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Wednesday, September 9, 2009
Sensory Memory
Saturday, August 29, 2009
Portable device to detect suicide bombers
Washington, June 28 (ANI): A group of students have developed a portable device to detect the weapons of suicide bombers.
Improvised explosive devices (IEDs), the weapons of suicide bombers, are a major cause of soldier casualties in Iraq and Afghanistan.
Now, a group of University of Michigan (U-M) engineering undergraduate students have developed a new way to detect them.
The students invented portable, palm-sized metal detectors that could be hidden in trash cans, under tables or in flower pots, for example.
The detectors are designed to be part of a wireless sensor network that conveys to a base station where suspicious objects are located and who might be carrying them.
Compared with existing technology, the sensors are cheaper, lower-power and longer-range. Each of the sensors weighs about 2 pounds.
“Their invention outperforms everything that exists in the market today,” said Nilton Renno, a professor in the U-M Department of Atmospheric, Oceanic and Space Sciences.
The students undertook this project in Renno’s Engineering 450 senior level design class.
“They clearly have an excellent understanding of the problem. They also thought strategically and designed and optimized their solution. The combination of a movable command center with a wireless sensor network can be easily deployed in the field and adapted to different situations,” said Renno.
The core technology is based on a magnetometer, or metal detector, explained Ashwin Lalendran, an engineering student who worked on the project and graduated in May.
“We built it entirely in-house - the hardware and the software,” Lalendran said.
“Our sensors are small, flexible to deploy, inexpensive and scalable. It’s extremely novel technology,” he added. (ANI)
Monday, August 24, 2009
Sensor Network Simulator and Emulator
The Necessity of Network Simulation
The emergence of wireless sensor networks brought many open issues to network designers. Traditionally, the three main techniques for analyzing the performance of wired and wireless networks are analytical methods, computer simulation, and physical measurement. However, because of many constraints imposed on sensor networks, such as energy limitation, decentralized collaboration and fault tolerance, algorithms for sensor networks tend to be quite complex and usually defy analytical methods that have been proved to be fairly effective for traditional networks. Furthermore, few sensor networks have come into existence, for there are still many unsolved research problems, so measurement is virtually impossible. It appears that simulation is the only feasible approach to the quantitative analysis of sensor networks.
Why a New Simulator
ns2, perhaps the most widely used network simulator, has been extended to include some basic facilities to simulate sensor networks. However, one of the problems of ns2 is its object-oriented design that introduces much unnecessary interdependency between modules. Such interdependency sometimes makes the addition of new protocol models extremely difficult, only mastered by those who have intimate familiarity with the simulator. Being difficult to extend is not a major problem for simulators targeted at traditional networks, for there the set of popular protocols is relatively small. For example, Ethernet is widely used for wired LAN, IEEE 802.11 for wireless LAN, TCP for reliable transmission over unreliable media. For sensor networks, however, the situation is quite different. There are no such dominant protocols or algorithms and there will unlikely be any, because a sensor network is often tailored for a particular application with specific features, and it is unlikely that a single algorithm can always be the optimal one under various circumstances.
Many other publicly available network simulators, such as JavaSim, SSFNet, Glomosim and its descendant Qualnet, attempted to address problems that were left unsolved by ns2. Among them, JavaSim developers realized the drawback of object-oriented design and tried to attack this problem by building a component-oriented architecture. However, they chose Java as the simulation language, inevitably sacrificing the efficiency of the simulation. SSFNet and Glomosim designers were more concerned about parallel simulation, with the latter more focused on wireless networks. They are not superior to ns2 in terms of design and extensibility.
Features of SENSE
SENSE is designed to be an efficient and powerful sensor network simulator that is also easy of use. We identify the three most critical factors as:
Extensibility: The enabling force behind the fully extensibility network simulation architecture is our progress on component-based simulation. We introduced a component-port model that frees simulation models from interdependency usually found in an object-oriented architecture, and then proposed a simulation component classification that naturally solves the problem of handling simulated time. The component-port model makes simulation models extensible: a new component can replace an old one if they have compatible interfaces, and inheritance is not required. The simulation component classification makes simulation engines extensible: advanced users have the freedom to develop new simulation engines that meet their needs.
Reusability: The removal of interdependency between models also promotes reusability. A component developed for one simulation can be used in another if it satisfies the latter's requirements on the interface and semantics. There is another level of reusability made possible by the extensive use of C++ template: a component is usually declared as a template class so that it can handle different type of data.
Scalability: Unlike many parallel network simulators, especially SSFNet and Glomosim, parallelization is provided as an option to the users of SENSE. The reflects our belief that completely automated parallelization of sequential discrete event models, however tempting it may seem, is impossible, just as automated parallelization of sequential programs. Even if it possible, it is doomed to be inefficient. Therefore, parallelizable models require more effort than sequential models, but a good portion of users are not interested in parallel simulation at all. In SENSE, a parallel simulation engine can only execute components of compatible components. If a user is content with the default sequential simulation engine, then every component in the model repository can be reused.
