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The Limits of Machine Learning

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Re: The Limits of Machine Learning

#41

Post by Animats »

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…

"The critiques of AI from Hubert Dreyfus have stood the test of time..."

This is the guy who in 1965 said "no computer can play even amateur chess". He was right. It took a lot longer than expected for computers to get good at chess. But they did. Now they're better, a lot better. Chess.com now says "any decent chess program could easily beat the world's top humans". No need for a supercomputer; Komodo costs $59.96 and running on a laptop will trounce any human.

Dreyfus is too much into "humans are special snowflakes", and he writes book-length arguments which assume you agree with that.

Re: The Limits of Machine Learning

#42

Post by xg15 »

Somewhat off-topic, this article mentions a property of samples that has puzzled me for a long time. Maybe someone can shed light on that?

[...] Field A would receive Fertilizer 1, Field B would receive Fertilizer 2, and so on.

But as Fisher pointed out, this type of experimentation was doomed to produce meaningless results. If the crops in Field A grew better than those in Field B, was that because Fertilizer 1 was better than Fertilizer 2? Or did Field A just happen to have richer soil?

[...] The way around the problem, Fisher concluded, was to apply different fertilizers to different small plots >at random

The article talks at length about how randomisation was the revolutionary new thing that Fisher introduced. Yet, in the given expample, it's the repetition of the experiment with different combinations of fields and fertilizers that does the trick, isn't it? It seems to me I should get the same results if I repeated the trial 50 times with Fertilizer 1 on field A and 50 times with 1 on field B.

So why is adding unpredictability (randomness) so important?

Re: The Limits of Machine Learning

#43

Post by Animats »

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…

If you're going to obsess over the nature/nuture thing, you have to consider the counterexample - animals that are born ready to go. These are called precocial species. Most of the large grazing mammals, including all the equines, are precocial.

You can see this by watching the first day of life of a horse. Within the first hour, the foal stands up by itself. This is a complex coordinated operation for an animal with such long legs. The sequence for doing this is dynamic (not statically stable and can't be done slowly), and clearly built-in, but requires tuning. The foal may fail the first few times, but eventually gets up on the long spindly legs and wobbles. The first few steps are tiny and cautious, but the nervous system calibrates rapidly. Stable walking is achieved quickly.

Trotting appears after a few more hours. Within a day or two, a newborn foal can run with the herd. At that point, all the locomotion functions are working - balance, coordination, visual foot placement, obstacle detection, and collision avoidance. That's a lot of capability. Having worked on both automatic driving and legged robot balance, I know how hard that is.

This demonstrates that mammal brains don't come up blank and learn. There's a lot of hard-wired capability.

(I sometimes comment on mobile robotics that the main thing is getting through the next 15 seconds of life without screwing up. If you can do that, you can then add task-oriented back-seat driving to get something done, and that's the easy part.)

Re: The Limits of Machine Learning

#44

Post by TheOtherHobbes »

pron wrote: post #21
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…

> 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…

Because it would be interesting to solve a wider class of problems. And having a deeper insight helps define which data to look at.

Having said that - I'm always bemused that AI is supposed to compete with a hypothetical single human brain, and that the Turing Test is supposed to prove we have AI when we can build a brain that appears human.

Learning is a social activity, not an individual one. Individual brains are useless without social training and access to culture.

And some humans are actually quite stupid.

A machine that passes the Turning Test for a hypothetical middle-of-the-bell curve human isn't so interesting a thing.

Re: The Limits of Machine Learning

#45

Post by xg15 »

xg15 wrote: post #42

Somewhat off-topic, this article mentions a property of samples that has puzzled me for a long time. Maybe someone can shed light on that? [...] Field A would receive Fertilizer 1, Field B would receive Fertilizer 2, and so on. But as Fisher pointed out, this type of experimentation was doomed to produce meaningless results. If the crops in Field A grew better than those in Field B, was that because Fertilizer 1 was…

... I didn't mean it to be that off topic. This got posted on the wrong thread, I'm sorry. If any mod could remove this, I'd be grateful.

Re: The Limits of Machine Learning

#46

Post by Dorisb85 »

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Re: The Limits of Machine Learning

#47

Post by eli_gottlieb »

mturmon wrote: post #8

Note to casual commenters: the precise real-world implications of the NFL theorems proved by Wolpert and collaborators have been difficult to appreciate, even to people well-versed in the computational learning world. Starting point: http://www.santafe.edu/media/workingpapers/12-10-017.pdf where we read: "However, arguably, much of that research has missed the most important implications of the theorems."

Well, the real-world implication (at least, as taught to me when I took my ML class) is that you have to make some assumption about P(f). If you assume that "reality" has to "spend energy" on building complicated f's ("reality functions" when we learned it), then you get usual probabilistic assumptions about function learning and everything goes back to normal (as we usually experience it in the real world). If you make other sorts of assumptions, you get many popular and useful ML algorithms.

It's all a matter of finding prior assumptions specific enough to bypass NFL, while still general enough to encompass useful real-world tasks.

Re: The Limits of Machine Learning

#48

Post by Chris2048 »

visarga wrote: post #36
tree_of_item wrote: post #13

Earlier quoted context omitted.

Christopher Columbus didn't discover "an entire new world", he was just one of the first Europeans to land on a continent that had already been there, with plenty of people, for thousands of years.

Well, it was a discovery, but just for Europe. American natives discovered Europe as well.

Unless American natives knew of Europe, it was a trivial discovery. When one continent discovers another, the relevance is the new link between them, and interactions between the two economies.

If we discover aliens on a new planet, the fact that the aliens knew about themselves before that doesn't change much - first contact may be when they discover us too.

But let's not pretend this isn't slightly about political correctness, and sensitivity over colonialism, which ruins the objectivity over the subject.

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