I can't do taxes in my head as fast as Turbo Tax, nor can my brain sniff out a bomb, but it can grasp the concept of both, among a zillion other things, and see corrolations between them, and build new ideas based upon both. Turbo Tax, left to it's own devices, will never be any more than Turbo Tax.
Yes and thanks for grasping my point. The analogy is simply a way to point out that any hand crafted software with the complexity of the brain would be so full of bugs, it would not even run. Robustness and reliability are measured in terms of errors vs. complexity. The brain is existence proof that complexity does not imply a lack of robustness. Its reliability is orders of magnitude greater than software. Just going down a flight of stairs or driving a car around town is much more complex than anything any software application can do. This fact squarely and decisively refutes Fred Brooks' "No Silver Bullet" nonsense.
No other computer expert in the annals of software engineering has had a more deleterious effect on our efforts to find a final solution to the software crisis than Fred Brookes. I don't care how respected the guy is in the computer science community. He single-handedly managed to convince almost the entire world that there is no hope. It's rather sad. Countless human beings will die as a result because the problem is not getting better. It's getting worse. Much worse.
I'll repeat with what I wrote with added emphasis. Complexity wise, there is nothing in the universe more robust and reliable than the brain. I'll leave it to you to figure it out. Although I don't hold much hope, in your case.
Software Is Bad Precisely Because Experts Like Brooks Teach that There Is No Silver Bullet
Software is bad because we are doing it wrong. We are doing it wrong because our so-called experts tell us that there is no silver bullet to the problem of unreliability. So everybody has given up on finding a solution. And now they want to bring in the lawyers and even more clueless experts. It won't work.
The brains of humans and animals are the existence proof that there is a silver bullet. Complexity wise, there is nothing in the universe more robust and reliable than the brain. In fact, the more complex the brain gets (as it learns), the more reliable it becomes. By contrast, the reliability of software gets worse as it gets more complex. Why is that?
The primary reason is that software is based on algorithms (threads) whereas the brain is a parallel, signal-based system. The reliability of the brain comes from the strict enforcement of precise signal timing: neurons always fire at the right time, under the right temporal conditions. With software, it is virtually impossible to guarantee the timing of various processes, which leads to program decisions happening at the wrong time.
The secondary reason for bad software has to do with connection types. In the brain, signal pathways are not connected willy nilly. Connections are made according to their types. We should do the same with software. All message connectors should have message types, and all connectors should be either male or female to ensure robust connectivity.
The Solution
We must imitate nature. We must forever stop basing computer programming on the algorithm and we must stop using algorithmic languages. Software should be more like hardware with parallel modules and their input and output connections. One of the reasons that electronic circuits are so much more reliable than software is that timing problems are immediately nipped at the bud due to the inherent parallelism of hardware. The circuit simply will not work if timing is wrong.
We can solve the software reliability crisis. We can create tools that automate over 90% of the software development process. Damn the Experts!
Software Is Bad Because the Experts Say There Is No Silver Bullet
Software is bad because we are doing it wrong. We are doing it wrong because our so-called experts tell us that there is no silver bullet to the problem of unreliability. So now they want to bring in the lawyers and even more clueless experts. It won't work.
The brains of humans and animals are the existence proof that there is a silver bullet. Complexity wise, there is nothing in the universe more robust and reliable than the brain. In fact, the more complex the brain gets (as it learns), the more reliable it becomes. By contrast, the reliability of software gets worse as it gets more complex. Why is that?
The primary reason is that software is based on algorithms (threads) whereas the brain is a parallel, signal-based system. The reliability of the brain comes from the strict enforcement of precise signal timing: neurons always fire at the right time, under the right temporal conditions. With algorithmic software, it is virtually impossible to guarantee the timing of various processes, which leads to program decisions happening at the wrong time.
The secondary reason for bad software has to do with connection types. In the brain, signal pathways are not connected willy nilly. Connections are made according to their types. We should do the same with software. All message connectors should have message types, and all connectors should be either male or female to ensure robust connectivity. Plug compatibility should automate over 90% of software construction.
The Solution
We must imitate nature. We must forever stop basing computer programming on the algorithm and we must stop using algorithmic languages. Software should be more like hardware with parallel modules and their input and output connections. One of the reasons that electronic circuits are so much more reliable than software is that timing problems are immediately nipped in the bud due to the inherent parallelism of hardware. The circuit simply will not work if timing is wrong. We can create the right software construction tools to solve the reliability crisis. Damn the experts!
