There are some conceptual differences. In RL, you define a value function for all possible states. In active inference, you make desirable sense data a priori likely. Sensory space is not only lower-dimensional than (unobserved) state space, but you only need to define a single point in it, rather than a function on the whole space. It's often a much more natural way of defining goals and is more similar to control theory than RL. You're directly optimizing for a desired (and known) outcome rather than having to figure out what to optimize for by reinforcement. For example, if you want a robot to walk to some goal point, RL would have... (read more)
Will scaling deep learning produce human-level generality, or do we need a new approach? You may have read the exchange between Scott Alexander and Gary Marcus, and felt that there are some good arguments on both sides, some bad ones, but few arguments that go beyond analogy and handwaving - arguments that would take what we know about deep learning and intelligence, and look at what that knowledge implies. If you haven’t read the exchange, here it is: SA, GM, SA, GM.
I will argue for Marcus’ position, but dive a little deeper than he does. I believe that symbolic representations, specifically programs, and learning as program synthesis, can provide data efficient and... (read more)
Hi,
I'm going to study at Aalto in Helsinki next semester, and I'd like to join your meetups. Are they primarily in Finnish or English?
OBS!: Studenterhuset closed on sundays, we moved to the nearest Baresso!
Nice, CAI is another similar approach, kind of in between the three already mentioned. I think "losing a degree of freedom" is very much a good thing, both computationally and functionally.