The Man behind Stable Diffusion

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#stablediffusion #ai #stabilityai An interview with Emad Mostaque, founder of Stability AI. OUTLINE: 0:00 - Intro 1:30 - What is Stability AI? 3:45 - Where does the money come from? 5:20 - Is this the CERN of AI? 6:15 - Who gets access to the resources? 8:00 - What is Stable Diffusion? 11:40 - What if your model produces bad outputs? 14:20 - Do you employ people? 16:35 - Can you prevent the corruption of profit? 19:50 - How can people find you? 22:45 - Final thoughts, let's destroy PowerPoint Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

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00:41:07

LLaMA: Open and Efficient Foundation Language Models (Paper Explained)

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92K views 3 years ago

,#ai #meta #languagemodel LLaMA is a series of large language models from 7B to 65B parameters, trained by Meta AI. They train for longer on more data and show that something like gpt-3 can be outperformed by significantly smaller models when trained like this. Meta also releases the trained models to the research community. OUTLINE: 0:00 - Introduction & Paper Overview 4:30 - Rant on Open-Sourcing 8:05 - Training Data 12:40 - Training Hyperparameters 14:50 - Architecture Modifications 17:10 - Optimizer 19:40 - Efficient Implementation 26:15 - Main Results 38:00 - Some more completions 40:00 - Conclusion Paper: https://arxiv.org/abs/2302.13971 Website: https://ai.facebook.com/blog/large-language-model-llama-meta-ai/ Abstract: We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community. Authors: Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

00:33:56

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (ML Research Paper Explained)

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1 views 5 years ago

,#nerf #neuralrendering #deeplearning View Synthesis is a tricky problem, especially when only given a sparse set of images as an input. NeRF embeds an entire scene into the weights of a feedforward neural network, trained by backpropagation through a differential volume rendering procedure, and achieves state-of-the-art view synthesis. It includes directional dependence and is able to capture fine structural details, as well as reflection effects and transparency. OUTLINE: 0:00 - Intro & Overview 4:50 - View Synthesis Task Description 5:50 - The fundamental difference to classic Deep Learning 7:00 - NeRF Core Concept 15:30 - Training the NeRF from sparse views 20:50 - Radiance Field Volume Rendering 23:20 - Resulting View Dependence 24:00 - Positional Encoding 28:00 - Hierarchical Volume Sampling 30:15 - Experimental Results 33:30 - Comments & Conclusion Paper: https://arxiv.org/abs/2003.08934 Website & Code: https://www.matthewtancik.com/nerf My Video on SIREN: https://youtu.be/Q5g3p9Zwjrk Abstract: We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x,y,z) and viewing direction (θ,ϕ)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons. Authors: Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/ BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

00:27:33

GPT-2: Language Models are Unsupervised Multitask Learners

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1 views 7 years ago

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Javier Milei on becoming president: I cut half the ministries | Lex Fridman Podcast Clips