Could the Sun Be the Culprit?
on
Baked Alaska
·
· Score: 2
When the pot gets too hot, the culprit is usually the fire. In my opinion, the sun, the main source of heat for earth's atmosphere, should be the primary suspect. Maybe the sun is going through a warming cycle.
This is lame. A much more interesting story would be that someone had written a program that could play world class chess without world class CPU horsepower.
I am working on just such a project. It's called Animal.
There is a lot more to knowledge than the classification of namable objects and their relationships. There is a huge amount of knowledge that cannot be formalized with symbols. For examples, playing soccer or football, recognizing a subtle fragrance, face or musical tune, manual dexterity, finding one's way around an unfamiliar neighborhood, etc..., in other words, the sort of common sense knowledge that can only be acquired through direct sensory experience.
The interconnectedness of human cognition is so astronomically complex as to be intractable to formal approaches. This realization immediately makes the use of symbolic knowledge representation approaches to creating human-like common sense in a machine look rather silly. That 25 million dollars of taxpayers money went into this Cyc thing is a testament to the effectiveness of the propaganda machine of the GOFAI community. Bravo!
There are now two major competing DLP technologies, one from Texas Instruments (Digital Micromirror Devices, DMD) and another from Digital Solutions (Grating Light Valves, GLV).
There are now two major competing DLP technologies, one from Texas Instruments (Digital Micromirror Devices, DMD) and another from Digital Solutions (Grating Light Valves, GLV). Other companies, notably Philips Research, are working on their own solutions.
I personally prefer the Digital Solutions (DS)approach because it's much cheaper and, if mass-produced, promises to bring super high-res projection TV to the masses for less than the price of a VCR. Sony has reportedly licensed GLV from DS, a sign that exciting things are in the works. I wouldn't mind a super hi-res rear projection adaptation as a replacement for my heavy and bulky monitor. I hope Sony is listening.:-)
6 to 12 second delay? That sounds awfully high. Are you sure that's not an exaggerated figure? If you recall the failed motorola iridium satellite phone system. The system failed not because of such high delays, but because of the high price/bulky equipment.
Well, we've all seen the six-second delay with the SAT phones used by news reporters during the war coverage of Afghanistan. The reason, I believe, mainly has to do with the high geosynchronous orbits of the satellites used. If I recall correctly, the Motorola Iridium satellites were positioned at much lower, non-geosynchronous orbits.
If you figure out what that Universal Syntax is (sorry, I forgot the exact term he used - it's been a while, and my university education was in Dutch), you can feed that into a computer and teach it to reduce all phonemes from a given language to it.
Syntax is not nearly enough. Unless you know what the individual words mean, you're shit out of luck. As an example, linguists pretty much had the syntax of Egyptian hieroglyphics figured out, but it took the discovery of the Rosetta stone for them to begin to understand what they were reading. Even then, there is still stuff they can't figure out. Something similar (old Spanish/Maya lexicons compiled by missionaries) was required to decipher Mayan symbols.
but what about a central supercomputer communicating via satellite with handheld devices?
An excellent idea. That would be fine if you can multitask several AIs on the same supercomputer because a huge number of people may want to access the same system simultaneously. Best bet might be to give everybody satellite access to his or her own supercomputer at home. Another twenty years or so should make that possible. Assuming, of course, that Moore's law continues unabated.
Of course, then it'd have to deal with noise as well
Digital communication is pretty much noiseless. A bigger problem is the annoying six to twelve seconds delay inherent in satellite communition.
Not unless they solve the general AI problem first. In other words, no. You need a machine with a HAL-like intelligence to do good real-time translation. The machine will need to go to school for a few years to learn the idiosynchracies of each language and culture. No way this technology can fit into a portable translator given the current state of computer technology. Besides, even human beings have a hard time interpreting languages that they are fluent in.
He's just trying to sell more copies of Mathematica!
That's not it. Wolfram is saying the exact opposite. He is saying the universe uses very little math. Just a few simple rules. Fancy math is a red herring, in my opinion. It explains nothing. On the contrary, it is our equations that are in dire need of an explanation, from Newton's gravity equation to Einstein's GR/SR equations. They only describe the evolution of matter but do not explain the causal mechanisms.
Real science is about causal mechanisms at the fundamental level where simple rules rule! This is where Wolfram's ideas are revolutionary. They will not be well received in academic circles. Academics hate simplicity because they can't show off with it.
