So Artificial Intelligence is a superset of Machine Learning. What are some AI algorithms that are still in use, that is not Machine Learning? Is there anything?
- Expert system / Rule-based system (CLIPS https://clipsrules.net/)
- Knowledge-based system
- Rational agent planning that doesn't involve ML (search, heuristics, MDP, POMDP)
- NLP that doesn't involve ML (Markov model, etc)
If you Google AI taxonomy you can get a good understanding of it.
Most so called algorithms taught in universities are after all some form of basic AI, there's very little conceptual difference between an A* planner and a minimax tree, yet one of these is essentially Stockfish, which is "smarter" than all humans when it comes to chess.
- Automated Theorem Proving
- Satisfiability (SAT) and constraint solving
I believe things come and go in cycles, so we might see the rise of non-ML solutions if the current circumstances change. Just like the last time: https://en.wikipedia.org/wiki/AI_winter.
Stuff like this: https://en.m.wikipedia.org/wiki/A*_search_algorithm
Such algorithms are still a viable approach in many situations (and I'm sure A* is used in production somewhere today).
Random forest can predict tabular data extremely well without overfitting, A* is the mathematical best approach to finding the shortest path through a graph, alfa-beta pruning with heuristics consistently beats any known neural net in chess, quicksort will be better than a neural sort because you don't have to first offload data onto a damn gpu, etc.
Even Tesla doesn't give the car's accelerator and steering reigns to the detection neural net outputs (that would be actually insane), instead it lets them extract data from the world into its vector space and then uses deterministic motion planning to determine where the car should drive based on A* GPS routing. That way the system won't do anything undefined and stupid, like neural nets are usually prone to.
DeepMind have downplayed the use of MCTS in their Alpha-x family, to the point of obfuscating the fact that is part of their system at all and have sowed much confusion about this, but their systems ain't going nowhere without good, old-fashion game-tree search.
Stockfish only recently adopted neural nets, btw.
Eg Wikipedia: "Reinforcement learning (RL) is an area of machine learning " https://en.m.wikipedia.org/wiki/Reinforcement_learning
In the spirit of the cutting edge, any chance you could give me a chain-of-reasoning on that inference?
It all depends on whether you consider the new use as a particular application of a more general thing or as a thing on its own. (But I agree that if you call it with than name it’s not that general.)
It seems we agree with Wikipedia that ML contains RL?
It's true RL is also studied in other fields.
I struggle to see how that means that RL is a good answer to "What are some AI algorithms that are still in use, that is not Machine Learning".
It may be the case if you define ML broadly enough. One may also define RL broadly to refer to things that existed well before ML was a thing (not that I would do it, but one may). I guess that may still be within the AI umbrella, but I’m not sure.
For a bit of history on machine learning I recommend Rodney Brooks' seminal series of articles on machine learning, beginnign here:
https://rodneybrooks.com/forai-machine-learning-explained/
The first article in the series, linked above has one section titled "Machine Learning Started with Games". In that section he goes over Arthur Samuel's checkers-playing program that beat a human champion in 1961.
The section also contains Brooks' description of Donald Michie's MENACE, which is widely considered to be one of the first reinforcement learning algorithms. For lack of a computer, it was implemented on a set of match boxes:
In 1960 Surgical Science did not have much pull in getting access to a digital computer. So Donald Michie himself built a machine that could learn to play the game of tic-tac-toe (Noughts and Crosses in British English) from 304 matchboxes, small rectangular boxes which were the containers for matches, and which had an outer cover and a sliding inner box to hold the matches. He put a label on one end of each of these sliding boxes, and carefully filled them with precise numbers of colored beads. With the help of a human operator, mindlessly following some simple rules, he had a machine that could not only play tic-tac-toe but could learn to get better at it.
The answer to the latter question is obviously “no”. Did anyone argue otherwise? mdp2021 suggested that redytedy may have meant that but what he or she actually wrote is “You can USE ML for RL, but the field itself is considered separate from ML and under AI in general.”
mdp2021: “The poster probably meant: Reinforcement Learning is not restricted to Neural Networks.”
feral: “Hmm, I don't see that.” [that RLinrtNN is probably what redytedy meant]
“In the spirit of the cutting edge, any chance you could give me a chain-of-reasoning on that inference?” [the inference that redytedy probably meant RLinrtNN]
I could be wrong.
However, I didn't think of reinforcement learning when compiling that list, because in my experience non-ML RL solutions are rarely better than ML solutions. Happy to be corrected on that front.
Probably not considered AI these days.