[agi] Re: Solomonoff Machines – up close and personal
Hi Ed, So is the real significance of the universal prior, not its probability value given in a given probability space (which seems relatively unimportant, provided is not one or close to zero), but rather the fact that it can model almost any kind of probability space? It just takes a binary string as input. If you can express your problem as one in which a binary string represents what has been observed so far, and a continuation of this string represents what happens next, then Solomonoff induction can deal with it. So you don't have to pick the space. You do however have to take your problem and represent it as binary data and feed it in, just as you do when you put any kind of data into a computer. The power of the universal prior comes from the fact that it takes all computable distributions into account. In a sense it contains all well defined hypotheses about what the structure in the string could be. This is a point that is worth contemplating for awhile. If there is any structure in there and this structure can be described by a program on a computer, even a probabilistic one, then it's already factored into the universal prior and the Solomonoff predictor is already taking it into account. How does the Kolmogorov complexity help deal with this problem? The key thing that Kolmogorov complexity provides is that it assigns a weighting to each hypothesis in the universal prior that is inversely proportional to the complexity of the hypothesis. This means that the Solomonoff predictor respects, in some sense, the principle of Occam's razor. That is, a priori, simpler things are considered more likely than complex ones. ED## ??Shane??, what are the major ways programs are used in a Solomonoff machine? Are they used for generating and matching patterns? Are they used for generating and creating context specific instantiations of behavioral patterns? Keep in mind that Solomonoff induction is not computable. It is not an algorithm. The role that programs play is that they are used to construct the universal prior. Once this is done, the Solomonoff predictor just takes the prior and conditions on the observed string so far to work out the distribution over the next bit. That's all. Lukasz## The programs are generally required to exactly match in AIXI (but not in AIXItl I think). ED## ??Shane??, could you please give us an assist on this one? Is exact matching required? And if so, is this something that could be loosened in a real machine? Exact pattern matching is required in the sense that if a hypothesis says that something cannot happen, and it does, then that hypothesis is effectively discarded. A real machine might have to loosen this, and many other things. Note that nobody I know is trying to build a real AGI machine based on Solomonoff's model. Isn't there a large similarity between a Solomonoff machine that could learn a hierarchy of pattern representing programs and Jeff Hawking's hierarchical learning (as represented in the Serre paper). One could consider the patterns at each level of the higherarchy as sub-routines. The system is designed to increase its representational efficiency by having representational subroutines available for use by multiple different patterns at higher compositional levels. To the extent that a MOSES-type evolutionary system could be set to work making such representations more compact, it would become clear how semi-Solomonoff machines could be made to work in the practical world. In think the point is that if you can do really really good general sequence prediction (via something impractical like Solomonoff induction, or practical like the cortex) then you're a long way towards being able to build a pretty impressive AGI. Some of Hutter's students are interested in the latter. The def of Solomonoff induction on the web and even in Shane Legg's paper Solomonoff induction make it sound like it is merely Bayesian induction, using the picking of priors based on Kolmogorov complexity. Yes, that's all it is. But statements made by Shane and Lukasz appears to imply that a Solomonoff machine uses programming and programming size as a tool for pattern representation, generalization, learning, inference, and more. All these programs are weighted into that universal prior. So I think (but I could well be wrong) I know what that means. Unfortunately I am a little fuzzy about whether NCD would take what information, what-with-what or binding information, or frequency information sufficiently into account to be an optimal measure of similarity. Is this correct? NCD is just a computable approximation. The universal similarity metric (in the Li and Vitanyi book that I cited) gives the pure incomputable version. The pure version basically takes all effective similarity metrics into account when working out how similar two things are. So if you have some concept of similarity that you're
[agi] Re: How valuable is Solmononoff Induction for real world AGI?
Hello Edward, I'm glad you found some of the writing and links interesting. Let me try to answer some of your questions. I understand the basic idea that if you are seeking a prior likelihood for the occurrence of an event and you have no data about the frequency of its occurrence -- absent any other knowledge -- some notion of the complexity, in information-theory terms, of the event might help you make a better guess. This makes sense because reality is a big computer, and complexity -- in terms of the number of combined events required to make reality cough up a given event , and the complexity of the space in which those events are to be combined -- should to some extent be related to the event's probability. I can understand how such complexity could be approximated by the length of code required in some a theoretical Universal computer to model such real world event-occurrence complexity. This seems like a reasonable summary to me. Ok, let's take your example as I think it captures the essence of what you are getting at: So what I am saying is, for example, that if you are receiving a sequence of bytes from a video camera, much of the complexity in the input stream might not be related to complexity-of-event-creation- or Occam's-razor-type issues, but rather to complexity of perception, or similarity understanding, or of appropriate context selection, factors which are not themselves necessarily related to complexity of occurrence. In short, yes. So, for example, if you hooked up a Solomonoff induction machine to a video camera it would first need to, in some sense, understand the nature of this input stream. This may be more complex than what the camera is actually looking at! Given that the Solomonoff machine starts from zero knowledge of the world, other than a special form of prior knowledge provided by the universal prior, there is no way around this problem. Somehow it has to learn this stuff. The good news, as Solomonoff proved, is that if the encoding of the video input stream isn't too crazy complex ( i.e. there exists a good algorithm to process the stream that isn't too long), then the Solomonoff machine will very quickly work out how to understand the video stream. Furthermore, if we put such a camera on a robot or something wandering around in the world, then it would not take long at all before the complexity of the observed world far surpassed the complexity of the video stream encoding. Perhaps what you should consider is a Solomonoff machine that has been pre-trained to do, say, vision. That is, you get the machine and train it up on some simple vision input so that it understands the nature of this input. Only then do you look at how well it performs at finding structure in the world though its visual input stream. Furthermore, I am saying that for an AGI it seems to me it would make much more sense to attempt to derive priors from notions of similarity, of probabilities of similar things, events, and contexts, and from things like causal models for similar or generalized classes. There is usually much from reality that we do know that we can, and do, use when learning about things we don't know. Yes. In essence what you seem to be saying is that our prior knowledge of the world strongly biases how we interpret new information. So, to use your example, we all know that people living on a small isolated island are probably genetically quite similar to each other. Thus, if we see that one has brown skin, we will guess that the others probably do also. However, weight is not so closely tied to genetics, and so if one is obese then this does not tell us much about how much other islanders weigh. Out-of-the-box a Solomonoff machine doesn't know anything about genetics and weight, so it can't make such inferences based on seeing just one islander. However, if it did have prior experience with genetics etc., then it too would generalise as you describe using context. Perhaps the best place to understand the theory of this is section 8.3 from An Introduction to Kolmogorov complexity and its Applications by Li and Vitanyi. You can also find some approximations to this theory that have been applied in practice to many diverse problems under the title of Normalized Compression Distance or NCD. A lot of this work has been done by Rudi Cilibrasi. HOW VALUABLE IS SOLMONONOFF INDUCTION FOR REAL WORLD AGI? Well ;-) In a certain literal sense, not much, as it is not computable. However, many practical methods in machine learning and statistics can be viewed as computable approximations of Solomonoff induction, and things like NCD have been used in practice with some success. And who knows, perhaps some very smart person will come up with a new version of Solomonoff induction that is much more practically useful. Personally, I suspect other approaches will reach human level AGI first. If you are interested in this topic, I'm currently
Re: [agi] How can you prove form/behaviour are disordered?
On 6/8/07, Matt Mahoney [EMAIL PROTECTED] wrote: The author has received reliable information, from a Source who wishes to remain anonymous, that the decimal expansion of Omega begins Omega = 0.998020554253273471801908... For which choice of universal Turing machine? It's actually 0.00787499699781238... At least when based on the Turing machine described here: http://www.emis.de/journals/EM/expmath/volumes/11/11.3/Calude361_370.pdf Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=e9e40a7e
Re: [agi] NARS: definition of intelligence
Pei, Yes, the book is the best source for most of the topics. Sorry for the absurd price, which I have no way to influence. It's $190. Somebody is making a lot of money on each copy and I'm sure it's not you. To get a 400 page hard cover published at lulu.com is more like $25. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=e9e40a7e
Re: [agi] Intelligence vs Efficient Intelligence
Matt, Shane Legg's definition of universal intelligence requires (I believe) complexity but not adaptability. In a universal intelligence test the agent never knows what the environment it is facing is. It can only try to learn from experience and adapt in order to perform well. This means that a system which is not adaptive will have a very low universal intelligence. Even within a single environment, some environments will change over time and thus the agent must adapt in order to keep performing well. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] definitions of intelligence, again?!
Eliezer, As the system is now solving the optimization problem in a much simpler way (brute force search), according to your perspective it has actually become less intelligent? It has become more powerful and less intelligent, in the same way that natural selection is very powerful and extremely stupid. I think the best way to resolve this is to be more specific about what we are calling powerful or stupid. At a micro level an individual act of selection, reproduction etc. that evolution is built upon is not powerful and is extremely stupid. At a macro level when we consider an entire environment that performs trillions of trillions of acts of selection, reproduction etc. over billions of years, that system as a whole is very powerful, intelligent and creative. The same can be said of the brain. At a micro level an individual act of Hebbian learning etc. on a synapse is not very powerful and quite stupid. However, at a macro level when you consider trillions of trillions of these acts in a system that has been trained over a couple of decades, the result is the brain of an adult which is indeed powerful and intelligent. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] definitions of intelligence, again?!
Pei, This just shows the complexity of the usual meaning of the word intelligence --- many people do associate with the ability of solving hard problems, but at the same time, many people (often the same people!) don't think a brute-force solution show any intelligence. I think this comes from the idea people have that things like intelligence and creativity must derive from some very clever process. A relatively dumb process implemented on a mind blowingly vast scale intuitively doesn't seem like it could be sufficient. I think the intelligent design movement gets its strength from this intuition. People think, How could something as complex and amazing as the human body and brain come out of not much more than random coin flips?!?!? They figure that the algorithm of evolution is just too simple and therefore dumb to do something as amazing as coming up with the human brain. Only something with super human intelligence could achieve such a thing. The solution I'm proposing is that we consider that relatively simple rules when implemented on sufficiently vast scales can be very intelligent. From this perspective, humans are indeed the product of intelligence, but the intelligence isn't God's, its a 4 billion year global scale evolutionary process. When intelligence is used on human, there is no problem, since few hard problem can be solved by the human mind by brute-force. Maybe humans are a kind of brute-force algorithm? Perhaps the important information processing that takes place in neurons etc. is not all that complex, the amazing power of the system largely comes from its gigantic scale? At this point, you see capability as more essential, while I see adaptivity as more essential. Yes, I take capability as primary. However, adaptivity is implied by the fact that being adaptable makes a system more capable. today, conventional computers solve many problems better than the human mind, but I don't take that as reason for them to be more intelligent. The reason for that, I believe, is because the set of problems that they can solve is far too narrow. If they were able to solve a very wide range of problems, through brute force or otherwise, I would be happy to call them intelligent. I suspect that most people, when faced with a machine that could solve amazingly difficult problems, pass a Turing test, etc..., would refer to the machine as being intelligent. They wouldn't really care if internally it was brute forcing stuff by running some weird quantum XYZ system that was doing 10^10^100 calculations per second. They would simply see that the machine seemed to be much smarter than themselves and thus would say it was intelligent. for most people, that will happen only when my system is producing results that they consider as impressive, which will not happen soon. Speaking of which, you're been working on NARS for 15 years! As the theory of NARS is not all that complex (at least that was my impression after reading you PhD thesis and a few other papers), what's the hold up. Even working part time I would have thought that 15 years would have been enough to complete the system and demonstrate its performance. In Ben's case I understand that psynet/webmind/novamente have all be fairly different to each other and complex. So I understand why it takes so long. But NARS seems to be much simpler and the design seems more stable over time? It seems to me that what you are defining would be better termed intelligence efficiency rather than intelligence. What if I suggest to rename your notion universal problem solver? ;-) To tell the truth, I wouldn't really mind too much! After all, once a sufficiently powerful all purpose problem solver exists I'll simply ask it to work out what the best way to define intelligence is and then ask it to build a machine according to this definition. See, even if my definition is wrong, a solution to my definition would still succeed in solving the problem. :-) but I really don't see how you can put the current AGI projects, which are as diverse one can image, into the framework you are proposing. If you simply say that the one that don't fit in are uninteresting to you, the others can say the same to your framework, right? Sure, they might not want to build something that is able to achieve an extremely wide range of goals in an extremely wide range of environments. All I'm saying is that this is something that is very interesting to me, and that it also seems like a pretty good definition of intelligence. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] Intelligence vs Efficient Intelligence
Ben, According to this distinction, AIXI and evolution have high intelligence but low efficient intelligence. Yes, and in the case of AIXI it is presumably zero given that the resource consumption is infinite. Evolution on the other hand is just efficient enough that when implemented on a crazy enough scale the results can be pretty amazing. If this hypothesis is correct then AIXI and the like don't really tell us much about what matters, which is the achievement of efficient intelligence in relevant real-world contexts... That might well be true. I don't want to give the impression that I don't care about the efficiency of intelligence. On any given hardware the most intelligent system will be the one that runs the algorithm with the greatest intelligence efficiency. Thus, if I want to see very intelligent systems then I need to care about how efficient they are. Nevertheless, it is still the end product raw intelligence generated by the system that really excites me, rather than statistics on its internal efficiency. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] definitions of intelligence, again?!
On 5/17/07, Pei Wang [EMAIL PROTECTED] wrote: Sorry, it should be I assume you are not arguing that evolution is the only way to produce intelligence Definitely not. Though the results in my elegant sequence prediction paper show that at some point math is of no further use due to Goedel incompleteness. To go beyond that point things like evolution may be necessary. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] definitions of intelligence, again?!
