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Much of the AI narrative revolves around architecture research, compute scaling & buying human data. Very few understand the deep technical discipline of "data research," which has driven many of the gains in model capabilities. Vivek Gopalan sits down with Mahesh Sathiamoorthy @ COLM2026 & Alex Dimakis, founders of...

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48,451 görüntüleme • 3 ay önce •via X (Twitter)

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@madiator We are very proud to partner with this team since the beginning!

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@vvkgopalan @madiator Data research is the silent engine behind recent capability jumps, scaling compute alone won’t close the gap

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@vvkgopalan @madiator 数据研究才是被严重低估的方向吧

Benzer Videolar

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.
1:05

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Base Labs

243,123 görüntüleme • 1 ay önce

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?
26:21

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

Pat Grady

66,691 görüntüleme • 1 ay önce

The models that will accelerate drug discovery, chip design, and energy are limited by the data they learn from. We led Proximal’s $15M seed as they build the data engine for the next generation of frontier AI and treat training data with the same rigor as model architecture. Watch quentinclark in conversation with Calvin Chen. Chapters 00:00 — Meet Proximal 03:45 — A Rainy Afternoon in South Park 05:19 — From Sneaker Bots to a First Exit 08:14 — Why Suffering Equals Growth 10:13 — The Path to Proximal 11:57 — How Models Learn 15:17 — Training Data as a Research Problem 23:24 — Building the Team in India
33:20

The models that will accelerate drug discovery, chip design, and energy are limited by the data they learn from. We led Proximal’s $15M seed as they build the data engine for the next generation of frontier AI and treat training data with the same rigor as model architecture. Watch quentinclark in conversation with Calvin Chen. Chapters 00:00 — Meet Proximal 03:45 — A Rainy Afternoon in South Park 05:19 — From Sneaker Bots to a First Exit 08:14 — Why Suffering Equals Growth 10:13 — The Path to Proximal 11:57 — How Models Learn 15:17 — Training Data as a Research Problem 23:24 — Building the Team in India

General Catalyst

13,212 görüntüleme • 7 gün önce

The Delta of Intelligence is Human Data! Curtis G. Northcutt is Director of AI Research at Handshake AI. In this episode, Curtis shares his unique perspectives on human data, RL envs, four quadrants of the AI market, journey of Handshake and much more. 0:00:00 - INTRO 0:02:08 - The Snowman Effect: From Rural Kentucky to 7 Years at MIT 0:09:50 - Confident Learning, Two Systems of Intelligence, Classical ML Has to Prevail 0:20:37 - Origin of CleanLab and Handshake acquisition 0:28:00 - How does GPT-5 become GPT-6? 0:31:20 - The Fastest growing Data Lab: What makes Handshake different? 0:35:32 - Difference between Good Data and Bad Data 0:38:17 - New kind of Data, IA and AI 0:42:04 - Scaling Coding Benchmarks and Efficiency in Long Horizon Tasks 0:49:12 - On Pre-Training, Misconception around Human Data 0:57:13 - Open Question: Taste and Personality, Quick Fire 01:05:15 - Advice to 20yo : Skill and Obsession
1:08:18

The Delta of Intelligence is Human Data! Curtis G. Northcutt is Director of AI Research at Handshake AI. In this episode, Curtis shares his unique perspectives on human data, RL envs, four quadrants of the AI market, journey of Handshake and much more. 0:00:00 - INTRO 0:02:08 - The Snowman Effect: From Rural Kentucky to 7 Years at MIT 0:09:50 - Confident Learning, Two Systems of Intelligence, Classical ML Has to Prevail 0:20:37 - Origin of CleanLab and Handshake acquisition 0:28:00 - How does GPT-5 become GPT-6? 0:31:20 - The Fastest growing Data Lab: What makes Handshake different? 0:35:32 - Difference between Good Data and Bad Data 0:38:17 - New kind of Data, IA and AI 0:42:04 - Scaling Coding Benchmarks and Efficiency in Long Horizon Tasks 0:49:12 - On Pre-Training, Misconception around Human Data 0:57:13 - Open Question: Taste and Personality, Quick Fire 01:05:15 - Advice to 20yo : Skill and Obsession

