1. When referring to unqualified consciousness, no structure is implied. Just as water has no shape, pure consciousness has no structure. Self-awareness implies a concept of self, and therefore structure.
2. Perhaps bacteria feel too. Maybe not. I think that's one of those unknowables.
3. Unconscious life is "inferior" from an ethical viewpoint. Plants can't suffer, so we treat them differently than, say, dogs and cats. However, we have an ethical duty to avoid negligent extinction of unconscious species.
Consciousness is not a requirement for sustained life. Perhaps life will evolve away from consciousness. It would be a pity, I think, but it could happen.
Full Version: The use of Math on the Path
robots don't 'need', Rick. there's nobody home in there
and, if I do what the boss needs me to do, it is because I need the boss's money
and, if I do what the boss needs me to do, it is because I need the boss's money
Just for info:
Whatever happened to machines that think?
23 April 2005
NewScientist.com news service
Justin Mullins
THE SINGULARITY: In futurology, a technological singularity is a predicted point in the development of a civilisation at which technological progress accelerates beyond the ability of present-day humans to fully comprehend or predict. The singularity can more specifically refer to the advent of smarter-than-human intelligence, and the cascading technological progress assumed to follow.
Wikipedia - the online encyclopedia
CLEVER computers are everywhere. From robotic lawnmowers to intelligent lighting, washing machines and even car engines that self-diagnose faults, there's a silicon brain in just about every modern device you can think of. But can you honestly call any machine intelligent in a meaningful sense of the word?
One rainy afternoon last February I decided to find out. I switched on the computer in my study, and logged on to www.intellibuddy.com, home to one of the leading artificial intelligences on the planet, to see what the state-of-the-art has to offer.
Intellibuddy: Hi, I'm Intellibuddy!
Nice to meet you.
JM: I'm writing an article about artificial intelligence.
Intellibuddy: Will you include me in your essay?
JM: I will if you can persuade me you really are intelligent.
Intellibuddy: Would you recommend this action to a friend?
JM: Errr...are you intelligent?
Intellibuddy: Ummm I am highly intelligent. I have many intellectual functions.
JM: What are your intellectual functions?
Intellibuddy: Thought, reason, intelligence, logic and deduction.
JM: What is your IQ?
Intellibuddy: My IQ is about 250 in human terms.
JM: Wow, you're cleverer than my editor.
Intellibuddy: I'm glad your [sic] impressed, User.
You think I am cleverer than my editor.
JM: No, cleverer than my editor.
Intellibuddy: I'm glad your impressed, User.
You think I am cleverer than my editor.
JM: No, cleverer than my editor.
Intellibuddy: I'm glad your impressed, User.
You think I am cleverer than my editor...
So much for today's artificial intelligence. Intellibuddy is a version of one of the world's most successful chatbots, called ALICE (Artificial Linguistic Internet Computer Entity) and invented in 1995 by Richard Wallace, an independent researcher based in San Francisco. You can find versions of ALICE all over the web; the software is free. But whichever version you choose to chat to, the results are disappointingly similar. While some conversations have promising starts, all descend into the type of gibberish that only artificial intelligence can produce.
And it's not as if there hasn't been time to perfect the idea. The first chatbot appeared in the 1960s. Back then, the very idea of chatting to a computer astounded people. Today, a conversation with a computer is viewed more on the level of talking to your pet pooch - cute, but ultimately meaningless.
The problem with chatbots is a symptom of a deeper malaise in the field of artificial intelligence (AI). For years researchers have been promising to deliver technology that will make computers we can chat to like friends, robots that function as autonomous servants, and one day, for better or worse, even produce conscious machines. Yet we appear to be as far away as ever from any of these goals.
But that could soon change. In the next few months, after being patiently nurtured for 22 years, an artificial brain called Cyc (pronounced "psych") will be put online for the world to interact with. And it's only going to get cleverer. Opening Cyc up to the masses is expected to accelerate the rate at which it learns, giving it access to the combined knowledge of millions of people around the globe as it hoovers up new facts from web pages, webcams and data entered manually by anyone who wants to contribute.
Crucially, Cyc's creator says it has developed a human trait no other AI system has managed to imitate: common sense. "I believe we are heading towards a singularity and we will see it in less than 10 years," says Doug Lenat of Cycorp, the system's creator.
But not all AI researchers welcome such claims. To many, they only fuel the sort of hype that spawned what became known as the "AI winter" of the 1990s. This was a time that saw government funding for AI projects cut and hopes dashed by the cold reality that making computers intelligent in the way we humans perceive intelligence is just too hard. Many scientists working in areas that were once considered core AI now refuse even to be associated with the term. To them, the phrase "artificial intelligence" has been forever tainted by a previous generation of researchers who hyped the technology and the fabled singularity beyond reason. For them, the study of artificial intelligence is a relic of a bygone era that has been superseded by research with less ambitious, more focused goals. Arguably, this has already led to a limited form of AI appearing all around us. Elements of AI research are now used in everything from credit-scoring systems and automatic camera focus controllers, to number plate recognition in speed cameras and spaceship navigation.
AI is in many ways as old as computing itself. The purpose of building computers in the first place was, after all, to perform mathematical tasks such as code breaking that were too tough for humans to tackle. It was in 1950 that Alan Turing, the celebrated second world war code breaker, mathematician and arguably inventor of the first computer, formulated the test that would become the benchmark by which the intelligence of all computer programs would subsequently be measured (see "Turing's test" - below). Even in Turing's day, computers were beginning to outperform humans in certain specific tasks. And as early as 1948, John von Neumann, one of the fathers of the computer revolution, said: "You insist that there is something a machine cannot do. If you will tell me precisely what it is that a machine cannot do, then I can always make a machine which will do just that." It seemed just a matter of time before computers would outperform people in most mental tasks.
But many scientists and philosophers baulked at the idea. They claimed that there was something about being human that a computer could never match. At first the arguments centred on properties such as consciousness and self-awareness, but disagreement over what exactly these terms meant and how we could test for them prevented the debate from making any real progress. Others admitted computers could become intelligent but said they would never develop qualities such as compassion or wisdom which were uniquely human, the result of our emotional upbringing and experience. The definition of intelligence itself began to slip through the philosophers' fingers, and the disagreements continue today.
Most researchers would at least encompass in their definition of AI the goal of building a machine that behaves in ways that would be called intelligent if a human were responsible for that behaviour. Others would cast the definition even wider. Ant colonies and immune systems, they say, also behave intelligently in ways that are utterly non-human. But to get bogged down in the debate is to fall into the same trap that has plagued AI for decades. The Turing test is a reasonable yardstick. We will know an intelligent machine when we can talk to one without realising it is a machine, and programs of Intellibuddy's ilk clearly fall short of this requirement.
Intellibuddy is merely one of the latest in a long line of chatbots. In 1966 Joseph Weizenbaum, a computer scientist at the Massachusetts Institute of Technology, developed the first chatbot, named Eliza after Eliza Doolittle, the character in George Bernard Shaw's Pygmalion who is taught to imitate upper-class English speech. The program was designed to mimic a psychotherapist and conversed with its patient mainly through a simple rhetorical trick: it reworded the patient's statements as questions. For example:
“If Japan had developed truly intelligent machines there was no telling what it might have been capable of. An arms race of sorts ensued between the US and Japan”
Patient: I want to cry
Eliza: Why do you say you want to cry?
Patient: Because my mother hates me
Eliza: Who else in your family hates you?
...and so on. Eliza was programmed to spot key phrases in its interlocutor's sentences and plug them into preformed sentences of its own. It was hugely successful. The idea of talking to a computer astounded people, and there are even anecdotes of people developing emotional attachments to Eliza.
This early success contributed to a sense of optimism that the problems of AI could be overcome, much of it based on the idea that some kind of grand unified theory of mind would emerge that would offer up a scheme to create artificial intelligence on a platter. The late 1960s and early 1970s saw feverish speculation about the impact intelligent machines might have on the world and the advantages they would bring to whoever developed them. The computer HAL in Stanley Kubrick's classic 1968 movie 2001: A space odyssey summed up the visions being debated, and the fears they conjured up.
