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