Currently Available Components and Simulation Engines (as of Oct 21, 2006)
Battery Model:
Linear Battery
Discharge Rate Dependent and/or Relaxation Battery
Application Layer:
Random Neighbor
Constant Bit Rate
Network Layer:
Simple Flooding
A simplified version of ADOV without route repairing
A simplified version of DSR without route repairing
Self Selective Routing (SSR)
Self Healing Routing (SHR)
MAC Layer:
NullMAC
IEEE 802.11 with DCF
Physical Layer: Duplex Transceiver
Wireless Channel:
Free Space
Adjacency Matrix
Simulation Engine: CostSimEng (sequential)
Sunday, August 16, 2009
The Future of Artificial Intelligence
Dr. Mark Humphrys
University of Edinburgh
Artificial Intelligence (AI) is a perfect example of how sometimes science moves more slowly than we would have predicted. In the first flush of enthusiasm at the invention of computers it was believed that we now finally had the tools with which to crack the problem of the mind, and within years we would see a new race of intelligent machines. We are older and wiser now. The first rush of enthusiasm is gone, the computers that impressed us so much back then do not impress us now, and we are soberly settling down to understand how hard the problems of AI really are.
What is AI? In some sense it is engineering inspired by biology. We look at animals, we look at humans and we want to be able to build machines that do what they do. We want machines to be able to learn in the way that they learn, to speak, to reason and eventually to have consciousness. AI is engineering but, at this stage, is it also science? Is it, for example, modeling in cognitive science? We would like to think that is both engineering and science but the contributions that is has made to cognitive science so far are perhaps weaker than the contributions that biology has given to the engineering.
The confused history of AI
Looking back at the history of AI, we can see that perhaps it began at the wrong end of the spectrum. If AI had been tackled logically, it would perhaps have begun as an artificial biology, looking at living things and saying "Can we model these with machines?". The working hypothesis would have been that living things are physical systems so let's try and see where the modeling takes us and where it breaks down. Artificial biology would look at the evolution of physical systems in general, development from infant to adult, self-organization, complexity and so on. Then, as a subfield of that, a sort of artificial zoology that looks at sensorimotor behavior, vision and navigation, recognizing, avoiding and manipulating objects, basic, pre-linguistic learning and planning, and the simplest forms of internal representations of external objects. And finally, as a further subfield of this, an artificial psychology that looks at human behavior where we deal with abstract reasoning, language, speech and social culture, and all those philosophical conundrums like consciousness, free will and so forth.
That would have been a logical progression and is what should have happened. But what did happen was that what people thought of as intelligence was the stuff that impresses us. Our peers are impressed by things like doing complex mathematics and playing a good chess game. The ability to walk, in contrast, doesn't impress anyone. You can't say to your friends, "Look, I can walk", because your friends can walk too.
So all those problems that toddlers grapple with every day were seen as unglamorous, boring, and probably pretty easy anyway. The really hard problems, clearly, were things demanding abstract thought, like chess and mathematical theorem proving. Everyone ignored the animal and went straight to the human, and the adult human too, not even the child human. And this is what `AI' has come to mean - artificial adult human intelligence. But what has happened over the last 40-50 years - to the disappointment of all those who made breathless predictions about where AI would go - is that things such as playing chess have turned out to be incredibly easy for computers, whereas learning to walk and learning to get around in the world without falling over has proved to be unbelievably difficult.
And it is not as if we can ignore the latter skills and just carry on with human-level AI. It has proved very difficult to endow machines with `common sense', emotions and those other intangibles which seem to drive much intelligent human behavior, and it does seem that these may come more from our long history of interactions with the world and other humans than from any abstract reasoning and logical deduction. That is, the animal and child levels may be the key to making really convincing, well-rounded forms of intelligence, rather than the intelligence of chess-playing machines like Deep Blue, which are too easy to dismiss as `mindless'.
In retrospect, the new view makes sense. It took 3 billion years of evolution to produce apes, and then only another 2 million years or so for languages and all the things that we are impressed by to appear. That's perhaps an indication that once you've got the mobile, tactile monkey, once you've got the Homo erectus, those human skills can evolve fairly quickly. It may be a fairly trivial matter for language and reasoning to evolve in a creature which can already find its way around the world.
The new AI, and the new optimism That's certainly what the history of AI has served to bear out. As a result, there has been a revolution in the field which goes by names such as Artificial Life (AL) and Adaptive Behavior, trying to re-situate AI within the context of an artificial biology and zoology (respectively). The basic philosophy is that we need much more understanding of the animal substrates of human behavior before we can fulfil the dreams of AI in replicating convincing well-rounded intelligence.
(Incidentally, the reader should note that the terminology is in chaos, as fields re-group and re-define themselves. For example, I work on artificial zoology but describe myself casually as doing AI. This chaos can, however, be seen as a healthy sign of a field which has not yet stabilized. Any young scientist with imagination should realize that these are the kind of fields to get into. Who wants to be in a field where everything was solved long ago?)