himanshu

22,264 görüntüleme • 5 ay önce

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview
4:45

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

Ray Fernando

42,398 görüntüleme • 6 ay önce

BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI
49:29

BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI

Molly O’Shea

70,094 görüntüleme • 1 yıl önce

Want world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, Gabe Pereyra shared Harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench, contracting, and the diligence dataset 03:57 Domain experts guiding synthetic data generation 05:23 Why Harvey open sourced its datasets 06:55 Working with the neo labs – and why more than one 08:20 Post-training in-house: building "Associate 1" 09:44 The model serving matrix: 60 countries, fallbacks, SLAs 11:05 Deciding what stays in production 12:29 Simple open source switches and model routing 13:55 Moneyball: "If we win on this budget, we change the game" 14:53 Q&A: Training with sensitive data 17:16 Q&A: Competing for research talent 18:46 Q&A: Designing rubrics that actually challenge frontier models 20:19 Q&A: Where the pipeline breaks — data, research, or infra 22:59 Q&A: The tension in open sourcing a benchmark 25:02 Q&A: Biggest remaining open problems 27:10 Q&A: Competing with horizontal products
29:20

Want world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, Gabe Pereyra shared Harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench, contracting, and the diligence dataset 03:57 Domain experts guiding synthetic data generation 05:23 Why Harvey open sourced its datasets 06:55 Working with the neo labs – and why more than one 08:20 Post-training in-house: building "Associate 1" 09:44 The model serving matrix: 60 countries, fallbacks, SLAs 11:05 Deciding what stays in production 12:29 Simple open source switches and model routing 13:55 Moneyball: "If we win on this budget, we change the game" 14:53 Q&A: Training with sensitive data 17:16 Q&A: Competing for research talent 18:46 Q&A: Designing rubrics that actually challenge frontier models 20:19 Q&A: Where the pipeline breaks — data, research, or infra 22:59 Q&A: The tension in open sourcing a benchmark 25:02 Q&A: Biggest remaining open problems 27:10 Q&A: Competing with horizontal products

Pat Grady

179,308 görüntüleme • 1 ay önce

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing
54:56

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing

Matt Turck

51,484 görüntüleme • 9 ay önce

Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI
1:13:56

Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI

Matt Turck

101,314 görüntüleme • 4 ay önce

Here's my conversation with Lucas Atkins and the team at Arcee.ai on their path to training and releasing Trinity Large today. From going all in on open models built end to end in the US 6 months ago to having the model in hand is no easy feet. I loved this conversation on how to design a startup around open models and take a bold step to scale it up. I'm openly an Arcee fan, watching them take risk and pull it off. We discuss: - The state (and future) of open vs. closed models, - The business of selling open models for on-prem deployments, - The story of Arcee AI & going “all-in” on this training run, - The ATOM project, - Building frontier model training teams in 6 months, - and other great topics. I really loved this one, and think you well too. Chapters: 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large 00:08:26 Transitioning a Company to Pre-training 00:13:00 Technical Decisions: Muon and MoE 00:18:41 Scaling and MoE Training Pain 00:23:14 Post-training and RL Strategies 00:28:09 Team Structure and Data Scaling 00:31:31 The Trinity Manifesto: US Open Weights 00:42:31 Specialized Models and Distillation 00:47:12 Infrastructure and Hosting 400B 00:50:53 Open Source as a Business Moat 00:56:31 Predictions: Best Model in 2026 01:02:29 Lightning Round & Conclusions More great open model builder podcasts coming soon!
1:12:15