It was against this backdrop that Japan's Ministry of International Trade and Industry announced, in 1982, a programme called the Fifth Generation Computer Systems project to develop massively parallel computers that would take computing and AI to a new level. The scale of the project and its ambition were unprecedented and raised fears in the west that Japan would end up dominating the computer industry in the same way it had taken the lead in the electronics and automotive industries. If it developed truly intelligent machines there was no telling what Japan might be capable of.
An arms race of sorts ensued in which the US and Japan vied for supremacy. The US Department of Justice even waived monopoly laws so that a group of American corporations that included giants such as Kodak and Motorola could join forces to match the Japanese research effort. Between them they set up the Microelectronics and Computer Technology Corporation (MCC) and asked Doug Lenat, then a computer scientist at Stanford University, to lead it. The Defense Advanced Research Projects Agency (DARPA), the Pentagon's research arm, also began to take an interest, and injected huge amounts of funding into the field.
But progress was frustratingly slow, and as the hoped-for breakthrough failed to materialise splits appeared between groups taking different approaches. On one side were those who believed that the key to intelligence lay in symbolic reasoning, a mathematical approach in which ideas and concepts are represented by symbols such as words, phrases or sentences, which are then processed according to the rules of logic. Given enough information, the hope was that these symbolic reasoning systems would eventually become intelligent. This approach appealed to many researchers because it meant that general proofs might eventually be found that could simultaneously revolutionise several branches of AI, such as natural language processing and machine vision.
Ignominious ending
But by the early 1990s, it had become clear that the Japanese project was not leading to any great leap forward in AI. Things were no better in the US, as most of DARPA's projects failed to produce significant advances and the agency withdrew much of its support. The repeated failures of these so-called expert systems - computer programs which, given specialist knowledge described by a human, use logical inference to answer queries - caused widespread disillusionment with symbolic reasoning. The human brain, many argued, obviously worked in a different way. This led to a spurt of enthusiasm for new approaches such as artificial neural networks, which at a rudimentary level imitate the way neurons in the brain work, and genetic algorithms, which imitate genetic inheritance and fitness to evolve better solutions to a problem with every generation.
Neural nets got off to a promising start and they are now used in everything from computer games to DNA sequencing systems. It was hoped that with sufficient complexity they could demonstrate intelligent behaviour. But these hopes were dashed because, though neural networks have the ability to learn from their mistakes, all existing models failed to develop long-term memory.
In the AI winter that followed, research funds became difficult to come by and many researchers focused their attention on more specific problems, such as computer vision, speech recognition and automatic planning, which had more clearly definable goals that they hoped would be easier to achieve. The effect was to fragment AI into numerous sub-disciplines. AI as an all-encompassing field died a sudden and undignified death.
This fragmentation has had some benefits. It has allowed researchers to create a critical mass of work aimed at solving well-defined problems. Computer vision, for example, is now a discipline with its own journals and conferences. "There are people who spend their whole careers on this one problem and never consider the other pieces of the puzzle," says Tom Mitchell, an expert on AI at Carnegie Mellon University in Pittsburgh, Pennsylvania. The same is true of speech recognition, text analysis and robot control.
Simple common sense
But Lenat refused to give up. As an academic he had been working on building a database of common-sense knowledge, which he believed would be the key to cracking artificial intelligence. When funding for MCC dried up, he decided in 1994 to go it alone and spin off a company called Cycorp, based in Austin, Texas, to continue the development of the AI system he named Cyc.
The driving philosophy behind Cyc is that it should be able to recognise that in the phrase "the pen is in the box", the pen is a small writing implement, while in the sentence "the box is in the pen", the pen is a much larger corral. Lenat reels off examples where such common-sense distinctions can make all the difference. In speech recognition, the best way to distinguish between the two spoken sentences "I've hired seven people" and "I fired seven people", which can sound virtually identical, is to analyse the context of the conversation and what it means, rather than just look at the words in isolation. "Humans would never have this problem, but a computer program might easily be confused," says Lenat. "There's almost no area of AI that wouldn't benefit. It's the difference between usability and non-usability."
Cyc has been meticulously assembled to relate each fact to others within the database. It knows for example, that in the sentence "each American has a president" there is only one president, whereas in the sentence "each American has a mother" there are many millions of mothers. Cyc's knowledge is stored in the form of logical clauses that assert truths it has learned. It is based on the symbolic reasoning systems that failed to deliver in the mid-1990s.
But Lenat and his team have made huge leaps since then. One of the curious things about Cyc is that the more it knows, the more easily it can learn. The volume of common sense it has accumulated means it can begin to make sense of things for itself. And every new fact that makes sense is incorporated and cross-referenced into its database. Lenat maintains that the rate at which the system can learn depends on the amount of common sense it has about the world, and that today's AI systems perform so badly because they have close to none. "The rate at which they can learn is close to zero," he says.
One of Cyc's most impressive features is the quality of the deductions it can make about things it has never learned about directly. For example, it can tell whether two animals are related without having been programmed with the explicit relationship between each animal we know of. Instead, it contains assertions that describe the entire Linnaean system of taxonomy of plants and animals, and this allows it to determine the answer through logical reasoning.
Cyc now contains 3 million assertions. Impressive as that is, sheer numbers are not the point. "We are not trying to maximise the number of assertions," Lenat says. Rather, he wants to limit them to the bare minimum that will allow Cyc to collect data on its own. He says Cyc is getting close to achieving that number, and it is already advanced enough to query each input itself, asking the human operator to clarify exactly what is meant.
Sometime this year it will be let loose onto the web, allowing millions of people to contribute to its fund of knowledge by submitting questions to Cyc through a web page and correcting it if it gets the answers wrong. "We're very close to a system that will allow the average person to enter knowledge," Lenat says. He envisages Cyc eventually being connected to webcams and other sensors monitoring environments around the globe, building its knowledge of the world more or less by itself.
When Cyc goes live, users should expect to get answers to their questions only some of the time because it won't yet have the common sense to understand every question or have the knowledge to answer it. But with the critical mass looming, in three to five years users should expect to get an answer most of the time. Lenat has pledged to make access to Cyc freely available, allowing developers of other AI systems to tap into its fund of common sense to improve the performance of their own systems.
“Cyc may eventually be connected to webcams and other sensors monitoring environments around the globe, building its knowledge of the world more or less by itself”
Lenat's optimism about Cyc is mirrored by a reawakening of interest in AI the world over. In Japan, Europe and the US, big, well-funded AI projects with lofty goals and grand visions for the future are once again gaining popularity. The renewed confidence stems from a new breed of systems that can deal with uncertainty - something humans have little trouble with, but which has till now brought computer programs grinding to a halt.
To cope with the uncertainty of the real world, the new programs employ statistical reasoning techniques: for example, a robot might measure the distance to a nearby wall, move and make another similar measurement. Is it seeing the same wall or a different one? At this stage it cannot tell, so it assigns a probability to each option, and then takes further measurements and assigns further probabilities. The trick, of course, is to ensure that this process converges on a solution - a map of the room. In practice these systems work most of the time, but the all too real fear is that the number of calculations could explode far beyond the robot's capabilities, leaving it hopelessly confused. Ways of coping with these situations are currently a hot topic of research.
Systems using the mathematical technique known as Bayesian inference have improved the performance of many AI programs to the point where they can be used in the real world. The despised Microsoft Office paper-clip assistant is based on Bayesian inference systems, as are pattern-recognition programs that can read text, or identify fingerprints and irises. Other approaches to AI have produced specialist programs that have played, and occasionally beaten, world champions in chess (New Scientist, 17 May 1997, p 13), draughts, and even poker (New Scientist, 20 December 2003, p 64).
But problems remain. Voice recognition only works usably in ideal conditions in which there is little or no background noise, and even then its accuracy is limited (New Scientist, 9 April, p 22). Chess programs are only capable of beating humans because the game itself can be reduced to a tree of possible moves, and given enough time computers are able to evaluate the outcome of each sequence of moves to find the one that is most likely to lead to checkmate. In the game of Go the board position is much harder for a computer to evaluate, and even modestly talented players can beat the most powerful computer programs. Robots have trouble negotiating obstacles that a five-year-old can dodge with ease. And if Intellibuddy is anything to go by, machines you can interact with, that understand what you are saying and react appropriately, are still some way off.