Third, speech is a very low bandwidth output compared to other solutions
It may be low bandwith but that does not mean it's not a powerful way of communicating. If I can say to my computer, "book me a flight to Miami for the weekend," it sure is a lot more powerful than going to Travelocity's or Yahoo' Travel's sites and make the travel arrangements on my own. That's the main difference between intelligent machines that truly understand natural languages and simple speech recognizers used for dictation and simple commands.
Hal was more than just speech recognition, it was more like a very clever secretary.
You are 100% right. If computers were smart enough to understand what we are saying, I certainly would not be sitting at work enunciating simple commands to my desktop machine to "scroll this page down a bit" or "load that document." Heck, I would not be working at all.
These things don't look any more superstitious to me than time travel, wormholes, black holes, parallel universes, quantum computing and the like. If scientists can openly believe in a bunch of crap without fear of being labeled crackpots, why can't the general public?
If a scientist has a theory for a natural phenomenon and yet cannot explain it in simple language that the average lay-person can understand, there is a 90% probability that it's crap. Furthermore, if the scientist claims that one needs math to understand the theory, then there is a 100% probability that he or she is as clueless about the phenomenon as everyone else.
Your argument that biological intelligence does not use symbolic manipulation is hollow. It's like saying computer intelligence does not use symbolic manipulation because it's just a bunch of transistors.
Thanks for making my point for me.
There has been plenty of great research in reinforcement learning. Isn't that what you mean by rewards and punishments?
If it is not done within the context of signal processing, i.e., by using spiking neural networks, it's crap. Spiking or pulsed networks did not come from the AI community, BTW. They came from the computational neuroscience community which is part of neurobiology. So don't try to get credit for something the GOFAI community has pretty much ignored for fifty years.
People have also tried to make neurologically inspired models for AI. Note that the transistor is even abstractly similar to the neuron.
The only neural networks that came from the GOFAI community (after Marvin Minsky and Seymour Papert tried to put a monkey wrench in neural network research) are the so-called ANNs. ANNs are a pathetic joke. They bear little ressemblance to biological neurons. Unless an AI researcher realizes that intelligence has to do with the temporal relationships between discrete signals, he or she is not doing AI. He or she is just spitting against the wind.
The AI community is in much better shape than you think.
I doubt that very much. Only a blind fool would believe in the AI community after fifty years of abject failure. They still don't have a clue and they don't seem interested in getting one either. Just recently, MIT Technology Review publisehed an article titled "AI Reboots" which is a PR piece for GOFAI guru Doug Lenat and that database, symbolic representation monstrosity of his called Cyc.
While you may not consider that to be a contribution, the same model may be used for more complicated things, such as piloting a spacecraft. I saw a talk the other day who trained a program to hover a remote-controlled helicopter in place. It performed better than the leading human controllers.
None of it has anything to do with intelligence. Again, to be intelligent, a program must not only be able to learn but it must learn anything and everything, not just a limited domain environment. But that is not all. It must be motivated and adapt to reward and punishment.
There's a lot more to AI than symbol manipulation. Knowledge representation is a very small subset of the field.
Neither symbol manipulation nor knowledge representation has anything to do with intelligence since the only intelligence we know of (biological intelligence) doesn't use any of it. First and foremost, intelligence has to do with discrete, temporal signal processing. The biological evidence is clear on this issue: neurons generate discrete signals or spikes. Second, intelligence has to do with motivation, i.e., it must react properly to reward and punishment. This is what psychology has taught us for the last one hundred years. The GOFAI crowd is not listening.
Calling AI researchers clueless for ignoring these areas is just revealing your ignorance about AI and your fanatacism against AI..
They're worse than clueless. They have wasted the taxpayer's money for fifty years. And, for the record, I am not against AI. Why lie about it when I have a site that promotes AI? On the contrary, I am trying to wake people up to the fact that they are being taken to cleaners by a bunch of clueless career propagandists. If you don't like my opinion on the matter, all can say to you is that it's that's too bad. I exercise my freedom of speech the way I see fit.
I know very few programmers outside of AI or mechanical engineering who can write a program to perform optimal cruise control given sensor/motor noise and unknown road conditions. There's more to it than "Read the speedometer. Accelerate if too slow, decelerate if too fast. Repeat."
There is not much more to it than that. So don't try to make it look like it's a harder problem than it actually is. Any junior programmer can write a good cruise control program through trial and error.
What if "optimal" were defined as some weighted combination making the ride smooth and conserving gas? Is that so trivial a program to write?
