Tools

Search

Explore

Videos Channels Figures

Atmrix

About

Tools

Search

Explore

Videos Channels Figures

Atmrix

About

LF

Lex Fridman Cast

02/03/25

@ Lex Fridman

The verification domains for AI models are extremely hard to set up, but there are basic expectations that suggest it could work.

Video

LF

DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459

@ Lex Fridman

DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459

02/03/25

Related Takeaways

SC

Scott Clark

05/23/25

@ a16z

Trust in AI systems involves verifying their reliability and consistency, which is crucial for user acceptance and enterprise success.

IS

Ion Stoica

05/29/25

@ a16z

Real-world testing is essential for ensuring that AI models are reliable and effective in practical applications.

TB

Trenton Bricken

05/23/25

@ Dwarkesh Patel

The bottleneck for AI development may shift from generating solutions to verifying their accuracy and reliability.

A

Anthropic Cast

03/18/25

@ Anthropic

It's difficult to find interesting and realistic threat models for AI systems that can effectively demonstrate their capabilities.

SA

Sam Altman

04/12/25

@ TED

It's essential to define rigorous testing for AI models to ensure society understands the risks associated with their deployment.

YC

Y Combinator Cast

06/18/25

@ Y Combinator

The AI industry suffers from a lack of rigorous evaluations and tests that demonstrate the frontier of model capabilities.

MM

Mira Murati

09/25/23

@ a16z

Building good products on top of AI models is incredibly difficult.

SC

Scott Clark

05/23/25

@ a16z

Testing is essential for ensuring that AI systems behave as expected, not just in terms of performance but also in their underlying behaviors.

DP

Dwarkesh Patel Cast

05/23/25

@ Dwarkesh Patel

The current capabilities of AI models are impressive, and skeptics should engage with them to understand their potential better.