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113 afleveringen, van nieuw naar oud
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Crucible Moments Returns for S2: The ServiceNow Story ft. CEO Frank Slootman & Founder Fred Luddy
3 september 2024 · 43 min
On Training Data, we learn from innovators pushing forward the frontier of AI’s capabilities. Today we’re bringing you something different. It’s the story of a company currently implementing AI at scale in the enterprise, and how it was built from a bootstrapped idea in the pre-AI era to a 150 billion dollar market cap giant. Lees meer
It’s the Season 2 premiere of Sequoia’s other podcast, Crucible Moments, where we hear from the founders and leaders of some legendary companies about the crossroads and inflection points that shaped their journeys. In this episode, you’ll hear from Fred Luddy and Frank Slootman about building and scaling ServiceNow. Listen to Crucible Moments wherever you get your podcasts or go to:
Spotify: https://open.spotify.com/show/40bWCUSan0boCn0GZJNpPn
Apple: https://podcasts.apple.com/us/podcast/crucible-moments/id1705282398Hosted by: Roelof Botha, Sequoia Capital
Transcript: https://www.sequoiacap.com/podcast/crucible-moments-servicenow/ -
Sierra Co-Founder Clay Bavor on Making Customer-Facing AI Agents Delightful
27 augustus 2024 · 1 uur 13 min
Customer service is hands down the first killer app of generative AI for businesses. The reasons are simple: the costs of existing solutions are so high, the satisfaction so low and the margin for ROI so wide. But trusting your interactions with customers to hallucination-prone LLMs can be daunting.
Enter Sierra. Co-founder Clay Bavor walks us through the sophisticated engineering challenges his team solved along the way to delivering AI agents for all aspects of the customer experience that are delightful, safe and reliable—and being deployed widely by Sierra’s customers. The Company’s AgentOS enables businesses to create branded AI agents to interact with customers, follow nuanced policies and even handle customer retention and upsell. Clay describes how companies can capture their brand voice, values and internal processes to create AI agents that truly represent the business.
Hosted by: Ravi Gupta and Pat Grady, Sequoia Capital Lees meerMentioned in this episode:
Bret Taylor: co-founder of Sierra
Towards a Human-like Open-Domain Chatbot: 2020 Google paper that introduced Meena, a predecessor of ChatGPT (followed by LaMDA in 2021)
PaLM: Scaling Language Modeling with Pathways: 2022 Google paper about their unreleased 540B parameter transformer model (GPT-3, at the time, had 175B)
Avocado chair: Images generated by OpenAI’s DALL·E model in 2022
Large Language Models Understand and Can be Enhanced by Emotional Stimuli: 2023 Microsoft paper on how models like GPT-4 can be manipulated into providing better results
𝛕-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains: 2024 paper authored by Sierra research team, led by Karthik Narasimhan (co-author of the 2022 ReACT paper and the 2023 Reflexion paper)
00:00:00 Introduction
00:01:21 Clay’s background
00:03:20 Google before the ChatGPT moment
00:07:31 What is Sierra?
00:12:03 What’s possible now that wasn’t possible 18 months ago?
00:17:11 AgentOS
00:23:45 The solution to many problems with AI is m… -
Phaidra’s Jim Gao on Building the Fourth Industrial Revolution with Reinforcement Learning
20 augustus 2024 · 51 min
After AlphaGo beat Lee Sedol, a young mechanical engineer at Google thought of another game reinforcement learning could win: energy optimization at data centers. Jim Gao convinced his bosses at the Google data center team to let him work with the DeepMind team to try. The initial pilot resulted in a 40% energy savings and led he and his co-founders to start Phaidra to turn this technology into a product. Lees meer
Jim discusses the challenges of AI readiness in industrial settings and how we have to build on top of the control systems of the 70s and 80s to achieve the promise of the Fourth Industrial Revolution. He believes this new world of self-learning systems and self-improving infrastructure is a key factor in addressing global climate change.