So AI is not dead, but re-grouping, and is still being driven, as always, by testable scientific models. Discussions on philosophical questions, such as `What is life?' or `What is intelligence?', change little over the years. There have been numerous attempts, from Roger Penrose to Gerald Edelman, to disprove AI (show that it is impossible) but none of these attempted revolutions has yet gathered much momentum. This is not just because of lack of agreement with their philosophical analysis (although there is plenty of that), but also perhaps because they fail to provide an alternative paradigm in which we can do science. Progress, as is normal in science, comes from building things and running experiments, and the flow of new and strange machines from AI laboratories is not remotely exhausted. On the contrary, it has been recently invigorated by the new biological approach.
In fact, the old optimism has even been resurrected. Professor Kevin Warwick of the University of Reading has recently predicted that the new approach will lead to human-level AI in our lifetimes. But I think we have learned our lesson on that one. I, and many like me in new AI, imagine that this is still Physics before Newton, that the field might have a good one or two hundred years left to run. The reason is that there is no obvious way of getting from here to there - to human-level intelligence from the rather useless robots and brittle software programs that we have nowadays. A long series of conceptual breakthroughs are needed, and this kind of thinking is very difficult to timetable. What we are trying to do in the next generation is essentially to find out what are the right questions to ask.
It may never happen (but not for the reasons you think)
I think that people who are worried about robots taking over the world should go to a robotics conference and watch these things try to walk. They fall over, bump into walls and end up with their legs thrashing or wheels spinning in the air. I'm told that in this summer's Robotic Football competition, the losing player scored all five goals - 2 against the opposing robot, and 3 against himself. The winner presumably just fell over.
Robots are more helpless than threatening. They are really quite sweet. I was in the MIT robotics laboratory once looking at Cog, Rodney Brooks' latest robot. Poor Cog has no legs. He is a sort of humanoid, a torso stuck on a stand with arms, grippers, binocular vision and so on. I saw Cog on a Sunday afternoon in a darkened laboratory when everyone had gone home and I felt sorry for him which I know is mad. But it was Sunday afternoon and no one was going to come and play with him. If you consider the gulf between that and what most animals experience in their lives, surrounded by a tribe of fellow infants and adults, growing up with parents who are constantly with them and constantly stimulating them, then you understand the incredibly limited kind of life that artificial systems have.
The argument I am developing is that there may be limits to AI, not because the hypothesis of `strong AI' is false, but for more mundane reasons. The argument, which I develop further on my website, is that you can't expect to build single isolated AI's, alone in laboratories, and get anywhere. Unless the creatures can have the space in which to evolve a rich culture, with repeated social interaction with things that are like them, you can't really expect to get beyond a certain stage. If we work up from insects to dogs to Homo erectus to humans, the AI project will I claim fall apart somewhere around the Homo erectus stage because of our inability to provide them with a real cultural environment. We cannot make millions of these things and give them the living space in which to develop their own primitive societies, language and cultures. We can't because the planet is already full. That's the main argument, and the reason for the title of this talk.
So what will happen?
So what will happen? What will happen over the next thirty years is that will see new types of animal-inspired machines that are more `messy' and unpredictable than any we have seen before. These machines will change over time as a result of their interactions with us and with the world. These silent, pre-linguistic, animal-like machines will be nothing like humans but they will gradually come to seem like a strange sort of animal. Machines that learn, familiar to researchers in labs for many years, will finally become mainstream and enter the public consciousness.
What category of problems could animal-like machines address? The kind of problems we are going to see this approach tackle will be problems that are somewhat noise and error resistant and that do not demand abstract reasoning. A special focus will be behavior that is easier to learn than to articulate - most of us know how to walk but we couldn't possibly tell anyone how we do it. Similarly with grasping objects and other such skills. These things involve building neural networks, filling in state-spaces and so on, and cannot be captured as a set of rules that we speak in language. You must experience the dynamics of your own body in infancy and thrash about until the changing internal numbers and weights start to converge on the correct behavior. Different bodies mean different dynamics. And robots that can learn to walk can learn other sensorimotor skills that we can neither articulate nor perform ourselves.
What are examples of these type of problems? Well, for example, there are already autonomous lawnmowers that will wander around gardens all afternoon. The next step might be autonomous vacuum cleaners inside the house (though clutter and stairs present immediate problems for wheeled robots). These are all sorts of other uses for artificial animals in areas where people find jobs dangerous or tedious - land-mine clearance, toxic waste clearance, farming, mining, demolition, finding objects and robotic exploration, for example. Any jobs done currently or traditionally by animals would be a focus. We are familiar already from the Mars Pathfinder and other examples that we can send autonomous robots not only to inhospitable places, but also send them there on cheap one-way `suicide' missions. (Of course, no machine ever `dies', since we can restore its mind in a new body on earth after the mission.)