It's in your head
Where could the secret to intelligence lie? According to Mitchell, the human brain is the place to look. He has been using functional magnetic resonance imaging (fMRI) to see which parts of the brain become active when a person thinks about a specific object. He has found that when people are asked to imagine a tool such as a hammer or a building such as a house, the same areas of the brain are activated as when they are shown a picture of these objects. He has also found that the area activated for each object - hammer or house - differs by a discernable amount depending on the object. "You can train a program to look at the brain images and determine with 90 per cent accuracy whether that person is thinking about a tool or a building," he says. Such a program could, eventually, literally read your mind.
For the moment the research is confined to concrete nouns associated with physical objects. Next Mitchell plans to see how verbs make the brain light up, and beyond that whether the same areas light up when these nouns and verbs are incorporated into sentences, or whether there is additional activation elsewhere in the brain. Mitchell hopes that this approach will resolve the fundamental differences between the symbolic reasoning approach to AI, and biologically inspired approaches such as neural nets.
The clue that Mitchell thinks is significant is that the same part of the brain seems to be responsible for both reasoning and perception. So when thinking about a hammer the brain is acting like a symbolic reasoning system, and when recognising a hammer the brain is acting like a neural network. "Knowing this could provide some guidance," says Mitchell. Exactly how the designers of neural networks might use such findings is not yet clear. But Mitchell is convinced that this type of insight from functional brain imaging is set to have a huge impact on the field.
Of course if Lenat's prediction proves true, by the time Mitchell's work bares fruit, Cyc may well have reached the singularity. The history of AI suggests that is unlikely, but after decades of faltering starts and failed promises, things are beginning to change. Finally, machines might soon start to think for themselves.
From issue 2496 of New Scientist magazine, 23 April 2005, page 32
A brief history of AI
1936 Alan Turing completes his paper "On computable numbers" which paves the way for artificial intelligence and modern computing
1942 Isaac Asimov sets out his three laws of robotics in the book I, Robot
1943 Warren McCulloch and Walter Pitts publish "A logical calculus of the ideas immanent in nervous activity" to describe neural networks that can learn
1950 Claude Shannon publishes an analysis of chess playing as a search process
1950 Alan Turing proposes the Turing test to decide whether a computer is exhibiting intelligent behaviour
1956 John McCarthy coins the phrase "artificial intelligence" at a conference at Dartmouth College, New Hampshire
1956 Demonstration of the first AI program, called Logic Theorist, created by Allen Newell, Cliff Shaw and Herbert Simon at the Carnegie Institute of Technology, now Carnegie Mellon University
1956 Stanislaw Ulam develops "Maniac I", the first chess program to beat a human player, at the Los Alamos National Laboratory
1965 Herbert Simon predicts that "by 1985 machines will be capable of doing any work a man can do"
1966 Joseph Weizenbaum, a computer scientist at the Massachusetts Institute of Technology, develops Eliza, the world's first chatbot
1969 Shakey, a robot built by the Stanford Research Institute in California, combines locomotion, perception and problem solving
1975 John Holland describes genetic algorithms in his book Adaptation in Natural and Artificial Systems
1979 A computer-controlled autonomous vehicle called the Stanford Cart, built by Hans Moravec at Stanford University, successfully negotiates a chair-filled room
1982 The Japanese Fifth Generation Computer project to develop massively parallel computers and a new artificial intelligence is born
Mid-1980s Neural networks become the new fashion in AI research
1992 Doug Lenat forms Cycorp to continue work on Cyc, an expert system that's learning common sense
1997 The Deep Blue chess program beats the then world chess champion, Garry Kasparov
1997 Microsoft's Office Assistant, part of Office 97, uses AI to offer customised help
1999 Remote Agent, an AI system, is given primary control of NASA's Deep Space 1 spacecraft for two days, 100 million kilometres from Earth
2001 The Global Hawk uncrewed aircraft uses an AI navigation system to guide it on a 13,000-kilometre journey from California to Australia
2004 In the DARPA Grand Challenge to build an intelligent vehicle that can navigate a 229-kilometre course in the Mojave desert, all the entrants fail to complete the course
2005 Cyc to go online
Turing test
In his essay "Computing Machinery and Intelligence", published in the philosophical journal Mind in 1950, the British mathematician Alan Turing argued that it would one day be possible for machines to think like humans. But if so, how would we ever tell? Turing suggested that we could consider a machine to be intelligent if its responses were indistinguishable from a human's. This has become the standard test for machine intelligence.
In 1990, Hugh Loebner, a New York philanthropist, offered a $100,000 prize for the first computer to beat the test and a $2000 annual prize for the best of the rest. Past winners include Joseph Weintraub, three-time winner who developed The PC Therapist, and Richard Wallace, the man behind ALICE, who has also won three times.
Whatever happened to machines that think?
23 April 2005
NewScientist.com news service
Justin Mullins
THE SINGULARITY: In futurology, a technological singularity is a predicted point in the development of a civilisation at which technological progress accelerates beyond the ability of present-day humans to fully comprehend or predict. The singularity can more specifically refer to the advent of smarter-than-human intelligence, and the cascading technological progress assumed to follow.
Wikipedia - the online encyclopedia
CLEVER computers are everywhere. From robotic lawnmowers to intelligent lighting, washing machines and even car engines that self-diagnose faults, there's a silicon brain in just about every modern device you can think of. But can you honestly call any machine intelligent in a meaningful sense of the word?
One rainy afternoon last February I decided to find out. I switched on the computer in my study, and logged on to www.intellibuddy.com, home to one of the leading artificial intelligences on the planet, to see what the state-of-the-art has to offer.
Intellibuddy: Hi, I'm Intellibuddy!
Nice to meet you.
JM: I'm writing an article about artificial intelligence.
Intellibuddy: Will you include me in your essay?
JM: I will if you can persuade me you really are intelligent.
Intellibuddy: Would you recommend this action to a friend?
JM: Errr...are you intelligent?
Intellibuddy: Ummm I am highly intelligent. I have many intellectual functions.
JM: What are your intellectual functions?
Intellibuddy: Thought, reason, intelligence, logic and deduction.
JM: What is your IQ?
Intellibuddy: My IQ is about 250 in human terms.
JM: Wow, you're cleverer than my editor.
Intellibuddy: I'm glad your [sic] impressed, User.
You think I am cleverer than my editor.
JM: No, cleverer than my editor.
Intellibuddy: I'm glad your impressed, User.
You think I am cleverer than my editor.
JM: No, cleverer than my editor.
Intellibuddy: I'm glad your impressed, User.
You think I am cleverer than my editor...
So much for today's artificial intelligence. Intellibuddy is a version of one of the world's most successful chatbots, called ALICE (Artificial Linguistic Internet Computer Entity) and invented in 1995 by Richard Wallace, an independent researcher based in San Francisco. You can find versions of ALICE all over the web; the software is free. But whichever version you choose to chat to, the results are disappointingly similar. While some conversations have promising starts, all descend into the type of gibberish that only artificial intelligence can produce.
And it's not as if there hasn't been time to perfect the idea. The first chatbot appeared in the 1960s. Back then, the very idea of chatting to a computer astounded people. Today, a conversation with a computer is viewed more on the level of talking to your pet pooch - cute, but ultimately meaningless.
The problem with chatbots is a symptom of a deeper malaise in the field of artificial intelligence (AI). For years researchers have been promising to deliver technology that will make computers we can chat to like friends, robots that function as autonomous servants, and one day, for better or worse, even produce conscious machines. Yet we appear to be as far away as ever from any of these goals.
But that could soon change. In the next few months, after being patiently nurtured for 22 years, an artificial brain called Cyc (pronounced "psych") will be put online for the world to interact with. And it's only going to get cleverer. Opening Cyc up to the masses is expected to accelerate the rate at which it learns, giving it access to the combined knowledge of millions of people around the globe as it hoovers up new facts from web pages, webcams and data entered manually by anyone who wants to contribute.
