Re: [agi] A Primary Distinction for an AGI
Jim, The importance of the point here is NOT primarily about AGI systems having to make this distinction. Yes, a real AGI robot will probably have to make this distinction as an infant does - but in terms of practicality, that's an awful long way away. The importance is this: real AGI is about dealing with a world of living creatures in a myriad ways - those living creatures, are all fundamentally unpredictable. Ergo most AGI activities and problems involve dealing with a fundamentally unpredictable world. Narrow AI - and all rational technology - and all attempts-at-AGI to date are predicated on dealing with a predictable world. (All the additions of probabilities and uncertainties to date do not change this basic assumption). All your personal logical and mathematical exercises are based on a predictable world. An AGI TSP equivalent for you would be what I already said - how would you deal with deciding a travel route to a set of *mobile*, *unpredictable* destinations? This recognition of fundamental unpredictability totally transforms the way you look at the world - and the kind of problems you have to deal with - makes you aware of the v. different, non-rational problems that real humans do deal with. And BTW it doesn't really matter if you are a determinist - for the plain reality of life is that the only evidence we have is of living creatures and humans behaving unpredictably. There might for argument's sake be some divine determinist plan revealing the underlying laws of living behaviour - but it sure as heck ain't available to anyone (not to mention that it doesn't exist) and we have to proceed accordingly. From: Jim Bromer Sent: Monday, June 28, 2010 5:20 PM To: agi Subject: Re: [agi] A Primary Distinction for an AGI On Mon, Jun 28, 2010 at 11:15 AM, Mike Tintner tint...@blueyonder.co.uk wrote: Inanimate objects normally move *regularly,* in *patterned*/*pattern* ways, and *predictably.* Animate objects normally move *irregularly*, * in *patchy*/*patchwork* ways, and *unbleedingpredictably* . This presumption looks similar (in some profound way) to many of the presumptions that were tried in the early days of AI, partly because computers lacked memory and they were very slow. It's unreliable just because we need the AGI program to be able to consider situations when, for example, inanimate objects move in patchy patchwork ways or in unpredictable patterns. Jim Bromer agi | Archives | Modify Your Subscription --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
Just off the cuff here - isn't the same true for vision? You can't learn vision from vision. Just as all NLP has no connection with the real world, and totally relies on the human programmer's knowledge of that world. Your visual program actually relies totally on your visual vocabulary - not its own. That is the inevitable penalty of processing unreal signals on a computer screen which are not in fact connected to the real world any more than the verbal/letter signals involved in NLP are. What you need to do - what anyone in your situation with anything like your asprations needs to do - is to hook up with a roboticist. Everyone here should be doing that. From: David Jones Sent: Tuesday, June 29, 2010 5:27 PM To: agi Subject: Re: [agi] A Primary Distinction for an AGI You can't learn language from language without embedding way more knowledge than is reasonable. Language does not contain the information required for its interpretation. There is no *reason* to interpret the language into any of the infinite possible interpretaions. There is nothing to explain but it requires explanatory reasoning to determine the correct real world interpretation On Jun 29, 2010 10:58 AM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: Natural language requires more than the words on the page in the real world. Of... Any knowledge that can be demonstrated over a text-only channel (as in the Turing test) can also be learned over a text-only channel. Cyc also is trying to store knowledge about a super complicated world in simplistic forms and al... Cyc failed because it lacks natural language. The vast knowledge store of the internet is unintelligible to Cyc. The average person can't use it because they don't speak Cycl and because they have neither the ability nor the patience to translate their implicit thoughts into augmented first order logic. Cyc's approach was understandable when they started in 1984 when they had neither the internet nor the vast computing power that is required to learn natural language from unlabeled examples like children do. Vision and other sensory interpretaion, on the other hand, do not require more info because that... Without natural language, your system will fail too. You don't have enough computing power to learn language, much less the million times more computing power you need to learn to see. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi a...@v2.listbox.c... Sent: Mon, June 28, 2010 9:28:57 PM Subject: Re: [agi] A Primary Distinction for an AGI Natural language requires more than the words on the page in the real world. Of course that didn't ... agi | Archives | Modify Your Subscription agi | Archives | Modify Your Subscription --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
Mike, THIS is the flawed reasoning that causes people to ignore vision as the right way to create AGI. And I've finally come up with a great way to show you how wrong this reasoning is. I'll give you an extremely obvious argument that proves that vision requires much less knowledge to interpret than language does. Let's say that you have never been to egypt, you have never seen some particular movie before. But if you see the movie, an alien landscape, an alien world, a new place or any such new visual experience, you can immediately interpret it in terms of spacial, temporal, compositional and other relationships. Now, go to egypt and listen to them speak. Can you interpret it? Nope. Why?! Because you don't have enough information. The language itself does not contain any information to help you interpret it. We do not learn language simply by listening. We learn based on evidence from how the language is used and how it occurs in our daily lives. Without that experience, you cannot interpret it. But with vision, you do not need extra knowledge to interpret a new situation. You can recognize completely new objects without any training except for simply observing them in their natural state. I wish people understood this better. Dave On Tue, Jun 29, 2010 at 12:51 PM, Mike Tintner tint...@blueyonder.co.ukwrote: Just off the cuff here - isn't the same true for vision? You can't learn vision from vision. Just as all NLP has no connection with the real world, and totally relies on the human programmer's knowledge of that world. Your visual program actually relies totally on your visual vocabulary - not its own. That is the inevitable penalty of processing unreal signals on a computer screen which are not in fact connected to the real world any more than the verbal/letter signals involved in NLP are. What you need to do - what anyone in your situation with anything like your asprations needs to do - is to hook up with a roboticist. Everyone here should be doing that. *From:* David Jones davidher...@gmail.com *Sent:* Tuesday, June 29, 2010 5:27 PM *To:* agi agi@v2.listbox.com *Subject:* Re: [agi] A Primary Distinction for an AGI You can't learn language from language without embedding way more knowledge than is reasonable. Language does not contain the information required for its interpretation. There is no *reason* to interpret the language into any of the infinite possible interpretaions. There is nothing to explain but it requires explanatory reasoning to determine the correct real world interpretation On Jun 29, 2010 10:58 AM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: Natural language requires more than the words on the page in the real world. Of... Any knowledge that can be demonstrated over a text-only channel (as in the Turing test) can also be learned over a text-only channel. Cyc also is trying to store knowledge about a super complicated world in simplistic forms and al... Cyc failed because it lacks natural language. The vast knowledge store of the internet is unintelligible to Cyc. The average person can't use it because they don't speak Cycl and because they have neither the ability nor the patience to translate their implicit thoughts into augmented first order logic. Cyc's approach was understandable when they started in 1984 when they had neither the internet nor the vast computing power that is required to learn natural language from unlabeled examples like children do. Vision and other sensory interpretaion, on the other hand, do not require more info because that... Without natural language, your system will fail too. You don't have enough computing power to learn language, much less the million times more computing power you need to learn to see. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi a...@v2.listbox.c... *Sent:* Mon, June 28, 2010 9:28:57 PM Subject: Re: [agi] A Primary Distinction for an AGI Natural language requires more than the words on the page in the real world. Of course that didn't ... *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
