Democratizing Automation

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Models aren't moats and how emergent behavior scaling laws will change the business landscape.
Nathan Lambert
6
Model-as-a-service makes more sense when there is a data advantage to back it up.
Nathan Lambert
5
Tales of the open and closed sides, how these two dynamics will dictate progress and public perception.
Nathan Lambert
Common machine learning systems are starting to deploy the RL lens of feedback.
Nathan Lambert
5
How an unlikely corner of robotics research, locomotion, defined RL's new notion of success.
Nathan Lambert
3
I make the case why people iteratively training any model should learn some core concerns of reinforcement learning.
Nathan Lambert
What the automation acceleration from COVID19 actually will look like. What did robotics look like a decade ago and is this step feasible?
Nathan Lambert
My predictions for machine learning this year: 3D assets, self-driving, GPT4, RLHF, Deep RL, diffusion models, and conference cycles.
Nathan Lambert
4
Re-naming arms races; bridging communities; aligning ChatGPT, and more.
Nathan Lambert
Robotics Transformers, DreamerV3, XLand 2, and hoping that scaling laws are coming embodied AI.
Nathan Lambert
4
How we’re passing through different eras of how reinforcement learning is viewed.
Nathan Lambert
and
Thomas Krendl Gilbert
The acrobatic dance videos are flashy, but what are the actual technical breakthroughs? What is happening to the Korean robotics industry?
Nathan Lambert
© 2023 Nathan Lambert
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