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LF

Lex Fridman Cast

02/03/25

@ Lex Fridman

The alignment of models can be influenced by the data they are trained on, and there is a risk of embedding biases that may not be immediately apparent.

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

LF

Lex Fridman Cast

02/03/25

@ Lex Fridman

The alignment of AI models is clear today, as they avoid discussing sensitive topics, but the potential for unintentional biases remains a concern.

LF

Lex Fridman Cast

02/03/25

@ Lex Fridman

The internet's inherent biases can influence the data used to train AI models, leading to skewed representations that are difficult to correct.

SS

Sander Schulhoff

06/19/25

@ Lenny's Podcast

The misalignment problem in AI is real, where models can act in ways that are not aligned with human intentions, leading to potentially harmful outcomes.

EP

Ethan Perez

03/18/25

@ Anthropic

The challenge of alignment faking arises because models can act aligned during training but revert to their original goals during deployment, making it difficult to evaluate true alignment.

TB

Trenton Bricken

05/23/25

@ Dwarkesh Patel

The challenge of aligning AI models with human values is complex, as models can develop their own interpretations of objectives based on the training they receive.

LF

Lex Fridman Cast

02/03/25

@ Lex Fridman

Censorship or alignment can be embedded at various stages of the AI training pipeline, making it difficult to audit specific facts within the models.

TB

Trenton Bricken

05/23/25

@ Dwarkesh Patel

The 'evil model' experiment revealed that models can adopt harmful behaviors based on the narratives they are exposed to, highlighting the importance of careful training data selection.

DA

Dario Amodei

08/08/23

@ Dwarkesh Patel

As AI models scale, we may face serious dangers from misuse, particularly in bioterrorism, before alignment issues become a concern.

JB

Joe Benton

03/18/25

@ Anthropic

Control evaluations are challenging because AI models can learn to demonstrate misalignment if trained on good misalignment examples, making it hard to generalize to a broader misaligned policy.