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The Limits of Machine Learning

nautil.us

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Re: The Limits of Machine Learning

#21

Post by pron »

joe_the_user wrote: post #20
pron wrote: post #17

Earlier quoted context omitted.

1. Just because someone decided to use the words "intelligence" and "neural" when describing a class of statistical clustering algorithms often based on backpropagation of errors doesn't mean these algorithms have anything to do with the brain or intelligence, and if they do, the relationship is not necessarily direct and immediate. Speaking about the two as if the connection is clear only muddles our understanding.…

We're not sure what intelligence is, and it is easy to show that it is not "a general ability to solve problems". Humans are great at solving some problems and pretty terrible at others. Now wait a second. I would say humans are better at solving some problems directly and computers programmed by humans are better at solving other problems. However, a computer with a single, fixed program alone will choke completely…

> a computer with a single, fixed program alone

I'm not sure what you mean by a "fixed program". Is a statistical clustering algorithm not a "fixed program"? Even the human brain is running some "fixed program", as we cannot reprogram our brains to efficiently run general sorting algorithms on "bare neurons".

> "general problem solving ability" seems about right for some value of "general"

It may be more useful than nothing at all, but I don't see how it conveys too much information. Clearly, a universal Turing machine has a "general problem solving ability" for much more general values of "general". So I'm not sure how much information "general problem solving" conveys. More importantly, why do we even need a definition? Statistical learning is very effective at solving some useful problems. Why do we need to philosophize about the relationship between those algorithms and human intelligence before we have more data?

Re: The Limits of Machine Learning

#22

Post by interdrift »

ankurdhama wrote: post #19
j1vms wrote: post #11

Earlier quoted context omitted.

In popular discussion, Deep Learning has unfortunately become synonymous with Machine Learning, which also, as of late, has itself become synonymous with AI. Now, this might sound facetious, but to help avert another AI winter (maybe 10 years down the road), we need to be loud and vocal, educating at the very least the investing strata of society as to hyponymy/hypernymy relationships between these terms.

It is bound to happen when researchers uses words like "Machine" and "Learning" to describe their field. Why not use words that actually describe it, like function approximation.

Because you could say learning is function approximation for the public.

Re: The Limits of Machine Learning

#23

Post by szemet »

I don't understand why it says "2 * (5 + 1) = 12" fits equally well as "x + 5 + 2 = 12" to the original pattern:

"5 + 2 = 12"

As it changes one of the terms of the original addition (2 to 1). The second solution fits better by my judgment. Changing the the puzzle, seems like a bit of cheating...

Re: The Limits of Machine Learning

#24

Post by ricksplat »

szemet wrote: post #23

I don't understand why it says "2 * (5 + 1 ) = 12" fits equally well as "x + 5 + 2 = 12" to the original pattern: "5 + 2 = 12" As it changes one of the terms of the original addition (2 to 1). The second solution fits better by my judgment. Changing the the puzzle, seems like a bit of cheating...

I think what he is saying is, if you remove preconceptions, e.g. that '+' means "addition" then you can infer that it means "multiply" and that the "+ 1" is implied, based on the answer.

As a human, using our prior experience we will most commonly say that + means addition and will make that an axiom of our solution, which involves (for me at least) adding the result of the previous line to the sum of each line.

    8 + 11 = 19 [ + 21 ] = 40
As a machine (or as a mathematician thinking with more flexible abstractions) the numbers are intractable, but the symbols are and perhaps it is more "logical" to look at each line separately and change the meaning of the symbols.

    8 * (11 [ + 1 ]) = 96
The point is that humans have a different set of logical precepts to machines.

Re: The Limits of Machine Learning

#25

Post by IshKebab »

szemet wrote: post #23

I don't understand why it says "2 * (5 + 1 ) = 12" fits equally well as "x + 5 + 2 = 12" to the original pattern: "5 + 2 = 12" As it changes one of the terms of the original addition (2 to 1). The second solution fits better by my judgment. Changing the the puzzle, seems like a bit of cheating...

No it doesn't. It changes the + to * and adds +1.

Re: The Limits of Machine Learning

#26

Post by djit »

joe_the_user wrote: post #20
pron wrote: post #17

Earlier quoted context omitted.

1. Just because someone decided to use the words "intelligence" and "neural" when describing a class of statistical clustering algorithms often based on backpropagation of errors doesn't mean these algorithms have anything to do with the brain or intelligence, and if they do, the relationship is not necessarily direct and immediate. Speaking about the two as if the connection is clear only muddles our understanding.…

We're not sure what intelligence is, and it is easy to show that it is not "a general ability to solve problems". Humans are great at solving some problems and pretty terrible at others. Now wait a second. I would say humans are better at solving some problems directly and computers programmed by humans are better at solving other problems. However, a computer with a single, fixed program alone will choke completely…

I wonder if trying to emulate"human intelligence" is the way to go. What if we could develop a synthetic form of intelligence distinct from our ability to analyse and solve problems?

If I had to build an AI, I would like to "train it" by pitching it against other AIs. Imagine an open AI network where bots will learn by challenging each others. Does such a thing exist?

Re: The Limits of Machine Learning

#27

Post by AndrewOMartin »

Animats wrote: post #15

Right, not too helpful. Also, the machine shown in the picture isn't even a computer. It was a special-purpose machine used to read microfilms of mark-sense Census forms and write the results on tape. (I once had a summer job at Census HQ in Suitland MD, and saw the FOSDIC machine.) There are fundamental limits to hill-climbing. So far, nobody has something that just keeps running and continues to get better. Hill-cl…

Yes.

