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Nah, no need for new ML approaches. It's just time for the pendulum to swing back again from the current craze of data-driven/"deep learning"/continuous modelin
by drewda 9y ago
Nah, no need for new ML approaches. It's just time for the pendulum to swing back again from the current craze of data-driven/"deep learning"/continuous modeling to the alternative of symbolic/"Good Old-Fashioned AI"/discrete modeling.
The switch happens every ~30 years: around the ~1950s, in the late 1980s, and perhaps again soon...
I realize I'm glossing over many nuances of how the most successful AI/ML approaches combine together continuous and discrete modeling, and symbolic and numeric techniques.
Still, this is a real cultural divide for researchers. The "deep learning" types just want to increase their match/success percentages as high as they will go--with little regard for how the sausage is made--while the symbolic types care about whether the model itself provides actual explanatory value.
- jacobush 9y agoEvery 30 years of Moore's gives enough oomph for the data driven stuff to step up one level in complexity?
- fooker 9y agoEven long after the 80's, AI meant decision trees and heuristics for graph algorithms.
- Don_Patrick 9y agoI participated in the WSC with GOFAI, though I am not representative of the state of the art. I would recommend GOFAI logic combined with a knowledge database assembled through machine learning (my database was virtually empty). However I found the main problem in this challenge to be that one had to solve all aspects of language processing before one could begin to solve the pronouns. My home-made parser just wasn't up for processing relative clauses of relative subclauses. https://artistdetective.wordpress.com/2016/07/31/winograd-schema-challenge-2016 https://artistdetective.wordpress.com/2016/07/31/winograd-sc...