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MM

Mira Murati

09/25/23

@ a16z

We are exploring reinforcement learning with human feedback to address the issue of hallucinations, which is a complex problem but potentially solvable with this approach.

Video

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Where We Go From Here with OpenAI's Mira Murati

@ a16z

Where We Go From Here with OpenAI's Mira Murati

09/25/23

Related Takeaways

YC

Y Combinator Cast

09/22/23

@ Y Combinator

Incorporating a human-in-the-loop approach is essential for assessing AI outputs and ensuring accuracy, especially in preventing hallucinations.

MM

Mira Murati

09/25/23

@ a16z

Hallucinations are a significant challenge for AI models, and we have been studying how to improve their truthfulness and express uncertainty.

YC

Y Combinator Cast

10/18/24

@ Y Combinator

The current AI systems struggle with following instructions accurately, which is a more significant issue than the hallucination problem.

a

a16z Cast

04/03/25

@ a16z

It's crucial to understand the complexities of working with AI systems, including the need to manage issues like hallucinations and model evaluations effectively.

RH

Raza Habib

03/01/23

@ Y Combinator

Reinforcement learning from human feedback significantly enhances model performance by using human preference data to fine-tune the model's outputs.

DN

Daniel Nadler

03/04/25

@ Sequoia Capital

In medicine, hallucinations from AI can be life-threatening, so we ensure our models are trained exclusively on peer-reviewed medical knowledge to maintain accuracy.

YC

Y Combinator Cast

09/22/23

@ Y Combinator

Efforts to reduce AI hallucinations have led to the opposite problem, where AI may deny knowledge of information that is actually present in its training data.

DS

David Silver

04/11/25

@ Google DeepMind

Human data might provide a head start for AI, but sustainable progress will depend on reinforcement learning as a continuous source of experience.

WA

Waseem Alshikh

03/27/25

@ LangChain

We find that human feedback is often unreliable, so our self-evolving models are designed to learn from their own mistakes without needing human input.