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> Machine learning is just one such technique which has gained popularity outside AI. I'd argue the machine learning label can be applied to any AI system that

> Machine learning is just one such technique which has gained popularity outside AI. I'd argue the machine learning label can be applied to any AI system that's data-driven in some sense (even self-generating the data using reinforcement learning). Wikipedia lists the following approaches to Machine Learning. Surely you wouldn't call them all 'one technique'?: Decision tree learning, Association rule learning, Artificial neural networks, Deep learning, Inductive logic programming, Support vector machines, Clustering, Bayesian networks, Reinforcement learning, Representation learning, Similarity and metric learning, Sparse dictionary learning, Genetic algorithms.

regularfry 9y ago
AI used to cover Expert Systems, which have nothing we'd recognise as a "learning" phase at all. Hell, it used to cover parsers.
YeGoblynQueenne 9y ago
Let's say that it's a class of techniques that learn models (or progams, in the case of ILP) from data. My main concern is not about defining what machine learning is, however. Rather, I'm worried about the definition of AI shrinking to "it's what we now call machine learning". Which is at the very least unhistorical.
- breatheoften 9y ago
  I thought it was common for people to think of AI as largely disjoint from machine learning ... AI covering all the expert/rule based systems, grammars, and search algorithms with ML being pretty strictly referring to techniques for building predictive models from datasets ...
  - YeGoblynQueenne 9y ago
    Well, machine learning arose partly from the need to find a better way to create rule-bases than hand-crafting them, painstakingly and at great cost. You can see this more clearly in the work of early pioneers like Ryszard Michalski, or in inductive inference (the field preoccupied with learning automata from examples). All these have always been well in the purview of classical AI. Then of course there's the connectionist branch that explicitly set out to take inspiration from the human brain. I don't think that anyone ever saw neural networks as anything but AI for the last 50 years or so. In any case, when the connectionist were having arguments, they were having them with other AI folks, not with, say, statisticians, or mathematicians. Then, if you think about the classic tasks that machine learning systems perform, they're all firmly classic AI tasks: classification, game-playing, machine vision, speech processing, etc etc. I mean, the fact that models are "predictive" on its own doesn't tell you that machine learning is separate from AI. After all, an intelligent agent must be able to make predictions about its environment and the outcome of its actions on this environment.

regularfry 9y ago

AI used to cover Expert Systems, which have nothing we'd recognise as a "learning" phase at all. Hell, it used to cover parsers.

YeGoblynQueenne 9y ago

Let's say that it's a class of techniques that learn models (or progams, in the case of ILP) from data. My main concern is not about defining what machine learning is, however. Rather, I'm worried about the definition of AI shrinking to "it's what we now call machine learning". Which is at the very least unhistorical.

breatheoften 9y ago
I thought it was common for people to think of AI as largely disjoint from machine learning ... AI covering all the expert/rule based systems, grammars, and search algorithms with ML being pretty strictly referring to techniques for building predictive models from datasets ...
- YeGoblynQueenne 9y ago
  Well, machine learning arose partly from the need to find a better way to create rule-bases than hand-crafting them, painstakingly and at great cost. You can see this more clearly in the work of early pioneers like Ryszard Michalski, or in inductive inference (the field preoccupied with learning automata from examples). All these have always been well in the purview of classical AI. Then of course there's the connectionist branch that explicitly set out to take inspiration from the human brain. I don't think that anyone ever saw neural networks as anything but AI for the last 50 years or so. In any case, when the connectionist were having arguments, they were having them with other AI folks, not with, say, statisticians, or mathematicians. Then, if you think about the classic tasks that machine learning systems perform, they're all firmly classic AI tasks: classification, game-playing, machine vision, speech processing, etc etc. I mean, the fact that models are "predictive" on its own doesn't tell you that machine learning is separate from AI. After all, an intelligent agent must be able to make predictions about its environment and the outcome of its actions on this environment.