Pei, However, in general I do think that, other things being equal, the system that uses less resources is more intelligent. Would the following be possible with your notion of intelligence: There is a computer system that does a reasonable job of solving some optimization problem. We go along and keep on plugging more and more RAM and CPUs into the computer. At some point the algorithm sees that it has enough resources to always solve the problem perfectly through brute force search and thus drops its more efficient but less accurate search strategy. As the system is now solving the optimization problem in a much simpler way (brute force search), according to your perspective it has actually become less intelligent? NARS can... - accept a number as input? - be instructed to try to maximise this input? - interact with its environment in order to try to do this? I assume NARS is able to do all of these things. Though NARS has the potential to work in the environment you specified, it is not designed to maximize a reward measurement given by the environment. Of course. If I want a general test, I can't assume that the systems to be tested were designed with my test in mind. Cheers Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] definitions of intelligence, again?!
Pei, No. To me that is not intelligence, though it works even better. This seems to me to be very divergent from the usual meaning of the word intelligence. It opens up the possibility that a super computer that is able to win a Nobel prize by running a somewhat efficient AI algorithm could be less intelligent than a basic pocket calculator that is solving optimization problems in a very efficient way. It seems to me that what you are defining would be better termed intelligence efficiency rather than intelligence. Let is also why I think the definition of intelligence in psychology cannot be directly accepted in AI. For human beings, problem-solving ability at a certain age can be used approximately to indicate the learning ability of a system (person), I don't see this. Unless they are designed to be highly culture neutral, such as a Raven test, IQ tests usually test for knowledge as well as problem solving. In this way they can estimate how well the individual was able to learn in the past. They don't need to have the test in mind, indeed, but how can you justify the authority and fairness of the testing results, if many systems are not built to achieve what you measure? I don't see that as a problem. By construction universal intelligence measures how well a system is able to act as an extremely general purpose problem solver (roughly stated). This is what I would like to have, and so universal intelligence is a good measure of what I am interested in achieving. I happen to believe that this is also a decent formalisation of the meaning of intelligence for machines. Some systems might be very good at what they have been designed to do, but what I want to know is how good are they as a general purpose problem solver? If I can't give them a problem, by defining a goal for them, and have them come up with a very clever solution to my problem, they aren't what I'm interested in with my AI work. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] definitions of intelligence, again?!
Pei, necessary to spend some time on this issue, since the definition of intelligence one accepts directly determines one's research goal and criteria in evaluating other people's work. Nobody can do or even talk about AI or AGI without an idea about what it means. This is exactly why I am interested in the definition of intelligence. I think the topic deserves far more time and thought in AI than it currently gets. Based on the above general consideration, I define intelligence as the ability to adapt and work with insufficient knowledge and resources According to my definition, a thermostat is not intelligence, and nor is an algorithm that provide optimum solutions by going through all possibilities and pick the best. If an optimisation algorithm searches some of a solution space (because of lack of computer power to search all of it) and then returns a solution, does this system have some intelligence according to your definition? Most optimisation algorithms have simple adaption (let's say that it's a genetic algorithm to make things concrete), and the system has insufficient knowledge and resources to directly search the whole space. To criticize his assumption as too far away from reality is a different matter, which is also why I don't agree with Hutter and Legg. Formal systems can be built on different assumptions, some of which are closer to reality than some others. Both AIXI and universal intelligence are too far away from reality to be directly implemented. I think we all agree on that. In their current form their main use is for theoretical study. In the case of AIXI, it seems to me that it would be difficult to build something that approximates these ideas in a way that produced a real working AI system. Or maybe I'm just not smart enough to see how it would be done. In the case of universal intelligence I think there is some hope due to the fact that the C-Test is based on quite similar ideas and this has been used to construct an intelligence test with sensible results. Sometime after my thesis I'm going to code up an intelligence test based on universal intelligence and see how well various AI algorithms perform. Cheers Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] definitions of intelligence, again?!
Mark, Gödel's theorem does not say that something is not true, but rather that it cannot be proven to be true even though it is true. Thus I think that the analogue of Gödel's theorem here would be something more like: For any formal definition of intelligence there will exist a form of intelligence that cannot be proven to be intelligent even though it is intelligent. Cheers Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] definitions of intelligence, again?!
Pei, Fully agree. The situation in mainstream AI is even worse on this topic, compared to the new AGI community. Will you write something for AGI-08 on this? Marcus suggested that I submit something to AGI-08. However I'm not sure what I could submit at the moment. I'll have a think about this after I've finished writing my thesis in a couple of months. if it searches different parts of the space in a context and experience sensitive manner, it is intelligent; if it doesn't only search among listed alternatives, but also find out new alternatives, it is much more intelligent. Hmmm. Ok, imagine that you have two optimization algorithms X and Y and they both solve some problem equally well. The difference is that Y uses twice as many resources as X to do it. As I understand your notion of intelligence, X would be considered more intelligent than Y. True? Essentially then, according to you intelligence depends on how well a system can perform per unit of resources consumed? beside input/output of the system, you assume the rewards to be maximized come from the environment in a numerical form, which is an assumption not widely accepted outside the reinforcement learning community. For example, NARS may interpret certain input as reward, and certain other input as punishment, but it depends on many factors in the system, and is not objective at all. For this kind of systems (I'm sure NARS isn't the only one), how can your evaluation framework be applied? NARS can... - accept a number as input? - be instructed to try to maximise this input? - interact with its environment in order to try to do this? I assume NARS is able to do all of these things. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] Determinism
On 5/14/07, David Clark [EMAIL PROTECTED] wrote: Even though I have a Math minor from University, I have used next to no Mathematics in my 30 year programming/design career. Yes, but what do you program? I've been programming for 24 years and I use math all the time. Recently I've been working with Marcus Hutter on a new learning algorithm based on a rather nasty mathematical derivation. The results kick butt. Another tricky derivation that Hutter did a few years back is now producing good results in processing gene expression data for cancer research. I could list many more... Anyway, my point is, whether you need math in your programing or not all depends on what it is that you are trying to program. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] Tommy
Josh, Interesting work, and I like the nature of your approach. We have essentially a kind of a pin ball machine at IDSIA and some of the guys were going to work on watching this and trying to learn simple concepts from the observations. I don't work on it so I'm not sure what the current state of their work is. When you publish something on this please let the list know! thanks Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] rule-based NL system
On 5/2/07, Mark Waser [EMAIL PROTECTED] wrote: One of the things that I think is *absolutely wrong* about Legg's paper is that he only uses more history as an example of generalization. I think that predictive power is test for intelligence (just as he states) but that it *must* include things that the agent has never seen before. In this sense, I think that Legg's paper is off the mark to the extent of being nearly useless (since you can see how it's has poisoned poor Matt's approach). Mark, Why do you think that a Legg-Hutter style intelligence test would not expose an agent to things it hadn't seen before? To have a significant level of intelligence an agent must be able to deal with environments that are full of surprises and unknowns. Agents that can't do this would only be able to deal with the most basic environments, and thus would have a relatively low universal intelligence value. Cheers Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] mouse uploading
Numbers for humans vary rather a lot. Some types of cells have up to 200,000 connections (Purkinje neurons) while others have very few. Thus talking about the number of synapses per neuron doesn't make much sense. It all depends on which type of neuron etc. you mean. Anyway, when talking about a global brain average I most often see the number 1,000. For rat cortex (which is a bit different to mouse cortex in terms of thickness and density) I usually see the number 10,000 as the average (just for cortex, not the whole brain). Shane On 4/29/07, Matt Mahoney [EMAIL PROTECTED] wrote: Does anyone know if the number of synapses per neuron (8000) for mouse cortical cells also apply to humans? This is the first time I have seen an estimate of this number. I believe the researchers based their mouse simulation on anatomical studies. --- J. Storrs Hall, PhD. [EMAIL PROTECTED] wrote: In case anyone is interested, some folks at IBM Almaden have run a one-hemisphere mouse-brain simulation at the neuron level on a Blue Gene (in 0.1 real time): http://news.bbc.co.uk/2/hi/technology/6600965.stm http://ieet.org/index.php/IEET/more/cascio20070425/ http://www.modha.org/papers/rj10404.pdf which reads in gist: Neurobiologically realistic, large-scale cortical and sub-cortical simulations are bound to play a key role in computational neuroscience and its applications to cognitive computing. One hemisphere of the mouse cortex has roughly 8,000,000 neurons and 8,000 synapses per neuron. Modeling at this scale imposes tremendous constraints on computation, communication, and memory capacity of any computing platform. We have designed and implemented a massively parallel cortical simulator with (a) phenomenological spiking neuron models; (b) spike-timing dependent plasticity; and (c) axonal delays. We deployed the simulator on a 4096-processor BlueGene/L supercomputer with 256 MB per CPU. We were able to represent 8,000,000 neurons (80% excitatory) and 6,300 synapses per neuron in the 1 TB main memory of the system. Using a synthetic pattern of neuronal interconnections, at a 1 ms resolution and an average firing rate of 1 Hz, we were able to run 1s of model time in 10s of real time! Josh - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?; -- Matt Mahoney, [EMAIL PROTECTED] - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?; - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] Circular definitions of intelligence
Mike, But interestingly while you deny that the given conception of intelligence is rational and deterministic.. you then proceed to argue rationally and deterministically. Universal intelligence is not based on a definition of what rationality is. It is based on the idea of achievement. I believe that if you start to behave irrationally (by any reasonable definition of the word) then your ability to achieve goals will go down and thus so will your universal intelligence. that actually you DON'T usually know what you desire. You have conflicting desires and goals. [Just how much do you want sex right now? Can you produce a computable function for your desire?] Not quite. Universal intelligence does not require that you personally can define your, or some other system's, goal. It just requires that the goal is well defined in the sense that a clear definition could be written down, even if you don't know what that would look like. If you want intelligence to include undefinable goals in the above weaker sense then you have this problem: Machine C is not intelligent because it cannot do X, where X is something that cannot be defined. I guess that this isn't a road you want to take as I presume that you think that machine intelligence is possible. And you have to commit yourself at a given point, but that and your priorities can change the next minute. A changing goal is still a goal, and as such is already taken care of by the universal intelligence measure. And vis-a-vis universal intelligence, I'll go with Ben According to Ben Goertzel, Ph. D, Since universal intelligence is only definable up to an arbitrary constant, it's of at best ~heuristic~ value in thinking about the constructure of real AI systems. In reality, different universally intelligent modules may be practically applicable to different types of problems. [8] http://www.sl4.org/archive/0104/1137.html Ben's comment is about AIXI, so I'll change to that for a moment. I'm going to have to be a bit more technical here. I think the compiler constant issue with Kolmogorov complexity is in some cases important, and in others it is not. In the case of Solomonoff's continuous universal prior (see my Scholarpedia article on algorithmic probability theory for details) the measure converges to the true measure very quickly for any reasonable choice of reference machine. With different choices of reference machine the compiler constant may mean that the system doesn't converge for a few more bytes of input. This isn't an issue for an AGI system that will be processing huge amounts of data over time. The optimality of its behaviour in the first hundred bytes of its existence really doesn't matter. Even incomputable super AIs go through an infantile stage, albeit a very short one. You seem to want to pin AG intelligence down precisely, I want to be more pluralistic - and recognize that uncertainty and conflict are fundamental to its operation. Yes, I would like to pin intelligence down as precisely as possible. I think that if somebody could do this it would be a great step forward. I believe that issues of definition and measurement are the bedrock of good science. Cheers Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] Circular definitions of intelligence
Ben, Are you claiming that the choice of compiler constant is not pragmatically significant in the definition of the Solomonoff-Levin universal prior, and in Kolmogorov complexity? For finite binary sequences... I really don't see this, so it would be great if you could elaborate. In some cases it matters, in others it doesn't. Solomonoff's prediction error theorem shows that the total summed expected squared prediction error is bounded by a constant when the true generating distribution \mu is computable. The constant is (ln 2)/2 K(\mu) bits. The K term in this bound depends on the choice of reference machine. For a reasonable choice of reference machine you might be able to push the bound up by something like 1000 bits. If you are considering long running systems that will process large amounts of data, that 1000 extra bits is tiny. On the other hand, if you want to know if K(10) K(147) then your answer will depend on which reference machine you use. In short: Kolmogorov complexity works well for reasonably big objects, it doesn't work well for small objects. Probably the best solution is to condition the measure with information about the world. In which case K(10|lots of world data) K(147|lots of world data) should work the way you expect. Google complexity works this way. In the case of Solomonoff induction, you let the predictor watch the world for a while before you start trying to get it to solve prediction tasks. In a practical Novamente context, it seems to make a big difference. If we make different choices regarding the internal procedure-representation language Novamente uses, this will make a big difference in what internally-generated programs NM thinks are simpler ... which will make a big difference in which ones it retains versus forgets; and which ones it focuses its attention on and prioritizes for generating actions. I think that the universal nature we see in Kolmogorov complexity should also apply to practical AGI systems. By that I mean the following: By construction, things which have high Kolmogorov complexity are complex with respect to any reasonable representation system. In essence, the reference machine is your representation system. Once an AGI system has spent some time learning about the world I expect that it will also find that there are certain types of representation systems that work well for certain kinds of problems. For example, it might encounter a problem that seems complex, but then it realises that, say, if it views the problem as a certain type of algebra problem then it knows how to find a solution quite easily. I think that the hardest part to finding a solution to a difficult problem often lies in finding the right way to view the problem, in order words, the right representation. Cheers Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] Circular definitions of intelligence
