[agi] The Missing Piece
Is there anyone out there who has a sense that most of the work being done in AI is still following the same track that has failed for fifty years now? The focus on logic as thought, or neural nets as the bottom-up, brain-imitating solution just isn't getting anywhere? It's the same thing, and it's never getting anywhere. The missing component is thought. What is thought, and how do human beings think? There is no reason that thought cannot be implemented in a sufficiently powerful computing machine -- the problem is how to implement it. Logical deduction or inference is not thought. It is mechanical symbol manipulation that can can be programmed into any scientific pocket calculator. Human intelligence is based on animal intelligence. We can perform logical calculations because we can see the symbols and their relations and move the symbols around in our minds to produce the results, but the intelligence is not the symbol manipulation, but our ability to see the relationships spatialy and decide if the pieces fit correctly throught the process. The world is continuous, spatiotemporal, and non-descrete, and simply is not describable in logical terms. A true AI system has to model the world in the same way -- spatiotemporal sensorimotor maps. Animal intelligence. This is short, and doesn't express my ideas in much detail. But I've been working alone for a long time now, and I think I have to find some people to talk to. I have an AGI project I've been developing, but I can't do it all by myself. If anyone has questions about what alternative ideas I have to the logical paradigm, I can clarify much further, as far as I can. I would just like to maybe make some connections and find some people who aren't stuck in the computational, symbolic mode. Ask some questions, and I'll tell you what I think. - 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: Languages for AGI [WAS Re: [agi] Priors and indefinite probabilities]
On 2/18/07, Charles D Hixson [EMAIL PROTECTED] wrote: You might check out D ( http://www.digitalmars.com/d/index.html ). Mind you, it's still in the quite early days, and missing a lot of libraries ... which means you need to construct interfaces to the C versions. Still, it answers several of your objections, and has partial answers to at least one of the others. I was going to try out D some time ago, but decided not to when I learned that they use Hans Boehm's conservative garbage collector. I find conservative garbage collection to be very inelegant and too error prone for my taste, even if it works well in practice for most projects... - 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
[agi] Larry Page, Google: We have some people at Google (who) are really trying to build artificial intelligence...
Larry Page, Google co-founder: We have some people at Google (who) are really trying to build artificial intelligence and to do it on a large scale, Page said to a packed Hilton ballroom of scientists. It's not as far off as people think. link: http://news.com.com/2100-11395_3-6160372.html - 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] The Missing Piece
On Mon, 19 Feb 2007, John Scanlon wrote: ) Is there anyone out there who has a sense that most of the work being ) done in AI is still following the same track that has failed for fifty ) years now? The focus on logic as thought, or neural nets as the ) bottom-up, brain-imitating solution just isn't getting anywhere? It's ) the same thing, and it's never getting anywhere. Yes, they are mostly building robots and trying to pick up blocks or catch balls. Visual perception and motor control for solving this task was first shown in a limited context in the 1960s. You are correct that the bottom up approach is not a theory driven approach. People talk about mystical words, such as Emergence or Complexity, in order to explain how their very simple model of mind can ultimately think like a human. Top-down design of an A.I. requires a theory of what abstract thought processes do. ) The missing component is thought. What is thought, and how do human ) beings think? There is no reason that thought cannot be implemented in ) a sufficiently powerful computing machine -- the problem is how to ) implement it. Right, there are many theories of how to implement an AI. I wouldn't worry too much about trying to define Thought. It has different definitions depending on the different problem solving contexts that it is used. If you focus on making a machine solve problems, then you might see some part of the machine you build will resemble your many uses for the term Thought. ) Logical deduction or inference is not thought. It is mechanical symbol ) manipulation that can can be programmed into any scientific pocket ) calculator. Logical deduction is only one way to think. As you say, there are many other ways to think. Some of these are simple reactive processes, while others are more deliberative and form multistep plans, while still others are reflective and react to problems in actual planning and inference processes. ) Human intelligence is based on animal intelligence. No. Human intelligence has evolved from animal intelligence. Human intelligence is not necessarily a simple subsumption of animal intelligence. ) The world is continuous, spatiotemporal, and non-descrete, and simply is ) not describable in logical terms. A true AI system has to model the ) world in the same way -- spatiotemporal sensorimotor maps. Animal ) intelligence. Logical parts of the world are describable in logical terms. We think in many different ways. Each of these ways uses different representations of the world. We have many specific solutions to specific types of problem solving, but to make a general problem solver we need ways to map these representations from one specific problem solver to another. This allows alternatives to pursue when a specific problem solver gets stuck. This type of robust problem solving requires reasoning by analogy. ) Ask some questions, and I'll tell you what I think. People always have a lot to say, but what we need more of are working algorithms and demonstrations of robust problem solving. - 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
[agi] Development Environments for AI (a few non-religious comments!)
