Gary Marcus

don’t understand, sorry. largest symbolic project you refer to is?

Watson. The only commonality between Watson and AlphaGo is the AI moniker, so it feels like an odd comparison

This is incorrect in several ways though. First, Watson seems to have been an everything and the kitchen sink approach. They used logistic regression, rule based, lexical databases, information retrieval, grammar based parsers, SVM based relation extractors etc.

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.