Mike, 1) It seems to assume that intelligence is based on a rational, deterministic program - is that right? Adaptive intelligence, I would argue, definitely isn't. There isn't a rational, right way to approach the problems adaptive intelligence has to deal with. I'm not sure what you mean by this. The agent that we measure the intelligence of does not have to be deterministic, nor does the environment. Indeed, the agent doesn't even have to have a computable probability distribution, it could work by magic for all we care. 2) It assumes that intelligent agents maximise their rewards. Wrong. You don't except in extreme situations try to maximise your rewards when you invest on the stockmarket - or invest in any other action. In the real world, you have to decide how to invest your time, energy and resources in taking/solving problematic decisions/problems (like how to invest on the stockmarket). Those decisions carry rewards, risks and uncertainty. The higher the rewards, the higher the risks (nor just of failure but of all kinds of danger). The lower the rewards, the lower the risks (and the greater the security). Let's say that you want to invest money in a low risk way that still has some minimal level of return. In other words, you don't want to simply maximise your expected return, rather you want to maximise some balance of return and risk (or any other things you also want to take into account such as time and energy). Take all these factors and define a utility function over the possible outcomes. If you can't do this then you don't really know exactly what it is that you desire. Now simply consider the reward signal from the environment to the agent to be exactly the utility function that you just defined. In order to perform well in this setting the agent must work out how to balance return on investment against risks etc. Moreover, this type of environment still has a computable measure and thus is already contained in our intelligence test. 3) And finally, just to really screw up this search for intelligence definitions - any definition will be fundamentally ARBITRARY There will always be conflicting ideals of what intelligent problem solving involves.. There is no such thing as a provably true definition. However some definitions are clearer, more general and more consistent with the informal usage than others. So let me put the challenge to you: Can you name one well defined process to do with intelligent problem solving that universal intelligence doesn't already test for? Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] Circular definitions of intelligence
Kaj, (Disclaimer: I do not claim to know the sort of maths that Ben and Hutter and others have used in defining intelligence. I'm fully aware that I'm dabbling in areas that I have little education in, and might be making a complete fool of myself. Nonetheless...) I'm currently writing my PhD thesis at the moment in which, at Hutter's request, I am going to provide what should be an easy to understand explanation of AIXI and the universal intelligence measure. Hopefully this will help make the subject more understandable to people outside the area of complexity theory. I'll let this list know when this is out. The intelligence of a system is a function of the amount of different arbitrary goals (functions that the system maximizes as it changes over time) it can carry out and the degree by which it can succeed in those different goals (how much it manages to maximize the functions in question) in different environments as compared to other systems. This is essentially what Hutter and I do. We measure the performance of the system for a given environment (which includes the goal) and then sum them up. The only additional thing is that we weight them according to the complexity of each environment. We use Kolmogorov complexity, but you could replace this with another complexity measure to get a computable intelligence measure. See for example the work of Hernandez (which I reference in my papers on this). Once I've finished my thesis, one thing that I plan to do is to write a program to test the universal intelligence of agents. This would eliminate a thermostat from being an intelligent system, since a thermostat only carries out one goal. Not really, it just means that the thermostat has an intelligence of one on your scale. I see no problem with this. In my opinion the important thing is that an intelligence measure orders things correct. For example, a thermostat should be more intelligent than a system that does nothing. A small machine learning algorithm should be smarter still, a mouse smarter still, and so on... Humans would be classified as relatively intelligent, since they can be given a wide variety of goals to achieve. It also has the benefit of assigning narrow-AI systems a very low intelligence, which is what we want it to do. Agreed. If you want to read about the intelligence measure that I have developed with Hutter check out the following. A summary set of talk slides: http://www.vetta.org/documents/Benelearn-UniversalIntelligence-Talk.pdf Or for a longer paper: http://www.vetta.org/documents/ui_benelearn.pdf Unfortunately the full length journal paper (50 pages) is still in review so I'm not sure when that will come out. But my PhD thesis will contain this material and that should be ready in a few months time. Cheers Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415user_secret=fabd7936
Re: [agi] Growing a Brain in Switzerland
On 4/4/07, Eugen Leitl [EMAIL PROTECTED] wrote: how do you reconcile the fact that babies are very stupid compared to adults? Babies have no less genetic hardware than adults but the difference The wiring is not determined by the genome, it's only a facility envelope. Some wiring is genetic, and some is not. On a large scale genes regulate how one part of the neocortex is wired to another part (there are even little tiny crawler things that do the wiring up during the prenatal development of the brain that sound totally science fiction and very cool, though the system isn't exactly perfect as they kill quite a few neurons when they try to craw around the brain hooking all the wiring up). At a micro scale each of the different types of neurons have different dendritic tree structures (which is genetic), and lie in particular layers of cortex (which is also genetic), and various other things. In short, it's not really genetic or due to adaption, it's a complex mixture of genetics and adaption that produces the wiring in the adult brain. The models are not complex. The emulation part is a standard numerics package. Heh. Come to Switzerland and talk to the Blue Brain guys at EPFL... Their model is very complex and definitely not simply some standard numerics package. They are working in collaboration with something like 400 researchers all around the world and the project will be going for at least several decades. Simple it is not. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] Growing a Brain in Switzerland
On 4/5/07, Eugen Leitl [EMAIL PROTECTED] wrote: I forget the exact number, but I think something like 20% of the human genome describes the brain. If somebody is interested in building a No, it codes for the brain tissue. That's something very different from describing the brain. See I didn't mean to imply that all this was for wiring, just that there is a sizable about of information used to construct the brain that comes from the genes. If you want to model the brain then this is the kind of information that you are going to have to put into your model. Why does the optic tract project to the lateral geniculate nucleus, the pretectum and the superior colliculus and not other places in the brain? Why does the lateral genicultate body project to striate and not other parts of cortex? Why does the magnocellular pathway project to layer 4Calpha, while the parvocullular pathway projects to 4A and 4Cbeta? Why does the cerebral cortex project to the putamen and caudate nucleus, but not the subthalamic nucleus? I could list pages and pages of examples of brain wiring that you were born with and that came from your genetics, it's basic neuro science. I don't clam that all wiring in the brain is genetic, or even a sizable proportion of it. What I am claiming is that the brain wiring that is genetic is non-trivial and cannot be ignored if somebody wants to build a working brain simulation. You remember the thread: complexity in the code versus complexity in the data? The Blue Brain complexity is all in the data. This is very different from the classical AI, which tends to obsessionate about lots of clever algorithms, but typically does sweep the data (state) under the carpet. Yes, I agree, it's in the data rather than the code. But I don't accept that you can say that their model is simple. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] My proposal for an AGI agenda
On 3/23/07, David Clark [EMAIL PROTECTED] wrote: Both the code and algorythmn must be good for any computer system to work and neither is easy. The bond formula was published for many years but this particular company certainly didn't have a copy of it inside a program they could use. The formula was 1 line of at least 4K lines of code. The program wasn't so trivial either :) The reason AGI doesn't exist yet is not because there aren't enough skilled programmers in the world. (Or that these people are using the wrong operating system or programming language etc... to address the rest of the discussion on this thread!) The problem is that people aren't exactly clear about what it is that has to be programmed. Time and time again in the field people have thought that they knew what had to be done, and yet when they finally got around to coding it the results weren't what they had hoped for. Is the research on AI full of Math because there are many Math professors that publish in the field or is the problem really Math related? Many PhDs in computer science are Math oriented exactly because the professors that deem their work worth a PhD are either Mathematicians or their sponsoring professor was. I don't know of any math profs who publish in artificial intelligence, though no doubt there are a few that do. No, thinking about it now I can think of a few. Even if you look at the work of my PhD supervisor Marcus Hutter, he's not a math prof, he's actually a physics PhD. His work might look very mathematical to a computer scientist, but he doesn't actually use much beyond about 4th year university level mathematics and statistics in his book. Indeed he likes to joke about mathematicians and how they are overly formal and concerned about details like whether he has properly deal with certain special cases on sets of measure 0 :-) So yeah, the math that you see in the field is almost always coming from people who are mathematically inclined, but aren't math profs. I should also note that the number of pure theory people in AI is very small. For example, I went to the ALT conference last year to present some work and there were only 40 people. This is the second biggest AI theory conference in the world (after COLT). Other areas like genetic algorithms attract thousands. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] My proposal for an AGI agenda
On 3/23/07, David Clark [EMAIL PROTECTED] wrote: I have a Math minor from University but in 32 years of computer work, I haven't used more than grade 12 Math in any computer project yet. ... I created a bond comparison program for a major wealth investment firm that used a pretty fancy formula at it's core but I just typed it in. I didn't have to create it, prove it or even understand exactly why it was any good. IMO, creating an AGI isn't really a programming problem. The hard part is knowing exactly what to program. The same was probably true of your bond program: The really hard part was originally coming up with that 'fancy formula' which you just had to type in. Thus far math has proven very useful in many areas of artificial intelligence, just pick up any book on machine learning such as Bishop's. Whether it will also be of large use for AGI... only time will tell. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] My proposal for an AGI agenda
On 3/21/07, Chuck Esterbrook [EMAIL PROTECTED] wrote: Sometimes the slowness of a program is not contained in a small portion of a program. Sure. For us however this isn't the case. Cobra looks nice, very clean to read, even more so than Python. However the fact that it's in beta and .NET sort of kills it for us. As we will be going into high performance computing, we have no choice but to do the core work in plain C running on Linux. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] My proposal for an AGI agenda
Ben, I didn't know you were a Ruby fan... After working in C# with Peter I'd say that's is a pretty good choice. Sort of like Java but you can get closer to the metal where needed quite easily. For my project we are using Ruby and C. Almost all the code can be in high level Ruby which is very fast to code and modify, and then the few parts of the code that consume 99.9% of the CPU time get converted into C and carefully optimised. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] one-shot Turing test
On 3/9/07, J. Storrs Hall, PhD. [EMAIL PROTECTED] wrote: Perhaps the ultimate Turing Test would be to make the system itself act as the interviewer for a Turing Test of another system. It's called an inverted Turing test. See: Watt, S. (1996) Naive-Psychology and the Inverted Turing Test. Psycoloquy 7(14) Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] Numenta (Hawkins) software released
The second scary bit, which I didn't mention above, is made clear in the blog post from the company CEO, Donna Dubinsky: Why do we offer you a license without deployment rights? Well, although we are very excited about the ultimate applications of this technology, we feel it is too early to expect commercial class applications. We do not want to mislead you as to the state of the technology. Perhaps we're wrong! If you find yourself closer to a commercial application than we expect, let us know. We promise that we'll speed up our commercial licensing plans!! which is from http://www.numenta.com/for-developers/blog.php In other words: Go make cool stuff with our technology, however if you are about to make lots of money, please let us know and we will then calculate how much it is going to cost you for a licence. There is no way I'd ever agree to terms like that, especially given that they cover not just code, but also all business ideas etc. related to this technology. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] Numenta (Hawkins) software released
It might however be worth thinking about the licence: Confidentiality. 1. Protection of Confidential Information. You agree that all code, inventions, algorithms, business concepts, workflow, ideas, and all other business, technical and financial information, including but not limited to the HTM Algorithms, HTM Algorithms Source Code, and HTM Technology, that you obtain or learn from Numenta in connection with this Agreement are the confidential property of Numenta (Confidential Information). Except as authorized herein, you will hold in confidence and not use, except as permitted or required in the Agreement, or disclose any Confidential Information and you will similarly bind your employees in writing. You will not be obligated under this Section 6 with respect to information that you can document: (i) is or has become readily publicly available without restriction through no fault of you or your employees or agents; or (ii) is received without restriction from a third party lawfully in possession of such information and lawfully empowered to disclose such information; or (iii) was rightfully in your possession without restriction prior to its disclosure by Numenta; or (iv) was independently developed by your employees or consultants without access to such Confidential Information. Shane On 3/7/07, J. Storrs Hall, PhD. [EMAIL PROTECTED] wrote: Just noticed this on Slashdot. Open source but not free software, for those of you for whom this makes a difference. http://www.numenta.com/for-developers/software.php Josh - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303 - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] Has anyone read On Intelligence
I think On Intelligence is a good book. It made an impact on me when I first read it, and it lead to me reading a lot more neuro science since then. Indeed in hindsight is seems strange to me that I was so interested in AGI and yet I hadn't seriously studied what is known about how the brain works. Indeed almost nobody in AI does, even people working on artificial neural networks. Anyway, having read a fair amount of neuro science since then it has become clear to me that while Hawkins' book is a good understandable summary of a particular view of neuro science, the claims he makes are all either well known facts, or things which a fair number of neuro scientists already believe. So there isn't anything really new in there that I know of. The other thing is that he presents a greatly simplified view of how things really work. This makes the book readable for non-scientists, which is great, however nobody really knows how much of all those details he glosses over are unimportant implementation stuff, and how much of it is critical to understanding how the whole system behaves. Of course, nobody will really know this for sure until the brain is fully understood. If you're read On Intelligence and are interested in a basic undergraduate overview of neuroscience I'd recommend the classic text book Essentials of Neural Science and Behavior by Kandel, Schwartz and Jessell. Once you've read that much of the scientific literature in the field is understandable. Shane On 2/21/07, Aki Iskandar [EMAIL PROTECTED] wrote: I'd be interested in getting some feedback on the book On Intelligence (author: Jeff Hawkins). It is very well written - geared for the general masses of course - so it's not written like a research paper, although it has the feel of a thesis. The basic premise of the book, if I can even attempt to summarize it in two statements (I wouldn't be doing it justice though) is: 1 - Intelligence is the ability to make predictions on memory. 2 - Artificial Intelligence will not be achieved by todays computer chips and smart software. What is needed is a new type of computer - one that is physically wired differently. I like the first statement. It's very concise, while capturing a great deal of meaning, and I can relate to it ... it jives. However, (and although Hawkins backs up the statements fairly convincingly) I don't like the second set of statements. As a software architect (previously at Microsoft, and currently at Charles Schwab where I am writing a custom business engine, and workflow system) it scares me. It scares me because, although I have no formal training in AI / Cognitive Science, I love the AI field, and am hoping that the AI puzzle is solvable by software. So - really, I'm looking for some of your gut feelings as to whether there is validity in what Hawkins is saying (I'm sure there is because there are probably many ways to solve these type of challenges), but also as to whether the solution(s) its going to be more hardware - or software. Thanks, ~Aki P.S. I remember a video I saw, where Dr. Sam Adams from IBM stated Hardware is not the issue. We have all the hardware we need. This makes sense. Processing power is incredible. But after reading Hawkins' book, is it the right kind of hardware to begin with? - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303 - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] Has anyone read On Intelligence
Sorry, the new version of the book I mentioned (I read the old one) is called Principles of Neural Science. With regards to computer power, I think it is very important. The average person doing research in AI (i.e. a PhD grad student) doesn't have access to much more than a PC or perhaps a small cluster of PCs. So it's all very well that IBM can build super computers with vast amounts of power, but most of us don't get access to such machines --- we're many orders of magnitude behind this. The other thing is that what is necessary for some algorithm to solve a problem is very different to what was needed to develop the algorithm in the first place. To develop a machine learning algorithm you might want to test it on 10 different data sets, with various different parameter settings, and a few different versions of the algorithm, and then run it many times in each of these configurations order to get accurate performance statistics. Then you look at the results and come up with some new ideas and repeat. Thus, even if algorithm Y is a decent AGI when run on hardware X, you probably want 100X computer power in order to develop algorithm Y. Shane - This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
Re: [agi] Why so few AGI projects?
