|
AI research has put hardly any effort into building goal-directed agents / A-Life since the advent of Machine Learning. A-Life was last really "looked into" in the '70s, back when "AI" meant Expert Systems and Behavior Trees. All the effort in AI research since the advent of Machine Learning, has been focused on making systems that — in neurological terms — are given a sensory stimulus of a question, and then passively "dream" a response to said question as a kind of autonomic "mind wandering" process. (And not even dynamic systems — these models always reach equilibrium with some answer and effectively halt, rather than continuing to "think" to produce further output.) I don't think there's a single dollar of funding in AI right now going to the "problem" of making an AI that 1. feeds data into a continuously-active dynamically-stable model, where this model 2. has terminal preferences, 3. sets instrumental goals to achieve those preferences, 4. iteratively observes the environment by snapshotting these continuous signals, and then 5. uses these snapshots to make predictions of 6. how well any possible chosen actions will help optimize the future toward its preferences, before 7. performing the chosen actions. That being said, this might not even be that hard a problem, compared to all the problems being solved in AI right now. A fruit fly is already a goal-directed agent in the sense described above. Yet a fruit fly has only 200K neurons, and very few of the connections between those neurons are dynamic; most are "hard wired" by [probably] genetics. If we want true ALife, we only need to understand what a fruit fly brain is doing, and then model it. And that model will then fit — with room to spare! — on a single GPU. From a decade ago. |
The entire (enormous) field of reinforcement learning begs to differ.