Training Data By cover art

Training Data

By: Sequoia Capital
Listen for free

  • Summary

  • Join us as we train our neural nets on the theme of the century: AI. Sonya Huang, Pat Grady and more Sequoia Capital partners host conversations with leading AI builders and researchers to ask critical questions and develop a deeper understanding of the evolving technologies—and their implications for technology, business and society. The content of this podcast does not constitute investment advice, an offer to provide investment advisory services, or an offer to sell or solicitation of an offer to buy an interest in any investment fund.
    Show more Show less
activate_primeday_promo_in_buybox_DT
Episodes
  • Zapier’s Mike Knoop launches ARC Prize to Jumpstart New Ideas for AGI

    Zapier’s Mike Knoop launches ARC Prize to Jumpstart New Ideas for AGI
    Jul 2 2024
    As impressive as LLMs are, the growing consensus is that language, scale and compute won’t get us to AGI. Although many AI benchmarks have quickly achieved human-level performance, there is one eval that has barely budged since it was created in 2019. Google researcher François Chollet wrote a paper that year defining intelligence as skill-acquisition efficiency—the ability to learn new skills as humans do, from a small number of examples. To make it testable he proposed a new benchmark, the Abstraction and Reasoning Corpus (ARC), designed to be easy for humans, but hard for AI. Notably, it doesn’t rely on language. Zapier co-founder Mike Knoop read Chollet’s paper as the LLM wave was rising. He worked quickly to integrate generative AI into Zapier’s product, but kept coming back to the lack of progress on the ARC benchmark. In June, Knoop and Chollet launched the ARC Prize, a public competition offering more than $1M to beat and open-source a solution to the ARC-AGI eval. In this episode Mike talks about the new ideas required to solve ARC, shares updates from the first two weeks of the competition, and shares why he’s excited for AGI systems that can innovate alongside humans. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models: The 2019 paper that first caught Mike’s attention about the capabilities of LLMs On the Measure of Intelligence: 2019 paper by Google researcher François Chollet that introduced the ARC benchmark, which remains unbeaten ARC Prize 2024: The $1M+ competition Mike and François have launched to drive interest in solving the ARC-AGI eval Sequence to Sequence Learning with Neural Networks: Ilya Sutskever paper from 2014 that influenced the direction of machine translation with deep neural networks. Etched: Luke Miles on LessWrong wrote about the first ASIC chip that accelerates transformers on silicon Kaggle: The leading data science competition platform and online community, acquired by Google in 2017 Lab42: Swiss AU lab that hosted ARCathon precursor to ARC Prize Jack Cole: Researcher on team that was #1 on the leaderboard for ARCathon Ryan Greenblatt: Researcher with current high score (50%) on ARC public leaderboard (00:00) Introduction (01:51) AI at Zapier (08:31) What is ARC AGI? (13:25) What does it mean to efficiently acquire a new skill? (19:03) What approaches will succeed? (21:11) A little bit of a different shape (25:59) The role of code generation and program synthesis (29:11) What types of people are working on this? (31:45) Trying to prove you wrong (34:50) Where are the big labs? (38:21) The world post-AGI (42:51) When will we cross 85% on ARC AGI? (46:12) Will LLMs be part of the solution? (50:13) Lightning round
    Show more Show less
    55 mins
  • Factory’s Matan Grinberg and Eno Reyes Unleash the Droids on Software Development

    Factory’s Matan Grinberg and Eno Reyes Unleash the Droids on Software Development
    Jun 25 2024
    Archimedes said that with a large enough lever, you can move the world. For decades, software engineering has been that lever. And now, AI is compounding that lever. How will we use AI to apply 100 or 1000x leverage to the greatest lever to move the world? Matan Grinberg and Eno Reyes, co-founders of Factory, have chosen to do things differently than many of their peers in this white-hot space. They sell a fleet of “Droids,” purpose-built dev agents which accomplish different tasks in the software development lifecycle (like code review, testing, pull requests or writing code). Rather than training their own foundation model, their approach is to build something useful for engineering orgs today on top of the rapidly improving models, aligning with the developer and evolving with them. Matan and Eno are optimistic about the effects of autonomy in software development and on building a company in the application layer. Their advice to founders, “The only way you can win is by executing faster and being more obsessed.” Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned: Juan Maldacena, Institute for Advanced Study, string theorist that Matan cold called as an undergrad SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent SWE-bench: Can Language Models Resolve Real-World GitHub Issues?, an evaluation framework for GitHub issues Monte Carlo tree search, a 2006 algorithm for solving decision making in games (and used in AlphaGo) Language agent tree search, a framework for LLM planning, acting and reasoning The Bitter Lesson, Rich Sutton’s essay on scaling in search and learning Code churn, time to merge, cycle time, metrics Factory thinks are important to eng orgs Transcript: https://www.sequoiacap.com/podcast/training-data-factory/ 00:00 Introduction 01:36 Personal backgrounds 10:54 The compound lever 12:41 What is Factory? 16:29 Cognitive architectures 21:13 800 engineers at OpenAI are working on my margins 24:00 Jeff Dean doesn't understand your code base 25:40 Individual dev productivity vs system-wide optimization 30:04 Results: Factory in action 32:54 Learnings along the way 35:36 Fully autonomous Jeff Deans 37:56 Beacons of the upcoming age 40:04 How far are we? 43:02 Competition 45:32 Lightning round 49:34 Bonus round: Factory's SWE-bench results
    Show more Show less
    59 mins
  • LangChain’s Harrison Chase on Building the Orchestration Layer for AI Agents

    LangChain’s Harrison Chase on Building the Orchestration Layer for AI Agents
    Jun 18 2024
    Last year, AutoGPT and Baby AGI captured our imaginations—agents quickly became the buzzword of the day…and then things went quiet. AutoGPT and Baby AGI may have marked a peak in the hype cycle, but this year has seen a wave of agentic breakouts on the product side, from Klarna’s customer support AI to Cognition’s Devin, etc. Harrison Chase of LangChain is focused on enabling the orchestration layer for agents. In this conversation, he explains what’s changed that’s allowing agents to improve performance and find traction. Harrison shares what he’s optimistic about, where he sees promise for agents vs. what he thinks will be trained into models themselves, and discusses novel kinds of UX that he imagines might transform how we experience agents in the future. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned: ReAct: Synergizing Reasoning and Acting in Language Models, the first cognitive architecture for agents SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent from researchers at Princeton Devin, autonomous software engineering from Cognition V0: Generative UI agent from Vercel GPT Researcher, a research agent Language Model Cascades: 2022 paper by Google Brain and now OpenAI researcher David Dohan that was influential for Harrison in developing LangChain Transcript: https://www.sequoiacap.com/podcast/training-data-harrison-chase/ 00:00 Introduction 01:21 What are agents? 05:00 What is LangChain’s role in the agent ecosystem? 11:13 What is a cognitive architecture? 13:20 Is bespoke and hard coded the way the world is going, or a stop gap? 18:48 Focus on what makes your beer taste better 20:37 So what? 22:20 Where are agents getting traction? 25:35 Reflection, chain of thought, other techniques? 30:42 UX can influence the effectiveness of the architecture 35:30 What’s out of scope? 38:04 Fine tuning vs prompting? 42:17 Existing observability tools for LLMs vs needing a new architecture/approach 45:38 Lightning round
    Show more Show less
    50 mins
Economics
Show more Show less

What listeners say about Training Data

Average customer ratings

Reviews - Please select the tabs below to change the source of reviews.

Audible.com reviews

Amazon reviews
No Reviews are Available