Eliezer,Shane, what would you do if you had your headway?Say, you won the lottery tomorrow (ignoring the fact that no rational person would buy aticket).Not just AGI - what specifically would you sit down and doall day?I've got a list of things I'd like to be working on. For example, I'd like to try to build a universal test of machine intelligence, I've also got ideas inthe area of genetic algorithms, neural network architectures, and somemore theoretical things related to complexity theory and AI. I also want to spend more time learning neuroscience. I think my best shot at buildingan AGI will involve bringing ideas from many of these areas together. Indeed not.It takes your first five years simply to figure out whichway is up.But Shane, if you restrict yourself to results you canregularly publish, you couldn't work on what you really wanted to do,even if you had a million dollars. If I had a million dollars I wouldn't care so much about my careeras I wouldn't be dependent on the academic system to pay my bills.As such I'd only publish once, or perhaps twice, a year and would spend more time on areas of research that were more likely to failor would require large time investments before seeing results.Shane This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Why so few AGI projects?
This is a question that I've thought about from time to time. The conclusionI've come to is that there isn't really one or two reasons, there are many.Surprisingly, most people in academic AI aren't really all that into AI. It's a job. It's more interesting than doing database programming ina bank, but at the end of the day it's just a job. They're not out tochange the world or do anything amazing, it's hard enough just trying to get a paper into conference X or Y. It's true that they are skepticalabout whether AI will make large progress towards human levelintelligence in their life times, however I think the more important pointis that they simply don't even think about this question. They're just not interested. I'd say that this is about 19 out of every 20 people in academicAI. Of course there are thousands of people working in academic AIaround the world, so 1 out of 19 is still a sizable number of people in total. Funding is certainly a problem. I'd like to work on my own AGI ideasafter my PhD is over next year... but can I get money to do that? Probablynot. So as a compromise I'll have to work on something else in AI during the day, and spend my weekends doing the stuff I'd really like to be doing.Currently I code my AI at nights and weekends.Pressure to publish is also a problem. I need results on a regular basisthat I can publish otherwise my career is over. AGI is not really short term results friendly.Another thing is visibility. Of the academic people I know who are tryingto build a general artificial intelligence (although probably not saying quitethat in their papers), I would be surprised if any of them were known to anybody on this list. These a non-famous young researchers, and becausethey can't publish papers saying that they want build a thinking machine,you'd only know this if you were to meet them in person. One thing that people who are not involved in academic AI often don'tappreciate is just how fractured the field is. I've seen plenty of exampleswhere there are two sub-fields that are doing almost the same thing but which are using different words for things, go to different conferences,and cite different sets of people. I bring this up because I sometimesget the feeling that some people think that academic AI is some sort of definable group. In reality, most academics lack of knowledge aboutAGI is no different to their lack of knowledge of many other areas of AI.In other words, they aren't ignoring AGI any more than they are ignoring twenty other areas in the field.Shane This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Definitions of intelligence
Pei, I'll correct your definition and add the new ones youcite on Monday.ThanksShaneOn 9/1/06, Pei Wang [EMAIL PROTECTED] wrote:Shane,Thanks for the great job! It will be a useful resource for all of us. In my definition, I didn't use the word agent, but system.You may also want to consider the 8 definitions listed in AIMA(http://aima.cs.berkeley.edu/ ), page 2.PeiOn 9/1/06, Shane Legg [EMAIL PROTECTED] wrote: As part of some research I've been doing with Prof. Hutter on AIXI and formal definitions of machine intelligence, I've been collecting definitions of intelligence that have been proposed by psychologists and AI researchers.I now have about 60 definitions.At least to the best of my knowledge, this makes it the largest and most fully referenced collection that there is. If you're interested I've put it online:http://www.idsia.ch/~shane/intelligence.html Of course to really understand the definitions you will need to follow the references back to the sources.Nevertheless, this collection provides a compact overview. Corrects etc. are of course welcomed, as are new definitions, provided that they have been published somewhere so that I can properly cite them.Also the individual must be of a certain significance the area, e.g. be a psychologist, or an academic AI researcher, run an AI company etc. Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]---To unsubscribe, change your address, or temporarily deactivate your subscription,please go to http://v2.listbox.com/member/[EMAIL PROTECTED] To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Marcus Hutter's lossless compression of human knowledge prize
Ben,So you think that, Powerful AGI == good Hutter test resultBut you have a problem with the reverse implication,good Hutter test result =/= Powerful AGIIs this correct? Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Marcus Hutter's lossless compression of human knowledge prize
That seems clear.Human-level AGI =/= Good Hutter test result just asHuman =/= Good Hutter test resultMy suggestion then is to very slightly modify the test as follows: Instead of just getting the raw characters, what you get is thesequence of characters and the probability distribution over the next character as predicted by a standard compressor. You(meaning the algorithm or person being tested) can then chooseto modify this distribution before it is used for compression.So, for example, when the compressor is extremely certain that the next characters are a href="" then you just let the compressordo its thing. But when the string so far is 3x7= and the compressordoesn't seem to know what the next characters are, you push the compressor in the right direction.I'm pretty sure that such a combination would easily beat the bestcompressors available when used with a human, or a human levelAGI with world knowledge for that matter. Indeed I think somebody has already done something like this before with humans. Maybeone of the references that Matt gives above. However, I am uncertain whether Amazingly outstanding Hutter test result == powerful AGIAt least I think you'll agree that an amazingly outstanding Huttertest result (possibly on an even larger text corpus that included conversations etc.) would allow you to then construct a machinethat would pass the Turing test?Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Marcus Hutter's lossless compression of human knowledge prize
Yes, I think a hybridized AGI and compression algorithm could dobetter than either one on its ownHowever, this might result in an incredibly slow compression process, depending on how fast the AGIthinks.(It would take ME a long time to carry out this process overthe whole Hutter corpus...)Estimate the average compression by sampling. Also, not all narrow-AI compression algorithms will necessarily beable to produce output in the style you describe above.Standard LZ Sure, use a PPM compressor perhaps. At least I think you'll agree that an amazingly outstanding Hutter test result (possibly on an even larger text corpus that included conversations etc.) would allow you to then construct a machine that would pass the Turing test?I agree ONLY in the context of a vastly larger text corpus --- and I wonder just how large a corpus would be required ... quite possibly,one much larger than all text ever produced in the history of thehuman race...I don't think it's anywhere near that much. I read at about 2 KB per minute, and I listen to speech (if written down as plain text)at a roughly similar speed. If you then work it out, buy the timeI was 20 I'd read/heard not more than 2 or 3 GB of raw text.If you could compress/predict everything that I'd read or heard until I was 20 years old *amazingly well*, then I'm sure you'dbe able to use this predictive model to easily pass a Turing test.Indeed it's trivially true: Just have me sit a Turing test when Iwas 19. Naturally I would have passed it, and thus so would the compressor/predictor (assuming that it's amazingly good,or at least as good at predicting my responses as I would be).Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Marcus Hutter's lossless compression of human knowledge prize
But Shane, your 19 year old self had a much larger and more diversevolume of data to go on than just the text or speech that you ingested...I would claim that a blind and deaf person at 19 could pass aTuring test if they had been exposed to enough information overthe years. Especially if they had the ability to read everything that was ever spoken to them. So I don't see why you wouldneed a corpus billions of times larger than this, as you suggested. And, of course, your ability to predict your next verbal response isNOT a good indicator of your ability to adaptively deal with newsituations...All I'm talking about is predicting well enough to pass a Turing test... that was my claim: That with an amazingly good compressormy life's spoken and written words you could construct a machinethat would pass a Turing test. I do not assume that an outstanding compressor of your verbal inputsand outputs would necessarily be a great predictor of your futureverbal inputs and outputs -- because there is much more to you thanverbalizations.It might make bad errors in predicting your responses in situations different from ones you had previously experienced... orin situations similar to situations you had previously experienced butthat did not heavily involve verbiage...But if it can't make good predictions to random questions given to me in a Turing test, then it's not an amazingly good compressorof the first 20 years of my life. Indeed the first 20 years of my lifewould involve tens of thousands of conversations, and I presume on all of them my responses would have been good enough to pass aTuring test.Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] [META] Is there anything we can do to keep junk out of the AGI Forum?
Basically, as you can all probably see, Davy has written a chat bot typeof program. If you email him he'll send you a copy --- he says it's a bitover 1.5 MB and runs on XP.It's a bit hard to understand how it works, partly because (by his own confession) he doesn't know much about AI and so doesn't know toproperly describe what he's doing.The machine translation is made considerably worse by the fact that he'snot writing in proper Italian --- he's using abbreviations for words, not using standard vowel accents, punctuation, capitalisation etc...ShaneOn 7/26/06, Davy Bartoloni - Minware S.r.l. [EMAIL PROTECTED] wrote:i Used a very Poor Translator...- Original Message - From: BillK [EMAIL PROTECTED]To: agi@v2.listbox.comSent: Wednesday, July 26, 2006 4:08 PMSubject: Re: [agi] [META] Is there anything we can do to keep junk out of the AGI Forum? On 7/26/06, Richard Loosemore wrote: I am beginning to wonder if this forum would be better off with a restricted membership policy. Richard LoosemoreDavy Bartoloni - Minware S.r.l. wrote: Which thing we want from a IA? , we want TRULY something? thedoubt rises me that nobody affidera' never to the words of a program, e' piu' easy to entrust itself to a book. the book from the emergency, its wordscannot change, what there e' written e' nearly sure reliable. it isentrusted to us of piu' to the handbook of the bimby in order making the mousse of snip It *might* sound a bit better in the original Italian??? But Babelfish / Google translate has made a real mess of it. BillK --- To unsubscribe, change your address, or temporarily deactivate yoursubscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED] -- No virus found in this incoming message. Checked by AVG Free Edition. Version: 7.1.394 / Virus Database: 268.10.4/399 - Release Date: 25/07/2006 ---To unsubscribe, change your address, or temporarily deactivate your subscription,please go to http://v2.listbox.com/member/[EMAIL PROTECTED] To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Flow charts? Source Code? .. Computing Intelligence? How too? ................. ping
On 7/25/06, Ben Goertzel [EMAIL PROTECTED] wrote: Hmmm...About the measurement of general intelligence in AGI's ...I would tend to advocate a vectorial intelligence approachI'm not against a vector approach. Naturally every intelligent system will have domains in which it is stronger than others.Knowing what these are is useful and important. A single numbercan't capture this. One might also define a domain-transcending intelligence, measuredby supplying a system with tasks involving learning how to solveproblems in totally new areas it has never seen before.This would be very hard to measure but perhaps not impossible.However, in my view, this domain-transcending intelligence -- thoughperhaps the most critical part of general intelligence -- should I think this most critical part, as you put it, is what's missing ina lot of AI systems. It's why people look at a machine that cansolve difficult calculus problems in the blink of a second and say that it's not really intelligent.This is the reason I think there's value in having an overall generalmeasure of intelligence --- to highlight the need to put the G backinto AI.Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Flow charts? Source Code? .. Computing Intelligence? How too? ................. ping
James,Currently I'm writing a much longer paper (about 40 pages) on intelligencemeasurement. A draft version of this will be ready in about a month whichI hope to circulate around a bit for comments and criticism. There is also another guy who has recently come to my attention who is doing verysimilar stuff. He has a 50 page paper on formal measures of machineintelligence that should be coming out in coming months.I'll make a post here when either of these papers becomes available. Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Flow charts? Source Code? .. Computing Intelligence? How too? ................. ping
On 7/13/06, Pei Wang [EMAIL PROTECTED] wrote: Shane,Do you mean Warren Smith?Yes.Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Universal Test for AI?...... AGI bottlenecks
For a universal test of AI, I would of course suggest universal intelligenceas defined in this report:http://www.idsia.ch/idsiareport/IDSIA-10-06.pdf ShaneOn Fri, 02 Jun 2006 09:15:26 -500, [EMAIL PROTECTED] [EMAIL PROTECTED] wrote:What is the universal test for the ability of any given AI SYSTEM to Perceive Reason and Act?Is there such a test?What is the closest test known to date?Dan Goe To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Who's watching us?
Jiri, I would have assumed that to be the case, like what Ben said. I guess they have just decided that my research is sufficiently interesting to keep up to date on. Though getting hits from these people on a daily basis seems a bit over the top. I only publish something once every few months or so! Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Who's watching us?
Daniel, It seems to be a combination of things. For example, my most recent hits from military related computers came from an air force base just a few hours ago: px20o.wpafb.af.mil - - [19/Dec/2005:12:07:41 +] GET /documents/42.pdf HTTP/1.1 200 50543 - Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:07:58 +] GET /documents/f92-legg.pdf HTTP/1.1 200 206778 - Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:08:02 +] GET /documents/IDSIA-04-04.pdf HTTP/1.1 200 338053 - Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:08:10 +] GET /documents/calude99solving.pdf HTTP/1.1 200 192729 - Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:08:27 +] GET /documents/96JC-SL-IHW-MDL.pdf HTTP/1.1 200 101489 - Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:08:31 +] GET /documents/smith94objective.pdf HTTP/1.1 200 42124 - Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)px20o.wpafb.af.mil - - [19/Dec/2005:12:08:51 +] GET /documents/disSol.pdf HTTP/1.1 200 202651 - Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322) I'm not sure if that's a spider or not. Some of my other visitors: hqda.pentagon.mil lanl.gov defence.gov.au robins.af.mil lockheedmartin.com The Pentagon looked like a spider as they hit multiple pages and selected out particular images and files all in one second. Other patterns of hits look like people with web browsers looking around. Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Who's watching us?