Here's my conversation with Lucas Atkins and the team at Arcee.ai on their path to training and releasing Trinity Large today. From going all in on open models built end to end in the US 6 months ago to having the model in hand is no easy feet. I loved this conversation on how to design a startup around open models and take a bold step to scale it up. I'm openly an Arcee fan, watching them take risk and pull it off. We discuss: - The state (and future) of open vs. closed models, - The business of selling open models for on-prem deployments, - The story of Arcee AI & going “all-in” on this training run, - The ATOM project, - Building frontier model training teams in 6 months, - and other great topics. I really loved this one, and think you well too. Chapters: 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large 00:08:26 Transitioning a Company to Pre-training 00:13:00 Technical Decisions: Muon and MoE 00:18:41 Scaling and MoE Training Pain 00:23:14 Post-training and RL Strategies 00:28:09 Team Structure and Data Scaling 00:31:31 The Trinity Manifesto: US Open Weights 00:42:31 Specialized Models and Distillation 00:47:12 Infrastructure and Hosting 400B 00:50:53 Open Source as a Business Moat 00:56:31 Predictions: Best Model in 2026 01:02:29 Lightning Round & Conclusions More great open model builder podcasts coming soon!

Nathan Lambert

26,872 görüntüleme • 8 ay önce

In the latest episode of Release Notes, Gemini's Dave Citron joins Logan Kilpatrick to deep dive into some of the latest Gemini updates. 🎙️ Learn more about Gemini with personalization, Canvas, Audio Overviews, Deep Research, and more: Timestamps: 0:00 Introduction 0:59 Recent Gemini app launches 2:00 Introducing Canvas 5:12 Canvas in action 8:46 Canvas examples 12:02 Enhanced capabilities with Thinking Models 15:12 Deep Research in action 20:27 The future of agentic experiences 22:12 Deep Research and Audio Overviews 24:11 Personalization in Gemini app 27:50 Personalization in action 29:58 How personalization works: user data and privacy 32:30 The future of personalization
36:53

In the latest episode of Release Notes, Gemini's Dave Citron joins Logan Kilpatrick to deep dive into some of the latest Gemini updates. 🎙️ Learn more about Gemini with personalization, Canvas, Audio Overviews, Deep Research, and more: Timestamps: 0:00 Introduction 0:59 Recent Gemini app launches 2:00 Introducing Canvas 5:12 Canvas in action 8:46 Canvas examples 12:02 Enhanced capabilities with Thinking Models 15:12 Deep Research in action 20:27 The future of agentic experiences 22:12 Deep Research and Audio Overviews 24:11 Personalization in Gemini app 27:50 Personalization in action 29:58 How personalization works: user data and privacy 32:30 The future of personalization

Google Gemini

119,472 görüntüleme • 1 yıl önce

When Mudith Jayasekara and I met Gabe Pereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion
41:50

When Mudith Jayasekara and I met Gabe Pereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion

Charlie O'Neill

93,422 görüntüleme • 2 ay önce

Why do even our best AI models need tens of thousands of examples to learn skills that a human picks up in a handful of tries? Solving this problem is one of the great open challenges in modern AI. World models, which give AI an internal simulation of its environment, are one of the most promising paths forward. In this episode of Decoded, YC's Ankit Gupta and Francois Chaubard discuss the intuition and math behind world models, new research, and current applications in self-driving, robotics, and more. 01:45 — What would perfect efficiency look like? 05:10 — World models in the human brain 09:20 — Control theory & the drone example 14:30 — When physics breaks down 17:45 — Chess, Go & the action space problem 24:10 — Why AlphaGo can't scale 28:00 — Monte Carlo tree search explained 34:00 — Self-Driving: state space is infinite 40:30 — Model-Free vs. Model-Based RL 44:00 — Why robotics is the hardest case 48:20 — World models that actually work 54:10 — JEPA & latent space tricks 59:00 — Open problems remaining 1:04:30 — Does this pass the squint test?
1:14:26

