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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.

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

Hallucination in AI refers to the generation of content that appears plausible but does not actually exist, highlighting the challenge of distinguishing fact from fiction.

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.

MA

Marc Andreessen

01/26/25

@ Lex Fridman

The concept of hallucinations in AI refers to the models generating incorrect or fabricated information, which has already led to legal issues, such as lawyers submitting false court citations.

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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.

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.

YC

Y Combinator Cast

12/14/24

@ Y Combinator

The reliability of AI models has improved, leading to widespread deployment in enterprises, which was previously hindered by concerns over hallucinations and risks.

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.

JH

Jake Heller

10/04/24

@ Y Combinator

Despite concerns about AI hallucinations and inaccuracies, there are pathways to improve these technologies, and the jobs they create will evolve to be more interesting rather than disappear.

RH

Raza Habib

03/01/23

@ Y Combinator

To mitigate the issue of hallucinations in language models, it's effective to provide factual context in the prompts given to the model, which can lead to more accurate outputs.