After a few hours digging around on the internet, what I found was thata number of popular blogs get hits from military DNSs. The most likelyreason seems to be that some people in the military who have office jobs spend a lot of time surfing the net. When they find something cool theytell all their other office worker military friends and before you know it,you're getting hits from 10 different military organisations. I'd never really thought about it before, but I guess people working in thePentagon get bored and read blogs during the day too ;-)I'll take off my tin foil hat now...Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] neural network and AGI
Ben,My suspicion is that in the brain knowledge is often stored on two levels: * specific neuronal groups correlated with specific informationIn terms of the activation of specific neurons indicating high level concepts,I think there is good evidence of this now. See for example the work of Gabriel Kreiman.* strange attractors spanning large parts of the brain, correlated with specific informationThe liquid computation model of Maass, Natschlager and Markram, which wasinspired by neuroscience work on neural microcircuits and cortical columns,shows how complex dynamics can be used to perform useful computations from random networks. Jaeger has studied how, essentially the same model,can be used as a short term memory using attractors in the system. Thus itseems reasonable that, at least at the columnar scale, attractors could play an important role in short term memory.Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] neural network and AGI
Hi Pei,Most of our disagreement seems to be about definitions and choicesof words, rather than facts. (1) My memo is not intend to cover every system labeled as neural network--- that is why I use a whole section to define what I mean by NNmodel discussed in the paper. I'm fully aware of the fact that given ...My strategy is to first discuss the most typical models of the neural network family (or the standard NN architectures, as Ben put it),My problem is this: At my research institute a large portion of the people workon neural networks. Things like, recurrent LSTM networks for continuous speech recognition and evolved echo state networks for real time adaptivecontrol problems. I also follow research on computational liquids, biologicallyplausible neural networks, neural microcircuit research, and the ideas of people like Jeff Hawkins. In my mind, this is all work on neural networks, andthe researchers themselves call it that, and publish in big NN conferences likeICANN, IJCNN and journals like Neural Networks. However none of this work is like the NN model you have defined. Thus to my mind, your NN modeldoes not represent modern neural network research. (3) Neuroscience results cannot be directly used to supportartificial neural networksI disagree as a number of the trends and new ideas in artificial neural networksthat I follow are coming from neuroscience research. If I had to sum up our differences: I'd say that what you call standard neuralnetworks and your NN model, and most of the problems you describe, wouldhave been reasonable in 2000... but not now, 5 to 6 years later. Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] neural network and AGI
Pei,To my mind the key thing with neural networks is that theyare based on large numbers of relatively simple units thatinteract in a local way by sending fairly simple messages.Of course that's still very broad. A CA could be considered a neural network according to this description, and indeedto my mind I don't see much difference between the two.Nevertheless, it does rule out many things --- just talk tosomebody who has tried to take a normal algorithm that does something and turn it into an algorithm that workson a massively parallel architecture using relatively simplecomputations units.As I see it neural networks are more a paradigm of computationrather than any specific AI method or technology. This means that talking about them in general is difficult.What you seem to be criticising in your memo is what I'd callfeed forward neural networks.Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] neural network and AGI
Hi Pei,As usual, I disagree! I think you are making a straw man argument.The problem is that what you describe as neural networks is just a certainlimited class of neural networks. That class has certain limitations, which you point out. However you can't then extend those conclusions to neuralnetworks in general. For example...You say, Starting from an initial state determined by an input vector...For recurrent NNs this isn't true, or at least I think that your description is confusing. The state is a product of the history of inputs, rather thanbeing determined by an input vector. Similarly I also wouldn't say thatNNs are about input-output function learning. Back prop NNs are about this when used in simple configurations. However this isn't true of NNsin general, in particular its not true of recurrent NNs. See for exampleliquid machines or echo state networks.I also wouldn't be so sure about neurons not being easily mapped to conceptual units. In recent years neuro scientists have found thatsmall groups of neurons in parts of the human brain correspond to veryspecific things. One famous case is the Bill Clinton neuron. Of course you're talking about artificial NNs not real brains. Nevertheless, if biologicialNNs can have this quasi-symbolic nature in places, then I can't see howyou could argue that artificial NNs can't do it due to some fundamental limitation.I have other things to say as well, but my main problem with the paperis what I've described above. I don't think your criticisms apply to neuralnetworks in general.Shane To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
[agi] Open AGI?
Hi all, I'm curious about the general sentiments that people have about the appropriate level of openness for an AGI project. My mind certainly isn't made up on the issue and I can see reasons for going either way. If a single individual or small group of people made a sudden break through in AGI design this would place a huge amount of power in their hands. I could easily see this situation being dangerous. On the other hand I'm not sure that I'd want too many people knowing how to do it either! Already the world seems to have a few too many people who have the detailed knowledge required to build a working nuclear weapon for example. What are your thoughts? Surely this has been debated many times before I suppose? Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Complexity of Evolving an AGI
Hi Ben, Thanks for the comments. I understand your perspective and I think it's a reasonable one. Well your thinking has surely left it's mark on my views ;-) I think that what you'll get from this approach, if you're lucky, is a kind of primitive brain, suitable to control something with general intelligence around that of a reptile or a very stupid mammal. Then, you can use the structures/dynamics of this primitive brain as raw materials, for constructing a more powerful general intelligence. Yes, this is the basic idea. Some structures and dynamics will tend to categorize and cluster information, some will tend to recognize temporal patterns, some might tend to store information, some will be mixtures of these things. Exactly what I'll find is hard to know in advance. As you say this isn't a powerful brain, but it might serve as a set of useful building blocks to construct more powerful general intelligence from. Your approach seem to be to skip this first step by careful design. The danger is that it's hard and you might mess it up. Some of the needed dynamics could be very subtle and easily missed. Also it's not clear just how small the fundamental units of representation have to be in order to be flexible and dynamic enough. The danger with my approach is that my theory might be too weak. This could leave me with a search space that is too big to effectively search. Also, if it turns out that my fundamental units of representation are smaller than what is really needed, I might produce very inefficient solutions. I also need to look more closely at why all the researchers who were evolving neural network dynamics seem to have given up and are now doing other things... that's not a good sign! One of the main guys who was doing this is at a research institute near here so I might have to go visit him for a chat about it. Heh, our approaches slowly get closer. Novamente is greatly simplified and cleaned up from the 1.5 million lines of code that was webmind, while vetta has very slowly been increasing in complexity since my webmind days. Still, this is only a weekend hobby for me --- I need to focus my energies on getting my PhD done. Building an AGI might have to wait. ciao Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] FYI: AAAI Symposium on Human-Level Intelligence
Thanks Pei. Following the links to the people who are running this I found a whole bunch of academic AI people who are interested in and working on general intelligence. Their approach seems to be very much based around the idea that powerful systems for vision, sound, speech, motor skills and so on form the basis for general intelligence. Somehow I'd managed to miss seeing these people and their projects in the past. For example: http://www.ai.mit.edu/projects/genesis/ http://www.ai.mit.edu/projects/HIE/index.html http://www.cassimatis.com/ Shane Pei Wang wrote: 2004 AAAI Fall Symposium Series Achieving Human-Level Intelligence through Integrated Systems and Research October 21-24, 2004 Washington D.C. See http://xenia.media.mit.edu/~nlc/conferences/fss04.html --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED] --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] FYI: AAAI Symposium on Human-Level Intelligence
Also I think this is pretty cool in case you miss it: http://www.ai.mit.edu/projects/genesis/movies.html --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
[agi] Two nice non-technical articles
Agi types might like these two articles, http://www.theregister.co.uk/content/4/33463.html http://www.theregister.co.uk/content/4/33486.html Shane Want to chat instantly with your online friends? Get the FREE Yahoo! Messenger http://mail.messenger.yahoo.co.uk --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Complexity of environment of agi agent
Arnoud, I'm not sure if this makes much sense. An ideal agent is not going to be a realistic agent. The bigger your computer and the better your software more complexity your agent will be able to deal with. With an ideal realistic agent I meant the best software we can make on the best hardware we can make. In which case I think the question is pretty much impossible to answer. Who knows what the best hardware we can make is? Who knows what the best software we can make is? Do I have to see it like something that the value of the nth bit is a (complex) function of all the former bits? Then it makes sense to me. After some length l of the pattern computation becomes unfeasible. But this is not the way I intend my system to handle patterns. It learns the pattern after a lot of repeted occurences of it (in perception). And then it just stores the whole pattern ;-) No compression there. But since the environment is made outof smaller patterns, the pattern can be formulated in those smaller patterns, and thus save memory space. This is ok, but it does limit the sorts of things that your system is able to do. I actually suspect that humans do a lot of very simple pattern matching like you suggest and in some sense fake being able to work out complex looking patterns. It's just that we have seen so many patterns in the past and that we are very good at doing fast and sometimes slightly abstract pattern matching on a huge database of experience. Nevertheless you need to be a little careful because some very simple patterns that don't repeat in a very explicit way could totally confuse your system: 123.9123 Your system, if I understand correctly, would not see the pattern until it had seen the whole cycle several times. Something like 5*100,000*2 = 1,000,000 characters into the sequence and even then it would need to remember 100,000 characters of information. A human would see the pattern after just a few characters with perhaps some uncertainly as to what will happen after the 9. The total storage required for the pattern with a human would be far less than 100,000 characters your system would need too. Yes, but in general you don't know the complexity of the simplest solution of the problem in advance. It's more likely that you get to know first what the complexity of the environment is. In general an agent doesn't know the complexity of its environment either. The strategy I'm proposing is: ignore everything that is too complex. Just forget about it and hope you can, otherwise it's just bad luck. Of course you want to do the very best to solve the problem, and that entails that some complex phenomenon that can be handled must not be ignored a priori; it must only be ignored if there is evidence that understanding that phenomenon does not help solving your the problem. In order for this strategy to work you need to know what the maximum complexity is an agent can handle, as a function of the resources of the agent: Cmax(R). And it would be very helpful for making design decisions to know Cmax(R) in advance. You can then build in that everything above Cmax(R) should be ignored; 'vette pech' as we say in Dutch if you then are not able to solve the problem. Why not just do this dynamically? Try to look at how much of the agent's resources are being used for something and how much benefit the agent is getting from this. If something else comes along that seems to have a better ratio of benefit to resource usage then throw away some of the older stuff to free up resources for this new thing. Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Complexity of environment of agi agent
Ciao Arnoud, Perhaps my pattern wasn't clear enough 1 2 3 4 . . . 00099 00100 00101 . . . 0 1 . . . 8 9 then repeat from the start again. However each character is part of the sequence. So the agent sees 10002300... So the whole pattern in some sense is 100,000 numbers each of 5 characters giving a 500,000 character pattern of digits from 0 to 9. A human can learn this reasonably easily but your AI won't. It would take something more like a mega byte to store the pattern. Actually with the overhead of all the rules it would be much bigger. Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Complexity of environment of agi agent