David Jones wrote: I wish people understood this better. For example, animals can be intelligent even though they lack language because they can see. True, but an AGI with language skills is more useful than one without. And yes, I realize that language, vision, motor skills, hearing, and all the other senses and outputs are tied together. Skills in any area make learning the others easier. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Tue, June 29, 2010 1:42:51 PM Subject: Re: [agi] A Primary Distinction for an AGI Mike, THIS is the flawed reasoning that causes people to ignore vision as the right way to create AGI. And I've finally come up with a great way to show you how wrong this reasoning is. I'll give you an extremely obvious argument that proves that vision requires much less knowledge to interpret than language does. Let's say that you have never been to egypt, you have never seen some particular movie before. But if you see the movie, an alien landscape, an alien world, a new place or any such new visual experience, you can immediately interpret it in terms of spacial, temporal, compositional and other relationships. Now, go to egypt and listen to them speak. Can you interpret it? Nope. Why?! Because you don't have enough information. The language itself does not contain any information to help you interpret it. We do not learn language simply by listening. We learn based on evidence from how the language is used and how it occurs in our daily lives. Without that experience, you cannot interpret it. But with vision, you do not need extra knowledge to interpret a new situation. You can recognize completely new objects without any training except for simply observing them in their natural state. I wish people understood this better. Dave On Tue, Jun 29, 2010 at 12:51 PM, Mike Tintner tint...@blueyonder.co.uk wrote: Just off the cuff here - isn't the same true for vision? You can't learn vision from vision. Just as all NLP has no connection with the real world, and totally relies on the human programmer's knowledge of that world. Your visual program actually relies totally on your visual vocabulary - not its own. That is the inevitable penalty of processing unreal signals on a computer screen which are not in fact connected to the real world any more than the verbal/letter signals involved in NLP are. What you need to do - what anyone in your situation with anything like your asprations needs to do - is to hook up with a roboticist. Everyone here should be doing that. From: David Jones Sent: Tuesday, June 29, 2010 5:27 PM To: agi Subject: Re: [agi] A Primary Distinction for an AGI You can't learn language from language without embedding way more knowledge than is reasonable. Language does not contain the information required for its interpretation. There is no *reason* to interpret the language into any of the infinite possible interpretaions. There is nothing to explain but it requires explanatory reasoning to determine the correct real world interpretation On Jun 29, 2010 10:58 AM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: Natural language requires more than the words on the page in the real world. Of... Any knowledge that can be demonstrated over a text-only channel (as in the Turing test) can also be learned over a text-only channel. Cyc also is trying to store knowledge about a super complicated world in simplistic forms and al... Cyc failed because it lacks natural language. The vast knowledge store of the internet is unintelligible to Cyc. The average person can't use it because they don't speak Cycl and because they have neither the ability nor the patience to translate their implicit thoughts into augmented first order logic. Cyc's approach was understandable when they started in 1984 when they had neither the internet nor the vast computing power that is required to learn natural language from unlabeled examples like children do. Vision and other sensory interpretaion, on the other hand, do not require more info because that... Without natural language, your system will fail too. You don't have enough computing power to learn language, much less the million times more computing power you need to learn to see. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi a...@v2.listbox.c...sent: Mon, June 28, 2010 9:28:57 PM Subject: Re: [agi] A Primary Distinction for an AGI Natural language requires more than the words on the page in the real world. Of course that didn't ... agi | Archives | Modify Your Subscription agi | Archives | Modify Your Subscription agi | Archives | Modify Your Subscription agi | Archives | Modify Your Subscription
Re: [agi] A Primary Distinction for an AGI
The point I was trying to make is that an approach that tries to interpret language just using language itself and without sufficient information or the means to realistically acquire that information, *should* fail. On the other hand, an approach that tries to interpret vision with minimal upfront knowledge needs *should* succeed because the knowledge required to automatically learn to interpret images is amenable to preprogramming. In addition, such knowledge must be pre-programmed. The knowledge for interpreting language though should not be pre-programmed. Dave On Tue, Jun 29, 2010 at 2:51 PM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: I wish people understood this better. For example, animals can be intelligent even though they lack language because they can see. True, but an AGI with language skills is more useful than one without. And yes, I realize that language, vision, motor skills, hearing, and all the other senses and outputs are tied together. Skills in any area make learning the others easier. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Tue, June 29, 2010 1:42:51 PM *Subject:* Re: [agi] A Primary Distinction for an AGI Mike, THIS is the flawed reasoning that causes people to ignore vision as the right way to create AGI. And I've finally come up with a great way to show you how wrong this reasoning is. I'll give you an extremely obvious argument that proves that vision requires much less knowledge to interpret than language does. Let's say that you have never been to egypt, you have never seen some particular movie before. But if you see the movie, an alien landscape, an alien world, a new place or any such new visual experience, you can immediately interpret it in terms of spacial, temporal, compositional and other relationships. Now, go to egypt and listen to them speak. Can you interpret it? Nope. Why?! Because you don't have enough information. The language itself does not contain any information to help you interpret it. We do not learn language simply by listening. We learn based on evidence from how the language is used and how it occurs in our daily lives. Without that experience, you cannot interpret it. But with vision, you do not need extra knowledge to interpret a new situation. You can recognize completely new objects without any training except for simply observing them in their natural state. I wish people understood this better. Dave On Tue, Jun 29, 2010 at 12:51 PM, Mike Tintner tint...@blueyonder.co.ukwrote: Just off the cuff here - isn't the same true for vision? You can't learn vision from vision. Just as all NLP has no connection with the real world, and totally relies on the human programmer's knowledge of that world. Your visual program actually relies totally on your visual vocabulary - not its own. That is the inevitable penalty of processing unreal signals on a computer screen which are not in fact connected to the real world any more than the verbal/letter signals involved in NLP are. What you need to do - what anyone in your situation with anything like your asprations needs to do - is to hook up with a roboticist. Everyone here should be doing that. *From:* David Jones davidher...@gmail.com *Sent:* Tuesday, June 29, 2010 5:27 PM *To:* agi agi@v2.listbox.com *Subject:* Re: [agi] A Primary Distinction for an AGI You can't learn language from language without embedding way more knowledge than is reasonable. Language does not contain the information required for its interpretation. There is no *reason* to interpret the language into any of the infinite possible interpretaions. There is nothing to explain but it requires explanatory reasoning to determine the correct real world interpretation On Jun 29, 2010 10:58 AM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: Natural language requires more than the words on the page in the real world. Of... Any knowledge that can be demonstrated over a text-only channel (as in the Turing test) can also be learned over a text-only channel. Cyc also is trying to store knowledge about a super complicated world in simplistic forms and al... Cyc failed because it lacks natural language. The vast knowledge store of the internet is unintelligible to Cyc. The average person can't use it because they don't speak Cycl and because they have neither the ability nor the patience to translate their implicit thoughts into augmented first order logic. Cyc's approach was understandable when they started in 1984 when they had neither the internet nor the vast computing power that is required to learn natural language from unlabeled examples like children do. Vision and other sensory interpretaion, on the other hand, do not require more info because that... Without natural language, your system
Re: [agi] A Primary Distinction for an AGI
Experiments in text compression show that text alone is sufficient for learning to predict text. I realize that for a machine to pass the Turing test, it needs a visual model of the world. Otherwise it would have a hard time with questions like what word in this ernai1 did I spell wrong? Obviously the easiest way to build a visual model is with vision, but it is not the only way. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Tue, June 29, 2010 3:22:33 PM Subject: Re: [agi] A Primary Distinction for an AGI I certainly agree that the techniques and explanation generating algorithms for learning language are hard coded into our brain. But, those techniques alone are not sufficient to learn language in the absence of sensory perception or some other way of getting the data required. Dave On Tue, Jun 29, 2010 at 3:19 PM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: The knowledge for interpreting language though should not be pre-programmed. I think that human brains are wired differently than other animals to make language learning easier. We have not been successful in training other primates to speak, even though they have all the right anatomy such as vocal chords, tongue, lips, etc. When primates have been taught sign language, they have not successfully mastered forming sentences. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Tue, June 29, 2010 3:00:09 PM Subject: Re: [agi] A Primary Distinction for an AGI The point I was trying to make is that an approach that tries to interpret language just using language itself and without sufficient information or the means to realistically acquire that information, *should* fail. On the other hand, an approach that tries to interpret vision with minimal upfront knowledge needs *should* succeed because the knowledge required to automatically learn to interpret images is amenable to preprogramming. In addition, such knowledge must be pre-programmed. The knowledge for interpreting language though should not be pre-programmed. Dave On Tue, Jun 29, 2010 at 2:51 PM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: I wish people understood this better. For example, animals can be intelligent even though they lack language because they can see. True, but an AGI with language skills is more useful than one without. And yes, I realize that language, vision, motor skills, hearing, and all the other senses and outputs are tied together. Skills in any area make learning the others easier. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Tue, June 29, 2010 1:42:51 PM Subject: Re: [agi] A Primary Distinction for an AGI Mike, THIS is the flawed reasoning that causes people to ignore vision as the right way to create AGI. And I've finally come up with a great way to show you how wrong this reasoning is. I'll give you an extremely obvious argument that proves that vision requires much less knowledge to interpret than language does. Let's say that you have never been to egypt, you have never seen some particular movie before. But if you see the movie, an alien landscape, an alien world, a new place or any such new visual experience, you can immediately interpret it in terms of spacial, temporal, compositional and other relationships. Now, go to egypt and listen to them speak. Can you interpret it? Nope. Why?! Because you don't have enough information. The language itself does not contain any information to help you interpret it. We do not learn language simply by listening. We learn based on evidence from how the language is used and how it occurs in our daily lives. Without that experience, you cannot interpret it. But with vision, you do not need extra knowledge to interpret a new situation. You can recognize completely new objects without any training except for simply observing them in their natural state. I wish people understood this better. Dave On Tue, Jun 29, 2010 at 12:51 PM, Mike Tintner tint...@blueyonder.co.uk wrote: Just off the cuff here - isn't the same true for vision? You can't learn vision from vision. Just as all NLP has no connection with the real world, and totally relies on the human programmer's knowledge of that world. Your visual program actually relies totally on your visual vocabulary - not its own. That is the inevitable penalty of processing unreal signals on a computer screen which are not in fact connected to the real world any more than the verbal/letter signals involved in NLP are. What you need to do - what anyone in your situation with anything like your asprations needs to do - is to hook up with a roboticist
