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IBM's "Watson" finally ready for prime-time Jeopardy

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Re: IBM's "Watson" finally ready for prime-time Jeopardy

#141

Post by coffeemug »

kls wrote: post #25
JoeAltmaier wrote: post #12

Earlier quoted context omitted.

I deny that the human brain IS algorithms. It is neurons connected by dendrons/synapses. Right? Small bits of it can be algorithmically simulated. Large processes can be algorithmically simulated. But to call the algorithm "intelligence" is sympathetic magic. Algorithms work in a different way; they break in a different way; they are hard-coded so don't change. They are a simulation.

No disrespect intended but, I think you are lacking a fundamental understanding of machine learning, genetic algorithms, neural networks and AI in general. I was introduced to Watson when I took a sub-project from IBM related to Watson, I had no idea what it was untill taking the contract, at which point I was introduced to Watson and it is an impressive feat, it is with out a doubt the state of the art in NLP AI. Su…

I've only spend a relatively short amount of time studying the subjects you mentioned, so correct me if I'm wrong, but the meta-programming aspects you're mentioning here are either highly exaggerated, or significantly less impressive than you make them sound.

Machine learning is really computational statistics - it applies fairly standard and well understood techniques to fit a function to a noisy data set. Genetic algorithms and neural networks are really fancy words for optimization algorithms - they're merely a set of tools (not unlike hill-climbing) for searching a large space. The de-facto books on AI are PAIP and PPAI. I've read both, and example programs there, while very interesting, are not much different than a combination of reasonably clever techniques.

"Generating new code" is the same thing as generating a data structure and running a predefined interpreter over it. These systems do that, but in a much more restricted way than you imply. They certainly don't design new algorithms in an intelligent fashion, merely use a set of predefined inference rules, not unlike any other rewriting system.

I don't know anything about Watson, but it is a well understood fact that every AI system to date is nothing more than a clever marionette (and it's very unlikely that this will change for a very long time). You can't just throw terms around - show an example. In every case so far a result that initially appears impressive, when understood, is immediately disappointing. They're all clever, but they're a far cry from "self-learning systems" for any reasonable definition of the word "learning".

Re: IBM's "Watson" finally ready for prime-time Jeopardy

#142

Post by PakG1 »

azakai wrote: post #122
baddox wrote: post #102

Earlier quoted context omitted.

What led you to believe that I don't understand what's going on? I definitely understand and appreciate IBM's accomplishment. I'm not minimizing the coolness of Watson being able to come up with so many correct responses, I'm minimizing the importance of its actual Jeopardy! gameplay. I'm not sure you understand the real game mechanics of Jeopardy! With highly proficient contestants, it comes down to being able to bu…

I think you are exactly right. Other commenters seem to be misunderstanding you. It's clear that this is a huge achievement. However, it is different than a computer beating the human chess champion. All this will prove is that a computer is about the same as the best humans, not strictly better than them. (And, that it is strictly better at timing its buzzer response, which is completely not impressive for a machine…

I'd be curious in knowing the mean response time of Watson. That would shed some light on the topic.

Re: IBM's "Watson" finally ready for prime-time Jeopardy

#143

Post by SeanDav »

baddox wrote: post #65
acangiano wrote: post #3

This is going to be, for lack of better words, epic. On a personal level however, it's a reminder of how truly wondrous the future is. When Deep Blue beat Kasparov I was a kid in an Italian high school, trying to explain to disinterested fellow students why that was a big deal. Not even in my wildest dreams I would have imagined to be working for the company that made that and this event possible, in a different coun…

Watson's defeat of top Jeopardy! players, while an impressive feat of natural language processing, is not as impressive as a defeat of a grand master, for one reason. The reason is that when multiple contestants know the correct response in Jeopardy!, it all comes down to reflexive timing. It's no surprise that a computer could buzz in faster than a human being, and there's no evidence that Watson knows more correct…

Human reflexes, when ready for a signal can respond easily with 0.2 of a second and often much faster, this is the sum total of the advantage that Watson has. This is irrelevant, what matters is that Watson has probably reached the stage where it can compete with the best of humans in a game involving abstract reasoning and natural language processing and do it fast enough where people can start arguing about fractions of a second!

Re: IBM's "Watson" finally ready for prime-time Jeopardy

#145

Post by kls »

coffeemug wrote: post #141
kls wrote: post #25

Earlier quoted context omitted.

No disrespect intended but, I think you are lacking a fundamental understanding of machine learning, genetic algorithms, neural networks and AI in general. I was introduced to Watson when I took a sub-project from IBM related to Watson, I had no idea what it was untill taking the contract, at which point I was introduced to Watson and it is an impressive feat, it is with out a doubt the state of the art in NLP AI. Su…

I've only spend a relatively short amount of time studying the subjects you mentioned, so correct me if I'm wrong, but the meta-programming aspects you're mentioning here are either highly exaggerated, or significantly less impressive than you make them sound. Machine learning is really computational statistics - it applies fairly standard and well understood techniques to fit a function to a noisy data set. Genetic…

but the meta-programming aspects you're mentioning here are either highly exaggerated, or significantly less impressive than you make them sound.

I don't think that I have exaggerated, the fact that currently AI systems are built by developers with a predetermined set of rules in not in dispute, those rules constrain what an AI system will generate this is analogies to the function of serotonin in the brain, it's level directly affects a factor of our state (happiness, empathy). Machine learning employs the same factor, basic here is serotonin (data) and here is the serotonin regulation mechanism (algorithm) but the machine learns (abuse drugs) to defeat the regulation mechanism, none the less it has to work within the constraints of the system, just as our minds have to work with the constrains of their biological functions. It works with in the constraints of the system to find ways to adapt the system. Whether this is impressive or not is subjective to the observer (I personally think it is). When contrasted to a biological level, it is pretty primitive reward logic is pretty low level when it comes to biology. None the less I think it is impressive.