arnoud wrote: How large can those constants be? How complex may the environment be maximally for an ideal, but still realistic, agi agent (thus not a solomonof or AIXI agent) to be still succesful? Does somebody know how to calculate (and formalise) this? I'm not sure if this makes much sense. An ideal agent is not going to be a realistic agent. The bigger your computer and the better your software more complexity your agent will be able to deal with. The only way I could see that it would make sense would be if you could come up with an algorithm and prove that it made the best possible usage of time and space in terms of achieving its goals. Then the constants you are talking about would be set by this algorithm and the size of the biggest computer you could get. Not even an educated guess? But I think some things can be said: Suppose perception of the environment is just a bit at a time: ...010100010010010111010101010... In the random case: for any sequence of length l the number of possible patterns is 2^l. Completely hopeless, unless prediction precision need decreases also exponentially with l. But that is not realistic. You then know nothing, but you want nothing also. Yes, this defines the limiting case for Solomonoff Induction... in the logarithmic case: the number of possible patterns of length l increases logarithmically with l: #p constant * log(l). If the constant is not to high this environment can be learned easily. There is no need for vagueness Not true. Just because the sequence is very compressible in a Kolmogorov sense doesn't imply that it's easy to learn. For example you could have some sequence where the computation time of the n-th bit take n^1000 computation cycles. There is only one pattern and it's highly compressible as it has a pretty short algorithm however there is no way you'll ever learn what the pattern is. I suppose the point I'm trying to make is that complexity of the environment is not all. It's is also important to know how many of the complexity can be ignored. Yes. The real measure of how difficult an environment is is not the complexity of the environment, but rather the complexity of the simplest solution to the problem that you need to solve in that environment. Shane P.S. one of these days I'm going to get around to replying to your other emails to me!! sorry about the delay! --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] Discovering the Capacity of Human Memory
The total number of particles in the whole universe is usually estimated to be around 10^80. These guys claim that the storage of the brain is 10^8432 bits. That means that my brain has around 10^8352 bits of storage for every particle in the whole universe. I thought I was feeling smarter than usual this morning! Possible explanations: 1) The quote to totally wrong the the ^ should be a , ? 2) They got confused and thought it was 1 April 3) They are actually doing research into just how flaky AI researchers really are and how easy it is to publish mathematical nonsense in Mind and Brain Journal 4) The scientists somehow managed to get their PhDs without understanding how numbers work 5) They concluded that the brain is really analogue and so they worked out the volume of the skull at the Planck scale (actually that doesn't work either as the Planck length is far far far to large at 1.6 x 10^-35 m) and so on... Does anybody have a better explanation? Shane --- Amara D. Angelica [EMAIL PROTECTED] wrote: http://www.kurzweilai.net/news/news_printable.html?id=2417 Discovering the Capacity of Human Memory Brain and Mind, August 2003 The memory capacity of the human brain is on the order of 10^8432 bits, three scientists have estimated. Writing in the August issue of Brain and Mind, their OAR cognitive model asserts that human memory and knowledge are represented by a network of relations, i.e., connections of synapses between neurons, rather than by the neurons themselves as in the traditional information-container model (1 neuron = 1 bit). This explains why the magnitude of neurons in an adult brain seems stable; however, huge amount of information can be remembered throughout the entire life of a person, they point out. Based on the projected computer memory capacity of 8 x 10^12 bits in the next ten years, Yingxu Wang et al. conclude that the memory capacity of a human brain is equivalent to at least 10^8419 modern computersThis tremendous difference of memory magnitudes between human beings and computers demonstrates the efficiency of information representation, storage, and processing in the human brains. They also conclude that this new factor has revealed the tremendous quantitative gap between the natural and machine intelligence and that next-generation computer memory systems may be built according to their relational model rather than the traditional container metaphor because the former is more powerful, flexible, and efficient, and is capable of generating a mathematically unlimited memory capacity by using limited number of neurons in the brain or hardware cells in the next generation computers. Brain and Mind 4 (2): 189-198, August 2003 --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED] Want to chat instantly with your online friends? Get the FREE Yahoo! Messenger http://mail.messenger.yahoo.co.uk --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Discovering the Capacity of Human Memory
Thanks for the link Pei. The thing is that they are talking about the number of BITS not the number of POSSIBLE STATES. Given x bits the number of possible states is 2^x. For example with 32 bits you can have 2^32 different states... or about 4,000,000,000 possible states. Thus, if the brain has 10^8432 bits of storage as they claim, then the number of possible states is 2^(10^8432). To make things even worse, even if they realise their error and decided that they didn't understand what a bit is and that they actually meant possible states, the number of bits in this case then becomes just log_2 (10^8432) = 8432 * log_2 (10) = 28,010 bits or about 3.5 kilo bytes of storage. I'd like to think that I have more than a 3.5 Kb brain!! They really should have sanity checked their results. Shane --- Pei Wang [EMAIL PROTECTED] wrote: The paper can be accessed at http://www.enel.ucalgary.ca/People/wangyx/Publications/Papers/BM-Vol4.2-HMC.pdf Their conclusion is based on the assumptions that there are 10^11 neurons and their average synapses number is 10^3. Therefore the total potential relational combinations is (10^11)! / (10^3)! ((10^11)! - (10^3)!), which is approximately 10^8432. The model is obviously an oversimplification, and the number is way too big. Pei - Original Message - From: shane legg [EMAIL PROTECTED] To: [EMAIL PROTECTED] Sent: Tuesday, September 16, 2003 6:24 AM Subject: RE: [agi] Discovering the Capacity of Human Memory The total number of particles in the whole universe is usually estimated to be around 10^80. These guys claim that the storage of the brain is 10^8432 bits. That means that my brain has around 10^8352 bits of storage for every particle in the whole universe. I thought I was feeling smarter than usual this morning! Possible explanations: 1) The quote to totally wrong the the ^ should be a , ? 2) They got confused and thought it was 1 April 3) They are actually doing research into just how flaky AI researchers really are and how easy it is to publish mathematical nonsense in Mind and Brain Journal 4) The scientists somehow managed to get their PhDs without understanding how numbers work 5) They concluded that the brain is really analogue and so they worked out the volume of the skull at the Planck scale (actually that doesn't work either as the Planck length is far far far to large at 1.6 x 10^-35 m) and so on... Does anybody have a better explanation? Shane --- Amara D. Angelica [EMAIL PROTECTED] wrote: http://www.kurzweilai.net/news/news_printable.html?id=2417 Discovering the Capacity of Human Memory Brain and Mind, August 2003 The memory capacity of the human brain is on the order of 10^8432 bits, three scientists have estimated. Writing in the August issue of Brain and Mind, their OAR cognitive model asserts that human memory and knowledge are represented by a network of relations, i.e., connections of synapses between neurons, rather than by the neurons themselves as in the traditional information-container model (1 neuron = 1 bit). This explains why the magnitude of neurons in an adult brain seems stable; however, huge amount of information can be remembered throughout the entire life of a person, they point out. Based on the projected computer memory capacity of 8 x 10^12 bits in the next ten years, Yingxu Wang et al. conclude that the memory capacity of a human brain is equivalent to at least 10^8419 modern computersThis tremendous difference of memory magnitudes between human beings and computers demonstrates the efficiency of information representation, storage, and processing in the human brains. They also conclude that this new factor has revealed the tremendous quantitative gap between the natural and machine intelligence and that next-generation computer memory systems may be built according to their relational model rather than the traditional container metaphor because the former is more powerful, flexible, and efficient, and is capable of generating a mathematically unlimited memory capacity by using limited number of neurons in the brain or hardware cells in the next generation computers. Brain and Mind 4 (2): 189-198, August 2003 --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED] Want to chat instantly with your online friends? Get the FREE Yahoo! Messenger http://mail.messenger.yahoo.co.uk --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED] --- To unsubscribe, change your address, or temporarily deactivate
RE: [agi] Discovering the Capacity of Human Memory
Yeah, it's a bit of a worry. By the way, if anybody is trying to look it up, I spelt the guy's name wrong, it's actually Stirling's equation. You can find it in an online book here: http://www.inference.phy.cam.ac.uk/mackay/itprnn/book.html It's a great book, about 640 pages long. The result I used is equation 1.13 which is on page 2. Shane --- Brad Wyble [EMAIL PROTECTED] wrote: It's also disconcerting that something like this can make it through the review process. Transdisciplinary is oftentimes a pseudonym for combining half-baked and ill-formed ideas from multiple domains into an incoherent mess. This paper is an excellent example. (bad math + bad neuroscience != good paper) --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED] Want to chat instantly with your online friends? Get the FREE Yahoo! Messenger http://mail.messenger.yahoo.co.uk --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
[agi] Robert Hecht-Nielsen's stuff
A while back Rob Sperry posted a link to a video of a presentation by Robert Hecht-Nielsen. ( http://inc2.ucsd.edu/inc_videos/ ) In it he claims to have worked out how the brain thinks :) I didn't look at it at the time as it's 150MB+ and only had a dial up account, but checked it out the other day with my new ADSL account. Aside from his, well, rather over the top style of presentation, what do people think of this? I haven't seen any comment to this list and don't know much neuroscience myself either. He has a book too, has anybody read it? Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Robert Hecht-Nielsen's stuff
Brad Wyble wrote: Well the short gist of this guy's spiel is that Lenat is on the right track. My understanding was that he argues that Lenat is on the wrong track! Lenat is trying to accumulate a large body of relatively high level logical rules about the world. This is very hard to do and requires a vast amount of human labour to do. Hecht-Nielsen argues that the brain doesn't in fact logically reason (at least not directly or course) but rather does something simpler. Essentially some kind of pattern matching and association as I understand it. Also, the knowledge representation system is at a sub-logical being made up of relatively simple associations formed on the most part by experience. This is the complete opposite of Cyc. Indeed, he attributes much of the failure of AI to be due to the assumption that intelligent systems should work on some kind of logic and use logical expressions to represent knowledge. Surely Cyc is in this tradition and so, from Hecht-Nielsen's point of view, must be on the wrong track? Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Request for invention of new word
Semiborg? :) Shane Ben Goertzel wrote: Hi , For a speculative futuristic article I'm writing (for a journal issue edited by Francis Heylighen), I need a new word: a word to denote a mind that is halfway between an individual mind and a society of minds. Not a hive-mind, but rather a community of minds that exchange thoughts/ideas directly rather than thru language, and hence derive a degree of synergetic mutual intelligence much greater than that achievable in a society of separate minds I'm reviewing two possible future examples of such minds 1) a community of Novamente AI engines 2) a community of human minds that are enhanced with neurochips or related technologies and linked into a Net enabled with Kazaa for thought-complex-swapping Any suggestions? Also, any reference to prior (serious or science-fictional) developments of this theme? Thanks, Ben G --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED] --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] more interesting stuff
Kevin wrote: Kevin's random babbling follows: Is there a working definition of what complexity exactly is? It seems to be quite subjective to me. But setting that aside for the moment... I think the situation is similar to that with the concept of intelligence in the sense that it means different things to different people. Indeed there are many formal definitions of various kinds of complexity, each measuring different things. For example, is a book on number theory complex? Well in the everyday sense of the word most people would say that it is. In the Kolmogorov sense it's actually quite simple as the theorems all follow from a small set of axioms and hence it is quite compressible. In the logical depth sense (that is, how many computation cycles would be required to reproduce the work) the complexity is quite high. Another example might be breaking prime number based encryption systems -- there is little information complexity but a lot of computation time complexity. Anyway, something can be very complex in one sense while being very simple in another. This just seems to show that our intuitive vague notion of complexity seems to cover a number of very loosely related things. Which is a problem when people write about complexity without being precise about what kind of complexity they are taking about. Basically they could be talking about just about anything, which is one of the reasons that I gave up reading non-mathematical papers on complexity and systems. Strictly speaking, the noumenal and the phenomenal cannot be separated or thought of distinctly IMO. From this viewpoint, complexity is merely apparent and not fundamentally real complexity... I'd agree to an extent. Though if all viewpoints have some common deep down underlying basis of reference such as Turing computation then perhaps this is THE ultimate viewpoint with which to view the problem? This is essentially the approach Kolmogorov compelexity takes. As we move away from UTMs towards complex AGI systems the apparent complexity of things will of course start to change in the sense that you suggest. However many of the more fundamental kinds of complexity (like Kolmogorov and logical depth above) will still hold firm for very complex things. In other words: just because your AGI is really smart doesn't mean that it can now do a large NP hard problem in a small amount of time --- the problem is likely to remain very complex in some sense (excluding weird stuff like finding new laws of physics and computation etc... of course). Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] A probabilistic/algorithmic puzzle...