Wow, I leave off email for two days and a 55-message Religious War breaks out! ;-) I promise this is nothing to do with languages I do or do not like (i.e. it is non-religious...). As many people pointed out, programming language matters a good deal less that what you are going to use it for. In my case I am very clear about what I want to do, and it is very different from conventional AI. My own goals are to build an entire software development environment, as I said earlier, and the main reasons for this are: 1) I am working on a conceptual framework for developing a *class* of AI systems [NB: a class of systems, not just one system], and the best way to express a framework is by instantiating that framework in the form of a tool that allows systems within that framework to be constructed easily. 2) My intention is to do systematic experiments to investigate the behavior of systems within that class, so I need some way to easily do this systematic experimentation. I want, for example, to construct a particular mechanism and then look at the behavior of many variants of that mechanism. So, for example, a concept-learning mechanism that involves a parameter governing the number of daughter concepts that are grabbed in an abstraction event ... and I might be intersted in how the mechanism behaves when the number of daughters is 2, 3, 4, 5, or some random number in the vicinity of one of those). I need a tool that will let me quickly set up such simulation experiments without having to touch any low level code. 3) One reason that is almost tangential to AI itself, though related: I believe that conventional environments and languages are built by people who think like engineers, and do not have a good understanding of how a mind works when it is trying to comprehend the enormous complexity of computational systems. [I know, I said that in combative language: but try not to flame me just because I said it assertively ;-)]. So I am trying to use psychological principles to make the process of system design and programming into a task that does not constantly trap the designer/programmer into the most stupid of errors. I have a number of ideas in this respect, but since I am talking to some people about funding this project right now, I'd rather not go into detail. 4) I need particular primitives that are simply not available in conventional languages. The biggest example is a facility for massive asymmetric parallelism that is not going to fall flat on its face all the time (with deadlocks and livelocks). I realise that everyone and their grandmother would like to do massive parallel programming without all the usuall headaches, and that the general problem is horrendous... but I can actually solve the problem in my context because I do not have to create a general solution to the problem. There is a restriction in my case that enables me to get away without having to solve the general problem. Again, apologies for coyness: possible patent pending and all that. Richard Loosemore. - 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] The Missing Piece
John Scanlon wrote: Is there anyone out there who has a sense that most of the work being done in AI is still following the same track that has failed for fifty years now? The focus on logic as thought, or neural nets as the bottom-up, brain-imitating solution just isn't getting anywhere? It's the same thing, and it's never getting anywhere. The missing component is thought. What is thought, and how do human beings think? There is no reason that thought cannot be implemented in a sufficiently powerful computing machine -- the problem is how to implement it. Logical deduction or inference is not thought. It is mechanical symbol manipulation that can can be programmed into any scientific pocket calculator. Human intelligence is based on animal intelligence. We can perform logical calculations because we can see the symbols and their relations and move the symbols around in our minds to produce the results, but the intelligence is not the symbol manipulation, but our ability to see the relationships spatialy and decide if the pieces fit correctly throught the process. The world is continuous, spatiotemporal, and non-descrete, and simply is not describable in logical terms. A true AI system has to model the world in the same way -- spatiotemporal sensorimotor maps. Animal intelligence. This is short, and doesn't express my ideas in much detail. But I've been working alone for a long time now, and I think I have to find some people to talk to. I have an AGI project I've been developing, but I can't do it all by myself. If anyone has questions about what alternative ideas I have to the logical paradigm, I can clarify much further, as far as I can. I would just like to maybe make some connections and find some people who aren't stuck in the computational, symbolic mode. Ask some questions, and I'll tell you what I think. John, I have *some* sympathy for what you say, but I am not sure I can buy the commitment to spatiotemporal maps and animal intelligence, because there are many ways to build a mind that do not use symbolic logic, without on the other hand insisting that everything is continuous. You can have discrete symbols, but with internal structure, for example. This is kind of a big, wie open topic, so it might be better for you to write out an essay about what you have in mind when you imagine an alternative approach. Richard Loosemore. - 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] The Missing Piece