Why do even our best AI models need tens of thousands of examples to learn skills that a human picks up in a handful of tries? Solving this problem is one of the great open challenges in modern AI. World models, which give AI an internal simulation of its environment, are one of the most promising paths forward. In this episode of Decoded, YC's Ankit Gupta and Francois Chaubard discuss the intuition and math behind world models, new research, and current applications in self-driving, robotics, and more. 01:45 — What would perfect efficiency look like? 05:10 — World models in the human brain 09:20 — Control theory & the drone example 14:30 — When physics breaks down 17:45 — Chess, Go & the action space problem 24:10 — Why AlphaGo can't scale 28:00 — Monte Carlo tree search explained 34:00 — Self-Driving: state space is infinite 40:30 — Model-Free vs. Model-Based RL 44:00 — Why robotics is the hardest case 48:20 — World models that actually work 54:10 — JEPA & latent space tricks 59:00 — Open problems remaining 1:04:30 — Does this pass the squint test?

Y Combinator

112,091 görüntüleme • 2 ay önce

New podcast/lecture combo -- a case study in the messy details of Olmo 3 post training & DPO with Scott Geng. It's rare to make time for these discussions, but we cover: What it takes for a research idea to make it into a (near) frontier model. The messy side of DPO (usually data). Organizational challenges in multi-stage post-training recipes. Reflections on where research is heading today, and how people should think of DPO. DPO fundamentals (once more) Other topics. Scott is one of the great student's I've had the pleasure of working with for a few years! Chapters: 00:00 Introduction 04:32 Preference Tuning Basics 08:21 The DPO-Algorithm Zoo 17:06 Scaling Preference Data 21:55 Delta Learning Hypothesis 32:30: Applying it to Olmo 3 36:08 Challenge 1: Data Details 47:17 Challenge 2: Organization Management 51:45 Challenge 3: Scaling Issues 59:15 The Future of Academic Research Excited to keep making this content under the motivation of my book. Really plugging away now :)
1:06:30

New podcast/lecture combo -- a case study in the messy details of Olmo 3 post training & DPO with Scott Geng. It's rare to make time for these discussions, but we cover: What it takes for a research idea to make it into a (near) frontier model. The messy side of DPO (usually data). Organizational challenges in multi-stage post-training recipes. Reflections on where research is heading today, and how people should think of DPO. DPO fundamentals (once more) Other topics. Scott is one of the great student's I've had the pleasure of working with for a few years! Chapters: 00:00 Introduction 04:32 Preference Tuning Basics 08:21 The DPO-Algorithm Zoo 17:06 Scaling Preference Data 21:55 Delta Learning Hypothesis 32:30: Applying it to Olmo 3 36:08 Challenge 1: Data Details 47:17 Challenge 2: Organization Management 51:45 Challenge 3: Scaling Issues 59:15 The Future of Academic Research Excited to keep making this content under the motivation of my book. Really plugging away now :)

Nathan Lambert

24,873 görüntüleme • 2 ay önce

Interview with Jay Parikh, EVP of CoreAI Microsoft 00:00 - Intro 00:25 - Microsoft's Core AI Team 05:08 - In-Person Work Culture 10:15 - How AI Changes Engineering Roles 15:24 - Data Centers & Power Constraints 19:00 - AI Bubble & Dark GPUs 22:34 - Model efficiency 25:17 - Enterprise model rollout 28:17 - Open v Closed source 30:43 - Omni-model vs. multi-model 32:53 - Microsoft's relationship with OpenAI 34:31 - MAI and MSFT's frontier models 35:51 - AI safety 38:45 - Contrarian Take
40:23

Interview with Jay Parikh, EVP of CoreAI Microsoft 00:00 - Intro 00:25 - Microsoft's Core AI Team 05:08 - In-Person Work Culture 10:15 - How AI Changes Engineering Roles 15:24 - Data Centers & Power Constraints 19:00 - AI Bubble & Dark GPUs 22:34 - Model efficiency 25:17 - Enterprise model rollout 28:17 - Open v Closed source 30:43 - Omni-model vs. multi-model 32:53 - Microsoft's relationship with OpenAI 34:31 - MAI and MSFT's frontier models 35:51 - AI safety 38:45 - Contrarian Take