I think when talking about AI a lot of people confuse consciousness with intelligence. While evidence suggest that intelligence is a prerequisite of consciousness the converse cannot be said. I think we have made great strides simulating the constructs of intelligence on a mechanical level. As for consciousness, we have to master the former before we will know how to tackle the latter. And when most people think about AI they think about the latter which sets a pretty high bar when measuring the state of the art.

Re: IBM's "Watson" finally ready for prime-time Jeopardy

#146

Post by Dn_Ab »

coffeemug wrote: post #141
kls wrote: post #25

Earlier quoted context omitted.

No disrespect intended but, I think you are lacking a fundamental understanding of machine learning, genetic algorithms, neural networks and AI in general. I was introduced to Watson when I took a sub-project from IBM related to Watson, I had no idea what it was untill taking the contract, at which point I was introduced to Watson and it is an impressive feat, it is with out a doubt the state of the art in NLP AI. Su…

I've only spend a relatively short amount of time studying the subjects you mentioned, so correct me if I'm wrong, but the meta-programming aspects you're mentioning here are either highly exaggerated, or significantly less impressive than you make them sound. Machine learning is really computational statistics - it applies fairly standard and well understood techniques to fit a function to a noisy data set. Genetic…

You make a large mistake here. I feel that the only way your argument holds is by assuming its conclusion. Which would be: what is going on in [some/most] of our brain is something more than or very different from 'just' statistical inference and optimization. Right now, we don't know whether this is true or not but more people are beginning to think not.

For example let us consider that based on time of day and knock you can guess who is most likely at your door. How why, can you know this? Have you learned or figured out anything there?

Or say, I tell you that a person is of gender X, race Y and lives in city Z. You will automatically generate an idea of what this person is like. And it will be different from what I would generate and these data points would likely mean nothing of significance to a 3 year old. Why? Because we have learned a model from our past experience/data. Machine Learning also uses generative models to infer situations. And in fact, we humans perform a very weak form of machine learning. It goes by the name of stereotyping or profiling.

When you are trying to figure out something. The process is not some clean logical step by step deduction. It is more like a search with dead ends (local optimums), back tracking and restarts. Trying and throwing away different ideas. Or when trying to learn a new sport, dance or flip. You do not consider the physics of the situations to try and figure amount the correct amount of impulses to apply. You try again and again to learn or statistically generate a satisfactory approximate local optimum of the correct physics model for the situation at hand.

As for systems which generate code. We can look at it most literally in terms of those which evolve rules in some way or loosely by considering that all machine learning does is use lots of data to prevent the programmer from hand coding a giant restricted system. Regardless of your stance, these systems differ from mere rewriting in that they are not deterministic. They interact and respond to different situations in varying ways. The more sophisticated methods can develop new algorithms - a set of rules - that were not programmed and make no sense to the developer to develop behaviours to cope with their situations. It is true that we provide a base, but that does not mean some limited form of learning is not occurring. What Machine learning cannot do that we can is introspect, abstract and generalize across domains.

I am the reverse of you. Before I picked up machine learning I thought the brain was something special. But now I cant help but feel that we are just clever marionettes and that whats going on is simply mundane mathematics by clever co-opting of physics by nature. I find this fact to be amazingly beautiful.

Re: IBM's "Watson" finally ready for prime-time Jeopardy

#147

Post by Dn_Ab »

kls wrote: post #145
coffeemug wrote: post #141

Earlier quoted context omitted.

I've only spend a relatively short amount of time studying the subjects you mentioned, so correct me if I'm wrong, but the meta-programming aspects you're mentioning here are either highly exaggerated, or significantly less impressive than you make them sound. Machine learning is really computational statistics - it applies fairly standard and well understood techniques to fit a function to a noisy data set. Genetic…

but the meta-programming aspects you're mentioning here are either highly exaggerated, or significantly less impressive than you make them sound. I don't think that I have exaggerated, the fact that currently AI systems are built by developers with a predetermined set of rules in not in dispute, those rules constrain what an AI system will generate this is analogies to the function of serotonin in the brain, it's lev…

I agree. Consciousness is not required for intelligence. Before I read your post, I was going to write to mention as well that people's definition is not only extremely wide ranging - running the gamut from memorizing digits of pi or spitting out trivia to self awareness and introspection - but it is also inconsistent. Changing scope and form based on whether the entity in question is autistic or a machine.

I muse that there is a race of AI type intelligences where genius is measure only in their ability to create moving works of arts and any fool can perform advanced mathematics and exceedingly complex computations involving a vast amount of variables.

Re: IBM's "Watson" finally ready for prime-time Jeopardy

#148

Post by jhpriestley »

nl wrote: post #130
jhpriestley wrote: post #129

Earlier quoted context omitted.

I'm saying that Watson's advantage in trivia-answering comes mainly from aptitudes which are already well-known to favor the computer. Watson's human-level performance at Jeopardy comes from the combination of highly superhuman data retrieval with highly subhuman language processing. Watson is more a spectacle than an innovation.

And here we see the AI paradox.. As soon as AI succeeds at something, it is simple.

Are you an AI? Your response is completely generic, taking into account none of the specifics of my argument.
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