On 2/19/07, Bo Morgan [EMAIL PROTECTED] wrote: On Mon, 19 Feb 2007, John Scanlon wrote: ) Is there anyone out there who has a sense that most of the work being ) done in AI is still following the same track that has failed for fifty ) years now? The focus on logic as thought, or neural nets as the ) bottom-up, brain-imitating solution just isn't getting anywhere? It's ) the same thing, and it's never getting anywhere. Yes, they are mostly building robots and trying to pick up blocks or catch balls. Visual perception and motor control for solving this task was first shown in a limited context in the 1960s. You are correct that the bottom up approach is not a theory driven approach. People talk about mystical words, such as Emergence or Complexity, in order to explain how their very simple model of mind can ultimately think like a human. Top-down design of an A.I. requires a theory of what abstract thought processes do. ) The missing component is thought. What is thought, and how do human ) beings think? There is no reason that thought cannot be implemented in ) a sufficiently powerful computing machine -- the problem is how to ) implement it. Right, there are many theories of how to implement an AI. I wouldn't worry too much about trying to define Thought. It has different definitions depending on the different problem solving contexts that it is used. If you focus on making a machine solve problems, then you might see some part of the machine you build will resemble your many uses for the term Thought. ) Logical deduction or inference is not thought. It is mechanical symbol ) manipulation that can can be programmed into any scientific pocket ) calculator. Logical deduction is only one way to think. As you say, there are many other ways to think. Some of these are simple reactive processes, while others are more deliberative and form multistep plans, while still others are reflective and react to problems in actual planning and inference processes. ) Human intelligence is based on animal intelligence. No. Human intelligence has evolved from animal intelligence. Human intelligence is not necessarily a simple subsumption of animal intelligence. ) The world is continuous, spatiotemporal, and non-descrete, and simply is ) not describable in logical terms. A true AI system has to model the ) world in the same way -- spatiotemporal sensorimotor maps. Animal ) intelligence. Logical parts of the world are describable in logical terms. We think in many different ways. Each of these ways uses different representations of the world. We have many specific solutions to specific types of problem solving, but to make a general problem solver we need ways to map these representations from one specific problem solver to another. This allows alternatives to pursue when a specific problem solver gets stuck. This type of robust problem solving requires reasoning by analogy. I hope my ignorance does not bother this list too much. Regarding what or what may not be done through logical inference and other expressive enough symbolic approaches; given unlimited resources would it not be possible to implement an UTM with at most a finite overhead which in turn yields that any algorithm running on an UTM could also run on expressive enough symbolic systems, whether they learn or not? I do not argue that it is not inefficient, both for running and implementation speed. It's even so that the logical inference in such a case may be reduced entirely and proven to be more efficiently obviously, than to implement the system direcly on certain systems. I do not think however that such a strict and not well-formulated position is rationally justified since it's not clear (at least not to me) that the logical inference may be efficiently reduced for every algorithm expressed in the logical language. Just rambling and unrelated but perhaps the brain's operations do not even allow for UTMs since they are not so clear and there might not be appropriate transformations and if assume the Turing-Church thesis we might find that there are problems that artificial components may solve that humans cannot even given unlimited resources. Perhaps not very likely since we can simulate the process of an UTM by hand and even the errors may be corrected given enough time. ) Ask some questions, and I'll tell you what I think. People always have a lot to say, but what we need more of are working algorithms and demonstrations of robust problem solving. - 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] Re: Languages for AGI
On Sunday 18 February 2007 19:22, Ricardo Barreira wrote: You can spend all the time you want sharpening your axes, it'll do you no good if you don't know what you'll use it for... True enough. However, as I've also mentioned in this venue before, I want to be able to do general associative retrieval, interpolation, and extrapolation of time-varying trajectories of manifolds in n-dimensional spaces, and constructive solid geometry between them. I'm guessing that's about halfway to AI -- i.e. the amount of coding needed, with that as a primitive, needed for AI is about as much as required to get that from current programming tools. 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