Hi Cliff and others, As I came up with this kind of a test perhaps I should say a few things about its motivation... The problem was that the Webmind system had a number of proposed reasoning systems and it wasn't clear which was the best. Essentially the reasoning systems took as input a whole lot of data like: Fluffy is a Cat Snuggles is a Cat Tweety is a Bird Cats are animals Cats are mamals Cats are dogs and so on... This data might have errors, it might be very bias in its sample of the outside world, it might contain contradictions and so on... nevertheless we would expect some basic level of consistency to it. The reasoning systems take this and come up with all sorts of conclusions like: Fluffy is an animal based on the fact that Fluffy is a Cat and Cats seem to be animals... In a sense the reasoning system is trying to fill in the gaps in our data by looking at the data it has and drawing simple conclusions. So what I wanted to do is to some how artificially generate test sets that I could use to automatically test the systems against each other. I would vary the number of entities in the space (Fluffy, Cat, Bird...) the amount of noise in the data set, the number of data points and so on... Now the problem is that you can't just randomly generate any old data points, you actually need at least some kind of consistency which is a bit tricky when you have some A's being B's and most B's being C's and all B's not being D's but all D's being A's. Before long your data is totally self contradictorary are are basically just feeding your reasoning system complete junk and so it isn't a very interesting test of the system's ability. So my idea was basically to create a virtual Venn diagram using randomly placed rectangles as the sets used to compute the probability for each entity in the space and the conditional probabilities of their various intersections. This way your fundamental underlying system has consistent probabilities which is a good start. You can then randomly sample points from the space or directly compute the probabilities from the rectange areas (actually n dimensional rectanges as this gives more interesting intersection possibilities) and so on to get your data sets. You can then look at how well the system is able to approximate the true probabilities based on the incomplete data that it has been given (you can compute the true probabilities directly as you know the recatangle areas). I think I proposed about 6 or so basic variations on this theme to test the reasoning system's ability with deal with various level or noise and missing data... you can come up with all sorts of interesting variations with a bit of thought. Yeah, just a fancy Venn diagram really used to generate reasonably consistent data sets. Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] AIXI and Solomonoff induction
The other text book that I know is by Cristian S. Calude, the Prof. of complexity theory that I studied under here in New Zealand. A new version of this book just recently came out. Going by the last version, the book will be somewhat more terse than the Li and Vitanyi book and thus more appropriate for professional mathematicans who are used to that sort of style. The Li and Vitanyi book is also a lot broader in its content thus for you I'd recommend the Li and Vitanyi book which is without doubt THE book in the field, as James already pointed out. There should be a new verson (third edition) of Li and Vitanyi sometime this year which will be interesting. Li and Vitanyi have also written quite a few introductions to the basics of the field many of which you should be able to find on the internet. Cheers Shane The Li and Vitanyi book is actually intended to be a graduate-level text in theoretical computer science (or so it says on the cover) and is formatted like a math textbook. It assumes little and pretty much starts from the beginning of the field; you should have no problems accessing the content. It is a well-written book, which is a good thing since it is sort of THE text for the field with few other choices. Cheers, -James Rogers [EMAIL PROTECTED] --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED] --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] AIXI and Solomonoff induction
Hi Cliff, Sorry about the delay... I've been out sailing watching the America's Cup racing --- just a pity my team keeps losing to the damn Swiss! :( Anyway: Cliff Stabbert wrote: SL This seems to be problematic to me. For example, a random string SL generated by coin flips is not compressible at all so would you SL say that it's alive? No, although it does take something living to flip the coins; but presumably it's non-random (physically predictable by observing externals) from the moment the coin has been flipped. The decision to call heads or tails however is not at all as *easily* physically predictable, perhaps that's what I'm getting at. But I understand your point about compressibility (expanded below). Well I could always build a machine to flip coins... Or pulls lottery balls out of a spinning drum for that matter. Is such a thing predictable, at least in theory? I have read about this sort of thing before but to be perfectly honest I don't recall the details... perhaps the Heisenberg principle makes it impossible even in theory. You would need to ask a quantum physicist I suppose. more and more quickly: the tides are more predictable than the behaviour of an ant, the ants are more predictable than a wolf, the wolves are more predictable than a human in 800 B.C., and the human in 800 B.C. is more predictable than the human in 2003 A.D. In that sense, Singularity Theory seems to be a statement of the development of life's (Kolmogorov?) complexity over time. Well it's hard to say actually. An ant is less complex than a human, but an ant really only makes sense in the context of the nest that it belongs to and, if I remember correctly, the total neural mass of some ants nests is about the same as that of a human brain. Also whales have much larger brains than humans and so are perhaps more complex in some physical sense at least. A lot of people in complexity believed that there was an evolutionary pressure driving system to become more complex. As far as I know there aren't any particularly good results in this direction -- though I don't exactly follow it much. Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] AIXI and Solomonoff induction
Hi Cliff, So Solomonoff induction, whatever that precisely is, depends on a somehow compressible universe. Do the AIXI theorems *prove* something along those lines about our universe, AIXI and related work does not prove that our universe is compressible. Nor do they need to. The sun seems to come up most days, the text in this email is clearly compressible, laws of chemistry, biology, physics, economics and so on seem to work. So in short: our universe is VASTLY compressible. or do they *assume* a compressible universe (i.e. do they state IF the universe is somehow compressible, these algorithms (given infinite resources) can figure out how)? They assume that the environment (or universe) that they have to deal with is compressible. If it wasn't they (and indeed any computer based AI system) would be stuffed. However that's not a problem as the real world is clearly compressible... Assuming the latter, does that mean that there is a mathematical definition of 'pattern'? As I stated I'm not a math head, but with what little knowledge I have I find it hard to imagine pattern as a definable entity, somehow. Yes, there is a mathematical definition of 'pattern' (in fact there are a few but I'll just talk about the one that is important here). It comes from Kolmogorov complexity theory and is actually quite simple. Essentially it says that something is a pattern if it has an effective description (i.e. computer program for a Turing machine) that is significantly shorter than just describing the thing in full bit by bit. So for example: For x = 1 to 1,000,000,000,000 print 1 Next Describes a string of a trillion 1's. The description (i.e. the lenght of this program above in bits) is vastly shorter than a trillion and so a string of a trillion 1's is highly compressible and has a strong pattern. On the other hand if I flipped a coin a trillion times and used that to generate a string of 0's and 1's, it would be exceedingly unlikely that the resulting string would have any description much shorter than just listing the whole thing 01000010010010101... Thus this is not compressible and has no pattern -- it's random. There is a bit more to the story than that but not a lot more. OK, let's say you reward it for winning during the first 100 games, then punish it for winning / reward it for losing during the next 100, reward it for winning the next 100, etc. Can it perceive that pattern? Clearly this pattern is computationally expressible as so it's no problem at all. Of course it will take the AI a while to work out the rules of the game and on game 101 it will be surprised to be punished for winning. And probably for games 102 and a few more. After a while it will lose a game and realise that it needs to start losing games. At game 201 is will probably again get a surprise when it's punished for losing and will take a few games to realise that it needs to start winning again. By game 301 is will suspect that it need to start losing again and will switch over very quickly. By game 401 it would probably switch automatically as it will see the pattern. Essentially this is just another rule in the game. Of course these are not exact numbers, I'm just giving you an idea of what would in fact happen if you had an AIXI system. Given infinite resources, could it determine that I am deciding to punish or reward a win based on a pseudo-random (65536-cyclic or whatever it's called) random number generator? Yes. It's pseudo-random and thus computationally expressible and so again it's no problem for AIXI. In fact AIXItl would solve this just fine with only finite resources. And if the compressibility of the Universe is an assumption, is there a way we might want to clarify such an assumption, i.e., aren't there numerical values that attach to the *likelihood* of gravity suddenly reversing direction; numerical values attaching to the likelihood of physical phenomena which spontaneously negate like the chess-reward pattern; etc.? This depends on your view of statistics and probability. I'm a Bayesian and so I'd say that these things depend on your prior and how much evidence you have. Clearly the evidence that gravity stays the same is rather large and so the probability that it's going to flip is extremely super hyper low and the prior doesn't matter to much... In fact -- would the chess-reward pattern's unpredictability *itself* be an indication of life? I.e., doesn't Ockham's razor fail in the case of, and possibly *only* in the case of, conscious beings*? I don't see what you are getting at here. You might need to explain some more. (I understand Ockham's razor, you don't need to explain that part; actually it comes up a lot in the theory behind Solomonoff induction and AIXI...) Thanks for your comments. Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL
Re: [agi] Breaking AIXI-tl
Eliezer S. Yudkowsky wrote: Has the problem been thought up just in the sense of What happens when two AIXIs meet? or in the formalizable sense of Here's a computational challenge C on which a tl-bounded human upload outperforms AIXI-tl? I don't know of anybody else considering human upload vs. AIXI. Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] AIXI and Solomonoff induction
Cliff Stabbert wrote: [On a side note, I'm curious whether and if so, how, lossy compression might relate. It would seem that in a number of cases a simpler algorithm than expresses exactly the behaviour could be valuable in that it expresses 95% of the behaviour of the environment being studied -- and if such an algorithm can be derived at far lower cost in a certain case, it would be worth it. Are issues like this addressed in the AIXI model or does it all deal with perfect prediction?] Yes, stuff like this comes up a lot in MDL work which can be viewed as a computable approximation to Solomonoff induction. Perhaps at some point a more computable version of AIXItl might exist that is similar in this sense. Some results do exist on the relationship between Kolmogorov complexity and lossy compression but I can't remember much about it off the top of my head (I'm only just getting back into the whole area myself after a number of years doing other things!) What I'm getting at is an attempt at an external definition or at least telltale of conscious behaviour as either that which is not compressible or that which although apparently compressible for some period, suddenly changes later or perhaps that which is not compressible to less than X% of the original data where X is some largeish number like 60-90. This seems to be problematic to me. For example, a random string generated by coin flips is not compressible at all so would you say that it's alive? Back in the mid 90's when complexity theory was cool for a while after chaos theory there was a lot of talk about the edge of chaos. One way to look at this is to say that alive systems seem to have some kind of a fundamental balance between randomness and extreme compressibility. To me this seems obvious and I have a few ideas on the matter. Many others investigated the subject but as far as I know never got anywhere. Chaitin, one of the founders of Kolmogorov complexity theory did some similar work some time ago, http://citeseer.nj.nec.com/chaitin79toward.html The reason I'm thinking in these terms is because I suspected Ockham's razor to relate to the compressibility idea as you stated; and I've Sounds to me like you need to read Li and Vitanyi's book on Kolmogorov complexity theory :) http://www.cwi.nl/~paulv/kolmogorov.html Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] Godel and AIXI
Which is more or less why I figured you weren't going to do a Penrose on us as you would then fact the usual reply... Which begs the million dollar question: Just what is this cunning problem that you have in mind? :) Shane Eliezer S. Yudkowsky wrote: Shane Legg wrote: Eliezer S. Yudkowsky wrote: An intuitively fair, physically realizable challenge with important real-world analogues, solvable by the use of rational cognitive reasoning inaccessible to AIXI-tl, with success strictly defined by reward (not a Friendliness-related issue). It wouldn't be interesting otherwise. Give the AIXI a series mathematical hypotheses some of which are Godelian like statements and asking the AIXI it if each statement is true and then rewarding it for each correct answer? I'm just guessing here... this seems too Penrose like, I suppose you have something quite different? Indeed. Godel's Theorem is widely misunderstood. It doesn't show that humans can understand mathematical theorems which AIs cannot. It does not even show that there are mathematical truths not provable in the Principia Mathematica. Godel's Theorem actually shows that *if* mathematics and the Principia Mathematica are consistent, *then* Godel's statement is true, but not provable in the Principia Mathematica. We don't actually *know* that the Principia Mathematica, or mathematics itself, is consistent. We just know we haven't yet run across a contradiction. The rest is induction, not deduction. The only thing we know is that *if* the Principia is consistent *then* Godel's statement is true but not provable in the Principia. But in fact this statement itself can be proved in the Principia. So there are no mathematical truths accessible to human deduction but not machine deduction. Godel's statement is accessible neither to human deduction nor machine deduction. Of course, Godel's statement is accessible to human *induction*. But it is just as accessible to AIXI-tl's induction as well. Moreover, any human reasoning process used to assign perceived truth to mathematical theorems, if it is accessible to the combined inductive and deductive reasoning of a tl-bounded human, is accessible to the pure inductive reasoning of AIXI-tl as well. In prosaic terms, AIXI-tl would probably induce a Principia-like system for the first few theorems you showed it, but as soon as you punished it for getting Godel's Statement wrong, AIXI-tl would induce a more complex cognitive system, perhaps one based on induction as well as deduction, that assigned truth to Godel's statement. At the limit AIXI-tl would induce whatever algorithm represented the physically realized computation you were using to invent and assign truth to Godel statements. Or to be more precise, AIXI-tl would induce the algorithm the problem designer used to assign truth to mathematical theorems; perfectly if the problem designer is tl-bounded or imitable by a tl-bounded process; otherwise at least as well as any tl-bounded human could from a similar pattern of rewards. Actually, humans probably aren't really all that good at spot-reading Godel statements. If you get tossed a series of Godel statements and you learned to decode the diagonalization involved, so that you could see *something* was being diagonalized, then the inductive inertia of your success at declaring all those statements true would probably lead you to blindly declare the truth of your own unidentified Godel statement, thus falsifying it. Thus I'd expect AIXI-tl to far outperform tl-bounded humans on any fair Godel-statement-spotting tournament (arranged by AIXI, of course). --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] AIXI and Solomonoff induction
Hi Cliff, I'm not good at math -- I can't follow the AIXI materials and I don't know what Solomonoff induction is. So it's unclear to me how a certain goal is mathematically defined in this uncertain, fuzzy universe. In AIXI you don't really define a goal as such. Rather you have an agent (the AI) that interacts with a world and as part of that interaction the agent gets occasional reward signals. The agent's job is to maximise the amount of reward it gets. So, if the environment contains me and I show the AI chess positions and interpret its outputs as being moves that the AI wants to make and then give the AI reward when ever it wins... then you could say that the goal of the system is to win at chess. Equally we could also mathematically define the relationship between the input data, output data and the reward signal for the AI. This would be a mathematically defined environment and again we could interpret part of this as being the goal. Clearly the relationship between the input data, the output data and the reward signal has to be in some sense computable for such a system to work (I say in some sense as the environment doesn't have to be deterministic it just has to have computaionally compressible regularities). That might see restrictive but if it wasn't the case then AI on a computer is simply impossible as there would be no computationally expressible solution anyway. It's also pretty clear that the world that we live in does have a lot of computationally expressible regularities. What I'm assuming, at this point, is that AIXI and Solomonoff induction depend on operation in a somehow predictable universe -- a universe with some degree of entropy, so that its data is to some extent compressible. Is that more or less correct? Yes, if the universe is not somehow predicatble in the sense of being compressible then the AI will be screwed. It doesn't have to be prefectly predictable; it just can't be random noise. And in that case, goals can be defined by feedback given to the system, because the desired behaviour patterns it induces from the feedback *predictably* lead to the desired outcomes, more or less? Yeah. I'd appreciate if someone could tell me if I'm right or wrong on this, or point me to some plain english resources on these issues, should they exist. Thanks. The work is very new and there aren't, as far as I know, alternate texts on the subject, just Marcus Hutter's various papers. I am planning on writing a very simple introduction to Solomonoff Induction and AIXI before too long that leaves out a lot of the maths and concentrates on the key concepts. Aside from being a good warm up before I start working with Marcus soon, I think it could be useful as I feel that the real significance of his work is being missed by a lot of people out there due to all the math involved. Marcus has mentioned that he might write a book about the subject at some time but seemed to feel that the area needed more time to mature before then as there is still a lot of work to be done and important questions to explore... some of which I am going to be working on :) I should add, the example you gave is what raised my questions: it seems to me an essentially untrainable case because it presents a *non-repeatable* scenario. In what sense is it untrainable? The system learns to win at chess. It then start getting punished for winning and switches to losing. I don't see what the problem is. If I were to give to an AGI a 1,000-page book, and on the first 672 pages was written the word Not, it may predict that on the 673d page will be the word Not.. But I could choose to make that page blank, and in that scenario, as in the above, I don't see how any algorithm, no matter how clever, could make that prediction (unless it included my realtime brainscans, etc.) Yep, even an AIXI super AGI isn't going to be psychic. The thing is that you can never be 100% certain based on finite evidence. This is a central problem with induction. Perhaps in ten seconds gravity will suddernly reverse and start to repel rather than attract. Perhaps gravity as we know it is just a physical law that only holds for the first 13.7 billion years of the universe and then reverses? It seems very very very unlikely, but we are not 100% certain that it won't happen. Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] An Artificial General Intelligence in the Making
Daniel, An ARFF file is just a collection of n-tuple data items where each tuple dimension has defined type information. It also has a dimension that is marked as being the class of the data item. So because it's basically just a big table of data you could in theory put any kind of information you like in there provided that you are a little creative in the encoding. However while you could do something like that with an ARFF file it probably doesn't make much sense. ARFF files carry with them the implicit assumption that the data items are more or less i.i.d. and that you suspect that there is some sort of explicit relationship between the dimensions; in particular you usually are interested in the abilty to predict the class dimension using the other dimensions. This is how Weka classifiers interpret the files. So in short: I'm sure you could jam KNOW data into an Arff file but I don't really see why doing so would make much sense. Cheers Shane Daniel Colonnese wrote: For those of us who are following the KNOW thread, could somebody comment on the capabilities of KNOW beyond existing knowledge representation language such as the ARFF format for the popular WEKA system. I've input data into such a system before and while existing systems have extensive grammar for representing logical relations they have very limited capabilities for more ambiguous knowledge. The KNOW document Ben posted a link too says: Syntax to semantics mapping in the natural language module, in which the final result should be represented in this language; This kind of capabilities would certainly be a huge advance over something like ARFF. If anyone works with ARFF, could he or she comment on the possibilities of such translation with the ARFF grammar? Does anyone who's familiar with the technical workings of knowledge representation language have any idea on how this kind of mapping could be accomplished? -Daniel --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] C-T Thesis (or a version thereof) - Is it useable as anin-principle argument for strong AI?
