Latent Space
Deep technical AI engineering content. The go-to podcast for AI builders.
189 episodes curated
Episodes
Llama 2: The New Open LLM SOTA (ft. Nathan Lambert, Matt Bornstein, Anton Troynikov, Russell Kaplan, Whole Mars Catalog et al.)
As first discussed on our May Emergency pod and leaked 4 days ago , Llama (renamed from LLaMA) was upgraded to Llama 2 (pretraining on 2 trillion tokens with 2x the context length - bigger than any dataset discussed in Datasets 101 , and adding ~$20m of RLHF/preference annotation) and released for commercial use on 18 July. It immediately displaced Falcon-40B as the leading open LLM and was immediately converted/ quantized to GGML and other formats. Llama 2 seems to outperform all other open source models in their equivalent weight class : Why are open models important ? The intersection of Op
AI Fundamentals: Datasets 101
In April, we released our first AI Fundamentals episode: Benchmarks 101 . We covered the history of benchmarks, why they exist, how they are structured, and how they influence the development of artificial intelligence. Today we are (finally!) releasing Datasets 101 ! We’re really enjoying doing this series despite the work it takes - please let us know what else you want us to cover! Stop me if you’ve heard this before: “GPT3 was trained on the entire Internet”. Blatantly, demonstrably untrue : the GPT3 dataset is a little over 600GB, primarily on Wikipedia, Books corpuses, WebText and 2016-2
Code Interpreter == GPT 4.5 (w/ Simon Willison, Alex Volkov, Aravind Srinivas, Alex Graveley, et al.)
Code Interpreter is GA! As we do with breaking news, we convened an emergency pod and >17,000 people tuned in, by far our most biggest ever. This is a 2-for-1 post - a longform essay with our trademark executive summary and core insights - and a podcast capturing day-after reactions. Don’t miss either of them! Essay and transcript: https://latent.space/p/code-interpreter Podcast Timestamps [00:00:00] Intro - Simon and Alex [00:07:40] Code Interpreter for Edge Cases [00:08:59] Code Interpreter's Dependencies - Tesseract, Tensorflow [00:09:46] Code Interpreter Limitations [00:10:16] Uploading De
[Practical AI] AI Trends: a Latent Space x Practical AI crossover pod!
Part 2 of our podcast feed swap weekend! Check out Cognitive Revolution as well. "Data" Dan Whitenack has been co-host of the Practical AI podcast for the past 5 years, covering full journey of the modern AI wave post Transformers. He joined us in studio to talk about their origin story and highlight key learnings from past episodes, riff on the AI trends we are all seeing as AI practitioner-podcasters, and his passion for low-resource-everything! Subscribe on the Changelog , RSS , Apple Podcasts , Twitter , Mastodon , and wherever fine podcasts are sold! Show notes * Daniel Whitenack – Twitte
[Cognitive Revolution] The Tiny Model Revolution with Ronen Eldan and Yuanzhi Li of Microsoft Research
Thanks to the over 1m people that have checked out the Rise of the AI Engineer . It’s a long July 4 weekend in the US, and we’re celebrating with a podcast feed swap! We’ve been big fans of Nathan Labenz and Erik Torenberg’s work at the Cognitive Revolution podcast for a while, which started around the same time as we did and has done an incredible job of hosting discussions with top researchers and thinkers in the field, with a wide range of topics across computer vision (a special focus thanks to Nathan’s work at Waymark), GPT-4 (with exceptional insight due to Nathan’s time on the GPT-4 “ r
Commoditizing the Petaflop — with George Hotz of the tiny corp
We are now launching our dedicated new YouTube and Twitter ! Any help in amplifying our podcast would be greatly appreciated, and of course, tell your friends! Notable followon discussions collected on Twitter , Reddit , Reddit , Reddit , HN , and HN . Please don’t obsess too much over the GPT4 discussion as it is mostly rumor; we spent much more time on tinybox/tinygrad on which George is the foremost authority! We are excited to share the world’s first interview with George Hotz on the tiny corp ! If you don’t know George , he was the first person to unlock the iPhone, jailbreak the PS3, wen
Emergency Pod: OpenAI's new Functions API, 75% Price Drop, 4x Context Length (w/ Alex Volkov, Simon Willison, Riley Goodside, Joshua Lochner, Stefania Druga, Eric Elliott, Mayo Oshin et al)
Full Transcript and show notes: https://www.latent.space/p/function-agents?sd=pf Timestamps: [00:00:00] Intro [00:01:47] Recapping June 2023 Updates [00:06:24] Known Issues with Long Context [00:08:00] New Functions API [00:10:45] Riley Goodside [00:12:28] Simon Willison [00:14:30] Eric Elliott [00:16:05] Functions API and Agents [00:18:25] Functions API vs Google Vertex JSON [00:21:32] From English back to Code [00:26:14] Embedding Price Drop and Pinecone Perspective [00:30:39] Xenova and Huggingface Perspective [00:34:23] Function Selection [00:39:58] Designing Code Agents with Function API
From RLHF to RLHB: The Case for Learning from Human Behavior - with Jeffrey Wang and Joe Reeve of Amplitude