Whether these type of machines may have a future in the home is an interesting question. If it ever happens, I think it will be because the robot is treated as a kind of pet, so that a machine roaming the house is regarded as cute rather than creepy. Machines that learn tend to develop an individual, unrepeatable character which humans can find quite attractive. There are already a few games in software - such as the Windows-based game Creatures, and the little Tamagotchi toys - whose personalities people can get very attached to. A major part of the appeal is the unique, fragile and unrepeatable nature of the software beings you interact with. If your Creature dies, you may never be able to raise another one like it again. Machines in the future will be similar, and the family robot will after a few years be, like a pet, literally irreplaceable.
What will hold things up? There are many things that could hold up progress but hardware is the one that is staring us in the face at the moment. Nobody is going to buy a robotic vacuum cleaner that costs �5000 no matter how many big cute eyes are painted on it or even if it has a voice that says, "I love you". Many conceptual breakthroughs will be needed to create artificial animals. The major theoretical issue to be solved is probably representation: what is language and how do we classify the world. We say `That's a table' and so on for different objects, but what does an insect do, what is going on in an insect's head when it distinguishes objects in the world, what information is being passed around inside, what kind of data structures are they using. Each robot will have to learn an internal language customized for its sensorimotor system and the particular environmental niche in which it finds itself. It will have to learn this internal language on its own, since any representations we attempt to impose on it, coming from a different sensorimotor world, will probably not work.
Predictions
Finally, what will be the impact on society of animal-like machines? Let's make a few predictions that I will later look back and laugh at.
First, family robots may be permanently connected to wireless family intranets, sharing information with those who you want to know where you are. You may never need to worry if your loved ones are alright when they are late or far away, because you will be permanently connected to them. Crime may get difficult if all family homes are full of half-aware, loyal family machines. In the future, we may never be entirely alone, and if the controls are in the hands of our loved ones rather than the state, that may not be such a bad thing.
Slightly further ahead, if some of the intelligence of the horse can be put back into the automobile, thousands of lives could be saved, as cars become nervous of their drunk owners, and refuse to get into positions where they would crash at high speed. We may look back in amazement at the carnage tolerated in this age, when every western country had road deaths equivalent to a long, slow-burning war. In the future, drunks will be able to use cars, which will take them home like loyal horses. And not just drunks, but children, the old and infirm, the blind, all will be empowered.
Eventually, if cars were all (wireless) networked, and humans stopped driving altogether, we might scrap the vast amount of clutter all over our road system - signposts, markings, traffic lights, roundabouts, central reservations - and return our roads to a soft, sparse, eighteenth-century look. All the information - negotiation with other cars, traffic and route updates - would come over the network invisibly. And our towns and countryside would look so much sparser and more peaceful.
Conclusion
I've been trying to give an idea of how artificial animals could be useful, but the reason that I'm interested in them is the hope that artificial animals will provide the route to artificial humans. But the latter is not going to happen in our lifetimes (and indeed may never happen, at least not in any straightforward way).
In the coming decades, we shouldn't expect that the human race will become extinct and be replaced by robots. We can expect that classical AI will go on producing more and more sophisticated applications in restricted domains - expert systems, chess programs, Internet agents - but any time we expect common sense we will continue to be disappointed as we have been in the past. At vulnerable points these will continue to be exposed as `blind automata'. Whereas animal-based AI or AL will go on producing stranger and stranger machines, less rationally intelligent but more rounded and whole, in which we will start to feel that there is somebody at home, in a strange animal kind of way. In conclusion, we won't see full AI in our lives, but we should live to get a good feel for whether or not it is possible, and how it could be achieved by our descendants.
University of Edinburgh
Artificial Intelligence (AI) is a perfect example of how sometimes science moves more slowly than we would have predicted. In the first flush of enthusiasm at the invention of computers it was believed that we now finally had the tools with which to crack the problem of the mind, and within years we would see a new race of intelligent machines. We are older and wiser now. The first rush of enthusiasm is gone, the computers that impressed us so much back then do not impress us now, and we are soberly settling down to understand how hard the problems of AI really are.
What is AI? In some sense it is engineering inspired by biology. We look at animals, we look at humans and we want to be able to build machines that do what they do. We want machines to be able to learn in the way that they learn, to speak, to reason and eventually to have consciousness. AI is engineering but, at this stage, is it also science? Is it, for example, modeling in cognitive science? We would like to think that is both engineering and science but the contributions that is has made to cognitive science so far are perhaps weaker than the contributions that biology has given to the engineering.
The confused history of AI
Looking back at the history of AI, we can see that perhaps it began at the wrong end of the spectrum. If AI had been tackled logically, it would perhaps have begun as an artificial biology, looking at living things and saying "Can we model these with machines?". The working hypothesis would have been that living things are physical systems so let's try and see where the modeling takes us and where it breaks down. Artificial biology would look at the evolution of physical systems in general, development from infant to adult, self-organization, complexity and so on. Then, as a subfield of that, a sort of artificial zoology that looks at sensorimotor behavior, vision and navigation, recognizing, avoiding and manipulating objects, basic, pre-linguistic learning and planning, and the simplest forms of internal representations of external objects. And finally, as a further subfield of this, an artificial psychology that looks at human behavior where we deal with abstract reasoning, language, speech and social culture, and all those philosophical conundrums like consciousness, free will and so forth.