Re: [agi] A Primary Distinction for an AGI
the purpose of text is to convey something. It has to be interpreted. who cares about predicting the next word if you can't interpret a single bit of it. On Tue, Jun 29, 2010 at 3:43 PM, David Jones davidher...@gmail.com wrote: People do not predict the next words of text. We anticipate it, but when something different shows up, we accept it if it is *explanatory*. Using compression like algorithms though will never be able to do this type of explanatory reasoning, which is required to disambiguate text. It is certainly not sufficient for learning language, which is not at all about predicting text. On Tue, Jun 29, 2010 at 3:38 PM, Matt Mahoney matmaho...@yahoo.comwrote: Experiments in text compression show that text alone is sufficient for learning to predict text. I realize that for a machine to pass the Turing test, it needs a visual model of the world. Otherwise it would have a hard time with questions like what word in this ernai1 did I spell wrong? Obviously the easiest way to build a visual model is with vision, but it is not the only way. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Tue, June 29, 2010 3:22:33 PM *Subject:* Re: [agi] A Primary Distinction for an AGI I certainly agree that the techniques and explanation generating algorithms for learning language are hard coded into our brain. But, those techniques alone are not sufficient to learn language in the absence of sensory perception or some other way of getting the data required. Dave On Tue, Jun 29, 2010 at 3:19 PM, Matt Mahoney matmaho...@yahoo.comwrote: David Jones wrote: The knowledge for interpreting language though should not be pre-programmed. I think that human brains are wired differently than other animals to make language learning easier. We have not been successful in training other primates to speak, even though they have all the right anatomy such as vocal chords, tongue, lips, etc. When primates have been taught sign language, they have not successfully mastered forming sentences. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Tue, June 29, 2010 3:00:09 PM *Subject:* Re: [agi] A Primary Distinction for an AGI The point I was trying to make is that an approach that tries to interpret language just using language itself and without sufficient information or the means to realistically acquire that information, *should* fail. On the other hand, an approach that tries to interpret vision with minimal upfront knowledge needs *should* succeed because the knowledge required to automatically learn to interpret images is amenable to preprogramming. In addition, such knowledge must be pre-programmed. The knowledge for interpreting language though should not be pre-programmed. Dave On Tue, Jun 29, 2010 at 2:51 PM, Matt Mahoney matmaho...@yahoo.comwrote: David Jones wrote: I wish people understood this better. For example, animals can be intelligent even though they lack language because they can see. True, but an AGI with language skills is more useful than one without. And yes, I realize that language, vision, motor skills, hearing, and all the other senses and outputs are tied together. Skills in any area make learning the others easier. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Tue, June 29, 2010 1:42:51 PM *Subject:* Re: [agi] A Primary Distinction for an AGI Mike, THIS is the flawed reasoning that causes people to ignore vision as the right way to create AGI. And I've finally come up with a great way to show you how wrong this reasoning is. I'll give you an extremely obvious argument that proves that vision requires much less knowledge to interpret than language does. Let's say that you have never been to egypt, you have never seen some particular movie before. But if you see the movie, an alien landscape, an alien world, a new place or any such new visual experience, you can immediately interpret it in terms of spacial, temporal, compositional and other relationships. Now, go to egypt and listen to them speak. Can you interpret it? Nope. Why?! Because you don't have enough information. The language itself does not contain any information to help you interpret it. We do not learn language simply by listening. We learn based on evidence from how the language is used and how it occurs in our daily lives. Without that experience, you cannot interpret it. But with vision, you do not need extra knowledge to interpret a new situation. You can recognize completely new objects without any training except for simply observing them in their natural state. I wish people understood this better. Dave
Re: [agi] A Primary Distinction for an AGI
You're not getting where I'm coming from at all. I totally agree vision is far prior to language. (We and I've covered your points many times). That's not the point - wh. is that vision is nevertheless still vastly more complex, than you have any idea. For one thing, vision depends on perceptualising/ conceptualising the world - a schematic ontology of the world - image-schematic. It almost certainly has to be done in a certain order, gradually built up. No one in our culture has much idea of either what that ontology - a visual ontology - consists of, or how it's built up. And for the most basic thing, you still haven't registered that your computer program has ZERO VISION. It's not actually looking at the world at all. It's BLIND - if you take the time to analyse it. A pretty fundamental error/ misconception. Consequently, it also lacks a fundamental dimension of vision, wh. is POINT-OF-VIEW - distance of the visual medium (eg the retina) and viewing subject from the visual object. Get thee to a roboticist, make contact with the real world. From: David Jones Sent: Tuesday, June 29, 2010 6:42 PM To: agi Subject: Re: [agi] A Primary Distinction for an AGI Mike, THIS is the flawed reasoning that causes people to ignore vision as the right way to create AGI. And I've finally come up with a great way to show you how wrong this reasoning is. I'll give you an extremely obvious argument that proves that vision requires much less knowledge to interpret than language does. Let's say that you have never been to egypt, you have never seen some particular movie before. But if you see the movie, an alien landscape, an alien world, a new place or any such new visual experience, you can immediately interpret it in terms of spacial, temporal, compositional and other relationships. Now, go to egypt and listen to them speak. Can you interpret it? Nope. Why?! Because you don't have enough information. The language itself does not contain any information to help you interpret it. We do not learn language simply by listening. We learn based on evidence from how the language is used and how it occurs in our daily lives. Without that experience, you cannot interpret it. But with vision, you do not need extra knowledge to interpret a new situation. You can recognize completely new objects without any training except for simply observing them in their natural state. I wish people understood this better. Dave On Tue, Jun 29, 2010 at 12:51 PM, Mike Tintner tint...@blueyonder.co.uk wrote: Just off the cuff here - isn't the same true for vision? You can't learn vision from vision. Just as all NLP has no connection with the real world, and totally relies on the human programmer's knowledge of that world. Your visual program actually relies totally on your visual vocabulary - not its own. That is the inevitable penalty of processing unreal signals on a computer screen which are not in fact connected to the real world any more than the verbal/letter signals involved in NLP are. What you need to do - what anyone in your situation with anything like your asprations needs to do - is to hook up with a roboticist. Everyone here should be doing that. From: David Jones Sent: Tuesday, June 29, 2010 5:27 PM To: agi Subject: Re: [agi] A Primary Distinction for an AGI You can't learn language from language without embedding way more knowledge than is reasonable. Language does not contain the information required for its interpretation. There is no *reason* to interpret the language into any of the infinite possible interpretaions. There is nothing to explain but it requires explanatory reasoning to determine the correct real world interpretation On Jun 29, 2010 10:58 AM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: Natural language requires more than the words on the page in the real world. Of... Any knowledge that can be demonstrated over a text-only channel (as in the Turing test) can also be learned over a text-only channel. Cyc also is trying to store knowledge about a super complicated world in simplistic forms and al... Cyc failed because it lacks natural language. The vast knowledge store of the internet is unintelligible to Cyc. The average person can't use it because they don't speak Cycl and because they have neither the ability nor the patience to translate their implicit thoughts into augmented first order logic. Cyc's approach was understandable when they started in 1984 when they had neither the internet nor the vast computing power that is required to learn natural language from unlabeled examples like children do. Vision and other sensory interpretaion, on the other hand, do not require more info because that... Without natural language, your system will fail too. You don't have enough computing power to learn language, much less the million times more computing power you
