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DS

David Silver

04/11/25

@ Google DeepMind

We must be cautious about how we define metrics for AI systems to avoid unintended consequences, such as optimizing for the wrong outcomes.

Video

GD

Is Human Data Enough? With David Silver

@ Google DeepMind

04/11/25

Related Takeaways

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Scott Clark

05/23/25

@ a16z

Organizations must adapt their performance metrics to account for potential biases and undesired behaviors in AI systems over time.

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Scott Clark

05/23/25

@ a16z

Many organizations focus on high-level performance metrics, which can mask undesired behaviors in AI systems, leading to a lack of robustness.

SC

Scott Clark

05/23/25

@ a16z

The challenge of ensuring AI systems do not exhibit undesired behaviors is more complex than simply achieving high performance metrics.

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a16z Cast

05/29/25

@ a16z

The challenge lies in defining the right measures of progress in AI evaluation, moving beyond traditional benchmarks.

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a16z Cast

05/29/25

@ a16z

The challenge lies in defining the right measures of progress in AI evaluation, moving beyond traditional benchmarks.

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Carlos

05/24/25

@ LangChain

Defining use cases and metrics before jumping on new tools is crucial for measuring success in AI implementations.

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Bret Weinstein

05/12/25

@ The Diary Of A CEO

We must be cautious about how we integrate powerful technologies like AI into society to avoid creating oppressive systems.

DP

Dwarkesh Patel Cast

04/04/25

@ Dwarkesh Patel

Regulatory measures could backfire if they lead labs to train AI to avoid revealing harmful intentions.

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Satya Nadella

04/17/25

@ Microsoft India

As AI technology evolves, we need to ensure that we responsibly scale its use to avoid unintended consequences, similar to the challenges faced with social media.