Crucially, Cyc's creator says it has developed a human trait no other AI system has managed to imitate: common sense. "I believe we are heading towards a singularity and we will see it in less than 10 years," says Doug Lenat of Cycorp, the system's creator.
But not all AI researchers welcome such claims. To many, they only fuel the sort of hype that spawned what became known as the "AI winter" of the 1990s. This was a time that saw government funding for AI projects cut and hopes dashed by the cold reality that making computers intelligent in the way we humans perceive intelligence is just too hard. Many scientists working in areas that were once considered core AI now refuse even to be associated with the term. To them, the phrase "artificial intelligence" has been forever tainted by a previous generation of researchers who hyped the technology and the fabled singularity beyond reason. For them, the study of artificial intelligence is a relic of a bygone era that has been superseded by research with less ambitious, more focused goals. Arguably, this has already led to a limited form of AI appearing all around us. Elements of AI research are now used in everything from credit-scoring systems and automatic camera focus controllers, to number plate recognition in speed cameras and spaceship navigation.
AI is in many ways as old as computing itself. The purpose of building computers in the first place was, after all, to perform mathematical tasks such as code breaking that were too tough for humans to tackle. It was in 1950 that Alan Turing, the celebrated second world war code breaker, mathematician and arguably inventor of the first computer, formulated the test that would become the benchmark by which the intelligence of all computer programs would subsequently be measured (see "Turing's test" - below). Even in Turing's day, computers were beginning to outperform humans in certain specific tasks. And as early as 1948, John von Neumann, one of the fathers of the computer revolution, said: "You insist that there is something a machine cannot do. If you will tell me precisely what it is that a machine cannot do, then I can always make a machine which will do just that." It seemed just a matter of time before computers would outperform people in most mental tasks.
But many scientists and philosophers baulked at the idea. They claimed that there was something about being human that a computer could never match. At first the arguments centred on properties such as consciousness and self-awareness, but disagreement over what exactly these terms meant and how we could test for them prevented the debate from making any real progress. Others admitted computers could become intelligent but said they would never develop qualities such as compassion or wisdom which were uniquely human, the result of our emotional upbringing and experience. The definition of intelligence itself began to slip through the philosophers' fingers, and the disagreements continue today.
Most researchers would at least encompass in their definition of AI the goal of building a machine that behaves in ways that would be called intelligent if a human were responsible for that behaviour. Others would cast the definition even wider. Ant colonies and immune systems, they say, also behave intelligently in ways that are utterly non-human. But to get bogged down in the debate is to fall into the same trap that has plagued AI for decades. The Turing test is a reasonable yardstick. We will know an intelligent machine when we can talk to one without realising it is a machine, and programs of Intellibuddy's ilk clearly fall short of this requirement.
Intellibuddy is merely one of the latest in a long line of chatbots. In 1966 Joseph Weizenbaum, a computer scientist at the Massachusetts Institute of Technology, developed the first chatbot, named Eliza after Eliza Doolittle, the character in George Bernard Shaw's Pygmalion who is taught to imitate upper-class English speech. The program was designed to mimic a psychotherapist and conversed with its patient mainly through a simple rhetorical trick: it reworded the patient's statements as questions. For example:
“If Japan had developed truly intelligent machines there was no telling what it might have been capable of. An arms race of sorts ensued between the US and Japan”
Patient: I want to cry
Eliza: Why do you say you want to cry?
Patient: Because my mother hates me
Eliza: Who else in your family hates you?
...and so on. Eliza was programmed to spot key phrases in its interlocutor's sentences and plug them into preformed sentences of its own. It was hugely successful. The idea of talking to a computer astounded people, and there are even anecdotes of people developing emotional attachments to Eliza.
This early success contributed to a sense of optimism that the problems of AI could be overcome, much of it based on the idea that some kind of grand unified theory of mind would emerge that would offer up a scheme to create artificial intelligence on a platter. The late 1960s and early 1970s saw feverish speculation about the impact intelligent machines might have on the world and the advantages they would bring to whoever developed them. The computer HAL in Stanley Kubrick's classic 1968 movie 2001: A space odyssey summed up the visions being debated, and the fears they conjured up.
It was against this backdrop that Japan's Ministry of International Trade and Industry announced, in 1982, a programme called the Fifth Generation Computer Systems project to develop massively parallel computers that would take computing and AI to a new level. The scale of the project and its ambition were unprecedented and raised fears in the west that Japan would end up dominating the computer industry in the same way it had taken the lead in the electronics and automotive industries. If it developed truly intelligent machines there was no telling what Japan might be capable of.
An arms race of sorts ensued in which the US and Japan vied for supremacy. The US Department of Justice even waived monopoly laws so that a group of American corporations that included giants such as Kodak and Motorola could join forces to match the Japanese research effort. Between them they set up the Microelectronics and Computer Technology Corporation (MCC) and asked Doug Lenat, then a computer scientist at Stanford University, to lead it. The Defense Advanced Research Projects Agency (DARPA), the Pentagon's research arm, also began to take an interest, and injected huge amounts of funding into the field.
But progress was frustratingly slow, and as the hoped-for breakthrough failed to materialise splits appeared between groups taking different approaches. On one side were those who believed that the key to intelligence lay in symbolic reasoning, a mathematical approach in which ideas and concepts are represented by symbols such as words, phrases or sentences, which are then processed according to the rules of logic. Given enough information, the hope was that these symbolic reasoning systems would eventually become intelligent. This approach appealed to many researchers because it meant that general proofs might eventually be found that could simultaneously revolutionise several branches of AI, such as natural language processing and machine vision.
Ignominious ending
But by the early 1990s, it had become clear that the Japanese project was not leading to any great leap forward in AI. Things were no better in the US, as most of DARPA's projects failed to produce significant advances and the agency withdrew much of its support. The repeated failures of these so-called expert systems - computer programs which, given specialist knowledge described by a human, use logical inference to answer queries - caused widespread disillusionment with symbolic reasoning. The human brain, many argued, obviously worked in a different way. This led to a spurt of enthusiasm for new approaches such as artificial neural networks, which at a rudimentary level imitate the way neurons in the brain work, and genetic algorithms, which imitate genetic inheritance and fitness to evolve better solutions to a problem with every generation.
Neural nets got off to a promising start and they are now used in everything from computer games to DNA sequencing systems. It was hoped that with sufficient complexity they could demonstrate intelligent behaviour. But these hopes were dashed because, though neural networks have the ability to learn from their mistakes, all existing models failed to develop long-term memory.
In the AI winter that followed, research funds became difficult to come by and many researchers focused their attention on more specific problems, such as computer vision, speech recognition and automatic planning, which had more clearly definable goals that they hoped would be easier to achieve. The effect was to fragment AI into numerous sub-disciplines. AI as an all-encompassing field died a sudden and undignified death.
This fragmentation has had some benefits. It has allowed researchers to create a critical mass of work aimed at solving well-defined problems. Computer vision, for example, is now a discipline with its own journals and conferences. "There are people who spend their whole careers on this one problem and never consider the other pieces of the puzzle," says Tom Mitchell, an expert on AI at Carnegie Mellon University in Pittsburgh, Pennsylvania. The same is true of speech recognition, text analysis and robot control.
Simple common sense
But Lenat refused to give up. As an academic he had been working on building a database of common-sense knowledge, which he believed would be the key to cracking artificial intelligence. When funding for MCC dried up, he decided in 1994 to go it alone and spin off a company called Cycorp, based in Austin, Texas, to continue the development of the AI system he named Cyc.
The driving philosophy behind Cyc is that it should be able to recognise that in the phrase "the pen is in the box", the pen is a small writing implement, while in the sentence "the box is in the pen", the pen is a much larger corral. Lenat reels off examples where such common-sense distinctions can make all the difference. In speech recognition, the best way to distinguish between the two spoken sentences "I've hired seven people" and "I fired seven people", which can sound virtually identical, is to analyse the context of the conversation and what it means, rather than just look at the words in isolation. "Humans would never have this problem, but a computer program might easily be confused," says Lenat. "There's almost no area of AI that wouldn't benefit. It's the difference between usability and non-usability."
