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20VC: OpenAI's Newest Board Member, Zico Colter on The Biggest Bottlenecks to the Performance of Foundation Models | The Biggest Questions and Concerns in AI Safety | How to Regulate an AI-Centric World

Sep 4, 2024

Zico Colter, a Professor and Director of the Machine Learning Department at Carnegie Mellon University and an OpenAI board member, dives deep into AI's bottlenecks. He explores data utilization for model performance, challenges with current algorithms, and the future of compute as an essential resource. Colter also addresses misconceptions in AI safety that people overlook. Plus, he discusses the necessity of innovative approaches to data and the importance of global collaboration on safety standards in developing AI technologies.

01:00:23

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Zico Colter

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Quick takeaways

AI systems, particularly LLMs, face significant challenges with safety and reliability, raising concerns about their potential misuse if not properly managed.

Despite perceptions of data scarcity, there remains a wealth of untapped audio and visual data that can enhance AI model training and performance.

The necessity for robust and adaptive regulations for AI is crucial in mitigating risks associated with misinformation and ethical misuse as technologies evolve.

Deep dives

The Erosion of Trust in Information

The increasing prevalence of misinformation, especially with the rise of AI technologies, has led to a crisis of belief in the information people encounter daily. Many individuals now find it difficult to trust any content that does not align with their existing beliefs, resulting in a general skepticism toward news and media. This phenomenon, exacerbated by AI, reflects a historical backdrop where humans relied heavily on close associates for information. Consequently, society risks reverting to a state where trust is only placed in familiar sources, undermining the objective assessment of facts.

AGI: A Transformative Collaboration on the Horizon

01:31

Caution in Capability Releases

01:34

Models and Data: Rethinking Core Beliefs

01:05

Intro

4min

Understanding the Foundations of AI Through Language Models

3min

Navigating the AI Data Landscape

19min

Reassessing AI Beliefs and Trust

6min

Navigating AI Safety in a Digital World

22min

Shifting Perspectives on AI Models and Data

4min

Closing Reflections and Sponsor Highlights

3min

Zico Colter 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.

In Today's Episode with Zico Colter 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?

2. 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?"

3. 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?

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