DeepSeek: separating fact from hype
Today, we’re bringing you a bonus episode zeroing in on DeepSeek, the Chinese AI lab that’s recently taken over the news and the app stores, beating out OpenAI's ChatGPT. Max Zeff is talking about it all with Ion Stoica, Professor of Computer Science Division at UC Berkeley and the cofounder and executive chairman of software startup Databricks.
Listen to the full episode to hear more about:
Why Stoica believes the future of AI lies in "doubling down on open source."
Microsoft's decision to host DeepSeek on Azure.
What the U.S. can do to foster accelerated innovation – with a look back at SB-1047 and a look ahead to 2025.
The controversy surrounding claims that DeepSeek used OpenAI’s models to train its own.
Equity will be back next week, so stay tuned!
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Databricks Founder Ion Stoica: Turning Academic Open Source into Startup Success
Berkeley professor Ion Stoica, co-founder of Databricks and Anyscale, transformed the open source projects Spark and Ray into successful AI infrastructure companies. He talks about what mattered most for Databricks' success -- the focus on making Spark win and making Databricks the best place to run Spark. He highlights the importance of striking key partnerships -- the Microsoft partnership in particular that accelerated Databricks' growth and contributed to Spark's dominance among data scientists and AI engineers. He also shares his perspective on finding new problems to work on, which holds lessons for aspiring founders and builders: 1) building systems in new areas that, if widely adopted, put you in the best position to understand the new problem space, and 2) focusing on a problem that is more important tomorrow than today.
Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital
Mentioned in this episode:
Spark: The open source platform for data engineering that Databricks was originally based on.
Ray: Open source framework to manage, executes and optimizes compute needs across AI workloads, now productized through Anyscale
MosaicML: Generative AI startups founded by Naveen Rao that Databricks acquired in 2023.
Unity Catalog: Data and AI governance solution from Databricks.
CIB Berkeley: Multi-strategy hedge fund at UC Berkeley that commercializes research in the UC system.
Hadoop: A long-time leading platform for large scale distributed computing.
VLLM and Chatbot Arena: Two of Ion’s students’ projects that he wanted to highlight.
Ion Stoica — Spark, Ray, and Enterprise Open Source
Ion Stoica is co-creator of the distributed computing frameworks Spark and Ray, and co-founder and Executive Chairman of Databricks and Anyscale. He is also a Professor of computer science at UC Berkeley and Principal Investigator of RISELab, a five-year research lab that develops technology for low-latency, intelligent decisions.
Ion and Lukas chat about the challenges of making a simple (but good!) distributed framework, the similarities and differences between developing Spark and Ray, and how Spark and Ray led to the formation of Databricks and Anyscale. Ion also reflects on the early startup days, from deciding to commercialize to picking co-founders, and shares advice on building a successful company.
The complete show notes (transcript and links) can be found here: http://wandb.me/gd-ion-stoica
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Timestamps:
0:00 Intro
0:56 Ray, Anyscale, and making a distributed framework
11:39 How Spark informed the development of Ray
18:53 The story behind Spark and Databricks
33:00 Why TensorFlow and PyTorch haven't monetized
35:35 Picking co-founders and other startup advice
46:04 The early signs of sky computing
49:24 Breaking problems down and prioritizing
53:17 Outro
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a16z Podcast: On Data and Data Scientists in the Age of AI
Data, data, everywhere, nor any drop to drink. Or so would say Coleridge, if he were a big company CEO trying to use A.I. today -- because even when you have a ton of data, there's not always enough signal to get anything meaningful from AI.
Why? Because, "like they say, it's 'garbage in, garbage out' -- what matters is what you have in between," reminds Databricks co-founder (and director of the RISElab at U.C. Berkeley) Ion Stoica. And even then it's still not just about data operations, emphasizes SigOpt co-founder Scott Clark; your data scientists need to really understand "What's actually right for my business and what am I actually aiming for?" And then get there as efficiently as possible.
But beyond defining their goals, how do companies get over the "cold start" problem when it comes to doing more with AI in practice, asks a16z operating partner Frank Chen (who also released a microsite on getting started with AI earlier this year)? The guests on this short "a16z Bytes" episode of the a16z Podcast -- based on a conversation that took place at our recent annual Summit event -- share practical advice about this and more.
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a16z Podcast: A New Lab Rises
with Ion Stoica, Peter Levine, and Sonal Chokshi
We’ve already talked quite a bit about the Algorithms, Machines, and People lab at U.C. Berkeley (AMPLab) — all about making sense of big data — so what happens when the entire world moves towards artificial intelligence — and the need to make intelligent decisions on that data? That’s where the new RISElab (Real-time Intelligence Secure Execution) comes in.
But what is a good “decision”, exactly? Beyond the existential question of that, what specific attributes make a “good” decision, both computationally and humanly? In this episode of the a16z Podcast (in conversation with general partner Peter Levine and Sonal Chokshi), computer science professor, entrepreneur (co-founder of Databricks), and RISElab director Ion Stoica answers that question. He also shares the “ingredients” of a working research lab model (one, dare we say, could also apply to many types of institutions?); the role of open source and building community; and the evolution of labs today given intense competition from industry and others… as well as what interesting projects — really, trends in decision making with AI — are coming next.
The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.
Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.
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Ray: A Distributed Computing Platform for Reinforcement Learning with Ion Stoica - TWiML Talk #55
The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. In this episode, I talk with Ion Stoica, professor of computer science & director of the RISE Lab at UC Berkeley. Ion joined us after he gave his talk “Building reinforcement learning applications with Ray.” We dive into Ray, a new distributed computing platform for RL, as well as RL generally, along with some of the other interesting projects RISE Lab is working on, like Clipper & Tegra. This was a pretty interesting talk. Enjoy! The notes for this show can be found at twimlai.com/talk/55