Cyc has been meticulously assembled to relate each fact to others within the database. It knows for example, that in the sentence "each American has a president" there is only one president, whereas in the sentence "each American has a mother" there are many millions of mothers. Cyc's knowledge is stored in the form of logical clauses that assert truths it has learned. It is based on the symbolic reasoning systems that failed to deliver in the mid-1990s.
But Lenat and his team have made huge leaps since then. One of the curious things about Cyc is that the more it knows, the more easily it can learn. The volume of common sense it has accumulated means it can begin to make sense of things for itself. And every new fact that makes sense is incorporated and cross-referenced into its database. Lenat maintains that the rate at which the system can learn depends on the amount of common sense it has about the world, and that today's AI systems perform so badly because they have close to none. "The rate at which they can learn is close to zero," he says.
One of Cyc's most impressive features is the quality of the deductions it can make about things it has never learned about directly. For example, it can tell whether two animals are related without having been programmed with the explicit relationship between each animal we know of. Instead, it contains assertions that describe the entire Linnaean system of taxonomy of plants and animals, and this allows it to determine the answer through logical reasoning.
Cyc now contains 3 million assertions. Impressive as that is, sheer numbers are not the point. "We are not trying to maximise the number of assertions," Lenat says. Rather, he wants to limit them to the bare minimum that will allow Cyc to collect data on its own. He says Cyc is getting close to achieving that number, and it is already advanced enough to query each input itself, asking the human operator to clarify exactly what is meant.
Sometime this year it will be let loose onto the web, allowing millions of people to contribute to its fund of knowledge by submitting questions to Cyc through a web page and correcting it if it gets the answers wrong. "We're very close to a system that will allow the average person to enter knowledge," Lenat says. He envisages Cyc eventually being connected to webcams and other sensors monitoring environments around the globe, building its knowledge of the world more or less by itself.
When Cyc goes live, users should expect to get answers to their questions only some of the time because it won't yet have the common sense to understand every question or have the knowledge to answer it. But with the critical mass looming, in three to five years users should expect to get an answer most of the time. Lenat has pledged to make access to Cyc freely available, allowing developers of other AI systems to tap into its fund of common sense to improve the performance of their own systems.
“Cyc may eventually be connected to webcams and other sensors monitoring environments around the globe, building its knowledge of the world more or less by itself”
Lenat's optimism about Cyc is mirrored by a reawakening of interest in AI the world over. In Japan, Europe and the US, big, well-funded AI projects with lofty goals and grand visions for the future are once again gaining popularity. The renewed confidence stems from a new breed of systems that can deal with uncertainty - something humans have little trouble with, but which has till now brought computer programs grinding to a halt.
To cope with the uncertainty of the real world, the new programs employ statistical reasoning techniques: for example, a robot might measure the distance to a nearby wall, move and make another similar measurement. Is it seeing the same wall or a different one? At this stage it cannot tell, so it assigns a probability to each option, and then takes further measurements and assigns further probabilities. The trick, of course, is to ensure that this process converges on a solution - a map of the room. In practice these systems work most of the time, but the all too real fear is that the number of calculations could explode far beyond the robot's capabilities, leaving it hopelessly confused. Ways of coping with these situations are currently a hot topic of research.
Systems using the mathematical technique known as Bayesian inference have improved the performance of many AI programs to the point where they can be used in the real world. The despised Microsoft Office paper-clip assistant is based on Bayesian inference systems, as are pattern-recognition programs that can read text, or identify fingerprints and irises. Other approaches to AI have produced specialist programs that have played, and occasionally beaten, world champions in chess (New Scientist, 17 May 1997, p 13), draughts, and even poker (New Scientist, 20 December 2003, p 64).
But problems remain. Voice recognition only works usably in ideal conditions in which there is little or no background noise, and even then its accuracy is limited (New Scientist, 9 April, p 22). Chess programs are only capable of beating humans because the game itself can be reduced to a tree of possible moves, and given enough time computers are able to evaluate the outcome of each sequence of moves to find the one that is most likely to lead to checkmate. In the game of Go the board position is much harder for a computer to evaluate, and even modestly talented players can beat the most powerful computer programs. Robots have trouble negotiating obstacles that a five-year-old can dodge with ease. And if Intellibuddy is anything to go by, machines you can interact with, that understand what you are saying and react appropriately, are still some way off.
It's in your head
Where could the secret to intelligence lie? According to Mitchell, the human brain is the place to look. He has been using functional magnetic resonance imaging (fMRI) to see which parts of the brain become active when a person thinks about a specific object. He has found that when people are asked to imagine a tool such as a hammer or a building such as a house, the same areas of the brain are activated as when they are shown a picture of these objects. He has also found that the area activated for each object - hammer or house - differs by a discernable amount depending on the object. "You can train a program to look at the brain images and determine with 90 per cent accuracy whether that person is thinking about a tool or a building," he says. Such a program could, eventually, literally read your mind.
For the moment the research is confined to concrete nouns associated with physical objects. Next Mitchell plans to see how verbs make the brain light up, and beyond that whether the same areas light up when these nouns and verbs are incorporated into sentences, or whether there is additional activation elsewhere in the brain. Mitchell hopes that this approach will resolve the fundamental differences between the symbolic reasoning approach to AI, and biologically inspired approaches such as neural nets.
The clue that Mitchell thinks is significant is that the same part of the brain seems to be responsible for both reasoning and perception. So when thinking about a hammer the brain is acting like a symbolic reasoning system, and when recognising a hammer the brain is acting like a neural network. "Knowing this could provide some guidance," says Mitchell. Exactly how the designers of neural networks might use such findings is not yet clear. But Mitchell is convinced that this type of insight from functional brain imaging is set to have a huge impact on the field.
Of course if Lenat's prediction proves true, by the time Mitchell's work bares fruit, Cyc may well have reached the singularity. The history of AI suggests that is unlikely, but after decades of faltering starts and failed promises, things are beginning to change. Finally, machines might soon start to think for themselves.
From issue 2496 of New Scientist magazine, 23 April 2005, page 32
A brief history of AI
1936 Alan Turing completes his paper "On computable numbers" which paves the way for artificial intelligence and modern computing
1942 Isaac Asimov sets out his three laws of robotics in the book I, Robot
1943 Warren McCulloch and Walter Pitts publish "A logical calculus of the ideas immanent in nervous activity" to describe neural networks that can learn
1950 Claude Shannon publishes an analysis of chess playing as a search process
1950 Alan Turing proposes the Turing test to decide whether a computer is exhibiting intelligent behaviour
1956 John McCarthy coins the phrase "artificial intelligence" at a conference at Dartmouth College, New Hampshire
1956 Demonstration of the first AI program, called Logic Theorist, created by Allen Newell, Cliff Shaw and Herbert Simon at the Carnegie Institute of Technology, now Carnegie Mellon University
1956 Stanislaw Ulam develops "Maniac I", the first chess program to beat a human player, at the Los Alamos National Laboratory
1965 Herbert Simon predicts that "by 1985 machines will be capable of doing any work a man can do"
1966 Joseph Weizenbaum, a computer scientist at the Massachusetts Institute of Technology, develops Eliza, the world's first chatbot
1969 Shakey, a robot built by the Stanford Research Institute in California, combines locomotion, perception and problem solving
1975 John Holland describes genetic algorithms in his book Adaptation in Natural and Artificial Systems
1979 A computer-controlled autonomous vehicle called the Stanford Cart, built by Hans Moravec at Stanford University, successfully negotiates a chair-filled room
1982 The Japanese Fifth Generation Computer project to develop massively parallel computers and a new artificial intelligence is born
Mid-1980s Neural networks become the new fashion in AI research
1992 Doug Lenat forms Cycorp to continue work on Cyc, an expert system that's learning common sense
1997 The Deep Blue chess program beats the then world chess champion, Garry Kasparov
1997 Microsoft's Office Assistant, part of Office 97, uses AI to offer customised help
1999 Remote Agent, an AI system, is given primary control of NASA's Deep Space 1 spacecraft for two days, 100 million kilometres from Earth
2001 The Global Hawk uncrewed aircraft uses an AI navigation system to guide it on a 13,000-kilometre journey from California to Australia
2004 In the DARPA Grand Challenge to build an intelligent vehicle that can navigate a 229-kilometre course in the Mojave desert, all the entrants fail to complete the course
2005 Cyc to go online
Turing test
In his essay "Computing Machinery and Intelligence", published in the philosophical journal Mind in 1950, the British mathematician Alan Turing argued that it would one day be possible for machines to think like humans. But if so, how would we ever tell? Turing suggested that we could consider a machine to be intelligent if its responses were indistinguishable from a human's. This has become the standard test for machine intelligence.