Hosted by: Sonya Huang and Pat Grady, Sequoia Capital
Mentioned in this episode:
Mustafa Suleyman: Co-founder of DeepMind and Inflection AI and currently CEO of Microsoft AI, known to his friends as “Moose”
Joe Kava: Google VP of data centers who Jim sent his initial email to pitching the idea that would eventually become Phaidra
Constrained optimization: the class of problem that reinforcement learning can be applied to in real world systems
Vedavyas Panneershelvam: co-founder and CTO of Phaidra; one of the original engineers on the AlphaGo project
Katie Hoffman: co-founder, President and COO of Phaidra
Demis Hassabis: CEO of DeepMind
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Fireworks Founder Lin Qiao on How Fast Inference and Small Models Will Benefit Businesses
13 augustus 2024 · 39 min
In the first wave of the generative AI revolution, startups and enterprises built on top of the best closed-source models available, mostly from OpenAI. The AI customer journey moves from training to inference, and as these first products find PMF, many are hitting a wall on latency and cost. Lees meer
Fireworks Founder and CEO Lin Qiao led the PyTorch team at Meta that rebuilt the whole stack to meet the complex needs of the world’s largest B2C company. Meta moved PyTorch to its own non-profit foundation in 2022 and Lin started Fireworks with the mission to compress the timeframe of training and inference and democratize access to GenAI beyond the hyperscalers to let a diversity of AI applications thrive.
Lin predicts when open and closed source models will converge and reveals her goal to build simple API access to the totality of knowledge.
Hosted by: Sonya Huang and Pat Grady, Sequoia Capital
Mentioned in this episode:
Pytorch: the leading framework for building deep learning models, originated at Meta and now part of the Linux Foundation umbrella
Caffe2 and ONNX: ML frameworks Meta used that PyTorch eventually replaced
Conservation of complexity: the idea that that every computer application has inherent complexity that cannot be reduced but merely moved between the backend and frontend, originated by Xerox PARC researcher Larry Tesler
Mixture of Experts: a class of transformer models that route requests between different subsets of a model based on use case
Fathom: a product the Fireworks team uses for video conference summarization
LMSYS Chatbot Arena: crowdsourced open platform for LLM evals hosted on Hugging Face
00:00 - Introduction
02:01 - What is Fireworks?
02:48 - Leading Pytorch
05:01 - What do researchers like about PyTorch?
07:50 - How Fireworks compares to open source
10:38 - Simplicity scales
12:51 - From training to inference
17:46 - Will open and closed source converge?
22:18 - Can you match OpenAI on the Fireworks stack?
26:53 - What is your v… -
GitHub CEO Thomas Dohmke on Building Copilot, and the the Future of Software Development
6 augustus 2024 · 1 uur 8 min
GithHub invented collaborative coding and in the process changed how open source projects, startups and eventually enterprises write code. GitHub Copilot is the first blockbuster product built on top of OpenAI’s GPT models. It now accounts for more than 40 percent of GitHub revenue growth for an annual revenue run rate of $2 billion. Copilot itself is already a larger business than all of GitHub was when Microsoft acquired it in 2018. Lees meer
We talk to CEO Thomas Dohmke about how a small team at GitHub built on top of GPT-3 and quickly created a product that developers love—and can’t live without. Thomas describes how the product has grown from simple autocomplete to a fully featured workspace for enterprise teams. He also believes that tools like Copilot will bring the power of coding to a billion developers by 2030.
Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital
Mentioned in this episode:
Nat Friedman: Former Microsoft VP (and now investor) who came up with the idea that Microsoft should buy GitHub
Oege de Moor: Github developer (and now founder of XBOW) who came up with the idea of using GPT-3 for code and went on to create Copilot
Alex Graveley: principal engineer and Chief Architect for Copilot (now CEO of Minion.ai) who came up with the name Copilot (because his boss, Nat Firedman, is an amateur pilot)
Productivity Assessment of Neural Code Completion: Original GitHub research paper on the impact of Copilot on Developer productivity
Escaping a room in Minecraft with an AI-powered NPC: Recent Minecraft AI assistant demo from Microsoft
With AI, anyone can be a coder now: TED2024 talk by Thomas Dohmke
JFrog: The software supply chain platform that GitHub just partnered with
00:00:00 - Introduction
00:01:18 - Getting started with code
00:03:43 - Microsoft’s acquisition of GitHub
00:11:40 - Evolving Copilot beyond autocomplete
00:14:18 - In hindsight, you can always move faster
00:15:56 - Building on top of OpenAI
00:20:21 - The latest metrics
00:2… -
Meta’s Joe Spisak on Llama 3.1 405B and the Democratization of Frontier Models
30 juli 2024 · 42 min
As head of Product Management for Generative AI at Meta, Joe Spisak leads the team behind Llama, which just released the new 3.1 405B model. We spoke with Joe just two days after the model’s release to ask what’s new, what it enables, and how Meta sees the role of open source in the AI ecosystem. Lees meer
Joe shares that where Llama 3.1 405B really focused is on pushing scale (it was trained on 15 trillion tokens using 16,000 GPUs) and he’s excited about the zero-shot tool use it will enable, as well as its role in distillation and generating synthetic data to teach smaller models. He tells us why he thinks even frontier models will ultimately commoditize—and why that’s a good thing for the startup ecosystem.
Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital
Mentioned in this episode:
Llama 3.1 405B paper
Open Source AI Is the Way Forward: Mark Zuckerberg essay released with Llama 3.1.
Mistral Large 2
The Bitter Lesson by Rich Sutton00:00 Introduction
01:28 The Llama 3.1 405B launch
05:02 The open source license
07:01 What's in it for Meta?
10:19 Why not open source?
11:16 Will frontier models commoditize?
12:41 What about startups?
16:29 The Mistral team
19:36 Are all frontier strategies comparable?
22:38 Is model development becoming more like software development?
26:34 Agentic reasoning
29:09 What future levers will unlock reasoning?
31:20 Will coding and math lead to unlocks?
33:09 Small models
34:08 7X more data
37:36 Are we going to hit a wall?
39:49 Lightning round -
Klarna CEO Sebastian Siemiatkowski on Getting AI to Do the Work of 700 Customer Service Reps
23 juli 2024 · 52 min
In February, Sebastian Siemiatkowski boldly announced that Klarna’s new OpenAI-powered assistant handled two thirds of the Swedish fintech’s customer service chats in its first month. Not only were customer satisfaction metrics better, but by replacing 700 full-time contractors the bottom line impact is projected to be $40M. Since then, every company we talk to wants to know, “How do we get the Klarna customer support thing?” Lees meer
Co-founder and CEO Sebastian Siemiatkowski tells us how the Klarna team shipped this new product in record time—and how embracing AI internally with an experimental mindset is transforming the company. He discusses how AI development is proliferating inside the company, from customer support to marketing to internal knowledge to customer-facing experiences.
Sebastian also reflects on the impacts of AI on employment, society, and the arts while encouraging lawmakers to be open minded about the benefits.
Hosted by: Sonya Huang and Pat Grady, Sequoia Capital
Mentioned in this episode:
DeepL: Language translation app that Sebastian says makes 10,000 translators in Brussels redundant
The Klarna brand: The offbeat optimism that the company is now augmenting with AI
Neo4j: The graph database management system that Klarna is using to build Kiki, their internal knowledge base00:00 Introduction
01:57 Klarna’s business
03:00 Pitching OpenAI
08:51 How we built this
10:46 Will Klara ever completely replace its CS team with AI?
14:22 The benefits
17:25 If you had a policy magic wand…
21:12 What jobs will be most affected by AI?
23:58 How about marketing?
27:55 How creative are LLMs?
30:11 Klarna’s knowledge graph, Kiki
33:10 Reducing the number of enterprise systems
35:24 Build vs buy?
39:59 What’s next for Klarna with AI?
48:48 Lightning round -
Reflection AI’s Misha Laskin on the AlphaGo Moment for LLMs
16 juli 2024 · 1 uur 7 min
LLMs are democratizing digital intelligence, but we’re all waiting for AI agents to take this to the next level by planning tasks and executing actions to actually transform the way we work and live our lives. Lees meer
Yet despite incredible hype around AI agents, we’re still far from that “tipping point” with best in class models today. As one measure: coding agents are now scoring in the high-teens % on the SWE-bench benchmark for resolving GitHub issues, which far exceeds the previous unassisted baseline of 2% and the assisted baseline of 5%, but we’ve still got a long way to go.
