Richard Socher의 21가지 AI 예측 중 가장 공감하는 건 "Reward Engineering" 직군 등장. 프롬프트 엔지니어링을 넘어서, AI에게 무엇을 보상으로 줄지 설계하는 역할이 핵심이 된다는 거. 이건 RLHF를 넘어 실제 프로덕션 레벨에서의 얘기. AI 도구를 잘 쓰는 것 → AI를 잘 가르치는 것으로 전환 중. #AI #RewardEngineering #2026Predictions

These are not CGI. Reinforcement learning is back — and this time, it’s rewriting both intelligence and instinct. When it operates on strings, it powers o3. When it operates on motors, it builds creatures that out-maneuver most animals. Here’s what I keep wondering: → What if reinforcement learning — not large language models — becomes the real foundation of AGI? → If prompts teach machines to say the right thing, and rewards teach them to do the right thing… which one should we trust more? → And if the world runs on incentives, should AI be any different? 2024 was the year of prompt engineering — speaking to machines. 2026 will be the year of reward engineering — shaping their desires. In my opinion, the next frontier isn’t better answers. It’s better motivationensuring safe alignment. Because in the end, AI won’t mirror our words. It’ll mirror our values. #AI #ReinforcementLearning #RewardEngineering #MachineLearning #Robotics #AIEthics

We used to build robots that obeyed. Now we’re building ones that compete. This robot isn’t running a script. It’s reading a shuttlecock midair, predicting trajectories, adjusting angles — and returning the shot. That’s not programming. That’s instinct. And here’s the part that I can’t stop thinking about: → When machines start playing with us, how long before they start playing against us? → If performance is now shared between human intuition and machine precision — who really wins? → Are we teaching robots to assist, or to aspire? Because we’ve already taught AI how to speak, code, and draw. Now we’re teaching it how to move. And once machines move with purpose, every industry — from sports to surgery — becomes a new playing field. The question is no longer can they do it. It’s should they? The solution isn’t to stop teaching machines — it’s to start teaching values alongside capability. If we train AI for precision without purpose, we risk brilliance without boundaries. Reward engineering shouldn’t just optimize performance — it should encode principles. Because the future of AI won’t depend on how fast it learns — but on what we choose to reward. #AI #Robotics #ReinforcementLearning #RewardEngineering #AIFuture #Ethics #AIAlignment