This is what it comes down to. Until we agree on a testable definition of “intelligence” (or sentience, sapience, consciousness or just about any descriptor of human thought), it’s not really science. Even in nature, what we might consider intelligence manifests in different organisms in different ways.
We could assume that when people say intelligence they mean human-like intelligence. That might be narrow enough to test, but you’d probably still end up failing some humans and passing some trained models
It’s not that it’s not science. Different sciences simply define intelligence in different ways. In psychology it’s mostly the ability to solve problems by reasoning so ‘human like’ intelligence. They don’t care that computers can solve the same problems without reasoning (by brute force for example) because they don’t study computers. In computer science it’s more fuzzy but pretty much boils down to algorithms solving problems by using some sort of insights that are not simple step-by-step instructions. The problem is that with general AI we’re trying to unify those definitions but when you do this both lose it’s meanings.
You’re right, it’s very much context dependent, and I appreciate your incite on how this clash between psychology and computer science muddies the terms. As a CS guy myself who’s just dipping my toes into NN’s, I lean toward the psychology definition, where intelligence is measured by behavior.
In an artificial neural network, the algorithms that wrangle data and build a model aren’t really what makes the decisions, they just build out the “body” (model, generator functions) and “environment” (data format), so to speak. If anything that code is more comparable to DNA than any state of mind. Training on data is where the knowledge comes from, and by making connections the model can “reason” a good answer with the correlations it found. Those processes are vague enough that I don’t feel comfortable calling them algorithms, though. It’s pretty divorced from cold, hard code.
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This is what it comes down to. Until we agree on a testable definition of “intelligence” (or sentience, sapience, consciousness or just about any descriptor of human thought), it’s not really science. Even in nature, what we might consider intelligence manifests in different organisms in different ways.
We could assume that when people say intelligence they mean human-like intelligence. That might be narrow enough to test, but you’d probably still end up failing some humans and passing some trained models
It’s not that it’s not science. Different sciences simply define intelligence in different ways. In psychology it’s mostly the ability to solve problems by reasoning so ‘human like’ intelligence. They don’t care that computers can solve the same problems without reasoning (by brute force for example) because they don’t study computers. In computer science it’s more fuzzy but pretty much boils down to algorithms solving problems by using some sort of insights that are not simple step-by-step instructions. The problem is that with general AI we’re trying to unify those definitions but when you do this both lose it’s meanings.
You’re right, it’s very much context dependent, and I appreciate your incite on how this clash between psychology and computer science muddies the terms. As a CS guy myself who’s just dipping my toes into NN’s, I lean toward the psychology definition, where intelligence is measured by behavior.
In an artificial neural network, the algorithms that wrangle data and build a model aren’t really what makes the decisions, they just build out the “body” (model, generator functions) and “environment” (data format), so to speak. If anything that code is more comparable to DNA than any state of mind. Training on data is where the knowledge comes from, and by making connections the model can “reason” a good answer with the correlations it found. Those processes are vague enough that I don’t feel comfortable calling them algorithms, though. It’s pretty divorced from cold, hard code.