Comment by pron
3 days ago
> this implies higher intelligence
Not necessarily. The problem is that we can't precisely define intelligence (or, at least, haven't so far), and we certainly can't (yet?) measure it directly. And so what we have are certain tests whose scores, we believe, are correlated with that vague thing we call intelligence in humans. Except these test scores can correlate with intelligence (whatever it is) in humans and at the same time correlate with something that's not intelligence in machines. So a high score may well imply high intellignce in humans but not in machines (e.g. perhaps because machine models may overfit more than a human brain does, and so an intelligence test designed for humans doesn't necessarily measure the same thing we think of when we say "intelligence" when applied to a machine).
This is like the following situation: Imagine we have some type of signal, and the only process we know produces that type of signal is process A. Process A always produces signals that contain a maximal frequency of X Hz. We devise a test for classifying signals of that type that is based on sampling them at a frequency of 2X Hz. Then we discover some process B that produces a similar type of signal, and we apply the same test to classify its signals in a similar way. Only, process B can produce signals containing a maximal frequency of 10X Hz and so our test is not suitable for classifying the signals produced by process B (we'll need a different test that samples at 20X Hz).
My definition of intelligence is the capability to process and formalize a deterministic action from given inputs as transferable entity/medium. In other words knowing how to manipulate the world directly and indirectly via deterministic actions and known inputs and teach others via various mediums. As example, you can be very intelligent at software programming, but socially very dumb (for example unable to socially influence others).
As example, if you do not understand another person (in language) and neither understand the person's work or it's influence, then you would have no assumption on the person's intelligence outside of your context what you assume how smart humans are.
ML/AI for text inputs is stochastic at best for context windows with language or plain wrong, so it does not satisfy the definition. Well (formally) specified with smaller scope tend to work well from what I've seen so far. Known to me working ML/AI problems are calibration/optimization problems.
What is your definition?
Forming deterministic actions is a sign of computation, not intelligence. Intelligence is probably (I guess) dependent on the nondeterministic actions.
Computation is when you query a standby, doing nothing, machine and it computes a deterministic answer. Intelligence (or at least some sign of it) is when machine queries you, the operator, on it's own volition.
> Forming deterministic actions is a sign of computation, not intelligence.
What computations can process and formalize other computations as transferable entity/medium, meaning to teach other computations via various mediums?
> Intelligence is probably (I guess) dependent on the nondeterministic actions.
I do agree, but I think intelligent actions should be deterministic, even if expressing non-deterministic behavior.
> Computation is when you query a standby, doing nothing, machine and it computes a deterministic answer.
There are whole languages for stochastic programming https://en.wikipedia.org/wiki/Stochastic_programming to express deterministically non-deterministic behavior, so I think that is not true.
> Intelligence (or at least some sign of it) is when machine queries you, the operator, on it's own volition.
So you think the thing, who holds more control/force at doing arbitrary things as the thing sees fit, is more intelligent? That sounds to me more like the definition of power, not intelligence.
2 replies →
> My definition of intelligence is the capability to process and formalize a deterministic action from given inputs as transferable entity/medium.
I don't think that's a good definition because many deterministic processes - including those at the core of important problems, such as those pertaining to the economy - are highly non-linear and we don't necessarily think that "more intelligence" is what's needed to simulate them better. I mean, we've proven that predicting certain things (even those that require nothing but deduction) require more computational resources regardless of the algorithm used for the prediction. Formalising a process, i.e. inferring the rules from observation through induction, may also be dependent on available computational resources.
> What is your definition?
I don't have one except for "an overall quality of the mental processes humans present more than other animals".
> I mean, we've proven that predicting certain things (even those that require nothing but deduction) require more computational resources regardless of the algorithm used for the prediction.
I do understand proofs as formalized deterministic action for given inputs and processing as the solving of various proofs.
> Formalising a process, i.e. inferring the rules from observation through induction, may also be dependent on available computational resources.
Induction is only one way to construct a process and there are various informal processes (social norms etc). It is true, that the overall process depends on various things like available data points and resources.
> I don't have one except for "an overall quality of the mental processes humans present more than other animals".
How would your formalize the process of self-reflection and believing in completely made-up stories of humans often used as example that distinguishes animals from humans? It is hard to make a clear distinction in language and math, since we mostly do not understand animal language and math or other well observable behavior (based on that).
ML/AI is much less stochastic than an average human
Fair, I think it would be more appropriate to say higher capacity.
Ok, but the point of a test of this kind is to generalise its result. I.e. the whole point of an intelligence test is that we believe that a human getting a high score on such a test is more likely to do some useful things not on the test better than a human with a low score. But if the problem is that the test results - as you said - don't generalise as we expect them, then the tests are not very meaningful to begin with. If we don't know what to expect from a machine with a high test score when it comes to doing things not on the test, then the only "capacity" we're measuring is the capacity to do well on such tests, and that's not very useful.