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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 menti

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".

kls 16y ago
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.
- Dn_Ab 16y ago
  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.
Dn_Ab 16y ago
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.

kls 16y ago

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.

Dn_Ab 16y ago
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.

Dn_Ab 16y ago

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.