In 1990, Hugh Loebner, a New York philanthropist, offered a $100,000 prize for the first computer to beat the test and a $2000 annual prize for the best of the rest. Past winners include Joseph Weintraub, three-time winner who developed The PC Therapist, and Richard Wallace, the man behind ALICE, who has also won three times.
| QUOTE (Rick @ May 03, 02:00 PM) |
| ....All ideas must be physically encoded in order to know or communicate them. Our thoughts are brain encodings of ideas. Books are ink encodings of ideas. Computer files are digital encodings of ideas.... |
~ percept, recept, concept - and the mathimatical mechanics of consciousness.
| QUOTE (Rick @ May 04, 05:33 PM) |
| There is no known level of consciousness beyond the sixth one. |
Ah, the mental evolution of mankind, eh?
What is this sixth level? And, when is it reached (usually at what age)?
Does it include peak colour sense, sense of fragrances, and/or musical sense, etc.?
Are all the senses in permanent state of heightened activity?
Are you talking enlightenment and illumination?
| QUOTE (Hey Hey @ May 05, 10:19 AM) |
| ....but don't you imagine that AI will (and should) eventually be its own programmer and create its own image of I.... |
So they would have to be capable of replication of some sort and genetic/binary transfer of molecular information to its products of reproduction?
'The Horizon of Human Experience and the Levels of the Apriori'
http://www.members.shaw.ca/jgfriesen/Mainh...stemology5.html
http://www.members.shaw.ca/jgfriesen/Mainh...stemology5.html
| QUOTE (Trip like I do @ May 07, 02:50 PM) | ||
So they would have to be capable of replication of some sort and genetic/binary transfer of molecular information to its products of reproduction? |
Yes, quite easily imagined. This is the fate of humans, I fear.
Math is everywhere! We may not recognize it because in the world around us sometimes seems invisible. Math is present in our world all the time--in art, music, the workplace, in our homes, and life in general. Math is in your life from the time you wake until the time you go to sleep. You are using math each time you set your alarm, buy groceries, mix a baby's formula, keep score or time at an athletic event, wallpaper a room, decide what type of tennis shoe to buy, or wrap a present. Have you ever asked yourself, "Did I get the correct change?" or "Do I have enough gasoline to drive 20 miles?" or "Do I have enough juice to fill all my children's thermoses for lunch?" or "Do I have enough bread for the week?" Math is all of this. That's all there is to that problem. We know what math is here. It make us truly human.
The ideal problem solver in math is a child dividing food fairly before other children beat themselves to a pulp like adults. Trouble begins when anyone of any age lies and cheats. Bad math involves dividing up an entire country and claiming to be welcome when an obvious majority do not want you there. Fools pay for deficits. Math is the best defense. People in the fine arts also need math. They need math not only to survive in the world, but each of their areas of specialty requires an in-depth understanding of some math, from something as obvious as the size of a canvas, to the beats in music, to the number of seats in an audience, and our computer-generated artwork prove that it's fun.
Math begins with kids where kindergartners build bar graphs of birthday cakes to show which month has the most birthdays for the most children in the class. Second graders are using pizzas to learn fractions, and measurements are being taken using items other than rulers for example, like how to divide that pizza before bad division causes revolts and war. A problem solver is a person who questions, investigates, and explores solutions to problems; demonstrates the ability to stick with a problem for days, if necessary, to find a workable solution; uses different strategies to arrive at an answer; considers many different answers as possibilities; and applies math to everyday situations and uses it successfully.
The ideal problem solver in math is a child dividing food fairly before other children beat themselves to a pulp like adults. Trouble begins when anyone of any age lies and cheats. Bad math involves dividing up an entire country and claiming to be welcome when an obvious majority do not want you there. Fools pay for deficits. Math is the best defense. People in the fine arts also need math. They need math not only to survive in the world, but each of their areas of specialty requires an in-depth understanding of some math, from something as obvious as the size of a canvas, to the beats in music, to the number of seats in an audience, and our computer-generated artwork prove that it's fun.
Math begins with kids where kindergartners build bar graphs of birthday cakes to show which month has the most birthdays for the most children in the class. Second graders are using pizzas to learn fractions, and measurements are being taken using items other than rulers for example, like how to divide that pizza before bad division causes revolts and war. A problem solver is a person who questions, investigates, and explores solutions to problems; demonstrates the ability to stick with a problem for days, if necessary, to find a workable solution; uses different strategies to arrive at an answer; considers many different answers as possibilities; and applies math to everyday situations and uses it successfully.
| QUOTE (Trip like I do @ May 07, 04:59 PM) |
| 'The Horizon of Human Experience and the Levels of the Apriori' http://www.members.shaw.ca/jgfriesen/Mainh...stemology5.html |
Thanks for pointing out this page. I'll try and avoid it whenever possible. Perhaps Rick might like to take a look.
| QUOTE (Trip like I do @ May 08, 05:02 AM) |
| Math is everywhere! We may not recognize it because in the world around us sometimes seems invisible. Math is present in our world all the time--in art, music, the workplace, in our homes, and life in general. Math is in your life from the time you wake until the time you go to sleep. You are using math each time you set your alarm, buy groceries, mix a baby's formula, keep score or time at an athletic event, wallpaper a room, decide what type of tennis shoe to buy, or wrap a present. Have you ever asked yourself, "Did I get the correct change?" or "Do I have enough gasoline to drive 20 miles?" or "Do I have enough juice to fill all my children's thermoses for lunch?" or "Do I have enough bread for the week?" Math is all of this. That's all there is to that problem. We know what math is here. It make us truly human. |
this sounds like it came from an advertisement for CBS' new crime drama, "Numb3rs"
| QUOTE (Dan @ May 05, 08:09 PM) |
| robots don't 'need', Rick. there's nobody home in there and, if I do what the boss needs me to do, it is because I need the boss's money |
Antonio R. Damasio has established the distinction between emotions and feelings. Emotions come first and the associated feeling comes after the emotion occurs. Therefore, we can distinguish between the emotion of need and the feeling of need. When I say that the robot has a need to cross the parking lot, I mean that it has a state of need that motivates its planning and subquent motion. I don't mean that the robot has a feeling of need, which would require artificial consciousness.
The robot has a complex state, or configuration. That state is "who" is in there. That is, the container is the contained (in such a way as to avoid infinite regress). We've been through this in a similar way for the human condition, and the result is the same: denial of the homonculus.
| QUOTE (Hey Hey @ May 08, 04:11 PM) |
| Thanks for pointing out this page. I'll try and avoid it whenever possible. Perhaps Rick might like to take a look. |
Highly unsatisfying, like most religious writing.
| QUOTE (Rick @ May 09, 11:40 AM) |
| When I say that the robot has a need to cross the parking lot, I mean that it has a state of need that motivates its planning and subquent motion. |
There is no state of need in a robot. There is no 'who' in there. There is simply a group of interacting mechanisms operating out of ordinary physical action that, because of the complexity of the resultant behavior of the whole, might be confused by an observer as existing in a 'state of need'. The only state that exists is the state of lots of cellular automata interacting individually in such a way the emergent patterns look like the product of a subjectively singular entity acting out of need.
The key distinction that is fundamental to clearing the AI confusion (belief that organized behavior = 'who') can be delineated as such: it is simply a figment of an observer's imagination to see a 'robot experiencing need' out of a bunch of interacting machines whizzing about through purely predictable physical pathways, while my state of need is a subjectively singular state that is amplified through my body into action that can lie outside the predictibility of physics.
| QUOTE (Dan @ May 09, 09:54 PM) |
| The key distinction that is fundamental to clearing the AI confusion (belief that organized behavior = 'who') can be delineated as such: it is simply a figment of an observer's imagination to see a 'robot experiencing need' out of a bunch of interacting machines whizzing about through purely predictable physical pathways, while my state of need is a subjectively singular state that is amplified through my body into action that can lie outside the predictibility of physics. |
There are several errors here. First, the potential existence of AI does not require that organized behavior be equivalent to personality or "who." AI is simply intelligent behavior evidenced by artificial devices. The more intelligent the behavior, the more "AI." It's not an all-or-nothing proposition.
