Chelsea Finn
Person on 3 sites
Chelsea Finn is an American computer scientist, assistant professor of computer science and electrical engineering at Stanford University.
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6 facts on wiqqi (opens wiqqi.app)
- Current role: Assistant Professor of Computer Science and of Electrical Engineering, Stanford University (opens wiqqi.app)
- Company: Co-founder of Physical Intelligence (opens wiqqi.app)
- Born: 8 October 1992 (opens wiqqi.app)
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3 channels on ppll (opens ppll.app)
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x.com: @chelseabfinn (opens x.com)
“Generative value classifiers are a new parametrization of Q-functions that don't require action conditioning. We think these may combine gracefully with large, pre-trained policies and learn/generalize better from a…”
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GitHub: @cbfinn (opens github.com)
“maml_rl — Code for RL experiments in "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"”
- Site: Chelsea Finn (opens ai.stanford.edu)
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x.com: @chelseabfinn (opens x.com)
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22 beliefs on korrents (opens korrents.com)
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Every machine-learning deployment that has paid off so far left a person making the decision, which is the only reason imperfect models were useful. (opens korrents.com)
“And in all of these applications, the customer is making a decision based off of the recommendation of the AI model more or less. Uh, and this means that if the customer is ultimately like kind of making the decision,…”
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Physical AI has to make far fewer mistakes than any AI deployed so far, because it is only useful when it acts autonomously on the world. (opens korrents.com)
“Uh and this means that they're going to be far more useful when they're operating fully autonomously. And as a result, this requires us to develop physical AI systems that make far fewer mistakes than the machine…”
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Hand-tuning a robot's data set cannot reach very high reliability, because people get tired; the system has to seek out its own missing data. (opens korrents.com)
“Um, and while this generally improves the reliability of the model, uh, people eventually get tired and it's hard to get really, really high reliability with a person that's manually tuning this. And so what would be…”
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Every machine-learning deployment that has paid off so far left a person making the decision, which is the only reason imperfect models were useful. (opens korrents.com)