[polkapolka]

Symbolic AI is machine programming. Connectionist AI is machine learning.

Machine programming simply does not scale. It is also not biologically plausible: it is not as if God put symbols into our brain, these formed, are prefaced by, and were learned from neural activity.

Just try to solve the spam problem using symbolic AI. It will keep you busy (and paid) for a long time, while yielding subpar results.

Deep learning is the most promising direction AI went into since a long time. Finding flaws in DL does not point back to a programmer crafting handwritten rules to correct it. It merely points to more DL research needed to have machines correct this themselves. Preferrably differentiable.

[giardini]

polkapolka says>"Machine programming simply does not scale....Just try to solve the spam problem using symbolic AI. It will keep you busy (and paid) for a long time, while yielding subpar results."

1. We can scale "machine programming" by making computers faster and more complex,

2. The spam problem can be solved with Bayesian methods (which I consider to be part of "machine programming"): a connectionist solution is not necessary.

Certainly connectionist deep learning is exciting and should be investigated as far as possible but it is only one of many tools. We should avoid being the man with a hammer who thinks that every problem looks like a nail.

[polkapolka]

Faster and more complex computers does not make you faster at manual programming. Computer vision had this before the DL boom: engineers painfully crafting feature extractors. It went nowhere.

Bayesian models underperform to DL by a wide margin (though it is a step up from handwritten rules: if DEAR FRIEND then Spam score++.

[giardini]

polkapolka says >"Faster and more complex computers does not make you faster at manual programming. Computer vision had this before the DL boom: engineers painfully crafting feature extractors. It went nowhere."

Faster and more complex computers make manual programming faster and make software faster, including DL software. Without the faster computers of today we wouldn't be using or even discussing DL.

[hopler]

Why do you think human-style learning can scale? Human brains are terrible at basic arithmetic like raising 3 to the 6th power.

[polkapolka]

Statistical learning with connectionist architectures is driving current AI at scale.

To me, this paradigm is also the most promising: learn from data bottom-up, not from experts top-down.