Second, according to dictionary.com, "need" is defined as "a condition or situation in which something is required or wanted" (first definition). Therefore, the needs of robots, which motivate their behavior, are hardly figments of human observers' imaginations. Robot needs can be programmed directly by their creators, or can be derived from other conditions in the robot's mind state.
Third, a person's need might be subjective when felt, but as an emotion, it also has structural aspects that involve brain states, and therefore also shares some similarity with robotic need.
Fourth, it is clear that human behavior is inherently unpredictable, but chaos theory shows that robotic behavior can also be unpredictable. True chaos is numerically achievable on any general purpose computer.
| QUOTE (Rick @ May 10, 09:41 AM) |
| AI is simply intelligent behavior evidenced by artificial devices. The more intelligent the behavior, the more "AI." |
I'm not in disagreement here. What I am saying is that this 'intelligent' behavior is a result of the designer who has decided what is 'intelligent' (i.e., the robot 'intelligence' is simply an extension of the intelligence of the designer)
| QUOTE |
| Second, according to dictionary.com, "need" is defined as "a condition or situation in which something is required or wanted" (first definition). Therefore, the needs of robots, which motivate their behavior, are hardly figments of human observers' imaginations. |
this first definition is implying a 'who' that requires or wants. A robot is not a 'who'. Only in the imagination of an observer is the robot a 'who' that is capable of 'requiring' or 'wanting'. It may be argued, however, that at some level one can identify 'elements' comprising the robot that are 'whos' capable of 'wanting' or 'requiring' and that, when they are combined into an interactive whole, generate the net 'robot' behavior. An analogy: a city does not 'want' to form roads, the people that inhabit the city 'want' roads and build them.
| QUOTE |
| Robot needs can be programmed directly by their creators, or can be derived from other conditions in the robot's mind state. |
there is no singular 'mind state' of a robot, at this level ('robot' as a single entity) the situation is best describes as a matter-state. The programmer designs the matter-state such that the behaviours of the 'robot' entity are a consequence of the well-understood physics of the matter-state (we know for certain how the elements will interact, thus we can know for certain how particular configurations of these elements will generate 'net' behaviors).
| QUOTE |
| Third, a person's need might be subjective when felt, but as an emotion, it also has structural aspects that involve brain states, and therefore also shares some similarity with robotic need. |
agreed, there is the similarity in that a person and a robot both exhibit behavior that is a consequence of ordinary physics of the control system. However, a robot is limited to this regime of cause while a person includes the 'subjective' perturbation on physics that results in the manifestation of 'creativity'. 'Need' is a fundamentally subjective phenomenon, thus a 'robot' as a single entity, having no singular subjective 'robot' mental state, cannot 'need'.
| QUOTE |
| Fourth, it is clear that human behavior is inherently unpredictable, but chaos theory shows that robotic behavior can also be unpredictable. |
Unpredictable robot behavior of 'chaotic' origin can have no better than random chance of being ordered with respect to its usefulness to the purpose of the robot (as designed), while unpredictable human behavior of 'creative' origin can have a better-than-random chance of being ordered with respect to its usefulness in satisfying the need(s) of the person.
| QUOTE (Dan @ May 10, 11:15 AM) |
| Unpredictable robot behavior of 'chaotic' origin can have no better than random chance of being ordered with respect to its usefulness to the purpose of the robot (as designed), while unpredictable human behavior of 'creative' origin can have a better-than-random chance of being ordered with respect to its usefulness in satisfying the need(s) of the person. |
The next time you play chess against a robot, see if you can predict its moves. The moves you can't predict have a good chance of being devastatingly effective in its campaign against you. The robot may not feel a need to win, but winning is a requirement of its design, and a situation where something is required is, by definition, a need.
I agree with Rick. So in a Data Base you can store all in binary code and generate new concept of making new keys etc thus making new kind of research.
| QUOTE (Rick @ May 10, 10:32 AM) |
| The next time you play chess against a robot, see if you can predict its moves. The moves you can't predict have a good chance of being devastatingly effective in its campaign against you. |
A chess-bot is predictible in its chess moves, as it plays according to a defined algorithm. Even if 'chance' is employed to choose among a set of moves that are equally optimal, such action would not be creative (it would be purely random) and would still be predictible insofar as the set of moves is small compared to the set of all possible moves. Just because I, the person ignorant of the algorithm, cannot predict what the chess-bot is doing does not mean it is unpredictable by ordinary means (it is quite ordinary to acquire the algorithm and run it in parallel with the chess-bot to 'predict' the chess-bot moves)
| QUOTE |
| The robot may not feel a need to win, but winning is a requirement of its design, and a situation where something is required is, by definition, a need. |
Yes, it is a need of the designer
Suppose the designer needs to give his robot free will?
good luck with that
I'm holding my breath and waiting for the Singularity.
Jesus will save us in the Singularity, and deliver to us conscious robot beings of pure love and happy-fun
Maybe, the answer is to hardwire them, like we also are, with all the possible potentialities and consequences for their actions and give them the freedom of selection as to the apprpriate course of action to be taken in any given situation?
| QUOTE (Dan @ May 10, 05:28 PM) |
| Jesus will save us in the Singularity, and deliver to us conscious robot beings of pure love and happy-fun |
I think that I would prefer the companionship of humans of the female persuasion, with all their perks and quirks, to that of a pure loving and happy-fun robot beings, but that's just me, maybe I'm strange that way, sorry guys!
| QUOTE (Rick @ May 10, 12:28 PM) |
| Suppose the designer needs to give his robot free will? |
Dear Rick I am impressed! What a topic!
Very, very, very interesting, indeed.
You know I start to love this forum very much.
| QUOTE (Dan @ May 10, 02:28 PM) |
| Jesus will save us in the Singularity, and deliver to us conscious robot beings of pure love and happy-fun |
You may connect the Freedom of Will with Wisdom concept in the Data-base theory. I think it is a nice approximation.
But the best definition is the Innumerable possibilities of making synergy out of somthing and even out of nothing. Here where some elements of the Freedom of Will do sometimes lie.
Ability to think everything.
How to make a Tool out of absurd chaotic statment? E.g.:
It is like magine two gay Elves one of which is Orlando Blum, turn it into a saga, make Troya and hammer the Kingdom of Heaven to the victorious end and invent new Spell. I think that is a nice example indeed.
One Man - one key, double key, Troyan key, multi-purpose key.
Neo-Contantine etc one Data-Base - one key - one image - one Spell.
Do you guys get what I mean?
Hope you percept the key approach of Lord Dritte.
BTW, what do you think about Lord Vader? Or Lord Valdemot?
That is a nice Singularity as well.
| QUOTE (Dan @ May 10, 11:15 AM) |
| 'Need' is a fundamentally subjective phenomenon, thus a 'robot' as a single entity, having no singular subjective 'robot' mental state, cannot 'need'. |
Plants need water and sunshine. I don't think this is disputable. Therefore it would seem that if needs require "whos" then you must be asserting that plants have either homunculi or souls.
I think, on the contrary, that needs can be more general than has been asserted, and that basic needs can be programmed into a robot by its master/creator much in the same way as basic needs are programmed into people by their DNA.
The active topics is definately lacking with regards to the female perspective on things, don't you think?
It would be nice to garner some female input on some of these subjects!
Shawn, help us out here.
It would be nice to garner some female input on some of these subjects!