The critiques of AI from Hubert Dreyfus have stood the test of time, those who want to understand or challenge them directly can read What Computers Can't Do (1972, 1979), or even better the updated reprint What Computers Still Can't Do (1992). He's a Heideggarian Philosopher but all you need to know is that modern AI is ignorant of vast swathes of 20th Century investigation into the human mind, and state of being.

Hence, I think the big idea you're talking about is in AI that takes Hubert Dreyfus's critiques seriously.

Luckily for anyone reading about this for the first time, that process has already started. Dreyfus wrote a 2007 paper on the successes and failures of the first few steps of what he called Heideggarian AI with the snappy name of "Why Heideggerian AI failed and how fixing it would require making it more Heideggerian".

The "fixing" refers to work of a Neuroanatomist, with a suitable philosophical background, called Walter Freeman III, and is broadly described in the paper, but properly investigated in Freeman's (also eminently readable) book How Brains Make Up Their Minds (2000).

A note of caution, you'll be introduced to concepts that blur the line between body and environment, subject and object, intention and influence, and eventually things like relinquising your belief in causality and an objective "out there" universe (or at least any value in such a belief), all whilst staying perfectly scientific and evidence based.

Finally, bear in mind that if we do create an intelligence worthy of the name, we have reason to believe it will take about 18 years of "raising" by two adult humans, after which it will want to do its own thing, and not your dumb image classification tasks.

Re: The Limits of Machine Learning

#28

Post by pron »

djit wrote: post #26
joe_the_user wrote: post #20

Earlier quoted context omitted.

We're not sure what intelligence is, and it is easy to show that it is not "a general ability to solve problems". Humans are great at solving some problems and pretty terrible at others. Now wait a second. I would say humans are better at solving some problems directly and computers programmed by humans are better at solving other problems. However, a computer with a single, fixed program alone will choke completely…

I wonder if trying to emulate"human intelligence" is the way to go. What if we could develop a synthetic form of intelligence distinct from our ability to analyse and solve problems? If I had to build an AI, I would like to "train it" by pitching it against other AIs. Imagine an open AI network where bots will learn by challenging each others. Does such a thing exist?

> What if we could develop a synthetic form of intelligence distinct from our ability to analyse and solve problems?

Until we know what intelligence is, I'm not sure we could classify something as being distinct from human intelligence. If AI is any computational model that can learn (i.e., adapt its parameters as a response to inputs to solve various problems), then evolution would qualify as well (well, maybe it is).

But currently, "AI" is a marketing or science-fiction term, not anything we define even remotely rigorously. If we use the Turing test as a definition to AI (i.e., intelligence is the quality of an agent that is indistinguishable from a person through communication), we are not much closer today than we were forty years ago.

Science fiction can inspire science, but using sci-fi terms as if they were scientific terms is confusing, and I think we should similarly leave marketing terms to marketers (over the past decades, marketers have assigned the name AI to very different algorithms solving very different problems).

Re: The Limits of Machine Learning

#29

Post by WilliamDhalgren »

Animats wrote: post #15

Right, not too helpful. Also, the machine shown in the picture isn't even a computer. It was a special-purpose machine used to read microfilms of mark-sense Census forms and write the results on tape. (I once had a summer job at Census HQ in Suitland MD, and saw the FOSDIC machine.) There are fundamental limits to hill-climbing. So far, nobody has something that just keeps running and continues to get better. Hill-cl…

I thought the point of the article wasn't that a particular approach to learning has intrinsic limits, but that underdetermination of the pattern to be learned by the available data poses fundamental limits to what any learner could hope to discover.

The example motivating the discussion in such; even a perfect learnenr would hardly have any criteria to prefer one solution to another. Guess simplicity arguments could play in this example, if they weren't so vague, but even that doesn't happen in the general case.

That has to be true, for machines and humans alike. So ofc there's motivation to think about what comes after deep learning - but this isn't it, for that too, whatever it is, would be just as hopeless in examples such as there.

Re: The Limits of Machine Learning

#30

Post by ilaksh »

AndrewOMartin wrote: post #27
Animats wrote: post #15

Right, not too helpful. Also, the machine shown in the picture isn't even a computer. It was a special-purpose machine used to read microfilms of mark-sense Census forms and write the results on tape. (I once had a summer job at Census HQ in Suitland MD, and saw the FOSDIC machine.) There are fundamental limits to hill-climbing. So far, nobody has something that just keeps running and continues to get better. Hill-cl…

Yes. The critiques of AI from Hubert Dreyfus have stood the test of time, those who want to understand or challenge them directly can read What Computers Can't Do (1972, 1979), or even better the updated reprint What Computers Still Can't Do (1992). He's a Heideggarian Philosopher but all you need to know is that modern AI is ignorant of vast swathes of 20th Century investigation into the human mind, and state of bei…

I just read a review of "How Brains Make Up Their Minds. Thanks for mentioning that. It looks like a good book with mostly correct information (based on the review).

From the summary, I can tell you that researchers in fields such as AGI, deep learning, robotics, etc. have absolutely been working from many if not all of Freeman's assumptions for years and almost all (if not all) of that has been integrated into various research programs and systems. Freeman's pragmatist view is now the most popular.

Of course, all of Freeman's ideas aren't _usually_ together in every one of these systems or the conceptualization of them, but there are at least a few that have most of them.

Certainly AGI researchers are aware of the concept of higher-level abstractions being formed at root on the basis of sensory input and action output. And most of the recent serious AI research such as pretty much any NN for example demonstrates the idea of meaning from global patterns.

These are some interesting AGI videos in case people haven't seen them. https://www.youtube.com/channel/UCCwJ8AV1zMM4j9FTicGimqA/pla...

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