Welcome to the almost 3k latent space explorers that joined us last month! We’re holding our first SF listener meetup with Practical AI next Monday; join us if you want to meet past guests and put faces to voices! All events are in /community . Who among you regularly click the ubiquitous 👍 /👎 buttons in ChatGPT/Bard/etc? Anyone? I don’t see any hands up. OpenAI has told us how important reinforcement learning from human feedback (RLHF) is to creating the magic that is ChatGPT, but we know from our conversation with Databricks’ Mike Conover just how hard it is to get just 15,000 pieces of ex
Building the AI × UX Scenius — with Linus Lee of Notion AI
Read: https://www.latent.space/p/ai-interfaces-and-notion Show Notes * Linus on Twitter * Linus’ personal blog * Notion * Notion AI * Notion Projects * AI UX Meetup Recap Timestamps * [00:03:30] Starting the AI / UX community * [00:10:01] Most knowledge work is not text generation * [00:16:21] Finding the right constraints and interface for AI * [00:19:06] Linus' journey to working at Notion * [00:23:29] The importance of notations and interfaces * [00:26:07] Setting interface defaults and standards * [00:32:36] The challenges of designing AI agents * [00:39:43] Notion deep dive: “Blocks”, AI,
Debugging the Internet with AI agents – with Itamar Friedman of Codium AI and AutoGPT
We are hosting the AI World’s Fair in San Francisco on June 8th! You can RSVP here . Come meet fellow builders, see amazing AI tech showcases at different booths around the venue, all mixed with elements of traditional fairs: live music, drinks, games, and food! We are also at Amplitude’s AI x Product Hackathon and are hosting our first joint Latent Space + Practical AI Podcast Listener Meetup next month! We are honored by the rave reviews for our last episode with MosaicML! They are also welcome on Apple Podcasts and Twitter/HN/LinkedIn/Mastodon etc! We recently spent a wonderful week with It
MPT-7B and The Beginning of Context=Infinity — with Jonathan Frankle and Abhinav Venigalla of MosaicML
We are excited to be the first podcast in the world to release an in-depth interview on the new SOTA in commercially licensed open source models - MosiacML MPT-7B! The Latent Space crew will be at the NYC Lux AI Summit next week, and have two meetups in June. As usual, all events are on the Community page ! We are also inviting beta testers for the upcoming AI for Engineers course. See you soon! One of GPT3’s biggest limitations is context length - you can only send it up to 4000 tokens (3k words, 6 pages) before it throws a hard error, requiring you to bring in LangChain and other retrieval t
Guaranteed quality and structure in LLM outputs - with Shreya Rajpal of Guardrails AI
Tomorrow, 5/16, we’re hosting Latent Space Liftoff Day in San Francisco. We have some amazing demos from founders at 5:30pm, and we’ll have an open co-working starting at 2pm. Spaces are limited, so please RSVP here ! One of the biggest criticisms of large language models is their inability to tightly follow requirements without extensive prompt engineering. You might have seen examples of ChatGPT playing a game of chess and making many invalid moves, or adding new pieces to the board. Guardrails AI aims to solve these issues by adding a formalized structure around inference calls, which valid
The AI Founder Gene: Being Early, Building Fast, and Believing in Greatness — with Sharif Shameem of Lexica
Thanks to the over 42,000 latent space explorers who checked out our Replit episode ! We are hosting/attending a couple more events in SF and NYC this month. See you if in town! Lexica.art was introduced to the world 24 hours after the release of Stable Diffusion as a search engine for prompts, gaining instant product-market fit as a world discovering generative AI also found they needed to learn prompting by example. Lexica is now 8 months old, serving 5B image searches/day, and just shipped V3 of Lexica Aperture , their own text-to-image model! Sharif Shameem breaks his podcast hiatus with u
No Moat: Closed AI gets its Open Source wakeup call — ft. Simon Willison
It’s now almost 6 months since Google declared Code Red , and the results — Jeff Dean’s recap of 2022 achievements and a mass exodus of the top research talent that contributed to it in January, Bard’s rushed launch in Feb, a slick video showing Google Workspace AI features and confusing doubly linked blogposts about PaLM API in March, and merging Google Brain and DeepMind in April — have not been inspiring. Google’s internal panic is in full display now with the surfacing of a well written memo , written by software engineer Luke Sernau written in early April, revealing internal distress not
Training a SOTA Code LLM in 1 week and Quantifying the Vibes — with Reza Shabani of Replit
Latent Space is popping off! Welcome to the over 8500 latent space explorers who have joined us. Join us this month at various events in SF and NYC , or start your own! This post spent 22 hours at the top of Hacker News . As announced during their Developer Day celebrating their $100m fundraise following their Google partnership , Replit is now open sourcing its own state of the art code LLM: replit-code-v1-3b ( model card , HF Space ), which beats OpenAI’s Codex model on the industry standard HumanEval benchmark when finetuned on Replit data (despite being 77% smaller) and more importantly pa