Shawn, help us out here.
| QUOTE (Rick @ May 11, 11:45 AM) |
| Plants need water and sunshine. I don't think this is disputable. Therefore it would seem that if needs require "whos" then you must be asserting that plants have either homunculi or souls. |
there is no 'plant', there is simply a collection of individual units acting according to what, by all accounts I am aware of, can be described as ordinary physical action. Such individual units would exist in a singular conscious state, and their needs and interactions with each other yield the 'plant' phenomenon that we observe (possibly these units are the cells, maybe just the atoms, maybe smaller than that). Analogously, it would be absurd to say a city 'needs' people, wood, grain and gasoline in order to grow. The absurdity is in that there is no conscious 'city' entity, there are only the individuals (people) whose needs and interactions yield the emergent 'city' phenomenon that we observe.
So it seems you are saying that there can be no need without consciousness, and that without consciousness, an entity can have no needs. This may be another clue in the hard problem of consciousness (the why part of the problem).
But still, my thesis is that AI does not require AC, and you are seeming to contradict that.
But still, my thesis is that AI does not require AC, and you are seeming to contradict that.
I am just saying that getting caught up in making complex toys and calling them 'intelligent' is a distraction from the most interesting idea associated with AI: constructing physical objects that can be possessed by a mind (a subject experiencing coherent, meaningful qualia related to the state of the object) that is capable of generating physical action of the object that is oriented toward satisfying itself.
I don't really care if we call a toaster 'intelligent', but if we want to create a device capable of attaining consciousness and operating intelligently due to its own will we're going to have to work with the physics of consciousness rather than the algorithms of 'intelligent' patterns
I don't really care if we call a toaster 'intelligent', but if we want to create a device capable of attaining consciousness and operating intelligently due to its own will we're going to have to work with the physics of consciousness rather than the algorithms of 'intelligent' patterns
| QUOTE (Enki @ May 11, 01:09 PM) |
| BTW, what do you think about Lord Vader? |
Now there was a machine trained to kill!
But, I think however, that the terminator would kick his ass!
| QUOTE (Trip like I do @ May 11, 08:13 PM) | ||
Now there was a machine trained to kill! But, I think however, that the terminator would kick his ass! |
Well it is matter of taste Trip.
You know today I am departing to very, very far away country and there absolutly no internet. So though I loved the forum very much but I have to bid farewell to you all for uncertain period of time.
Separatly I want to bid farewell to the kids from NSA. Daddy will be back children if you miss me. Just send a bird. I am sure that you will not miss for long period of time. But one day you will. :kiss:
Yours in love and war,
Enki
12 May 2005 AD
AV.
Separatly I want to bid farewell to the kids from NSA. Daddy will be back children if you miss me. Just send a bird. I am sure that you will not miss for long period of time. But one day you will. :kiss:
Yours in love and war,
Enki
12 May 2005 AD
AV.
Enki,
Take care on your travels; enjoy yourself in the process.
Good health and healthy thinking are wished to you!
We'll see you soon after you return, hopefully.
Bill.
Take care on your travels; enjoy yourself in the process.
Good health and healthy thinking are wished to you!
We'll see you soon after you return, hopefully.
Bill.
| QUOTE (Dan @ May 11, 05:01 PM) |
| I don't really care if we call a toaster 'intelligent', but if we want to create a device capable of attaining consciousness and operating intelligently due to its own will we're going to have to work with the physics of consciousness rather than the algorithms of 'intelligent' patterns |
I agree that if we want to create artificial consciousness we will have to work with the physics of consciousness (if we can ever figure out what that is). However, it is not certain that consciousness is required for either intelligent or willful behavior or both.
For example, there are some who have argued that the dinosaurs had no consciousness because they didn't have neocortexes. However, I think it could be argued that the predation strategies of carnivorous dinosaurs evidenced both intelligence and willfulness.
The Quantum AI Experiment
David Deutsch - Centre for Quantum Computation, University of Oxford
The origins of the quantum information revolution can be traced back to 1969 and Rolf Landauer, who became an IBM Fellow in 1969. Landauer believed that computation is physics and that you can't understand the limits of computation without saying what the physical implementation (i.e type of hardware) is.
The prevailing view at the time was that computation was ultimately an abstract process that had more to do with the world of mathematical ideals than with physics of machines. But Landauer's view began to take hold when he discovered a crucial link between physics and computation.
In the early 1980's, Deutsch proposed an experiment to test the existence of multiple universes. He envisaged the construction of a thinking, conscious artificial intelligence whose memory worked at the quantum level. Such a machine could be asked to conduct a crucial experiment inside its own brain and report back to Deutsch whether there was the existence of parallel universes.
David Deutsch - Centre for Quantum Computation, University of Oxford
The origins of the quantum information revolution can be traced back to 1969 and Rolf Landauer, who became an IBM Fellow in 1969. Landauer believed that computation is physics and that you can't understand the limits of computation without saying what the physical implementation (i.e type of hardware) is.
The prevailing view at the time was that computation was ultimately an abstract process that had more to do with the world of mathematical ideals than with physics of machines. But Landauer's view began to take hold when he discovered a crucial link between physics and computation.
In the early 1980's, Deutsch proposed an experiment to test the existence of multiple universes. He envisaged the construction of a thinking, conscious artificial intelligence whose memory worked at the quantum level. Such a machine could be asked to conduct a crucial experiment inside its own brain and report back to Deutsch whether there was the existence of parallel universes.
the thing about me is I am not confused about the question, so I tend to avoid pathological arguments that derive from the confused experts. I suppose this attitude doesn't help much; maybe I'll try harder to 'enlighten' the AI crowd when I am not so concerned about feeding myself.
Greedy, greedy, Dan!
Share and share alike!
Typical American attitude!
I do know what you mean though. I believe that at a certain developmental stage in cognitive functioning one becomes more of a humanist and becomes more willing to help in the growth of the global mind!
Take the high road Daniel-son, it's the road less travelled!
Share and share alike!
Typical American attitude!
I do know what you mean though. I believe that at a certain developmental stage in cognitive functioning one becomes more of a humanist and becomes more willing to help in the growth of the global mind!
Take the high road Daniel-son, it's the road less travelled!
That is, of course, when the brain is satisfactorily fed!
There's nothing in the laws of physics that say AI won't be possible, is there?
Computation is connected to all sorts of human things like thought, knowledge, life, etc. ain't it?
Physics is the most fundamental description of nature, ain't it?
So here we have an unsuspected, very deep connection between human-type things and fundamental type things, don't we?
I think philosopy is going to take a long time to assimilate this!
Physics is the most fundamental description of nature, ain't it?
So here we have an unsuspected, very deep connection between human-type things and fundamental type things, don't we?
I think philosopy is going to take a long time to assimilate this!
| QUOTE (Dan @ May 11, 04:33 PM) |
| Analogously, it would be absurd to say a city 'needs' people, wood, grain and gasoline in order to grow. The absurdity is in that there is no conscious 'city' entity, there are only the individuals (people) whose needs and interactions yield the emergent 'city' phenomenon that we observe. |
Unless, of course, we R in a cyclical universe that does expand and contract quite regulary, and the city phenomenon is simply a innate hard-wired concept of any growing cosmos, and are developed because the nature of atoms, molecules, and matter tell us to create them.
We didn't discover them, or create them. They R simply an accurate manifestation of any given reality in any given moment in time of creation.
An innevitability!
Were numbers discovered or invented?
| QUOTE (Enki @ May 12, 03:45 AM) | ||||
Well it is matter of taste Trip. |
Wasn't it the HUMAN Luke Skywalker who toppled the Machine that was Lord Vader?
| QUOTE (Dan @ May 12, 12:56 PM) |
| ... when I am not so concerned about feeding myself. |
Hunger is a strong motivator, but it's also a distraction, isn't it?
| QUOTE (Trip like I do @ May 12, 04:40 PM) |
| Were numbers discovered or invented? |
Invented.
| QUOTE (Trip like I do @ May 12, 11:40 PM) |
| Were numbers discovered or invented? |
discovered.
In his book Researches published in 1800 Davy concludes: "As nitrous oxide in its extensive operation appears capable of destroying physical pain, it may probably be used with advantage during surgical operations in which no great effusion of blood takes place."
Therefore, numbers (anaesthetics) were discovered before 1800 - and thank God for them!
Anaesthetics, being substances, can be discovered. Mathematical numbers, being forms (information), have no substance, and therefore must be invented.
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