Re: Languages for AGI [WAS Re: [agi] Priors and indefinite probabilities]
Hi, I was offline and missed the large discussion so let me just add my 2c: Cobra is currently at a late alpha stage. There are some docs (including a comparison to Python) and examples. (And pardon my plain looking web site, but I have no graphics skills.) Here it is: http://cobralang.com/ Nice :). You might want to check another open-source .Net language called Nemerle (nemerle.org). It is quite stable now, reasonably efficient and has bindings to some IDEs (VS, monodevelop). It is majorly a functional language and not that python-like, but it has a special option that allows you to switch to python-like syntax (white-space and newline delimiters, etc.). And it has very nice lisp-like macros :). Far and away, the best answer to the best language question is the .NET framework. If you're using the framework, you can use any language that has been implemented on the framework (which includes everything from C# to the OCAML-like F# and nearly every language in between -- those obviously many implementations are better than others) AND you can easily intermix languages (so the answer to best language will vary from piece to piece). Unluckily, after being involved in .Net for quite some time, I do not share your optimism. In fact I came to think that .Net is not suitable for anything that requires really high performance and parallelism. Perhaps the problem is just that it is very very hard to build a really good VM and probably impossible to build one that will be good for more than one programming paradigm. As long as you do imperative OO programming .Net might be ok and your comments about mixing languages are right. But if you start doing functional and generative programming it will be a pain and a performance bottleneck. In that case you need things like MetaOCaml (www.metaocaml.org) for generative programming or OCamlP3l for easy parallelism (ocamlp3l.inria.fr/eng.htm). - lk - 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] Re: Languages for AGI
J. Storrs Hall, PhD. wrote: On Sunday 18 February 2007 19:22, Ricardo Barreira wrote: You can spend all the time you want sharpening your axes, it'll do you no good if you don't know what you'll use it for... True enough. However, as I've also mentioned in this venue before, I want to be able to do general associative retrieval, interpolation, and extrapolation of time-varying trajectories of manifolds in n-dimensional spaces, and constructive solid geometry between them. BTW, if you do make efficient tools for this, we could certainly use them within Novamente -- though perhaps for a more limited purpose than what you envision. I wouldn't try to get NM to represent general knowledge in this way, but, for representing knowledge about the physical environment and things observed and projected therein, having such operations to act on 3D manifolds would be quite valuable ben I'm guessing that's about halfway to AI -- i.e. the amount of coding needed, with that as a primitive, needed for AI is about as much as required to get that from current programming tools. 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] The Missing Piece
It's pretty clear that humans don't run FOPC as a native code, but that we can learn it as a trick. I disagree. I think that Hebbian learning between cortical columns is essentially equivalent to basic probabilistic term logic. Lower-level common-sense inferencing of the Clyde--elephant--gray type falls out of the representations and the associative operations. I think it falls out of the logic of spike timing dependent long term potentiation of bundles of synapses between cortical columns... -- Ben G - 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] The Missing Piece
On Monday 19 February 2007 16:08, Ben Goertzel wrote: It's pretty clear that humans don't run FOPC as a native code, but that we can learn it as a trick. I disagree. I think that Hebbian learning between cortical columns is essentially equivalent to basic probabilistic term logic. That's a tantalizing hint (not that I haven't been floating a few of my own :-). I tend to think of my n-D spaces as representing what a column does... CSG is exactly propositional logic if you think of each point as a proposition. It's the mappings between spaces that are the tricky part and give you the equivalent power of predicates, but not in just that form. I haven't looked it, but I'd bet that Hebbian learning is within hollering distance of some of my associative clustering operations, on a conceptual level. I wouldn't try to get NM to represent general knowledge in this way, but, for representing knowledge about the physical environment and things observed and projected therein, having such operations to act on 3D manifolds would be quite valuable True, but I'm envisioning going up to 1-D in some cases. The key problem, vis-a-vis a system that uses symbols as a base representation, is where do the symbols come from? My idea is to generalize operations that do recognition (e.g. of shapes, phonemes) from raw sense data (lots of nerve signals) -- and then to use the same operations all the way up, to form higher-level concepts from patterns of lower-level ones. Once you have symbols, i.e. once you've carved the world into concepts, things get a lot more straightforward. 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
