[inconfident2021]

I hate to say this but symbolic reasoning and planning has aged like milk. The use case is so limited and the only people using these are the people full of nostalgia. One paradigm shift into deep learning in 2012 and 10 years later we have something as general as GPT-4. Gatekeeping in academia is a real thing. Sad but true.

[vkazanov]

Symbolic reasoning and related fields are just areas to were important to be researched back in the day. There are some very interesting results that came of that family of research, especially on the intersection of traditional math and CS, i.e. logic stuff, proof assistants, various solvers, etc.

Machine learning and its flavours were always there. What is new in deep learning is the scale of everything: amount of compute made possible by clusters of GPU-centric machines and amount of data made available by the Internet.

The shocking thing here how almost boring the core of recent advances is: do more of the same, much, much more, and quantity will turn into new qualities yet again.

[FrustratedMonky]

Its amazing that everything is some conspiracy of 'they' just wouldn't let us advance. Most times these sentiments are from those that are just learning a field, and haven't realized that the 'establishment' wasn't stopping advancement, it was also advancing. Just because there is a big break through, doesn't mean that everything that came before wasn't needed to reach it.

[bmitc]

Machine learning has been around for decades. The real breakthrough has been the massive increase in compute power, the ubiquity of distributed systems, and the amount of data available.

[zelphirkalt]

Symbolic reasoning is for things we truly know how to do. It can be used as supplemental implementation for known special cases as well.

[jmeister]

There was no gate-keeping. Yann LeCun was doing neural networks in the late 80s.

[visarga]

Late 80s was the second AI winter, it wasn't all peaches. Why do you think just a handful of people were working on neural nets?

[YeGoblynQueenne]

Because before backpropagation (mid-80's) nobody could train multi-layer neural nets.

Although to be fair, there wasn't "just a handful of people". Just look at Geoff Hinton's citations count throughout the '80s.