is a prequel to Game of Thrones, and that series ended so badly I don’t want anything more to do with that fictional world. But maybe A.I. could change my mind?

The world of online fandom has come out against recent strides in disruptive technology: crusading against crypto, for instance, and protesting the widespread scraping of art for use in the training of visual AI programs.

People assume that adjusting to climate change means giving up stuff we want. But what if it means giving up things we’re well rid of?

The incessant hype over AI tools like ChatGPT is inspiring lots of bad opinions from people who have no idea what they’re talking about.

I won’t watch House of the Dragon, despite its high ratings. The HBO show is a prequel to Game of Thrones, and that series ended so badly I don’t want anything more to do with that fictional world. But maybe A.I. could change my mind?

To create is human. For the past 300,000 years we’ve been unique in our ability to make art, cuisine, manifestos, societies: to envision and craft something new where there was nothing before. Now we have company.