Yes it is. I've seen self-taught programmers do amazing things with a 1Mhz Apple II back in the early eighties. Things that are orders of magnitude more complex and clever than cruise control. They never claimed they were doing AI.
AI researchers have solved many important problems over the years, and I don't consider it at all a disappointment that we are not even close to approaching human intelligence.
It's worse than a disappointment. It's a pathetic failure. Many of us remember people like Minsky and others making outrageous claims about their ability to create human-level AI by the end of the last century. We all know about their promotion of the symbol manipulation and knowledge representation nonsense. It all turned out to be mostly worthless crap with little to do with intelligence. Those guys made it a point to ignore every significant advance in neurobiology and psychology over the last one hundred years. Talk about clueless!
Cruise control could be very well formulated as an AI problem.
One does not have to major in artificial intelligence at a university to write a cruise control program. Any competent programmer can write a program that accomplish safe and effective cruise control. It's a simple coding problem that does not require fifty years of DARPA funding to figure out. The only intelligence we know is animal intelligence. Biological intelligence is what we should be trying to emulate. Human intelligence is general, scalable and adaptive. I have seen nothing from the AI community that qualifies as true intelligence. All they have is hype and propaganda.
I'm sick of people asking "When will we see widespread commercial application of AI". AI researchers often quote the so-called "moving frontier" problem, that is, as soon as an AI application becomes useful enough to solve real-world problems, it ceases to be known as AI and looks a whole lot more mundane.
Could it be because it was never AI to begin with? I am sick and tired of the GOFAI (good old fashioned AI) community pasting the AI label on every clever computer application out there so they can cover up their failure to come up with human-level AI. People are not stupid. They can tell the difference between automatic cruise control and HAL. The former is not AI, it's just a clever hack. The latter has real intelligence. Let's face it. The GOFAI research community has failed. They had no clue as to what intelligence is about when they started the field fifty years ago and they have no clue now. We need new blood and new ideas in AI research.
The problem with connectionism is that it came from the same GOFAI crowd that gave us symbol manipulation and knowledge representation. Those guys made it a point to ignore every significant advance that happened in neurobiology and psychology over the last 100 years. ANNs are a joke. They have as much to do with animal intelligence as an alpha-beta tree-searching algorithm. Temporal, spiking neural networks are where it's at in the new AI, AKA computational neuroscience. Everything else is a joke. Like I said, we need new blood in AI. The old school has got to go.
Slashdot Mirror
I can't do taxes in my head as fast as Turbo Tax, nor can my brain sniff out a bomb, but it can grasp the concept of both, among a zillion other things, and see corrolations between them, and build new ideas based upon both. Turbo Tax, left to it's own devices, will never be any more than Turbo Tax.
Yes and thanks for grasping my point. The analogy is simply a way to point out that any hand crafted software with the complexity of the brain would be so full of bugs, it would not even run. Robustness and reliability are measured in terms of errors vs. complexity. The brain is existence proof that complexity does not imply a lack of robustness. Its reliability is orders of magnitude greater than software. Just going down a flight of stairs or driving a car around town is much more complex than anything any software application can do. This fact squarely and decisively refutes Fred Brooks' "No Silver Bullet" nonsense.
No other computer expert in the annals of software engineering has had a more deleterious effect on our efforts to find a final solution to the software crisis than Fred Brookes. I don't care how respected the guy is in the computer science community. He single-handedly managed to convince almost the entire world that there is no hope. It's rather sad. Countless human beings will die as a result because the problem is not getting better. It's getting worse. Much worse.
I'll repeat with what I wrote with added emphasis. Complexity wise, there is nothing in the universe more robust and reliable than the brain. I'll leave it to you to figure it out. Although I don't hold much hope, in your case.
Software Is Bad Precisely Because Experts Like Brooks Teach that There Is No Silver Bullet
Software is bad because we are doing it wrong. We are doing it wrong because our so-called experts tell us that there is no silver bullet to the problem of unreliability. So everybody has given up on finding a solution. And now they want to bring in the lawyers and even more clueless experts. It won't work.
The brains of humans and animals are the existence proof that there is a silver bullet. Complexity wise, there is nothing in the universe more robust and reliable than the brain. In fact, the more complex the brain gets (as it learns), the more reliable it becomes. By contrast, the reliability of software gets worse as it gets more complex. Why is that?