That would have been a logical progression and is what should have happened. But what did happen was that what people thought of as intelligence was the stuff that impresses us. Our peers are impressed by things like doing complex mathematics and playing a good chess game. The ability to walk, in contrast, doesn't impress anyone. You can't say to your friends, "Look, I can walk", because your friends can walk too.
So all those problems that toddlers grapple with every day were seen as unglamorous, boring, and probably pretty easy anyway. The really hard problems, clearly, were things demanding abstract thought, like chess and mathematical theorem proving. Everyone ignored the animal and went straight to the human, and the adult human too, not even the child human. And this is what `AI' has come to mean - artificial adult human intelligence. But what has happened over the last 40-50 years - to the disappointment of all those who made breathless predictions about where AI would go - is that things such as playing chess have turned out to be incredibly easy for computers, whereas learning to walk and learning to get around in the world without falling over has proved to be unbelievably difficult.
And it is not as if we can ignore the latter skills and just carry on with human-level AI. It has proved very difficult to endow machines with `common sense', emotions and those other intangibles which seem to drive much intelligent human behavior, and it does seem that these may come more from our long history of interactions with the world and other humans than from any abstract reasoning and logical deduction. That is, the animal and child levels may be the key to making really convincing, well-rounded forms of intelligence, rather than the intelligence of chess-playing machines like Deep Blue, which are too easy to dismiss as `mindless'.
In retrospect, the new view makes sense. It took 3 billion years of evolution to produce apes, and then only another 2 million years or so for languages and all the things that we are impressed by to appear. That's perhaps an indication that once you've got the mobile, tactile monkey, once you've got the Homo erectus, those human skills can evolve fairly quickly. It may be a fairly trivial matter for language and reasoning to evolve in a creature which can already find its way around the world.
The new AI, and the new optimism That's certainly what the history of AI has served to bear out. As a result, there has been a revolution in the field which goes by names such as Artificial Life (AL) and Adaptive Behavior, trying to re-situate AI within the context of an artificial biology and zoology (respectively). The basic philosophy is that we need much more understanding of the animal substrates of human behavior before we can fulfil the dreams of AI in replicating convincing well-rounded intelligence.
(Incidentally, the reader should note that the terminology is in chaos, as fields re-group and re-define themselves. For example, I work on artificial zoology but describe myself casually as doing AI. This chaos can, however, be seen as a healthy sign of a field which has not yet stabilized. Any young scientist with imagination should realize that these are the kind of fields to get into. Who wants to be in a field where everything was solved long ago?)
So AI is not dead, but re-grouping, and is still being driven, as always, by testable scientific models. Discussions on philosophical questions, such as `What is life?' or `What is intelligence?', change little over the years. There have been numerous attempts, from Roger Penrose to Gerald Edelman, to disprove AI (show that it is impossible) but none of these attempted revolutions has yet gathered much momentum. This is not just because of lack of agreement with their philosophical analysis (although there is plenty of that), but also perhaps because they fail to provide an alternative paradigm in which we can do science. Progress, as is normal in science, comes from building things and running experiments, and the flow of new and strange machines from AI laboratories is not remotely exhausted. On the contrary, it has been recently invigorated by the new biological approach.
In fact, the old optimism has even been resurrected. Professor Kevin Warwick of the University of Reading has recently predicted that the new approach will lead to human-level AI in our lifetimes. But I think we have learned our lesson on that one. I, and many like me in new AI, imagine that this is still Physics before Newton, that the field might have a good one or two hundred years left to run. The reason is that there is no obvious way of getting from here to there - to human-level intelligence from the rather useless robots and brittle software programs that we have nowadays. A long series of conceptual breakthroughs are needed, and this kind of thinking is very difficult to timetable. What we are trying to do in the next generation is essentially to find out what are the right questions to ask.
It may never happen (but not for the reasons you think)
I think that people who are worried about robots taking over the world should go to a robotics conference and watch these things try to walk. They fall over, bump into walls and end up with their legs thrashing or wheels spinning in the air. I'm told that in this summer's Robotic Football competition, the losing player scored all five goals - 2 against the opposing robot, and 3 against himself. The winner presumably just fell over.
Robots are more helpless than threatening. They are really quite sweet. I was in the MIT robotics laboratory once looking at Cog, Rodney Brooks' latest robot. Poor Cog has no legs. He is a sort of humanoid, a torso stuck on a stand with arms, grippers, binocular vision and so on. I saw Cog on a Sunday afternoon in a darkened laboratory when everyone had gone home and I felt sorry for him which I know is mad. But it was Sunday afternoon and no one was going to come and play with him. If you consider the gulf between that and what most animals experience in their lives, surrounded by a tribe of fellow infants and adults, growing up with parents who are constantly with them and constantly stimulating them, then you understand the incredibly limited kind of life that artificial systems have.