Re: [agi] A Primary Distinction for an AGI
On Tue, Jun 29, 2010 at 3:33 PM, Mike Tintner tint...@blueyonder.co.ukwrote: You're not getting where I'm coming from at all. I totally agree vision is far prior to language. (We and I've covered your points many times). That's not the point - wh. is that vision is nevertheless still vastly more complex, than you have any idea. whatever you say. That has nothing to do with whether it should be pursued this way or not. For one thing, vision depends on perceptualising/ conceptualising the world - a schematic ontology of the world - image-schematic. It almost certainly has to be done in a certain order, gradually built up. how is that, even remotely, a reason to change the way I do my research? It doesn't even logically follow... No one in our culture has much idea of either what that ontology - a visual ontology - consists of, or how it's built up. Again, how is that an argument for changing my research? It's not. It does not follow again. And for the most basic thing, you still haven't registered that your computer program has ZERO VISION. It's not actually looking at the world at all. It's BLIND - if you take the time to analyse it. A pretty fundamental error/ misconception. Not an argument again. It has nothing to do with whether my approach will or will not provide the valuable knowledge and foundation required to solve the fundamental problems of general vision. Consequently, it also lacks a fundamental dimension of vision, wh. is POINT-OF-VIEW - distance of the visual medium (eg the retina) and viewing subject from the visual object. AGAIN. Not an argument against my approach. It simply doesn't logically follow anything. How is having a point of view in example problems prove that anything learned or developed isn't applicable to general vision? Get thee to a roboticist, make contact with the real world. Get yourself to a psychologist so that they can show you how flawed your reasoning is. Fallacy upon fallacy. You are not in touch with reality. *From:* David Jones davidher...@gmail.com *Sent:* Tuesday, June 29, 2010 6:42 PM *To:* agi agi@v2.listbox.com *Subject:* Re: [agi] A Primary Distinction for an AGI Mike, THIS is the flawed reasoning that causes people to ignore vision as the right way to create AGI. And I've finally come up with a great way to show you how wrong this reasoning is. I'll give you an extremely obvious argument that proves that vision requires much less knowledge to interpret than language does. Let's say that you have never been to egypt, you have never seen some particular movie before. But if you see the movie, an alien landscape, an alien world, a new place or any such new visual experience, you can immediately interpret it in terms of spacial, temporal, compositional and other relationships. Now, go to egypt and listen to them speak. Can you interpret it? Nope. Why?! Because you don't have enough information. The language itself does not contain any information to help you interpret it. We do not learn language simply by listening. We learn based on evidence from how the language is used and how it occurs in our daily lives. Without that experience, you cannot interpret it. But with vision, you do not need extra knowledge to interpret a new situation. You can recognize completely new objects without any training except for simply observing them in their natural state. I wish people understood this better. Dave On Tue, Jun 29, 2010 at 12:51 PM, Mike Tintner tint...@blueyonder.co.ukwrote: Just off the cuff here - isn't the same true for vision? You can't learn vision from vision. Just as all NLP has no connection with the real world, and totally relies on the human programmer's knowledge of that world. Your visual program actually relies totally on your visual vocabulary - not its own. That is the inevitable penalty of processing unreal signals on a computer screen which are not in fact connected to the real world any more than the verbal/letter signals involved in NLP are. What you need to do - what anyone in your situation with anything like your asprations needs to do - is to hook up with a roboticist. Everyone here should be doing that. *From:* David Jones davidher...@gmail.com *Sent:* Tuesday, June 29, 2010 5:27 PM *To:* agi agi@v2.listbox.com *Subject:* Re: [agi] A Primary Distinction for an AGI You can't learn language from language without embedding way more knowledge than is reasonable. Language does not contain the information required for its interpretation. There is no *reason* to interpret the language into any of the infinite possible interpretaions. There is nothing to explain but it requires explanatory reasoning to determine the correct real world interpretation On Jun 29, 2010 10:58 AM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: Natural language requires more than the words on the page in the real world. Of... Any
Re: [agi] A Primary Distinction for an AGI
Answering questions is the same problem as predicting the answers. If you can compute p(A|Q) where Q is the question (and previous context of the conversation) and A is the answer, then you can also choose an answer A from the same distribution. If p() correctly models human communication, then the response would be indistinguishable from a human in a Turing test. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Tue, June 29, 2010 3:43:53 PM Subject: Re: [agi] A Primary Distinction for an AGI the purpose of text is to convey something. It has to be interpreted. who cares about predicting the next word if you can't interpret a single bit of it. On Tue, Jun 29, 2010 at 3:43 PM, David Jones davidher...@gmail.com wrote: People do not predict the next words of text. We anticipate it, but when something different shows up, we accept it if it is *explanatory*. Using compression like algorithms though will never be able to do this type of explanatory reasoning, which is required to disambiguate text. It is certainly not sufficient for learning language, which is not at all about predicting text. On Tue, Jun 29, 2010 at 3:38 PM, Matt Mahoney matmaho...@yahoo.com wrote: Experiments in text compression show that text alone is sufficient for learning to predict text. I realize that for a machine to pass the Turing test, it needs a visual model of the world. Otherwise it would have a hard time with questions like what word in this ernai1 did I spell wrong? Obviously the easiest way to build a visual model is with vision, but it is not the only way. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Tue, June 29, 2010 3:22:33 PM Subject: Re: [agi] A Primary Distinction for an AGI I certainly agree that the techniques and explanation generating algorithms for learning language are hard coded into our brain. But, those techniques alone are not sufficient to learn language in the absence of sensory perception or some other way of getting the data required. Dave On Tue, Jun 29, 2010 at 3:19 PM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: The knowledge for interpreting language though should not be pre-programmed. I think that human brains are wired differently than other animals to make language learning easier. We have not been successful in training other primates to speak, even though they have all the right anatomy such as vocal chords, tongue, lips, etc. When primates have been taught sign language, they have not successfully mastered forming sentences. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Tue, June 29, 2010 3:00:09 PM Subject: Re: [agi] A Primary Distinction for an AGI The point I was trying to make is that an approach that tries to interpret language just using language itself and without sufficient information or the means to realistically acquire that information, *should* fail. On the other hand, an approach that tries to interpret vision with minimal upfront knowledge needs *should* succeed because the knowledge required to automatically learn to interpret images is amenable to preprogramming. In addition, such knowledge must be pre-programmed. The knowledge for interpreting language though should not be pre-programmed. Dave On Tue, Jun 29, 2010 at 2:51 PM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: I wish people understood this better. For example, animals can be intelligent even though they lack language because they can see. True, but an AGI with language skills is more useful than one without. And yes, I realize that language, vision, motor skills, hearing, and all the other senses and outputs are tied together. Skills in any area make learning the others easier. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Tue, June 29, 2010 1:42:51 PM Subject: Re: [agi] A Primary Distinction for an AGI Mike, THIS is the flawed reasoning that causes people to ignore vision as the right way to create AGI. And I've finally come up with a great way to show you how wrong this reasoning is. I'll give you an extremely obvious argument that proves that vision requires much less knowledge to interpret than language does. Let's say that you have never been to egypt, you have never seen some particular movie before. But if you see the movie, an alien landscape, an alien world, a new place or any such new visual experience, you can immediately interpret it in terms of spacial, temporal, compositional and other relationships. Now, go to egypt and listen to them speak. Can you interpret it? Nope
Re: [agi] A Primary Distinction for an AGI