Matthew Berman

40,712 görüntüleme • 10 ay önce

Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience leading pre-training efforts for Google’s PaLM and early Gemini models to argue that pre-training itself must be rethought to move beyond static benchmarks. We explore the limitations of next-token prediction for multi-step workflows and examine how attention mechanisms, loss objectives, and training data must evolve to support long-form reasoning and planning. Aakanksha shares insights on the difference between context retrieval and actual reasoning, the importance of "trajectory" training data, and why scaling remains essential for discovering emergent agentic capabilities like error recovery and dynamic tool learning. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 02:26 - Reflection 04:54 - Limitations of post-training for building agents 07:31 - Rethinking pre-training in agents 10:51 - Scaling 11:27 - Evolving attention mechanisms for agentic capabilities 12:39 - Memory as a tool 14:13 - Loss objectives and training data 15:50 - Fine-tuning loss in agent performance 19:37 - Training data 21:29 - Augmenting dominant training data source 24:11 - Overcoming challenges in training on synthetic data 25:47 - Benchmarks 30:44 - Scaling laws in large models versus small models 33:20 - Long-form versus short-form reasoning 37:57 - Agent’s ability to recover from failure 40:15 - Hallucinations and failure recovery 43:53 - Tool use in agents 46:38 - Coding agents 48:37 - How researchers can contribute to agentic AI
51:54

Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience leading pre-training efforts for Google’s PaLM and early Gemini models to argue that pre-training itself must be rethought to move beyond static benchmarks. We explore the limitations of next-token prediction for multi-step workflows and examine how attention mechanisms, loss objectives, and training data must evolve to support long-form reasoning and planning. Aakanksha shares insights on the difference between context retrieval and actual reasoning, the importance of "trajectory" training data, and why scaling remains essential for discovering emergent agentic capabilities like error recovery and dynamic tool learning. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 02:26 - Reflection 04:54 - Limitations of post-training for building agents 07:31 - Rethinking pre-training in agents 10:51 - Scaling 11:27 - Evolving attention mechanisms for agentic capabilities 12:39 - Memory as a tool 14:13 - Loss objectives and training data 15:50 - Fine-tuning loss in agent performance 19:37 - Training data 21:29 - Augmenting dominant training data source 24:11 - Overcoming challenges in training on synthetic data 25:47 - Benchmarks 30:44 - Scaling laws in large models versus small models 33:20 - Long-form versus short-form reasoning 37:57 - Agent’s ability to recover from failure 40:15 - Hallucinations and failure recovery 43:53 - Tool use in agents 46:38 - Coding agents 48:37 - How researchers can contribute to agentic AI

The TWIML AI Podcast

45,911 görüntüleme • 9 ay önce

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing
41:16

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

Latent.Space

62,796 görüntüleme • 3 ay önce

Ilya Sutskever says the AI scaling era is running out of road because pre-training is about to hit a finite-data wall. "At some point though, pre-training will run out of data. The data is very clearly finite." "Then, okay, what do you do next? Either you do some kind of a souped-up pre-training... or you're doing RL, or maybe something else." "In some sense, we are back to the age of research." His timeline is the part most AI investors and founders should hear: "From 2012 to 2020, it was the age of research. Now from 2020 to 2025, it was the age of scaling... But now the scale is so big." "Is the belief really that... if you had 100x more everything would be so different?" "I don't think that's true. So it's back to the age of research again, just with big computers."
1:10

Ilya Sutskever says the AI scaling era is running out of road because pre-training is about to hit a finite-data wall. "At some point though, pre-training will run out of data. The data is very clearly finite." "Then, okay, what do you do next? Either you do some kind of a souped-up pre-training... or you're doing RL, or maybe something else." "In some sense, we are back to the age of research." His timeline is the part most AI investors and founders should hear: "From 2012 to 2020, it was the age of research. Now from 2020 to 2025, it was the age of scaling... But now the scale is so big." "Is the belief really that... if you had 100x more everything would be so different?" "I don't think that's true. So it's back to the age of research again, just with big computers."

Karl Mehta

200,003 görüntüleme • 3 ay önce