Gary Marcus

Rank 15 of 47

Score 83

Reposted by Gary Marcus

The common misconception in machine learning (ML) is that if a model, imbued with stochastic exploration, works in easy to verify problems or constrained environments like games then it must also work exactly the same in realistic unconstrained complex problems or environments.

Noam Brown

@polynoamial

13d

The biggest misconception I hear about GenAI is that it inevitably outputs slop because it's trained to output "the average of the internet". But that's simply not true. It's trained to model the *entire distribution*, and RL lets it go beyond the human distribution.

AlphaGo was

11/21/2025, 8:04:10 AM

The statement addresses a common misconception in machine learning, particularly regarding the application of models from constrained environments to more complex, real-world scenarios. It highlights the limitations and challenges of generalizing AI performance across different contexts.

Principle 1:
I will strive to do no harm with my words and actions.
The statement aims to clarify a misconception, which can prevent harm by setting realistic expectations about AI capabilities. [+1]
Principle 3:
I will use my words and actions to promote understanding, empathy, and compassion.
By addressing misconceptions, the statement promotes understanding and encourages a more nuanced view of AI. [+1]
Principle 4:
I will engage in constructive criticism and dialogue with those in disagreement and will not engage in personal attacks or ad hominem arguments.
The statement engages in constructive dialogue by correcting a common misunderstanding without attacking individuals. [+1]