Scratch my statement about it being useless :) It's useful, but no where near sufficient for AGI like understanding. On Tue, Jun 29, 2010 at 4:58 PM, David Jones davidher...@gmail.com wrote: notice how you said *context* of the conversation. The context is the real world, and is completely missing. You cannot model human communication using text alone. The responses you would get back would be exactly like eliza. Sure, it might be pleasing to someone that has never seen AI before, but its certainly not answering any questions. This reminds me of the Bing search engine commercials where people ask a question and get responses that include the words they asked about, but in a completely wrong context. Predicting the next word and understanding the question are completely different and cannot be solved the same way. In fact, predicting the next word is altogether useless (at least by itself) in my opinion. Dave On Tue, Jun 29, 2010 at 4:50 PM, Matt Mahoney matmaho...@yahoo.comwrote: Answering questions is the same problem as predicting the answers. If you can compute p(A|Q) where Q is the question (and previous context of the conversation) and A is the answer, then you can also choose an answer A from the same distribution. If p() correctly models human communication, then the response would be indistinguishable from a human in a Turing test. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Tue, June 29, 2010 3:43:53 PM *Subject:* Re: [agi] A Primary Distinction for an AGI the purpose of text is to convey something. It has to be interpreted. who cares about predicting the next word if you can't interpret a single bit of it. On Tue, Jun 29, 2010 at 3:43 PM, David Jones davidher...@gmail.comwrote: People do not predict the next words of text. We anticipate it, but when something different shows up, we accept it if it is *explanatory*. Using compression like algorithms though will never be able to do this type of explanatory reasoning, which is required to disambiguate text. It is certainly not sufficient for learning language, which is not at all about predicting text. On Tue, Jun 29, 2010 at 3:38 PM, Matt Mahoney matmaho...@yahoo.comwrote: Experiments in text compression show that text alone is sufficient for learning to predict text. I realize that for a machine to pass the Turing test, it needs a visual model of the world. Otherwise it would have a hard time with questions like what word in this ernai1 did I spell wrong? Obviously the easiest way to build a visual model is with vision, but it is not the only way. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Tue, June 29, 2010 3:22:33 PM *Subject:* Re: [agi] A Primary Distinction for an AGI I certainly agree that the techniques and explanation generating algorithms for learning language are hard coded into our brain. But, those techniques alone are not sufficient to learn language in the absence of sensory perception or some other way of getting the data required. Dave On Tue, Jun 29, 2010 at 3:19 PM, Matt Mahoney matmaho...@yahoo.comwrote: David Jones wrote: The knowledge for interpreting language though should not be pre-programmed. I think that human brains are wired differently than other animals to make language learning easier. We have not been successful in training other primates to speak, even though they have all the right anatomy such as vocal chords, tongue, lips, etc. When primates have been taught sign language, they have not successfully mastered forming sentences. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Tue, June 29, 2010 3:00:09 PM *Subject:* Re: [agi] A Primary Distinction for an AGI The point I was trying to make is that an approach that tries to interpret language just using language itself and without sufficient information or the means to realistically acquire that information, *should* fail. On the other hand, an approach that tries to interpret vision with minimal upfront knowledge needs *should* succeed because the knowledge required to automatically learn to interpret images is amenable to preprogramming. In addition, such knowledge must be pre-programmed. The knowledge for interpreting language though should not be pre-programmed. Dave On Tue, Jun 29, 2010 at 2:51 PM, Matt Mahoney matmaho...@yahoo.comwrote: David Jones wrote: I wish people understood this better. For example, animals can be intelligent even though they lack language because they can see. True, but an AGI with language skills is more useful than one without. And yes, I realize that language, vision, motor skills, hearing
Re: [agi] A Primary Distinction for an AGI
Mike, Alive vs. dead? As I've said before, there is no actual difference. It is not a qualitative difference that makes something alive or dead. It is a quantitative difference. They are both controlled by physics. I don't mean the nice clean physics rules that we approximate things with, I mean the real dynamics of matter. Neither moves any more regularly or irregularly than the other. It is harder to define why something alive moves because the mechanism is normally too complex. If you didn't realize, there are life forms that don't really move, such as viruses. Viruses are controlled by the liquid that contains them. Yet, viruses are arguably alive. Some plants or algae don't really move either. They may just grow in some direction, which is not quite the same as movement. Likewise, your analogy of this to AGI fails. You think there is a difference, but there is none. You may think a fractal is more AGI than a simple, low noise black square, but that is not the case. It is completely besides the point. I can easily add noise to my experiments. I can simulate the noise of light, camera lenses, blurring, etc. But, why should I when, even without noise, there is a clear unsolved AGI challenge. The explanatory reasoning required to solve even zero noise problems is still required for full complexity problems. If you can't solve it for 2 squares on a screen, what makes you think you can solve it for real images? Your grasp of reality regarding AGI is quite poor, in my opinion. Your main claim is that the problems I am working on are not representative or applicable to AGI. But, you fail to see that they really are. The abductive reasoning required to solve these extremely simplified problems is required for every other AGI problem as well. These problems might be solvable using methods that don't apply to AGI. But, that's why it is important to force oneself to solve them in such a way that it IS applicable to AGI. It doesn't mean that you have to choose a problem that is so hard you can't cheat. It's unnecessary to do that unless you can't control your desire to cheat. I can. Developing in this way, such as an implementation of explanatory based reasoning, is very much applicable to AGI. Dave On Mon, Jun 28, 2010 at 11:15 AM, Mike Tintner tint...@blueyonder.co.ukwrote: The recent Core of AGI exchange has led me IMO to a beautiful conclusion - to one of the most basic distinctions a real AGI system must make, and also a simple way of distinguishing between narrow AI and real AGI projects of any kind. Consider - you have a) Dave's square moving across a screen b) my square moving across a screen (it was a sort-of-Pong-player line, but let's make it a square box). How do you distinguish which is animate or inanimate, alive or dead? A very early distinction an infant must make. Remember inanimate objects move (or are moved) too, and in this case you can only see them in motion, - so the self-starting distinction is out. Well, obviously, if Dave's moves *regularly* (like a train or falling stone), it's probably inanimate. If mine moves *irregularly*, - if it stops and starts, or slows and accelerates in irregular, even if only subtly jerky fashion (like one operated by a human Pong player) - it's probably inanimate. That's what distinguishes the movement of life. Inanimate objects normally move *regularly,* in *patterned*/*pattern* ways, and *predictably.* Animate objects normally move *irregularly*, * in *patchy*/*patchwork* ways, and *unbleedingpredictably* . (IOW Newton is wrong - the laws of physics do not apply to living objects as whole objects - that's the fundamental way we know they are living, because they visibly don't obey those laws - they don't normally move regularly like a stone falling to earth, or thrown through the sky. And we're v. impressed when humans like dancers or soldiers do manage by dint of great effort and practice to move with a high though not perfect degree of regularity and smoothness). And now we have such a simple way of distinguishing between narrow AI and real AGI projects. Look at their objects. The really narrow AI-er will always do what Dave did - pick objects that are shaped regularly, move and behave regularly, are patterned, and predictable. Even at as simple a level as plain old squares. And he'll pick closed, definable sets of objects. He'll do this instinctively, because he doesn't know any different - that's his intellectual, logicomathematical world - one of objects that no matter how complex (like fractals) are always regular in shape, movement, patterned, come in definable sets and are predictable. That's why Ben wants to see the world only as structured and patterned even though there's so much obvious mess and craziness everywhere - he's never known any different intellectually. That's why Michael can't bear to even contemplate a world in which things and people behave unpredictably. (And
Re: [agi] A Primary Distinction for an AGI
On Mon, Jun 28, 2010 at 11:15 AM, Mike Tintner tint...@blueyonder.co.ukwrote: Inanimate objects normally move *regularly,* in *patterned*/*pattern* ways, and *predictably.* Animate objects normally move *irregularly*, * in *patchy*/*patchwork* ways, and *unbleedingpredictably* . This presumption looks similar (in some profound way) to many of the presumptions that were tried in the early days of AI, partly because computers lacked memory and they were very slow. It's unreliable just because we need the AGI program to be able to consider situations when, for example, inanimate objects move in patchy patchwork ways or in unpredictable patterns. Jim Bromer --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
Well, I see that Mike did say normally move... so yes that type of principle could be used in a more flexible AGI program (although there is still a question about the use of any presumptions that go into this level of detail about their reference subjects. I would not use a primary reference like Mike's in my AGI program just because it is so presumptuous about animate and inanimate objects). But anyway, my criticism then is that the presumption is not really superior - in any way - to the run of the mill presumptions that you often hear considered in discussions about AGI programs. For example, David never talked about distinguishing between animate and inanimate objects (in the sense of the term 'animate' that Mike is using the words,) and his reference was only made to an graphics example to present the idea that he was talking about. Jim Bromer On Mon, Jun 28, 2010 at 12:20 PM, Jim Bromer jimbro...@gmail.com wrote: On Mon, Jun 28, 2010 at 11:15 AM, Mike Tintner tint...@blueyonder.co.ukwrote: Inanimate objects normally move *regularly,* in *patterned*/*pattern* ways, and *predictably.* Animate objects normally move *irregularly*, * in *patchy*/*patchwork* ways, and *unbleedingpredictably* . This presumption looks similar (in some profound way) to many of the presumptions that were tried in the early days of AI, partly because computers lacked memory and they were very slow. It's unreliable just because we need the AGI program to be able to consider situations when, for example, inanimate objects move in patchy patchwork ways or in unpredictable patterns. Jim Bromer --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
