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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

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?

watersb 4y ago
Production-rule systems can do inference that isn't from unsupervised neural net training. It's just matching up the rules. Probably not considered AI these days.
paradite 4y ago
- Sufficiently complex "if else statements" (Decision tree) - Expert system / Rule-based system (CLIPS https://clipsrules.net/ 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.
- brutus1213 4y ago
  You covered it in your high level list above (which is pretty good) but I wanted to point out Planners as being an important subfield cutting across different applications.
  - moffkalast 4y ago
    I think those would fall under the first and last point, depending on implementation. 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.
- YeGoblynQueenne 4y ago
  Good list. Also: - Automated Theorem Proving - Satisfiability (SAT) and constraint solving
- petilon 4y ago
  How many of these are in common use today? You rarely hear about things like Expert Systems today. Didn't prolog die in the 1990's?
  - paradite 4y ago
    You don't hear a lot about them nowadays because the hype now is ML, even though things like search or decision tree are probably more widely adopted than ML. 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 https://en.wikipedia.org/wiki/AI_winter.
jrumbut 4y ago
When I took an AI course in 2009 it was largely about game playing algorithms that use graph traversal (possibly guided by heuristics). Stuff like this: https://en.m.wikipedia.org/wiki/A*_search_algorithm 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).
- moffkalast 4y ago
  In practice these "traditional AI" algorithms are far more common because they're well defined, low cost to implement and run (no gpu instances required lol), and give useful results faster. Proper neural nets are still more buzzwords than workhorses these days, especially when you stray away from image processing where they really shine. 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.
- YeGoblynQueenne 4y ago
  Search-based algorithms continue to dominate traditional board games. Stockfish, AlphaGo and friends are hybrid systems combining a game-tree search algorithm with a neural net. The neural net is trained (by self-play) to learn an evaluation function for the game-tree search algorithm (more precisely, the neural nets learn a classifier for board positions as leading to a win, loss or draw). The game-tree search algorithms are alpha-beta minimax in Stockfish and Monte Carlo Tree Search in AlphaGo and family. Far as I know anyway. 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.
redytedy 4y ago
Other replies are missing an explicit call-out to Reinforcement Learning. You can USE ML for RL, but the field itself is considered separate from ML and under AI in general.
- feral 4y ago
  RL is generally considered as a type of ML. Eg Wikipedia: "Reinforcement learning (RL) is an area of machine learning " https://en.m.wikipedia.org/wiki/Reinforcement_learning https://en.m.wikipedia.org/wiki/Reinforcement_learning
  - mdp2021 4y ago
    The poster probably meant: Reinforcement Learning is not restricted to Neural Networks.
    - feral 4y ago
      Hmm, I don't see that. In the spirit of the cutting edge, any chance you could give me a chain-of-reasoning on that inference?
      - kgwgk 4y ago
        From your link: “Due to its generality, reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics.” 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.)
        feral 4y ago
        The original comment was: "So Artificial Intelligence is a superset of Machine Learning. What are some AI algorithms that are still in use, that is not Machine Learning" 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".
isolli 4y ago
Optical character recognition, applied to scanning documents or automatically reading license plates and postal addresses.
optimalsolver 4y ago
Symbolic regression/program synthesis/genetic programming.

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?

watersb 4y ago

Production-rule systems can do inference that isn't from unsupervised neural net training. It's just matching up the rules. Probably not considered AI these days.

paradite 4y ago

- Sufficiently complex "if else statements" (Decision tree) - Expert system / Rule-based system (CLIPS https://clipsrules.net/ 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.

brutus1213 4y ago
You covered it in your high level list above (which is pretty good) but I wanted to point out Planners as being an important subfield cutting across different applications.
- moffkalast 4y ago
  I think those would fall under the first and last point, depending on implementation. 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.
YeGoblynQueenne 4y ago
Good list. Also: - Automated Theorem Proving - Satisfiability (SAT) and constraint solving
petilon 4y ago
How many of these are in common use today? You rarely hear about things like Expert Systems today. Didn't prolog die in the 1990's?
- paradite 4y ago
  You don't hear a lot about them nowadays because the hype now is ML, even though things like search or decision tree are probably more widely adopted than ML. 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 https://en.wikipedia.org/wiki/AI_winter.

jrumbut 4y ago

When I took an AI course in 2009 it was largely about game playing algorithms that use graph traversal (possibly guided by heuristics). Stuff like this: https://en.m.wikipedia.org/wiki/A*_search_algorithm 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).

moffkalast 4y ago
In practice these "traditional AI" algorithms are far more common because they're well defined, low cost to implement and run (no gpu instances required lol), and give useful results faster. Proper neural nets are still more buzzwords than workhorses these days, especially when you stray away from image processing where they really shine. 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.
YeGoblynQueenne 4y ago
Search-based algorithms continue to dominate traditional board games. Stockfish, AlphaGo and friends are hybrid systems combining a game-tree search algorithm with a neural net. The neural net is trained (by self-play) to learn an evaluation function for the game-tree search algorithm (more precisely, the neural nets learn a classifier for board positions as leading to a win, loss or draw). The game-tree search algorithms are alpha-beta minimax in Stockfish and Monte Carlo Tree Search in AlphaGo and family. Far as I know anyway. 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.

redytedy 4y ago

Other replies are missing an explicit call-out to Reinforcement Learning. You can USE ML for RL, but the field itself is considered separate from ML and under AI in general.

feral 4y ago
RL is generally considered as a type of ML. Eg Wikipedia: "Reinforcement learning (RL) is an area of machine learning " https://en.m.wikipedia.org/wiki/Reinforcement_learning https://en.m.wikipedia.org/wiki/Reinforcement_learning
- mdp2021 4y ago
  The poster probably meant: Reinforcement Learning is not restricted to Neural Networks.
  - feral 4y ago
    Hmm, I don't see that. In the spirit of the cutting edge, any chance you could give me a chain-of-reasoning on that inference?
    - kgwgk 4y ago
      From your link: “Due to its generality, reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics.” 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.)
      - feral 4y ago
        The original comment was: "So Artificial Intelligence is a superset of Machine Learning. What are some AI algorithms that are still in use, that is not Machine Learning" 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".

isolli 4y ago

Optical character recognition, applied to scanning documents or automatically reading license plates and postal addresses.

optimalsolver 4y ago

Symbolic regression/program synthesis/genetic programming.