The argument I am developing is that there may be limits to AI, not because the hypothesis of `strong AI' is false, but for more mundane reasons. The argument, which I develop further on my website, is that you can't expect to build single isolated AI's, alone in laboratories, and get anywhere. Unless the creatures can have the space in which to evolve a rich culture, with repeated social interaction with things that are like them, you can't really expect to get beyond a certain stage. If we work up from insects to dogs to Homo erectus to humans, the AI project will I claim fall apart somewhere around the Homo erectus stage because of our inability to provide them with a real cultural environment. We cannot make millions of these things and give them the living space in which to develop their own primitive societies, language and cultures. We can't because the planet is already full. That's the main argument, and the reason for the title of this talk.
So what will happen?
So what will happen? What will happen over the next thirty years is that will see new types of animal-inspired machines that are more `messy' and unpredictable than any we have seen before. These machines will change over time as a result of their interactions with us and with the world. These silent, pre-linguistic, animal-like machines will be nothing like humans but they will gradually come to seem like a strange sort of animal. Machines that learn, familiar to researchers in labs for many years, will finally become mainstream and enter the public consciousness.
What category of problems could animal-like machines address? The kind of problems we are going to see this approach tackle will be problems that are somewhat noise and error resistant and that do not demand abstract reasoning. A special focus will be behavior that is easier to learn than to articulate - most of us know how to walk but we couldn't possibly tell anyone how we do it. Similarly with grasping objects and other such skills. These things involve building neural networks, filling in state-spaces and so on, and cannot be captured as a set of rules that we speak in language. You must experience the dynamics of your own body in infancy and thrash about until the changing internal numbers and weights start to converge on the correct behavior. Different bodies mean different dynamics. And robots that can learn to walk can learn other sensorimotor skills that we can neither articulate nor perform ourselves.
What are examples of these type of problems? Well, for example, there are already autonomous lawnmowers that will wander around gardens all afternoon. The next step might be autonomous vacuum cleaners inside the house (though clutter and stairs present immediate problems for wheeled robots). These are all sorts of other uses for artificial animals in areas where people find jobs dangerous or tedious - land-mine clearance, toxic waste clearance, farming, mining, demolition, finding objects and robotic exploration, for example. Any jobs done currently or traditionally by animals would be a focus. We are familiar already from the Mars Pathfinder and other examples that we can send autonomous robots not only to inhospitable places, but also send them there on cheap one-way `suicide' missions. (Of course, no machine ever `dies', since we can restore its mind in a new body on earth after the mission.)
Whether these type of machines may have a future in the home is an interesting question. If it ever happens, I think it will be because the robot is treated as a kind of pet, so that a machine roaming the house is regarded as cute rather than creepy. Machines that learn tend to develop an individual, unrepeatable character which humans can find quite attractive. There are already a few games in software - such as the Windows-based game Creatures, and the little Tamagotchi toys - whose personalities people can get very attached to. A major part of the appeal is the unique, fragile and unrepeatable nature of the software beings you interact with. If your Creature dies, you may never be able to raise another one like it again. Machines in the future will be similar, and the family robot will after a few years be, like a pet, literally irreplaceable.
What will hold things up? There are many things that could hold up progress but hardware is the one that is staring us in the face at the moment. Nobody is going to buy a robotic vacuum cleaner that costs �5000 no matter how many big cute eyes are painted on it or even if it has a voice that says, "I love you". Many conceptual breakthroughs will be needed to create artificial animals. The major theoretical issue to be solved is probably representation: what is language and how do we classify the world. We say `That's a table' and so on for different objects, but what does an insect do, what is going on in an insect's head when it distinguishes objects in the world, what information is being passed around inside, what kind of data structures are they using. Each robot will have to learn an internal language customized for its sensorimotor system and the particular environmental niche in which it finds itself. It will have to learn this internal language on its own, since any representations we attempt to impose on it, coming from a different sensorimotor world, will probably not work.
Predictions
Finally, what will be the impact on society of animal-like machines? Let's make a few predictions that I will later look back and laugh at.
First, family robots may be permanently connected to wireless family intranets, sharing information with those who you want to know where you are. You may never need to worry if your loved ones are alright when they are late or far away, because you will be permanently connected to them. Crime may get difficult if all family homes are full of half-aware, loyal family machines. In the future, we may never be entirely alone, and if the controls are in the hands of our loved ones rather than the state, that may not be such a bad thing.
Slightly further ahead, if some of the intelligence of the horse can be put back into the automobile, thousands of lives could be saved, as cars become nervous of their drunk owners, and refuse to get into positions where they would crash at high speed. We may look back in amazement at the carnage tolerated in this age, when every western country had road deaths equivalent to a long, slow-burning war. In the future, drunks will be able to use cars, which will take them home like loyal horses. And not just drunks, but children, the old and infirm, the blind, all will be empowered.
Eventually, if cars were all (wireless) networked, and humans stopped driving altogether, we might scrap the vast amount of clutter all over our road system - signposts, markings, traffic lights, roundabouts, central reservations - and return our roads to a soft, sparse, eighteenth-century look. All the information - negotiation with other cars, traffic and route updates - would come over the network invisibly. And our towns and countryside would look so much sparser and more peaceful.
