Latent Space
Deep technical AI engineering content. The go-to podcast for AI builders.
189 episodes curated
Episodes
Mapping the future of *truly* Open Models and Training Dolly for $30 — with Mike Conover of Databricks
The race is on for the first fully GPT3/4-equivalent, truly open source Foundation Model! LLaMA’s release proved that a great model could be released and run on consumer-grade hardware (see llama.cpp ), but its research license prohibits businesses from running it and all it’s variants (Alpaca, Vicuna, Koala, etc) for their own use at work. So there is great interest and desire for *truly* open source LLMs that are feasible for commercial use (with far better customization, finetuning, and privacy than the closed source LLM APIs). The previous leading contenders were Eleuther’s GPT-J and Neo o
AI-powered Search for the Enterprise — with Deedy Das of Glean
The most recent YCombinator W23 batch graduated 59 companies building with Generative AI for everything from sales, support, engineering, data, and more: Many of these B2B startups will be seeking to establish an AI foothold in the enterprise. As they look to recent success, they will find Glean, started in 2019 by a group of ex-Googlers to finally solve AI-enabled enterprise search. In 2022 Sequoia led their Series C at a $1b valuation and Glean have just refreshed their website touting new logos across Databricks, Canva, Confluent, Duolingo, Samsara, and more in the Fortune 50 and announcing
Segment Anything Model and the Hard Problems of Computer Vision — with Joseph Nelson of Roboflow
2023 is the year of Multimodal AI , and Latent Space is going multimodal too! * This podcast comes with a video demo at the 1hr mark and it’s a good excuse to launch our YouTube - please subscribe! * We are also holding two events in San Francisco — the first AI | UX meetup next week (already full; we’ll send a recap here on the newsletter) and Latent Space Liftoff Day on May 4th ( signup here ; but get in touch if you have a high profile launch you’d like to make). * We also joined the Chroma/OpenAI ChatGPT Plugins Hackathon last week where we won the Turing and Replit awards and met some of
AI Fundamentals: Benchmarks 101
We’re trying a new format, inspired by Acquired.fm ! No guests, no news, just highly prepared, in-depth conversation on one topic that will level up your understanding. We aren’t experts, we are learning in public. Please let us know what we got wrong and what you think of this new format! When you ask someone to break down the basic ingredients of a Large Language Model, you’ll often hear a few things: You need lots of data. You need lots of compute. You need models with billions of parameters. Trust the Bitter Lesson , more more more, scale is all you need . Right? Nobody ever mentions the s
Grounded Research: From Google Brain to MLOps to LLMOps — with Shreya Shankar of UC Berkeley
We are excited to feature our first academic on the pod! I first came across Shreya when her tweetstorm of MLOps principles went viral: Shreya’s holistic approach to production grade machine learning has taken her from Stanford to Facebook and Google Brain, being the first ML Engineer at Viaduct, and now a PhD in Databases (trust us, its relevant) at UC Berkeley with the new EPIC Data Lab . If you know Berkeley’s history in turning cutting edge research into gamechanging startups, you should be as excited as we are! Recorded in-person at the beautiful StudioPod studios in San Francisco. Full t
Emergency Pod: ChatGPT's App Store Moment (w/ OpenAI's Logan Kilpatrick, LindyAI's Florent Crivello and Nader Dabit)
This blogpost has been updated since original release to add more links and references. The ChatGPT Plugins announcement today could be viewed as the launch of ChatGPT’s “App Store”, a moment as significant as when Apple opened its App Store for the iPhone in 2008 or when Facebook let developers loose on its Open Graph in 2010. With a dozen lines of simple JSON and a mostly-english prompt to help ChatGPT understand what the plugin does, developers will be able to add extensions to ChatGPT to get information and trigger actions in the real world. OpenAI itself launched with some killer first pa
From Astrophysics to AI: Building the future AI Data Stack — with Sarah Nagy of Seek.ai
If Text is the Universal Interface , then Text to SQL is perhaps the killer B2B business usecase for Generative AI. You may have seen incredible demos from Perplexity AI , OSS Insights , and CensusGPT where the barrier of learning SQL and schemas goes away and you can intuitively converse with your data in natural language. But in the multi-billion dollar data engineering industry, Seek.ai has emerged as the forerunner in building a conversational engine and knowledge base that truly democratizes data insights. We’re proud to present our first remote interview with Sarah Nagy to learn how AI c
97% Cheaper, Faster, Better, Correct AI — with Varun Mohan of Codeium
OpenAI just rollicked the AI world yet again yesterday — while releasing the long awaited ChatGPT API, they also priced it at $2 per million tokens generated, which is 90% cheaper than the text-davinci-003 pricing of the “GPT3.5” family. Their blogpost on how they did it is vague: Through a series of system-wide optimizations, we’ve achieved 90% cost reduction for ChatGPT since December; we’re now passing through those savings to API users. We were fortunate enough to record Episode 2 of our podcast with someone who routinely creates 90%+ improvements for their customers, and in fact have star
ChatGPT, GPT4 hype, and Building LLM-native products — with Logan Kilpatrick of OpenAI
We’re so glad to launch our first podcast episode with Logan Kilpatrick ! This also happens to be his first public interview since joining OpenAI as their first Developer Advocate. Thanks Logan! Recorded in-person at the beautiful StudioPod studios in San Francisco. Full transcript is below the fold. Timestamps * 00:29: Logan’s path to OpenAI * 07:06: On ChatGPT and GPT3 API * 16:16: On Prompt Engineering * 20:30: Usecases and LLM-Native Products * 25:38: Risks and benefits of building on OpenAI * 35:22: OpenAI Codex * 42:40: Apple's Neural Engine * 44:21: Lightning Round Show notes * Sam Altm