Re: Mystical Emergence/Complexity [WAS Re: [agi] The Missing Piece]
Bo Morgan wrote: On Mon, 19 Feb 2007, Richard Loosemore wrote: ) Bo Morgan wrote: ) ) On Mon, 19 Feb 2007, John Scanlon wrote: ) ) ) Is there anyone out there who has a sense that most of the work being ) ) done in AI is still following the same track that has failed for ) ) fifty years now? The focus on logic as thought, or neural nets as the ) ) bottom-up, brain-imitating solution just isn't getting anywhere? ) ) It's the same thing, and it's never getting anywhere. ) ) Yes, they are mostly building robots and trying to pick up blocks or catch ) balls. Visual perception and motor control for solving this task was first ) shown in a limited context in the 1960s. You are correct that the bottom up ) approach is not a theory driven approach. People talk about mystical words, ) such as Emergence or Complexity, in order to explain how their very simple ) model of mind can ultimately think like a human. Top-down design of an A.I. ) requires a theory of what abstract thought processes do. ) ) It is interesting that you would say this. ) ) My first reaction was to simply declare that I completely disagree with your ) ...mystical words, such as Emergence or Complexity... comments, but that ) would not have been very constructive. ) ) I am more interested in *why* you would say that. What approaches do you have ) in mind, that are lacking in theory? Who, of all the researchers you had in ) mind, are the ones you most consider to be using those words in a mystical ) way? I think that describing the ways that humans solve problems will help us to understand how they are intelligent. If we have a sufficient description of how humans solve problems then we will have a theory of how humans solve problems. For example, answers to these questions: How do children attach to their parents and not strangers? How do children learn morals and values? How do children learn how to stack blocks? How do children do visual analogy completion problems? How do parents feel anxious when they hear their child crying? Why do our mental processes seem so simple when they are very intricate processes of control, such as making a turn while walking. How do we learn new ways to learn how to think? How do we reflect on our planning mistakes in order to make a better plan next time? We need to describe these processes and view the architecture of human thinking from an implementation point of view. I think that too many people are focusing on simple components that learn to do very simple tasks, such as recognizing handwriting characters or answering questions such as Is there an animal in this picture?. I disagree with an approach that has solved a simple problem and then claims that by massive scaling, massive parallelism, a humanly intelligent thinking process will Emerge. ) More pointedly, would you be able to give a statement of what *they* would ) claim was their most definitive, non-mystical statement of the meaning of ) terms like complexity or emergence, and could you explain why you feel ) that, neverthless, they are saying nothing beyond the vague and mystical? One example of Emergence would be a recurrent neural network that has a given number of stable oscillating states. People use these stable oscillating states instead of using symbols. They invent recurrent neural networks that can transition from one symbol to the next. This is fine work, but we already have symbols and the ability to actually describe human thought in symbolic systems. RNNs have their time and place, but focusing solely on them is a bottom-up approach without a larger theory of mind. Without a larger theory of how humans think these networks will not become humanly intelligent magically. ) I ask this in a genuine spirit of inquiry: I am puzzled as to why people say ) this stuff about complexity, because it has a very, very clear, non-mystical ) meaning. But maybe you are using those words to refer to something different ) than what I mean so I am trying to find out. I'm not saying that complexity is ill-defined. I'm saying that people make a leap such as: Humans are complex systems, which as far as I understand is roughly equivalent to the statement Humans have a lot of degrees of freedom. They use this statement to draw an analogy between a human mind and a neural network with a billion nodes with no description of any organizing structure. What are a few hundred computational elements that a neural network would need to implement? These are the answers to the questions above. That was a surprise: the things that you were referring to when you used the words emergence and complexity are in fact very different from the meanings that a lot of others use, especially when they are making the mystical processes criticism. Your beef is not the same as theirs, by a long way. I work on a complex systems approach to cognition, but from my point of view I am
Re: [agi] The Missing Piece