Conclusion
I've been trying to give an idea of how artificial animals could be useful, but the reason that I'm interested in them is the hope that artificial animals will provide the route to artificial humans. But the latter is not going to happen in our lifetimes (and indeed may never happen, at least not in any straightforward way).
In the coming decades, we shouldn't expect that the human race will become extinct and be replaced by robots. We can expect that classical AI will go on producing more and more sophisticated applications in restricted domains - expert systems, chess programs, Internet agents - but any time we expect common sense we will continue to be disappointed as we have been in the past. At vulnerable points these will continue to be exposed as `blind automata'. Whereas animal-based AI or AL will go on producing stranger and stranger machines, less rationally intelligent but more rounded and whole, in which we will start to feel that there is somebody at home, in a strange animal kind of way. In conclusion, we won't see full AI in our lives, but we should live to get a good feel for whether or not it is possible, and how it could be achieved by our descendants.
Friday, August 7, 2009
THE FUTURE OF SENSOR NETWORKS
From thermostats in building automation to computer numerical controls in factory automation, device and sensor information is traveling over the same technology that is powering our e-world. But how well is it working, and where is the trend taking us?
Mark Fondl, ICT and Lynn Linse, Lantronix, Inc.
The volume of data carried on a network increases as the devices on it become more sophisticated. Low-end devices may transmit data in 1 bit increments, indicating a simple on/off condition. High-end sensors, on the other hand, contain local intelligence and transmit complex data types measured in bytes (see Figure 1.)
To meet the need for more complex data communications, the industry has looked to other networks. In the process, many have asked: Can Ethernet/TCP/IP be used to replace some of these networks? Can some of the networks be integrated into higher level Ethernet architectures (e.g., DeviceNet over Ethernet, Interbus-S over Ethernet, LonWorks over Ethernet)? Some of the answers to these questions can be found in an examination of implementation costs, ease of use, performance, and vendor support.
Implemention Costs
Ethernet costs are not inherently lower than the other networks. For the foreseeable future, cost can be justified only by concentrating multiple sensors on one Ethernet interface.
Other factors contributing to the cost of implementation are the CPU resources. Here, Ethernet does not compare favorably with an architecture such as DeviceNet. For example, DeviceNet can run on a CPU with 4000 bytes of code and 176 bytes of RAM. Ethernet, though, requires a minimum of 64,000 bytes of code and 64,000 bytes of RAM. Here, many implementers insist the minimum is more like 256 KB each, but they would prefer 2–4 MB of code and RAM. If the volumes are low and the margins are high, the simpler software offsets Ethernet’s greater CPU requirements. But as volumes rise and margins shrink, the lower resource needs of something like DeviceNet will force a price premium for Ethernet with the same sales volumes.
Consideration of connection costs—especially for bit-level sensors in industrial environments—causes some to favor ASI and DeviceNet wiring. Optimized for machines in which many discrete sensors are located in a relatively small area (50 m), these sensor networks are ideal. But extending their range poses some difficulty, and based on response times of these clusters, bridging with Ethernet may provide value.
Ease of Use
Here the focus is on long-term support of software configuration. This has multiple facets:
TCP/IP ease of use is based on the wide availability of skilled technicians and tools.
But TCP/IP (at the moment) lacks the high-level standards that allow auto-replacement, which is supported in DeviceNet and ASI.
The complexity of the options found in TCP/IP can overwhelm inexperienced users.
Systems such as DeviceNet and ASI are well suited for applications in which the communications are kept on a local scale. But when the data travel into extended areas and applications in which specialized network skills are required, then a commercial TCû/IP network becomes attractive. Network evaluation can be as simple as a “ping” from almost any computer. Commercial technologies aimed at simple diagnostics will eventually become common. Training individuals to support TCP/IP will be much easier. Device networks feeling this pressure will undoubtedly develop simpler and even browser-based tools.
Performance
With a well-designed network, TCP/IP will perform quite well. But the network must be well designed. An isolated subnet with limited or master/slave functions can expect reliable 2–5 ms times. But the instant you add routers or other noncontrol traffic (including Web servers), you can expect delays of 500 ms or more. So Ethernet performs only as well as the user designs it to perform.
A sparsely designed Ethernet, which underuses its capacity, can rival or beat any deterministic control network. But a poorly designed Ethernet can be an operational nightmare.
Web access via TCP/IP is a common unrealistic hope. With control traffic running at 5–10,000 Bps, users often overlook the fact that a Web page can attempt to force millions of bytes of data through a network at the same time that control data are being transmitted, dwarfing the control traffic. Users and vendors still have to learn the tradeoffs here. Some Web access is wonderful, but this needs to be shared/supplemented with Web resources stored off the control network.
To improve performance on the sensor level, automation companies are experimenting with UDP and variations of limited TCP/IP stacks. These stacks listen only to certain types of transmissions, ignoring others and eliminating a retry structure for a high-speed master-slave structure. This is similar to how I/O has worked for years with PLCs. The architecture and wiring are Ethernet, but the openness is traded for performance.
