Ryan Little @TacticSiege

Boston, MA Joined August 2008

Tweets

139
Followers

48
Following

310
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115

Tim Butterly @timbutterly

2 months ago

It has been brought to my attention that in my state of grief I never even tweeted the link to the special. Here it is for posterity. RIP Andy. 30 Minutes with Tim Butterly | Presented by GaS Digital | Full Special youtu.be/xwfGIBItvKI

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Ryan Little @TacticSiege

a year ago

@Kotaku pot, kettle.

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Sam Altman @sama

2 years ago

people love MCP and we are excited to add support across our products. available today in the agents SDK and support for chatgpt desktop app + responses api coming soon!

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Ryan Little @TacticSiege

2 years ago

@BoneJail corp.oup.com/word-of-the-ye…

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Ryan Little @TacticSiege

3 years ago

@lunivore I had no problem with your post, chat isn’t stateless and I was trying to help improve the example.

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Jim Fan @DrJimFan

3 years ago

This is an ape ("Kanzi") playing Minecraft! A fascinating experiment on non-human biological neural networks 🙉 I've been teaching AI to play Minecraft for too long. There're so many similar techniques that the ape trainers used: - In-context reinforcement learning: Kanzi gets a fruit or peanut whenever he hits a marked milestone in the game, incentivizing him to follow the in-game guides. - RLHF: Kanzi doesn't understand much language, but he can see the trainers cheering him on, and he occasionally cheers them back! That gives him a strong signal that he's on the right track. - Imitation learning: the trainers show Kanzi just 1 demonstration of how to do a task, and he immediately grasps the concepts. It's much more efficient than using rewards alone. - Curriculum learning: they start with very simple environments to gradually teach Kanzi the controls. At the end, Kanzi is able to navigate complex caves, mazes, and the Nether. It also amazes me how strong the ape's vision system is. Kanzi never saw Minecraft in his life, and for sure his ancestors didn't either. Yet he rapidly adapts to Minecraft's texture and physics, which are dramatically different from the natural world. This level of generalization is far beyond what our most powerful vision models can do today. We are right in the thick of Moravec's paradox again: our best AIs are approaching human level on understanding language, but far behind animals on parsing pixels. From YouTube channel "ChrisDaCow": youtube.com/watch?v=UKpFoY… Researchers are from the Ape Initiative, a non-profit org.