Ben Goertzel wrote: It's pretty clear that humans don't run FOPC as a native code, but that we can learn it as a trick. I disagree. I think that Hebbian learning between cortical columns is essentially equivalent to basic probabilistic term logic. Lower-level common-sense inferencing of the Clyde--elephant--gray type falls out of the representations and the associative operations. I think it falls out of the logic of spike timing dependent long term potentiation of bundles of synapses between cortical columns... The original suggestion was (IIANM) that humans don't run FOPC as a native code emat the level of symbols and concepts/em (i.e. the concept-stuff that we humans can talk about because we have introspective access at that level of our systems). Now, if you are going to claim that spike-timing-dependent LTP between columns is where some probabilistic term logic is happening ON SYMBOLS, then what you have to do is buy into a story about where symbols are represented and how. I am not clear about whether you are suggesting that the symbols are represented at: (1) the column level, or (2) the neuron level, or (3) the dendritic branch level, or (4) the synapse level, or (perhaps) (5) the spike-train level (i.e. spike trains encode symbol patterns). If you think that the logical machinery is visible, can you say which of these levels is the one where you see it? As I see it, ALL of these choices have their problems. In other words, if the machinery of logical reasoning is actually visible to you in the naked hardware at any of these levels, I reckon that you must then commit to some description of how symbols are implemented, and I think all of them look like bad news. THAT is why, each time the subject is mentioned, I pull a sucking-on-lemons face and start bad-mouthing the neuroscientists. ;-) I don't mind there being some logic-equivalent machinery down there, but I think it would be strictly sub-cognitive, and not relevant to normal human reasoning at all .. and what I find frustrating is that (some of) the people who talk about it seem to think that they only have to find *something* in the neural hardware that can be mapped onto *something* like symbol-manipulation/logical reasoning, and they think they are half way home and dry, without stopping to consider the other implications of the symbols being encoded at that hardware-dependent level. I haven't seen any neuroscientists who talk that way show any indication that they have a clue that there are even problems with it, let alone that they have good answers to those problems. In other words, I don't think I buy it. Richard Loosemore. - 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] Development Environments for AI (a few non-religious comments!)
Richard Loosemore wrote: There is a restriction in my case that enables me to get away without having to solve the general problem. I am curious to know what that restriction is? Offlist would be welcomed. Thanks Anna:) On 2/19/07, Richard Loosemore [EMAIL PROTECTED] wrote: Wow, I leave off email for two days and a 55-message Religious War breaks out! ;-) I promise this is nothing to do with languages I do or do not like (i.e. it is non-religious...). As many people pointed out, programming language matters a good deal less that what you are going to use it for. In my case I am very clear about what I want to do, and it is very different from conventional AI. My own goals are to build an entire software development environment, as I said earlier, and the main reasons for this are: 1) I am working on a conceptual framework for developing a *class* of AI systems [NB: a class of systems, not just one system], and the best way to express a framework is by instantiating that framework in the form of a tool that allows systems within that framework to be constructed easily. 2) My intention is to do systematic experiments to investigate the behavior of systems within that class, so I need some way to easily do this systematic experimentation. I want, for example, to construct a particular mechanism and then look at the behavior of many variants of that mechanism. So, for example, a concept-learning mechanism that involves a parameter governing the number of daughter concepts that are grabbed in an abstraction event ... and I might be intersted in how the mechanism behaves when the number of daughters is 2, 3, 4, 5, or some random number in the vicinity of one of those). I need a tool that will let me quickly set up such simulation experiments without having to touch any low level code. 3) One reason that is almost tangential to AI itself, though related: I believe that conventional environments and languages are built by people who think like engineers, and do not have a good understanding of how a mind works when it is trying to comprehend the enormous complexity of computational systems. [I know, I said that in combative language: but try not to flame me just because I said it assertively ;-)]. So I am trying to use psychological principles to make the process of system design and programming into a task that does not constantly trap the designer/programmer into the most stupid of errors. I have a number of ideas in this respect, but since I am talking to some people about funding this project right now, I'd rather not go into detail. 4) I need particular primitives that are simply not available in conventional languages. The biggest example is a facility for massive asymmetric parallelism that is not going to fall flat on its face all the time (with deadlocks and livelocks). I realise that everyone and their grandmother would like to do massive parallel programming without all the usuall headaches, and that the general problem is horrendous... but I can actually solve the problem in my context because I do not have to create a general solution to the problem. There is a restriction in my case that enables me to get away without having to solve the general problem. Again, apologies for coyness: possible patent pending and all that. Richard Loosemore. - 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] The Missing Piece