Most systems don’t need this level of performance and should stay with standard Ethernet. As the technology continues to improve, you can imagine a time when a conventional approach will surpass proprietary methods.
Service and Support
Support for Ethernet TCP/IP systems is good, but the actual media (e.g., cables, connectors, and power) are rather unindustrial. Many TCP/IP experts have an IT mentality, not a plant-floor mindset, so they misunderstand what users want or modify existing systems in ways that hurt the industrial user in an effort to improve the system according to other criteria.
«o users still need to learn about the technology and watch over the shoulders of the experts. Ask questions, and make sure the IT experts learn how you view the problem.
Openness
Ethernet TCP/IP systems provide an open network platform, but high-level application standards are still in flux. TCP/IP is often viewed as a false open standard because its higher layers are proprietary. Modbus/TCP and some of the new encapsulations of òeviceNet, Foundation Fieldbus, and Profibus will help in this area by providing interfaces that will allow different protocols to communicate with each other. But that still leaves a lot of application standards that are not interoperable.
Many of these network architectures encapsulate other protocols, but the interoperability does not extend to the physical and transport layers. This prevents the various buses from communicating with each other. So there is still a great deal of work to be done in this area.
Flexibility
Just about anything is possible with global TCP/IP, but the reliability of its performance depends on the skill of the implementer. However, there is still a need for a more industrial and optimized sensor bus.
As data requirements increase, hybrid and direct Ethernet systems will become commonplace. High-level sensors with serial ports are already being linked over Ethernet. The protocols are transported transparently on top of TCP/IP and delivered to a host, which in some cases is unaware that they were carried over a LAN.
Friday, July 10, 2009
Monday, June 29, 2009
Purdue, Japanese Researchers To Create More Human-Like Robots
Purdue University is leading a four-year project to enable humanoid robots to move more like people and adapt quickly to new situations so that they can complete a variety of tasks they weren't specifically programmed to perform.
"We are trying to give humanoid robots the ability to behave and move more like human beings, to have the skill-learning capabilities of humans," said C.S. George Lee, a Purdue professor of electrical and computer engineering who specializes in robotics.
Purdue will collaborate with researchers from the Advanced Institute of Science and Technology in Japan, which leads the world in humanoid-robot research.
"What we are going to try to do is capture the essence of how people learn movement skills," said Howard Zelaznik, a Purdue professor of health and kinesiology.
The work is funded with a four-year, $900,000 grant from the National Science Foundation's Information Technology Research program.
Humans are able to automatically combine a series of basic movements, such as pushing, lifting or grasping, to perform new tasks on the fly.
"For example, if I asked you to open a door and you were carrying two bags of groceries, you would know how to do that the first time through because you have in your repertoire the flexibility to combine old skills into new ones," Zelaznik said. "We'd like to see whether we can figure out if there is a computationally reasonable way for a robot to take a set of skills and combine them into new skills rather efficiently, flexibly and quickly."
Humanoid robots are robots that resemble people. A popular example of such robots is Honda Motor Co.'s "ASIMO," which walks upright, has two arms, two legs and a head.
Although today's humanoid robots represent an engineering feat, they do not move the way people do.
"They are very stiff and mechanical," Lee said.
One important reason to teach humanoid robots how to quickly learn new movements is so that they will be better able to assist people.
"Imagine that a person in a wheelchair has just dropped his or her keys under the wheelchair, and the robot wasn't programmed specifically to retrieve them from that location," Zelaznik said. "We are trying to figure out how best to make that robot adaptable so that it can learn new skills quickly."
The Purdue team, which includes four doctoral students, will use specialized equipment to record human movements in three dimensions.
Tiny coiled wire "receivers" will be placed around certain body parts, such as fingers and arms, as a person moves in a low-level magnetic field. As the person moves, these coiled wire receivers will induce a current, which will be tracked by laboratory computers. Lee and Zelaznik will then be able to see the basic movement patterns and hope to use that information to build mathematical models to make robots move more like people.
The ultimate goal is to create software that enables robots to combine several of the most "primitive" skills to perform more complex movements.
"We are not trying to make the robot perfect," Zelaznik said. "People are not perfect. When we move, we are variable, we are imprecise, we make errors. We don't exactly do the same thing time in and time out. We believe it is this imperfection that allows us the capability to be flexible."
C. S. George Lee, a Purdue professor of electrical and computer engineering, from left, and Howard Zelaznik, a professor of health and kinesiology, work with doctoral student Nicole Rheaume to study how humans learn movement skills. Rheaume draws s circle with a pen equipped with a tiny coiled wire "receiver." A nearby magnet induces a low-level magnetic field. As Rheaume moves the pen, the wire coil induces an electrical current that enables researchers to track the movements. The ultimate goal is to create software that enables robots to combine several of the most "primitive" skills to perform more complex movements, much as people are able to combine a series of basic movements to perform specific tasks. (Purdue News Service photo/David Umberger)
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