Hi, This isn't something that I really know much about, but I'll put my understanding of the issue down in the hope that if I'm missing something then somebody will point it out and I'll learn something :) The literal Church-Turing thesis states that all formal models of what constitutes a well defined process are in fact equivalent to the Turing machine model. This thesis came about after it was discovered that all the various formal models (lambda calculus, recursive function theory and many others) that had been proposed were provably equivalent to the TM model. It is worth noting that nobody has actually proven that this claim is true, it's more the case that all efforts to find formal model of well defined processes that's more powerful than a Turing machine model have all failed and so people assume that the thesis probably true. Some people take this a step further and claim that not only are all formal models of well defined processes equivalent, but in fact all well defined physical processes are also equivalent to the Turing machine model. This appears to be supported by the fact that no well defined physical process has ever been found that is more powerful than the Turing machine model. Thus in a sense this claim is very similar to the one above as it essentially rests on empirical evidence rather than hard proof. If this physical interpretation of the Church-Turing thesis is accepted then it follows that if the physical brain and its operation is a well defined process then it must be possible to implement the process that the brain carries out on a Turing machine. This is the claim of Strong AI. Does that sounds correct to people? Cheers Shane Anand AI wrote: Hi everyone, After having read quite a bit about the the C-T Thesis, and its different versions, I'm still somewhat confused on whether it's useable as an in-principle argument for strong AI. Why or why isn't it useable? Since I suspect this is a common question, any good references that you have are appreciated. (Incidentally, I've read Copeland's entry on the C-T Thesis in SEoC (plato.standford.edu).) I'll edit any answers for SL4's Wiki (http://sl4.org/bin/wiki.pl?HomePage), and thanks very much in advance. Best wishes, Anand --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] AI and computation (was: The Next Wave)
Pei Wang wrote: In my opinion, one of the most common mistakes made by people is to think AI in terms of computability and computational complexity, using concepts like Turing machine, algorithm, and so on. For a long argument, see http://www.cis.temple.edu/~pwang/551-PT/Lecture/Computation.pdf. Comments are welcome. It's difficult for me to attack a specific point after reading through your paper because I find myself at odds with your views in many places. My views seem to be a lot more orthodox I suppose. Perhaps where our difference is best highlighted is in the following quote that you use: something can be computational at one level, but not at another level [Hofstadter, 1985] To this I would say: Something can LOOK like computation at one level, but not LOOK at computation at another level. Nevertheless it still is computation and any limits due to the fundamental properties of computation theory still apply. Or to use an example from another field: A great painting involves a lot more than just knowledge of the physical properties of paint. Nevertheless, a good painter will know the physical properties of his paints well because he knows that the product of his work is ultimately constrained by these. That's one half of the story anyway; the other part is that I believe that intelligence is definable at a pretty fundamental level (i.e. not much higher than the concept of universal Turing computation) but I'll leave that part for now and focus on this first issue. Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] Early Apps.
Alan Grimes wrote: According to my rule of thumb, If it has a natural language database it is wrong, I more or less agree... Currently I'm trying to learn Italian before I leave New Zealand to start my PhD. After a few months working through books on Italian grammar and trying to learn lots of words and verb forms and stuff and not really getting very far, I've come to realise just how complex language is! Many of you will have learnt a second language as an adult yourselves and will know what I mean - natual languages are massively complex things. I worked out that I know about 25,000 words in English, many with multiple means, many having huges amounts of symbol grounding information and complex relationships with other things I know, then there is spelling information and grammar knowledge and I'm told that English grammar isn't too complex, but my Italian grammar reference book is 250 pages of very dense information on irregular verbs and tenses etc... and of course even that is only a high level ridged structure description not how the language is actually used. Natural languages are hard - really hard. Humans have special brain areas that are set up to solve just this kind of problem and even then it takes a really long time to get good at it, perhaps ten years! To work something that complex out using a general intelligence rather than specialised systems would require a computer that was amazingly smart in my opinion. One other thing; if one really is focused on natural language learning why not make things a little easier and use an artificial language like Esperanto? Unlike like highly artificial languages like logic based or maths based etc. languages, Esperanto is just like a normal natural language in many ways. You can get novels written in it, you can speak it, some children have even grown up speaking it as one of their first languages along side other natural languages. However the language is extremely regular compared to a real natural language. For example there are only 16 rules of grammar - they can fit onto an single sheet of paper! All the verbs and adverbs and pronouns and so on obey neat and tidy patterns and rules. I'm told that after two weeks somebody can become comfortable enough with the grammar to be able to hold a conversation and then after a few months of learning more words is able to communicate quite freely and read books and so on. Why not aim at this and make the job much easier? If you ever did build a computer that could hold a good conversation in Esperanto I'm sure moving to a natural language would only be a matter of taking what you already had and increasing the level of complexity to deal with all the additional messiness required. Enough rating for today! :) Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] Language and AGI (was Re: Early Apps)
I suspect that Esperanto will not be much more difficult to tackle than any current existing language, or at best a *tiny* bit easier. The greatest difficulty of language is not grammar, or spelling, punctuation, etc. To get an AGI to the point of using _any_ language naturally on the level humans use it is the big challenge. It can be ancient Greek or Latin with all its declensions and exceptions; the difficulty lies in the use of language per se. In case my position isn't clear, I think that any language will be too difficult to start with and development should be focused on playing a wide range of simple games instead. However I have been really struck by the fact that Esperanto (and no doubt many other artificial languages) can be equal to a natural language in terms of the role they play and yet are something like ten times less complex than a real natural language in terms of language structure. I'm sure a reasonably powerful AGI would be able to infer the Esperanto rule for forming the plural of a noun (you add j to the end of the word) but I think it would struggle to work out how to do it in Italian (it's about six pages of rules in my Italian grammar book and than doesn't cover all the weird cases like when a word changes gender conditionally when forming a plural depending on the context). Sure, getting a computer to speak Esperanto would still be *really* hard, but having hundreds of pages of grammar rules that serve no real purpose other than to add a truck load of complexity to an already difficult problem just seems absurd. I guess people continue to do AI with languages like English because that is what is of practical use and where more money is likely to be. Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] TLoZ: Link's Awakening.
I don't think this is all that crazy an idea. A reasonable number of people think that intelligence is essentailly about game playing in some sense, I happen to be one. I actually used to play The Legend of Zelda many years back. Not a bad game from what I remember. However I'm not convinced that this is the best game for this purpose as, if I remember correctly, there were quite a few things in the game that had meaning to me as a player because they related to things in the external world. I'm talking about different sorts of objects etc. that you could pick up and use. Thus as a player I had a reasonable amount of background knowledge and understanding of what various objects were for and what their properties were likely to be that was based on my knowledge of the real world. An AGI wouldn't have this and so playing the game would be a lot harder. Perhaps then PacMan would be a better game? When you walk into a wall that hurts (pain), when you eat a dot (?) that's pleasure, eating a cherry is lots of pleasure and running into a ghost and losing a life is lots of pain. With a little experimentation the AGI would be able to quickly figure all this out without needing background knowledge from the real world to start with. My other point is that an AGI has to be a General Intelligence. So being able to just play PacMan isn't really enough, what we would really need is huge collection games like this that exercised the AI's brain in all sorts of slightly different ways with different types of simple learning problems. We need somebody to build a collection of simple games with a common simple API. A standard AGI test bed of sorts. (in case those with a theoretical bent are wondering: yes, I'm very much an RL, AIXI model of intelligence kind of a guy, in fact it's my PhD area) Cheers Shane Alan Grimes wrote: In 1986 Nintendo released a game called The Legend of Zelda. It remained on the top-10 list for the next five years. So why do I mention this totally irrelevant game on this list? Well, I'ts become apparent that I am well suited for a niche on list-ecology that is responsible for throwing up a semi-crazy idea and provoking useful discussion. This aims to be such a posting. The basic problem of a baby AI mind is that you want to give it some interactive environment that is heavy on feedback but doesn't require it to understand abstract relationships right off the bat. A game such as Dragon Warrior would not be good at all because it relies heavily on textual clues. A game such as the legend of Zelda, however, is excelent because you hardly have to be literate at all to begin to play it. There may be a game that better-maches this criterian but lets stick to this one. The game's ROM was only 160k and the NES is easily emulated on a PC. As there are open-source interpriters available, it should be feasable to adapt it to serve an AI's needs. One would need to hack the rom a bit to lay down traps for certain events such as bumping into something but that shouldn't be to terrably hard. The idea is to then take all the IO+hacks, and then map them onto your AI's simulated spinal chord. If Link bumps into something, the event is trapped and sent to the AI's mind and thus it learns... (It would also corelate this experience with the audio and visual feedback). The output would be the directional buttons, A, B, [select] and [start]. This approach is rather limiting as it doesn't give the AI any real-world capabilities but it would serve quite well for demonstration purposes. The AI would need to demonstrate basic planning skills (ie: you should restore your health and pick up some potions before attempting a big level), as well as navigation using the map systems. My godforsaken develment machine (if it ever works) should be well suited to this type of experament. Currently I am planning an AI based on an architecture that I call mind-2. It is an attempt at a high-level brain emulation. It will not use neurons but rather vectors and registers to achieve functional equivalence to the apparent CAM organization of the brain. This Mind-2 architecture is not a strong AI but it should be no less general than the human brain. I've shifted my focus to it because it doesn't require nearly as deep an understanding of the function of the brain as would a strong AI. The mere fact that we have no AI at present makes it a useful project. A mind-2 architecture for Link can be greatly simplified next to the complexity required for dealing with the real world. The organization of this can be a small fraction of the size of a real-world intelligence. --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] TLoZ: Link's Awakening.
One addition/correction: Shane Legg wrote: An AGI wouldn't have this and so playing the game would be a lot harder. Of course and AGI *could* have this... but you need to build a big knowledge base into your system and that's a big big job... or custom build a knowledge base for this particular game into your system, but is that cheating? Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] AI on TV
maitri wrote: The second guy was from either England or the states, not sure. He was working out of his garage with his wife. He was trying to develop robot AI including vision, speech, hearing and movement. This one's a bit more difficult, Steve Grand perhaps? http://www.cyberlife-research.com/people/steve/ Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] AI on TV
Gary Miller wrote: On Dec. 9 Kevin said: It seems to me that building a strictly black box AGI that only uses text or graphical input\output can have tremendous implications for our society, even without arms and eyes and ears, etc. Almost anything can be designed or contemplated within a computer, so the need for dealing with analog input seems unnecessary to me. Eventually, these will be needed to have a complete, human like AI. It may even be better that these first AGI systems will not have vision and hearing because it will make it more palatable and less threatening to the masses My understanding is that this current trend came about as follows: Classical AI system where either largely disconnected from the physical world or lived strictly in artificial mirco worlds. This lead to a number of problems including the famous symbol grounding problem where the agent's symbols lacked any grounding in an external reality. As a reaction to these problems many decided that AI agents needed to be more grounded in the physical world, embodiment as they call it. Some now take this to an extreme and think that you should start with robotic and sensory and control stuff and forget about logic and what thinking is and all that sort of thing. This is what you see now in many areas of AI research, Brooks and the Cog project at MIT being one such example. Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] AI on TV
I think my position is similar to Ben's; it's not really what you ground things in, but rather that you don't expose your limited little computer brain to an environment that is too complex -- at least not to start with. Language, even reasonably simple context free languages, could well be too rich for a baby AI. Trying to process 3D input is far too complex. Better then to start with something simple like 2D pixel patterns as Ben suggests. The A2I2 project by Peter Voss is taking a similar approach. Once very simple concepts and relations have been formed at this level then I would expect an AI to be better able to start dealing with richer things like basic language using what it learned previously as a starting point. For example, relating simple patterns of language that have an immediate and direct relation to the visual environment to start with and slowly building up from there. Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] Inventory of AGI projects
I think the key fact is that most of these projects are currently relatively inactive --- plenty of passion out there, just not a lot of resources. The last I heard both the HAL project and the CAM-brain project where pretty much at a stand still due to lack of funding? Perhaps a good piece of information to add to a list of AGI projects would be an indication of the level of resources that the project has. (I'm currently between places and only on the internet via cafes... So I won't be very active on this list for a few weeks at least) I suppose I should give a short who-am-I for those who don't know: I'm a New Zealand mathematician/AI kind of a guy, worked for Ben for a few years on Webmind and spend most of this year working for Peter Voss on the A2I2 project. I'm into complexity and intelligence and am starting a PhD with Marcus Hutter at IDSIA in a few months working on a mathematical definition of intelligence that he's come up with. Cheers Shane --- Ben Goertzel [EMAIL PROTECTED] wrote: Hi, Inspired by a recent post, here is my attempt at a list of serious AGI projects underway on the planet at this time. If anyone knows of anything that should be added to this list, please let me know. . Novamente ... · Pei Wangs NARS system · Peter Vosss A2I2 project · Jason Hutchens intelligent chat bots, an ongoing project that for a while was carried out at www.a-i.com · Doug Lenats Cyc project · The most serious traditional AI systems: SOAR and ACT-R · Hugo de Gariss artificial brain · James Rogers information theory based AGI effort · Eliezer Yudkowskys DGI project · Sam Adams experiential learning project at IBM · The algorithmic information theory approach to AGI theory, carried out by Juergen Schmidhuber and Marcus Hutter at IDSIA . The Cog project at MIT -- Ben --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/ __ Do You Yahoo!? Everything you'll ever need on one web page from News and Sport to Email and Music Charts http://uk.my.yahoo.com --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/