Sorry, I was slow to read. Working on a thought is what makes it maybe one day a realtiy. Nice post. Thanks. Anna:) On 2/19/07, John Scanlon [EMAIL PROTECTED] wrote: Eliezer S. Yudkowsky wrote: John Scanlon wrote: Is there anyone out there who has a sense that most of the work being done in AI is still following the same track that has failed for fifty years now? The focus on logic as thought, or neural nets as the bottom-up, brain-imitating solution just isn't getting anywhere? It's the same thing, and it's never getting anywhere. The missing component is thought. What is thought, and how do human beings think? There is no reason that thought cannot be implemented in a sufficiently powerful computing machine -- the problem is how to implement it. No, that's not it. I know because I once built a machine with thoughts in it and it still didn't work. Do you have any other ideas? Okay, that was a nice, quick dismissive statement. And you're right -- just insert the element of thought, and voila you have intelligence, or in the case of the machine you once built -- nothing. That's not what I mean. I've read some of your stuff, and you know a lot more about computer science and science in general than I may ever know. I don't mean that the missing ingredient is simply the mystical idea of thought. I mean that thought is something different than calculation. Human intelligence is built on animal intelligence -- and what I mean by that is that there was animal intelligence, the same kind of intelligence that can be seen today in apes, before the development of language that was the substrate that allowed the use of language. Language is the manipulation of symbols. When you think of how a non-linguistic proto-human species first started using language, you can imagine creatures associating sounds with images -- oog is the big hairy red ape who's always trying to steal your women. akk is the action of hitting him with a club. The symbol, the sound, is associated with a sensorimotor pattern. The visual pattern is the big hairy red ape you know, and the motor pattern is the sequence of muscle activations that swing the club. In order to use these symbols effectively, you have to have a sensorimotor image or pattern that the symbols are attached to. That's what I'm getting at. That is thought. We already know how to get computers to carry out very complex logical calculations, but it's mechanical, it's not thought, and they can't navigate themselves (with any serious competence) around a playground. Language and logical intelligence is built on visual-spatial modeling. That's why children learn their ABC's by looking at letters drawn on a chalkboard and practicing the muscle movements to draw them on paper. I think that the key to AI is to implement this sensorimotor, spatiotemporal modeling in software. That means data structures that represent the world in three spatial dimensions and one temporal dimension. This modeling can be done. It's done every day in video games. But obviously that's not enough. There is the element of probability -- what usually occurs, what might occur, and how my actions might affect what might occur. Okay -- so what I am focused on is creating data structures that can take sensorimotor patterns and put them into a knowledge-representation system that can remember events, predict events, and predict how motor actions will affect events. And it is all represented in terms of sensorimotor images or maps. I don't have it all figured out right now, but this is what I'm working on. - Original Message - From: Eliezer S. Yudkowsky To: agi@v2.listbox.com Sent: Monday, February 19, 2007 9:12 PM Subject: Re: [agi] The Missing Piece John Scanlon wrote: Is there anyone out there who has a sense that most of the work being done in AI is still following the same track that has failed for fifty years now? The focus on logic as thought, or neural nets as the bottom-up, brain-imitating solution just isn't getting anywhere? It's the same thing, and it's never getting anywhere. The missing component is thought. What is thought, and how do human beings think? There is no reason that thought cannot be implemented in a sufficiently powerful computing machine -- the problem is how to implement it. No, that's not it. I know because I once built a machine with thoughts in it and it still didn't work. Do you have any other ideas? -- Eliezer S. Yudkowsky http://singinst.org/ Research Fellow, Singularity Institute for Artificial Intelligence - 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] Development Environments for AI (a few non-religious comments!)