Yeah. I forgot to mention that robots are not aalive yet could act indistinguishably from what is alive. The concept of alive is likely something that requires inductive type reasoning and generalization to learn. Categorization, similarity analysis, etc could assist in making such distinctions as well. The point is that agi is not defined by any particular problem. It is defined by how you solve problems, even simple ones. Which is why your claim that my problems are not agi is simply wrong. On Jun 28, 2010 12:22 PM, Jim Bromer jimbro...@gmail.com wrote: On Mon, Jun 28, 2010 at 11:15 AM, Mike Tintner tint...@blueyonder.co.uk wrote: Inanimate objects normally move *regularly,* in *patterned*/*pattern* ways, and *predictably This presumption looks similar (in some profound way) to many of the presumptions that were tried in the early days of AI, partly because computers lacked memory and they were very slow. It's unreliable just because we need the AGI program to be able to consider situations when, for example, inanimate objects move in patchy patchwork ways or in unpredictable patterns. Jim Bromer *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
On Mon, Jun 28, 2010 at 11:15 AM, Mike Tintner tint...@blueyonder.co.ukwrote: Inanimate objects normally move *regularly,* in *patterned*/*pattern* ways, and *predictably.* Animate objects normally move *irregularly*, * in *patchy*/*patchwork* ways, and *unbleedingpredictably* . I think you made a major tactical error and just got caught acting the way you are constantly criticizing everyone else for acting. --(Busted)-- You might say my interest is: how do we get a contemporary computer problem to deal with situations in which a prevailing (or presumptuous) point of view should be reconsidered from different points of view, when the range of reasonable ways to look at a problem is not clear and the possibilities are too numerous for a contemporary computer to examine carefully in a reasonable amount of time. For example, we might try opposites, and in this case I wondered about the case where we might want to consider a 'supposedly inanimate object' that moves in an irregular and unpredictable way. Another example: Can unpredictable itself be considered predictable? To some extent the answer is, of course it can. The problem with using opposites is that it is an idealization of real world situations and where using alternative ways of looking at a problem may be useful. Can an object be both inanimate and animate (in the sense Mike used the term)? Could there be another class of things that was neither animate nor inanimate? Is animate versus animate really the best way to describe living versus non living? No? Given that the possibilities could quickly add up and given that they are not clearly defined, it presents a major problem of complexity to the would be designer of a true AGI program. The problem is that it is just not feasible to evaluate millions of variations of possibilities and then find the best candidates within a reasonable amount of time. And this problem does not just concern the problem of novel situations but those specific situations that are familiar but where there are quite a few details that are not initially understood. While this is -clearly- a human problem, it is a much more severe problem for contemporary AGI. Jim Bromer --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
On Mon, Jun 28, 2010 at 4:54 PM, David Jones davidher...@gmail.com wrote: But, that's why it is important to force oneself to solve them in such a way that it IS applicable to AGI. It doesn't mean that you have to choose a problem that is so hard you can't cheat. It's unnecessary to do that unless you can't control your desire to cheat. I can. That would be relevant if it was entirely a problem of willpower and self-discipline, but it isn't. It's also a problem of guidance. A real problem gives you feedback at every step of the way, it keeps blowing your ideas out of the water until you come up with one that will actually work, that you would never have thought of in a vacuum. A toy problem leaves you guessing, and most of your guesses will be wrong in ways you won't know about until you come to try a real problem and realize you have to throw all your work away. Conversely, a toy problem doesn't make your initial job that much easier. It means you have to write less code, sure, but what of it? That was only ever the lesser difficulty. The main reason toy problems are easier is that you can use lower grade methods that could never scale up to real problems -- in other words, precisely that you can 'cheat'. But if you aren't going to cheat, you're sacrificing most of the ease of a toy problem, while also sacrificing the priceless feedback from a real problem -- the worst of both worlds. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
I also want to mention that I develop solutions to the toy problems with the real problems in mind. I also fully intend to work my way up to the real thing by incrementally adding complexity and exploring the problem well at each level of complexity. As you do this, the flaws in the design will be clear and I can retrace my steps to create a different solution. The benefit to this strategy is that we fully understand the problems at each level of complexity. When you run into something that is not accounted, you are much more likely to know how to solve it. Despite its difficulties, I prefer my strategy to the alternatives. Dave On Mon, Jun 28, 2010 at 3:56 PM, David Jones davidher...@gmail.com wrote: That does not have to be the case. Yes, you need to know what problems you might have in more complicated domains to avoid developing completely useless theories on toy problems. But, as you develop for full complexity problems, you are confronted with several sub problems. Because you have no previous experience, what tends to happen is you hack together a solution that barely works and simply isn't right or scalable because we don't have a full understanding of the individual sub problems. Having experience with the full problem is important, but forcing yourself to solve every sub problem at once is not a better strategy at all. You may think my strategies has flaws, but I know that and still chose it because the alternative strategies are worse. Dave On Mon, Jun 28, 2010 at 3:41 PM, Russell Wallace russell.wall...@gmail.com wrote: On Mon, Jun 28, 2010 at 4:54 PM, David Jones davidher...@gmail.com wrote: But, that's why it is important to force oneself to solve them in such a way that it IS applicable to AGI. It doesn't mean that you have to choose a problem that is so hard you can't cheat. It's unnecessary to do that unless you can't control your desire to cheat. I can. That would be relevant if it was entirely a problem of willpower and self-discipline, but it isn't. It's also a problem of guidance. A real problem gives you feedback at every step of the way, it keeps blowing your ideas out of the water until you come up with one that will actually work, that you would never have thought of in a vacuum. A toy problem leaves you guessing, and most of your guesses will be wrong in ways you won't know about until you come to try a real problem and realize you have to throw all your work away. Conversely, a toy problem doesn't make your initial job that much easier. It means you have to write less code, sure, but what of it? That was only ever the lesser difficulty. The main reason toy problems are easier is that you can use lower grade methods that could never scale up to real problems -- in other words, precisely that you can 'cheat'. But if you aren't going to cheat, you're sacrificing most of the ease of a toy problem, while also sacrificing the priceless feedback from a real problem -- the worst of both worlds. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
Yes I have. But what I found is that real vision is so complex, involving so many problems that must be solved and studied, that any attempt at general vision is beyond my current abilities. It would be like expecting a single person, such as myself, to figure out how to build the h-bomb all by themselves back before it had ever been done. It is the same scenario because it involves many engineering and scientific problems that must all be solved and studied. You see in real vision you have a 3D world, camera optics, lighting issues, noise, blurring, rotation, distance, projection, reflection, shadows, occlusion, etc, etc, etc. It is many magnitudes more difficult than the problems I'm studying. Yet, really consider the two black squares problem. Its hard! It's so simple, yet so hard. I still haven't fully defined how to do it algorithmically... I will get to that in the coming weeks. So, to work on the full problem is practically impossible for me. Seeing as though there isn't a lot of support for AGI research such as this, I am much better served by proving the principle rather than implementing the full solution to the real problem. If I can even prove how vision works on simple black squares, I might be able to get help in my research... without a proof of concept, no one will help. If I can prove it on screenshots, even better. It would be a very significant achievement, if done in a truly general fashion (keeping in mind that truly general is not really possible). A great example of what happens when you work with real images is this... Look at the current solutions. They use features, such as sift. Using sift features, you might be able to say that an object exists with 70% certainty, or something like that. But, it won't be able to tell you what the object looks like, whats behind it. What is it occluding. What's next to it. What color is it. What pixels in the image belong to it. How are those parts attached. Etc. etc. etc. Now do you see why it makes little sense to tackle the full problem? Even the state of the art in computer vision sucks. It is great at certain narrow applications, but no where near where it needs to be for AGI. Dave On Mon, Jun 28, 2010 at 4:00 PM, Russell Wallace russell.wall...@gmail.comwrote: On Mon, Jun 28, 2010 at 8:56 PM, David Jones davidher...@gmail.com wrote: Having experience with the full problem is important, but forcing yourself to solve every sub problem at once is not a better strategy at all. Certainly going back to a toy problem _after_ gaining some experience with the full problem would have a much better chance of being a viable strategy. Have you tried that with what you're doing, i.e. having a go at writing a program to understand real video before going back to black squares and screen shots to improve the fundamentals? --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
