Tools

Search

Explore

Videos Channels Figures

Atmrix

About

Tools

Search

Explore

Videos Channels Figures

Atmrix

About

A

Anthropic Cast

03/27/25

@ Anthropic

Even when we open the black box of AI, interpreting the internal workings can be challenging without proper tools.

Video

A

Tracing the thoughts of a large language model

@ Anthropic

Tracing the thoughts of a large language model

03/27/25

Related Takeaways

A

Anthropic Cast

03/27/25

@ Anthropic

AI is often described as a black box, where inputs lead to outputs, but the reasoning behind its responses remains unclear.

YC

Y Combinator Cast

03/06/25

@ Y Combinator

The ability to debug and understand underlying systems is crucial, especially as AI tools become more prevalent in coding practices.

DA

Dario Amodei

08/08/23

@ Dwarkesh Patel

Mechanistic interpretability aims to understand what's happening inside AI models at the level of individual circuits, but we still don't know enough to ensure alignment.

AA

Anima Anandkumar

05/04/25

@ EO

AI tools are improving, but they still require clear instructions and descriptions of tasks. The ability to articulate what AI should do remains crucial.

YC

Y Combinator Cast

05/23/25

@ Y Combinator

We need to stop treating AI prompts like black boxes; users should be able to see and understand what the AI is instructed to do.

a

a16z Cast

05/16/25

@ a16z

When using AI for coding, there is often a gap between the code generated by AI and the user's ability to modify it effectively, highlighting the need for better tools.

YC

Y Combinator Cast

10/18/24

@ Y Combinator

Debugging AI agents is challenging due to the complexity of visualizing their processes, which often leads to difficulties in understanding their behavior.

A

Anthropic Cast

03/27/25

@ Anthropic

To enhance the usefulness, reliability, and security of AI, we need to understand the reasoning behind their actions.

SW

Scott Wu

05/04/25

@ Lenny's Podcast

The development of AI tools like Devin is not just about coding but also about understanding the complexities of real-world engineering.