Why is that? What do we need to truly unlock agentic capability for LLMs? What can we learn from researchers who have built both the most powerful agents in the world, like AlphaGo, and the most powerful LLMs in the world?
To find out, we’re talking to Misha Laskin, former research scientist at DeepMind. Misha is embarking on his vision to build the best agent models by bringing the search capabilities of RL together with LLMs at his new company, Reflection AI. He and his cofounder Ioannis Antonoglou, co-creator of AlphaGo and AlphaZero and RLHF lead for Gemini, are leveraging their unique insights to train the most reliable models for developers building agentic workflows.
Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital
00:00 Introduction
01:11 Leaving Russia, discovering science
10:01 Getting into AI with Ioannis Antonoglou
15:54 Reflection AI and agents
25:41 The current state of Ai agents
29:17 AlphaGo, AlphaZero and Gemini
32:58 LLMs don’t have a ground truth reward
37:53 The importance of post-training
44:12 Task categories for agents
45:54 Attracting talent
50:52 How far away are capable agents?
56:01 Lightning roundMentioned:
The Feynman Lectures on Physics: The classic text that got Misha interested in science.
Mastering the game of Go with deep neural networks and tree search: The original 2016 AlphaGo paper.
Mastering the game of Go without human knowledge: 2017 A…
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Microsoft CTO Kevin Scott on How Far Scaling Laws Will Extend
9 juli 2024 · 1 uur
The current LLM era is the result of scaling the size of models in successive waves (and the compute to train them). It is also the result of better-than-Moore’s-Law price vs performance ratios in each new generation of Nvidia GPUs. The largest platform companies are continuing to invest in scaling as the prime driver of AI innovation. Lees meer
Are they right, or will marginal returns level off soon, leaving hyperscalers with too much hardware and too few customer use cases? To find out, we talk to Microsoft CTO Kevin Scott who has led their AI strategy for the past seven years. Scott describes himself as a “short-term pessimist, long-term optimist” and he sees the scaling trend as durable for the industry and critical for the establishment of Microsoft’s AI platform.
Scott believes there will be a shift across the compute ecosystem from training to inference as the frontier models continue to improve, serving wider and more reliable use cases. He also discusses the coming business models for training data, and even what ad units might look like for autonomous agents.
Hosted by: Pat Grady and Bill Coughran, Sequoia Capital
Mentioned:
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, the 2018 Google paper that convinced Kevin that Microsoft wasn’t moving fast enough on AI.
Dennard scaling: The scaling law that describes the proportional relationship between transistor size and power use; has not held since 2012 and is often confused with Moore’s Law.
Textbooks Are All You Need: Microsoft paper that introduces a new large language model for code, phi-1, that achieves smaller size by using higher quality “textbook” data.
GPQA and MMLU: Benchmarks for reasoning
Copilot: Microsoft product line of GPT consumer assistants from general productivity to design, vacation planning, cooking and fitness.
Devin: Autonomous AI code agent from Cognition Labs that Microsoft recently announced a partnership with.
Ray Solomonoff: Participant in the 1956 Dart… -
Zapier’s Mike Knoop launches ARC Prize to Jumpstart New Ideas for AGI
2 juli 2024 · 55 min
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. Lees meer
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 platfo…
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Factory’s Matan Grinberg and Eno Reyes Unleash the Droids on Software Development
25 juni 2024 · 59 min
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? Lees meer
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 enginee… -
LangChain’s Harrison Chase on Building the Orchestration Layer for AI Agents
18 juni 2024 · 50 min
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. Lees meer
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 needi… -
Introducing "Training Data"
6 juni 2024 · 1 min
Join us as we train our neural nets on the theme of the century: AI. Sequoia Capital partners Sonya Huang and Pat Grady 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. Lees meer
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.