*nods* So you have tried the full problem, and caught up with the current state-of-the-art in techniques for it? In that case... ... well, honestly, I still don't think your approach with black squares and screenshots is going to produce any useful results. But given the above, I no longer think you are being irrational in pursuing it. I think, as you said, you have looked at the alternatives, all of which are very tough, and your judgment disagrees with mine about which is the least bad. On Mon, Jun 28, 2010 at 9:15 PM, David Jones davidher...@gmail.com wrote: Yes I have. But what I found is that real vision is so complex, involving so many problems that must be solved and studied, that any attempt at general vision is beyond my current abilities. It would be like expecting a single person, such as myself, to figure out how to build the h-bomb all by themselves back before it had ever been done. It is the same scenario because it involves many engineering and scientific problems that must all be solved and studied. You see in real vision you have a 3D world, camera optics, lighting issues, noise, blurring, rotation, distance, projection, reflection, shadows, occlusion, etc, etc, etc. It is many magnitudes more difficult than the problems I'm studying. Yet, really consider the two black squares problem. Its hard! It's so simple, yet so hard. I still haven't fully defined how to do it algorithmically... I will get to that in the coming weeks. So, to work on the full problem is practically impossible for me. Seeing as though there isn't a lot of support for AGI research such as this, I am much better served by proving the principle rather than implementing the full solution to the real problem. If I can even prove how vision works on simple black squares, I might be able to get help in my research... without a proof of concept, no one will help. If I can prove it on screenshots, even better. It would be a very significant achievement, if done in a truly general fashion (keeping in mind that truly general is not really possible). A great example of what happens when you work with real images is this... Look at the current solutions. They use features, such as sift. Using sift features, you might be able to say that an object exists with 70% certainty, or something like that. But, it won't be able to tell you what the object looks like, whats behind it. What is it occluding. What's next to it. What color is it. What pixels in the image belong to it. How are those parts attached. Etc. etc. etc. Now do you see why it makes little sense to tackle the full problem? Even the state of the art in computer vision sucks. It is great at certain narrow applications, but no where near where it needs to be for AGI. Dave On Mon, Jun 28, 2010 at 4:00 PM, Russell Wallace russell.wall...@gmail.com wrote: On Mon, Jun 28, 2010 at 8:56 PM, David Jones davidher...@gmail.com wrote: Having experience with the full problem is important, but forcing yourself to solve every sub problem at once is not a better strategy at all. Certainly going back to a toy problem _after_ gaining some experience with the full problem would have a much better chance of being a viable strategy. Have you tried that with what you're doing, i.e. having a go at writing a program to understand real video before going back to black squares and screen shots to improve the fundamentals? --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
There would be an insidious problem with programming computers to play poker that in Sid's opinion would raise the Turing test to a higher level. The problem would not be whether people could figure out if they were up against a computer. It would be whether the computer could figure out people, particularly the ever-changing social dynamics in a randomly selected group of people. Nobody at a poker table would care whether or not the computer would play poker like a person. In fact, people would welcome a computer, since computers would tend to play predictably. Computers would be, by definition, predictable, which would be the meaning of the word 'programmed. ' If you would play a computer simulation for a short amount of time, you would learn the machine's betting patterns, adjust would mean the computer would be distinguishable from a person. Many people would play poker as predictably as a computer. They would be welcomed at the table, too. If you would find a predictable poker opponent and would learn his or her patterns, you could exploit that knowledge for profit. Most people,however, have been unpredictable and human unpredictability would be an advantage at poker. To play poker successfully, computers would not only have to develop human unpredictability, hey would have to learn to adjust to human unpredictability as well. Computers would fail miserably at the problem of adjusting to ever changing social conditions that would result from human interactions. That would be why beating a computer at poker has been so easy. Of course, the same requirement, the ability to adjust unpredictability, would apply to poker playing humans who would want to be successful. You should go back and study how Sid had adjusted each hour in his poker session. However, as humans, we have been more accustomed to human unpredictability, so we have been far better at learning how to adjust. http://www.holdempokergame.poker.tj/adjust-your-play-to-conditions-1.html Of course, he's talking about dumb narrow AI purely-predicting-and-predictable computers, we're all interested in building AGI computers that expect-unpredictability-and-can-react-unpredictably, right? (Wh. means being predicting-and-predictable some of the time too. The real world is complicated.). From: Jim Bromer Sent: Monday, June 28, 2010 6:35 PM To: agi Subject: Re: [agi] A Primary Distinction for an AGI On Mon, Jun 28, 2010 at 11:15 AM, Mike Tintner tint...@blueyonder.co.uk wrote: Inanimate objects normally move *regularly,* in *patterned*/*pattern* ways, and *predictably.* Animate objects normally move *irregularly*, * in *patchy*/*patchwork* ways, and *unbleedingpredictably* . I think you made a major tactical error and just got caught acting the way you are constantly criticizing everyone else for acting. --(Busted)-- You might say my interest is: how do we get a contemporary computer problem to deal with situations in which a prevailing (or presumptuous) point of view should be reconsidered from different points of view, when the range of reasonable ways to look at a problem is not clear and the possibilities are too numerous for a contemporary computer to examine carefully in a reasonable amount of time. For example, we might try opposites, and in this case I wondered about the case where we might want to consider a 'supposedly inanimate object' that moves in an irregular and unpredictable way. Another example: Can unpredictable itself be considered predictable? To some extent the answer is, of course it can. The problem with using opposites is that it is an idealization of real world situations and where using alternative ways of looking at a problem may be useful. Can an object be both inanimate and animate (in the sense Mike used the term)? Could there be another class of things that was neither animate nor inanimate? Is animate versus animate really the best way to describe living versus non living? No? Given that the possibilities could quickly add up and given that they are not clearly defined, it presents a major problem of complexity to the would be designer of a true AGI program. The problem is that it is just not feasible to evaluate millions of variations of possibilities and then find the best candidates within a reasonable amount of time. And this problem does not just concern the problem of novel situations but those specific situations that are familiar but where there are quite a few details that are not initially understood. While this is -clearly- a human problem, it is a much more severe problem for contemporary AGI. Jim Bromer agi | Archives | Modify Your Subscription --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id
Re: [agi] A Primary Distinction for an AGI
David Jones wrote: I also want to mention that I develop solutions to the toy problems with the real problems in mind. I also fully intend to work my way up to the real thing by incrementally adding complexity and exploring the problem well at each level of complexity. A little research will show you the folly of this approach. For example, the toy approach to language modeling is to write a simplified grammar that approximates English, then write a parser, then some code to analyze the parse tree and take some action. The classic example is SHRDLU (blocks world, http://en.wikipedia.org/wiki/SHRDLU ). Efforts like that have always stalled. That is not how people learn language. People learn from lots of examples, not explicit rules, and they learn semantics before grammar. For a second example, the toy approach to modeling logical reasoning is to design a knowledge representation based on augmented first order logic, then write code to implement deduction, forward chaining, backward chaining, etc. The classic example is Cyc. Efforts like that have always stalled. That is not how people reason. People learn to associate events that occur in quick succession, and then reason by chaining associations. This model is built in. People might later learn math, programming, and formal logic as rules for manipulating symbols within the framework of natural language learning. For a third example, the toy approach to modeling vision is to segment the image into regions and try to interpret the meaning of each region. Efforts like that have always stalled. That is not how people see. People learn to recognize visual features that they have seen before. Features are made up of weighted sums of lots of simpler features with learned weights. Features range from dots, edges, color, and motion at the lowest levels, to complex objects like faces at the higher levels. Vision is integrated with lots of other knowledge sources. You see what you expect to see. The common theme is that real AGI consists of a learning algorithm, an opaque knowledge representation, and a vast amount of training data and computing power. It is not an extension of a toy system where you code all the knowledge yourself. That doesn't scale. You can't know more than an AGI that knows more than you. So I suggest you do a little research instead of continuing to repeat all the mistakes that were made 50 years ago. You aren't the first person to do these kinds of experiments. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Mon, June 28, 2010 4:00:24 PM Subject: Re: [agi] A Primary Distinction for an AGI I also want to mention that I develop solutions to the toy problems with the real problems in mind. I also fully intend to work my way up to the real thing by incrementally adding complexity and exploring the problem well at each level of complexity. As you do this, the flaws in the design will be clear and I can retrace my steps to create a different solution. The benefit to this strategy is that we fully understand the problems at each level of complexity. When you run into something that is not accounted, you are much more likely to know how to solve it. Despite its difficulties, I prefer my strategy to the alternatives. Dave On Mon, Jun 28, 2010 at 3:56 PM, David Jones davidher...@gmail.com wrote: That does not have to be the case. Yes, you need to know what problems you might have in more complicated domains to avoid developing completely useless theories on toy problems. But, as you develop for full complexity problems, you are confronted with several sub problems. Because you have no previous experience, what tends to happen is you hack together a solution that barely works and simply isn't right or scalable because we don't have a full understanding of the individual sub problems. Having experience with the full problem is important, but forcing yourself to solve every sub problem at once is not a better strategy at all. You may think my strategies has flaws, but I know that and still chose it because the alternative strategies are worse. Dave On Mon, Jun 28, 2010 at 3:41 PM, Russell Wallace russell.wall...@gmail.com wrote: On Mon, Jun 28, 2010 at 4:54 PM, David Jones davidher...@gmail.com wrote: But, that's why it is important to force oneself to solve them in such a way that it IS applicable to AGI. It doesn't mean that you have to choose a problem that is so hard you can't cheat. It's unnecessary to do that unless you can't control your desire to cheat. I can. That would be relevant if it was entirely a problem of willpower and self-discipline, but it isn't. It's also a problem of guidance. A real problem gives you feedback at every step of the way, it keeps blowing your ideas out of the water until you come up with one that will actually work, that you would never have