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1 views 1 year ago

,Lex Fridman Podcast full episode: https://www.youtube.com/watch?v=8NLzc9kobDk Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/cv8258-sb See below for guest bio, links, and to give feedback, submit questions, contact Lex, etc. *GUEST BIO:* Javier Milei is the President of Argentina. This episode is available in both English and Spanish. *CONTACT LEX:* *Feedback* - give feedback to Lex: https://lexfridman.com/survey *AMA* - submit questions, videos or call-in: https://lexfridman.com/ama *Hiring* - join our team: https://lexfridman.com/hiring *Other* - other ways to get in touch: https://lexfridman.com/contact *EPISODE LINKS:* Javier Milei's X: https://x.com/JMilei Javier Milei's Instagram: https://instagram.com/javiermilei Javier Milei's Facebook: https://facebook.com/JavierMileiEconomista *SPONSORS:* To support this podcast, check out our sponsors & get discounts: *Eight Sleep:* Temp-controlled smart mattress. Go to https://lexfridman.com/s/eight_sleep-cv8258-sb *NetSuite:* Business management software. Go to https://lexfridman.com/s/netsuite-cv8258-sb *BetterHelp:* Online therapy and counseling. Go to https://lexfridman.com/s/betterhelp-cv8258-sb *AG1:* All-in-one daily nutrition drinks. Go to https://lexfridman.com/s/ag1-cv8258-sb *LMNT:* Zero-sugar electrolyte drink mix. Go to https://lexfridman.com/s/lmnt-cv8258-sb *PODCAST LINKS:* - Podcast Website: https://lexfridman.com/podcast - Apple Podcasts: https://apple.co/2lwqZIr - Spotify: https://spoti.fi/2nEwCF8 - RSS: https://lexfridman.com/feed/podcast/ - Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 - Clips Channel: https://www.youtube.com/lexclips *SOCIAL LINKS:* - X: https://x.com/lexfridman - Instagram: https://instagram.com/lexfridman - TikTok: https://tiktok.com/@lexfridman - LinkedIn: https://linkedin.com/in/lexfridman - Facebook: https://facebook.com/lexfridman - Patreon: https://patreon.com/lexfridman - Telegram: https://t.me/lexfridman - Reddit: https://reddit.com/r/lexfridman

00:03:04

Great Moments in Unintended Consequences: Coin Mail, Fertilizer Ban, and Hawaiian Mongoose (Vol. 14)

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1 views 2 years ago

,Good intentions, bad results. Watch the whole series: https://www.youtube.com/watch?v=1lUrH4Sbgh8&list=PLBuns9Evn1w9XhnH7vVh_7C65wJbaBECK&index=1 Do you know a great moment in unintended consequences? Leave a comment or email us at comedy@reason.com. ----- Part One: Pros and Coins The Year: 2008 The Problem: People aren't switching to dollar coins, even though they're heavier! The Solution: Allow the general public to purchase the coins at face value, directly from the U.S. Mint, and cover the cost of shipping. Sounds like a great idea, with the best of intentions. What could possibly go wrong? Turns out, people like free stuff! And some credit cards offer rewards. Consumers realized they could charge thousands of dollars worth of coins and then immediately pay off the balance with those very same coins, earning loads of free airline miles. Eventually, the Mint caught on, changed their rules and blocked suspicious buyers from further purchases, but not before many had jacked up their reward points banking free trips around the world. One frequent flier even is said to have bought $800,000 in coins helping him earn lifetime platinum-elite status on American Airlines. Now that's a coin trick. Part Two: Crop Drop The Year: 2021 The Problem: Sri Lanka's farms aren't organic. The Solution: An immediate nationwide ban on synthetic fertilizer and pesticides—which coincidently saves Sri Lanka $400 million in annual subsidy costs while they wrestle with a deep fiscal crisis. Sounds like a great idea, with the best of intentions. What could possibly go wrong? Turns out synthetic fertilizers and pesticides actually work! Six months after the presidential decree, Sri Lanka's biggest export, tea, was down 18%. Rice production plunged 20%, forcing the once proudly self-sufficient country to import rice at a cost of $450 million. Due to public backlash, the government was forced to reverse their ban and pay out hundreds of millions to compensate farmers—deepening the country's economic crisis, and fanning protests that led to the president's resignation and temporary exile. Rice knowing ya! Part Three: Wild Mongoose Chase The Year: 1883 The Problem: Rats in Hawaii are damaging valuable sugar crops. The Solution: Control the rodents by introducing a new predatory species: the Small Indian Mongoose. Sounds like…let's be honest we all know where this is going. It turns out rats are generally nocturnal while the Small Indian Mongoose is active during the day. So the two species rarely met! Luckily for the mongooses, Hawaii is full of appetizing native wildlife including sea turtle eggs and numerous species of endangered birds. With no natural predators, the Mongoose thrived! A hundred years later, they remain a massive problem on many of the islands. Great moments in unintended consequences: good intentions, bad results.