Gary Marcus

Part of the etc. is their heavy use of simulations to fit parameters for their strategy modules. I'll have to recheck but I recall the use of bayesian methods, reinforcement learning, Neural networks and monte carlo search in the game strategies paper: https://ieeexplore.ieee.org/document/6177733 …

I'm sure they did lots of things to make it work. It's still the most symbolic and least DRL system coming out of any modern research lab. It's quite a bit closer to the Gary's cognitive hybrid systems approach than anything out of DeepMind.

hype aside, alphago is a hybrid, with a monte carlo tree sim backbone that traversed trees out of symbol CS 101, alongside the DRL.

That's an odd argument to make since you also claim DeepMind relies too much on DRL.

Both can be & are true. Really telling to me is that DRL on its own worked for Atari games but not Go— and that DM’s spin on Go really downplayed the hybrid aspect that was essential to its success. (It was also apparently necessary to build in the rules for Go, unlike Atari.)

So we build hybrid systems but are also too reliant on DRL? How can both of those be true?

It’s question of emphasis, in part, but if I were running your ship I would spend more time exploring principled ways of building hybrids, and more kinds of of hybrids, and more time on on open-ended problems.

Other than Neural Turing Machines, AlphaGo, GraphNets, GQN, SPIRAL, etc? I'm sure you'd run things differently, but this is a far cry from the DRL centric narrative of the Wired article.