Re: [agi] A Primary Distinction for an AGI
Interestingly, the world's best AI poker program *does* work by applying sophisticated Bayesian probability analysis to social modeling... http://pokerparadime.com/ -- Ben On Mon, Jun 28, 2010 at 7:02 PM, Mike Tintner tint...@blueyonder.co.ukwrote: There would be an insidious problem with programming computers to play poker that in Sid’s opinion would raise the Turing test to a higher level. The problem would not be whether people could figure out if they were up against a computer. It would be whether the computer could figure out people, particularly the ever-changing social dynamics in a randomly selected group of people. Nobody at a poker table would care whether or not the computer would play poker like a person. In fact, people would welcome a computer, since computers would tend to play predictably. Computers would be, by definition, predictable, which would be the meaning of the word ‘programmed. ’ If you would play a computer simulation for a short amount of time, you would learn the machine’s betting patterns, adjust would mean the computer would be distinguishable from a person. Many people would play poker as predictably as a computer. They would be welcomed at the table, too. If you would find a predictable poker opponent and would learn his or her patterns, you could exploit that knowledge for profit. Most people,however, have been unpredictable and human unpredictability would be an advantage at poker. To play poker successfully, computers would not only have to develop human unpredictability, hey would have to learn to adjust to human unpredictability as well. Computers would fail miserably at the problem of adjusting to ever changing social conditions that would result from human interactions. That would be why beating a computer at poker has been so easy. Of course, the same requirement, the ability to adjust unpredictability, would apply to poker playing humans who would want to be successful. You should go back and study how Sid had adjusted each hour in his poker session. However, as humans, we have been more accustomed to human unpredictability, so we have been far better at learning how to adjust. http://www.holdempokergame.poker.tj/adjust-your-play-to-conditions-1.html Of course, he's talking about dumb narrow AI purely-predicting-and-predictable computers, we're all interested in building AGI computers that expect-unpredictability-and-can-react-unpredictably, right? (Wh. means being predicting-and-predictable some of the time too. The real world is complicated.). *From:* Jim Bromer jimbro...@gmail.com *Sent:* Monday, June 28, 2010 6:35 PM *To:* agi agi@v2.listbox.com *Subject:* Re: [agi] A Primary Distinction for an AGI On Mon, Jun 28, 2010 at 11:15 AM, Mike Tintner tint...@blueyonder.co.uk wrote: Inanimate objects normally move *regularly,* in *patterned*/*pattern* ways, and *predictably.* Animate objects normally move *irregularly*, * in *patchy*/*patchwork* ways, and *unbleedingpredictably* . I think you made a major tactical error and just got caught acting the way you are constantly criticizing everyone else for acting. --(Busted)-- You might say my interest is: how do we get a contemporary computer problem to deal with situations in which a prevailing (or presumptuous) point of view should be reconsidered from different points of view, when the range of reasonable ways to look at a problem is not clear and the possibilities are too numerous for a contemporary computer to examine carefully in a reasonable amount of time. For example, we might try opposites, and in this case I wondered about the case where we might want to consider a 'supposedly inanimate object' that moves in an irregular and unpredictable way. Another example: Can unpredictable itself be considered predictable? To some extent the answer is, of course it can. The problem with using opposites is that it is an idealization of real world situations and where using alternative ways of looking at a problem may be useful. Can an object be both inanimate and animate (in the sense Mike used the term)? Could there be another class of things that was neither animate nor inanimate? Is animate versus animate really the best way to describe living versus non living? No? Given that the possibilities could quickly add up and given that they are not clearly defined, it presents a major problem of complexity to the would be designer of a true AGI program. The problem is that it is just not feasible to evaluate millions of variations of possibilities and then find the best candidates within a reasonable amount of time. And this problem does not just concern the problem of novel situations but those specific situations that are familiar but where there are quite a few details that are not initially understood. While this is -clearly- a human problem, it is a much more severe problem for contemporary
Re: [agi] A Primary Distinction for an AGI
Natural language requires more than the words on the page in the real world. Of course that didn't work. Cyc also is trying to store knowledge about a super complicated world in simplistic forms and also requires more data to get right. Vision and other sensory interpretaion, on the other hand, do not require more info because that is where the experience comes from. On Jun 28, 2010 8:52 PM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: I also want to mention that I develop solutions to the toy problems with the re... A little research will show you the folly of this approach. For example, the toy approach to language modeling is to write a simplified grammar that approximates English, then write a parser, then some code to analyze the parse tree and take some action. The classic example is SHRDLU (blocks world, http://en.wikipedia.org/wiki/SHRDLU ). Efforts like that have always stalled. That is not how people learn language. People learn from lots of examples, not explicit rules, and they learn semantics before grammar. For a second example, the toy approach to modeling logical reasoning is to design a knowledge representation based on augmented first order logic, then write code to implement deduction, forward chaining, backward chaining, etc. The classic example is Cyc. Efforts like that have always stalled. That is not how people reason. People learn to associate events that occur in quick succession, and then reason by chaining associations. This model is built in. People might later learn math, programming, and formal logic as rules for manipulating symbols within the framework of natural language learning. For a third example, the toy approach to modeling vision is to segment the image into regions and try to interpret the meaning of each region. Efforts like that have always stalled. That is not how people see. People learn to recognize visual features that they have seen before. Features are made up of weighted sums of lots of simpler features with learned weights. Features range from dots, edges, color, and motion at the lowest levels, to complex objects like faces at the higher levels. Vision is integrated with lots of other knowledge sources. You see what you expect to see. The common theme is that real AGI consists of a learning algorithm, an opaque knowledge representation, and a vast amount of training data and computing power. It is not an extension of a toy system where you code all the knowledge yourself. That doesn't scale. You can't know more than an AGI that knows more than you. So I suggest you do a little research instead of continuing to repeat all the mistakes that were made 50 years ago. You aren't the first person to do these kinds of experiments. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Mon, June 28, 2010 4:00:24 PM Subject: Re: [agi] A Primary Distinction for an AGI I also want to mention that I develop solutions to the toy problems with the real problems in mind On Mon, Jun 28, 2010 at 3:56 PM, David Jones davidher...@gmail.com wrote: That does not have to be the case. Yes, you need to know what problems you might have in more co... On Mon, Jun 28, 2010 at 3:41 PM, Russell Wallace russell.wall...@gmail.com wrote: On Mon, Jun 28, 2010 at 4:54 PM, David Jones davidher...@gmail.com wrote: But, that's w... --- agi Archives: https://www.listbox.com/mem... Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com agi | Archives | Modify Your Subscription *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] A Primary Distinction for an AGI
On Mon, 2010-06-28 at 16:15 +0100, Mike Tintner wrote: That's why Michael can't bear to even contemplate a world in which things and people behave unpredictably. (And Ben can't bear to contemplate a stockmarket that is obviously unpredictable). If he were an artist his instincts would be the opposite - he'd go for the irregular and patchy and unpredictable twists. If he were drawing a box going across a screen, he would have to put some irregularity in omewhere - put in some fits and starts and stops - there's always an irregular twist in the picture or the tale. An artist has to put some surprise and life into what he does - You patternise the things that are patternisable - like an erratic waving arm is still an arm, and it's pattern is erratic. Also, note I used to be a art and animation lecturer for 2 years ;) --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