On 2/19/07, Richard Loosemore [EMAIL PROTECTED] wrote: Wow, I leave off email for two days and a 55-message Religious War breaks out! ;-) I promise this is nothing to do with languages I do or do not like (i.e. it is non-religious...). As many people pointed out, programming language matters a good deal less that what you are going to use it for. In my case I am very clear about what I want to do, and it is very different from conventional AI. My own goals are to build an entire software development environment, as I said earlier, and the main reasons for this are: 1) I am working on a conceptual framework for developing a *class* of AI systems [NB: a class of systems, not just one system], and the best way to express a framework is by instantiating that framework in the form of a tool that allows systems within that framework to be constructed easily. Can't comment on this one as it's too high level for me to do so. 2) My intention is to do systematic experiments to investigate the behavior of systems within that class, so I need some way to easily do this systematic experimentation. I want, for example, to construct a particular mechanism and then look at the behavior of many variants of that mechanism. So, for example, a concept-learning mechanism that involves a parameter governing the number of daughter concepts that are grabbed in an abstraction event ... and I might be intersted in how the mechanism behaves when the number of daughters is 2, 3, 4, 5, or some random number in the vicinity of one of those). I need a tool that will let me quickly set up such simulation experiments without having to touch any low level code. I've done this for financial analysis and genetic algorithm projects that had parameters that could be varied. It can be glued on to just about any system. Define your parameters by name, type, required-or-not and optionally (when applicable) min and max. Then provide some code that reads the parameter definitions and does (at least) the following: * complains about violations (missing value, value of out range) * interprets looping values like a (start, stop, step) for numeric parameters or an (a, b, c) for enums or strings * executes the program with each combination of values, storing the parameter sets with the results The inputs could be done via a text file that is parsed and interpreted. And/or a web or gui form could be generated from the defs. My real point is that you don't really need a new dev env for this. 3) One reason that is almost tangential to AI itself, though related: I believe that conventional environments and languages are built by people who think like engineers, and do not have a good understanding of how a mind works when it is trying to comprehend the enormous complexity of computational systems. [I know, I said that in combative language: but try not to flame me just because I said it assertively ;-)]. So I am trying to use psychological principles to make the process of system design and programming into a task that does not constantly trap the designer/programmer into the most stupid of errors. I have a number of ideas in this respect, but since I am talking to some people about funding this project right now, I'd rather not go into detail. This is the most interesting point in your list. Too bad we can't get the details yet. :-) I don't know what such an environment would look like, but I don't see why it couldn't exist. Developers have to keep a lot of stuff in their head as they work on a project and I'm positive that current IDEs aren't doing as much as they could to help visualize, manage and develop a project. Not nearly as much as they could! I sincerely wish you luck as I'd like to take such an environment for a drive. 4) I need particular primitives that are simply not available in conventional languages. The biggest example is a facility for massive asymmetric parallelism that is not going to fall flat on its face all the time (with deadlocks and livelocks). I realise that everyone and their grandmother would like to do massive parallel programming without all the usuall headaches, and that the general problem is horrendous... but I can actually solve the problem in my context because I do not have to create a general solution to the problem. There is a restriction in my case that enables me to get away without having to solve the general problem. Again, apologies for coyness: possible patent pending and all that. That feels likes something that can be done via a library, although I appreciate that some things can only be done at the language level or are simply best done there. (And perhaps at the environment level or some mix of all of these.) Feel free to keep us informed of any technical and business developments... -Chuck - 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
