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TB

Trenton Bricken

05/23/25

@ Dwarkesh Patel

Imbuing models with a sense of taste and setting up effective feedback loops is an ongoing challenge in AI development.

Video

DP

Is RL + LLMs enough for AGI? – Sholto Douglas & Trenton Bricken

@ Dwarkesh Patel

Is RL + LLMs enough for AGI? – Sholto Douglas & Trenton Bricken

05/23/25

Related Takeaways

TB

Trenton Bricken

05/23/25

@ Dwarkesh Patel

Models can struggle with tasks requiring expertise and taste, such as evaluating art, which presents a significant challenge for AI.

PB

Paul Buchheit

01/25/25

@ Y Combinator

The ability to prompt AI effectively is crucial, as it determines the quality of the output, which is a reflection of agency and taste in AI development.

C(

Chungin (Roy) Lee

05/18/25

@ EO

The user experience of AI tools should be seamless and intuitive, focusing on taste rather than just technical challenges. This requires continuous iterations to improve usability.

YC

Y Combinator Cast

02/28/25

@ Y Combinator

Feedback mechanisms in AI interfaces can help users refine their prompts and improve interaction with the AI, enhancing the overall experience.

MB

Matt Biilmann

03/08/25

@ a16z

The skill of prompting AI models is crucial; those who understand how to communicate effectively with these models can create more sophisticated and creative outputs.

SA

Sam Altman

04/12/25

@ TED

The iterative process of deploying AI systems, gathering feedback, and learning is crucial for building safe systems as we advance into more complex AI.

YC

Y Combinator Cast

09/22/23

@ Y Combinator

An iterative approach is essential in working with AI; continuously debugging and refining prompts can lead to more reliable outcomes.

YC

Y Combinator Cast

09/22/23

@ Y Combinator

Fine-tuning AI models on new types of data is crucial to maintain accuracy, especially as trends and terminology evolve rapidly.

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.