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The Twenty Minute VCThe Twenty Minute VC

Zico Kolter: OpenAI's Newest Board Member on The Biggest Questions and Concerns in AI Safety | E1197

Zico Kolter is a Professor and the Director of the Machine Learning Department at Carnegie Mellon University.

Zico KolterguestHarry Stebbingshost
Sep 4, 20241h 3mWatch on YouTube ↗

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Episode Details

EPISODE INFO

Released
September 4, 2024
Duration
1h 3m
Channel
The Twenty Minute VC
Watch on YouTube
▶ Open ↗

EPISODE DESCRIPTION

Zico Kolter is a Professor and the Director of the Machine Learning Department at Carnegie Mellon University. His research spans several topics in AI and machine learning, including work in AI safety and robustness, LLM security, the impact of data on models, implicit models, and more. He also serves on the Board of OpenAI, as a Chief Expert for Bosch, and as Chief Technical Advisor to Gray Swan, a startup in the AI safety space. ----------------------------------------------- Timestamps: (00:00) Intro (01:29) Understanding the Basics Behind Modern AI Technology (04:17) Data Availability & Synthetic Data (09:08) Why AI Performance Doesn't Plateau Despite Data Limits (16:14) How Will AI Models Evolve Amid Rapid Commoditization (19:09) Are Corporations Pursuing AGI or Profitable AI Products? (27:55) The Danger of Misinformation & Lack of Trust in Objective Reality (37:14) The Concerns and Hierarchy of Safety in AI (44:45) The Considerations of Releasing Open-Source Models (59:10) Quick-Fire Round ----------------------------------------------- In Today’s Episode with Zico Kolter We Discuss:

1. Model Performance: What are the Bottlenecks: Data: To what extent have we leveraged all available data? How can we get more value from the data that we have to improve model performance? Compute: Have we reached a stage of diminishing returns where more data does not lead to an increased level of performance? Algorithms: What are the biggest problems with current algorithms? How will they change in the next 12 months to improve model performance?

1. Sam Altman, Sequoia and Frontier Models on Data Centres: Sam Altman: Does Zico agree with Sam Altman’s statement that “compute will be the currency of the future?” Where is he right? Where is he wrong? David Cahn @ Sequoia: Does Zico agree with David’s statement; “we will never train a frontier model on the same data centre twice?”

1. AI Safety: What People Think They Know But Do Not: What are people not concerned about today which is a massive concern with AI? What are people concerned about which is not a true concern for the future? Does Zico share Arvind Narayanan’s concern, “the biggest danger is not that people will believe what they see, it is that they will not believe what they see”? Why does Zico believe the analogy of AI to nuclear weapons is wrong and inaccurate? ----------------------------------------------- Subscribe on Spotify: https://open.spotify.com/show/3j2KMcZTtgTNBKwtZBMHvl?si=85bc9196860e4466 Subscribe on Apple Podcasts: https://podcasts.apple.com/us/podcast/the-twenty-minute-vc-20vc-venture-capital-startup/id958230465 Follow Harry Stebbings on Twitter: https://twitter.com/HarryStebbings Follow Zico Kolter on Twitter: https://twitter.com/zicokolter Follow 20VC on Instagram: https://www.instagram.com/20vchq Follow 20VC on TikTok: https://www.tiktok.com/@20vc_tok Visit our Website: https://www.20vc.com Subscribe to our Newsletter: https://www.thetwentyminutevc.com/contact ----------------------------------------------- #20vc #harrystebbings #zicokolter #venturecapital #professor #ai #openai #samaltman #compute

SPEAKERS

  • Zico Kolter

    guest
  • Harry Stebbings

    host
  • Narrator

    other

EPISODE SUMMARY

In this episode of The Twenty Minute VC, featuring Zico Kolter and Harry Stebbings, Zico Kolter: OpenAI's Newest Board Member on The Biggest Questions and Concerns in AI Safety | E1197 explores OpenAI Board Member Zico Kolter Dissects Data, Safety, and AGI Futures Zico Kolter, head of CMU’s Machine Learning Department and new OpenAI board member, explains why next-word prediction LLMs are a profound scientific discovery and why we are far from hitting hard limits on data, models, or compute.

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