The primary reason is that software is based on algorithms (threads) whereas the brain is a parallel, signal-based system. The reliability of the brain comes from the strict enforcement of precise signal timing: neurons always fire at the right time, under the right temporal conditions. With software, it is virtually impossible to guarantee the timing of various processes, which leads to program decisions happening at the wrong time.
The secondary reason for bad software has to do with connection types. In the brain, signal pathways are not connected willy nilly. Connections are made according to their types. We should do the same with software. All message connectors should have message types, and all connectors should be either male or female to ensure robust connectivity.
The Solution
We must imitate nature. We must forever stop basing computer programming on the algorithm and we must stop using algorithmic languages. Software should be more like hardware with parallel modules and their input and output connections. One of the reasons that electronic circuits are so much more reliable than software is that timing problems are immediately nipped at the bud due to the inherent parallelism of hardware. The circuit simply will not work if timing is wrong.
We can solve the software reliability crisis. We can create tools that automate over 90% of the software development process. Damn the Experts!
Software Is Bad Because the Experts Say There Is No Silver Bullet
Software is bad because we are doing it wrong. We are doing it wrong because our so-called experts tell us that there is no silver bullet to the problem of unreliability. So now they want to bring in the lawyers and even more clueless experts. It won't work.
The brains of humans and animals are the existence proof that there is a silver bullet. Complexity wise, there is nothing in the universe more robust and reliable than the brain. In fact, the more complex the brain gets (as it learns), the more reliable it becomes. By contrast, the reliability of software gets worse as it gets more complex. Why is that?
The primary reason is that software is based on algorithms (threads) whereas the brain is a parallel, signal-based system. The reliability of the brain comes from the strict enforcement of precise signal timing: neurons always fire at the right time, under the right temporal conditions. With algorithmic software, it is virtually impossible to guarantee the timing of various processes, which leads to program decisions happening at the wrong time.
The secondary reason for bad software has to do with connection types. In the brain, signal pathways are not connected willy nilly. Connections are made according to their types. We should do the same with software. All message connectors should have message types, and all connectors should be either male or female to ensure robust connectivity. Plug compatibility should automate over 90% of software construction.
The Solution
We must imitate nature. We must forever stop basing computer programming on the algorithm and we must stop using algorithmic languages. Software should be more like hardware with parallel modules and their input and output connections. One of the reasons that electronic circuits are so much more reliable than software is that timing problems are immediately nipped in the bud due to the inherent parallelism of hardware. The circuit simply will not work if timing is wrong. We can create the right software construction tools to solve the reliability crisis. Damn the experts!
When the pot gets too hot, the culprit is usually the fire. In my opinion, the sun, the main source of heat for earth's atmosphere, should be the primary suspect. Maybe the sun is going through a warming cycle.
At least according to Active Media Research and the folks at MIT Technology Review:
Mobile Robotics: The Next Revolution
This is lame. A much more interesting story would be that someone had written a program that could play world class chess without world class CPU horsepower.
I am working on just such a project. It's called Animal.
There is a lot more to knowledge than the classification of namable objects and their relationships. There is a huge amount of knowledge that cannot be formalized with symbols. For examples, playing soccer or football, recognizing a subtle fragrance, face or musical tune, manual dexterity, finding one's way around an unfamiliar neighborhood, etc..., in other words, the sort of common sense knowledge that can only be acquired through direct sensory experience.
The interconnectedness of human cognition is so astronomically complex as to be intractable to formal approaches. This realization immediately makes the use of symbolic knowledge representation approaches to creating human-like common sense in a machine look rather silly. That 25 million dollars of taxpayers money went into this Cyc thing is a testament to the effectiveness of the propaganda machine of the GOFAI community. Bravo!
I wrote:
There are now two major competing DLP technologies, one from Texas Instruments (Digital Micromirror Devices, DMD) and another from Digital Solutions (Grating Light Valves, GLV).
Replace 'Digital Solutions' above with Silicon Light Machines.
There are now two major competing DLP technologies, one from Texas Instruments (Digital Micromirror Devices, DMD) and another from Digital Solutions (Grating Light Valves, GLV). Other companies, notably Philips Research, are working on their own solutions.
:-)
I personally prefer the Digital Solutions (DS)approach because it's much cheaper and, if mass-produced, promises to bring super high-res projection TV to the masses for less than the price of a VCR. Sony has reportedly licensed GLV from DS, a sign that exciting things are in the works. I wouldn't mind a super hi-res rear projection adaptation as a replacement for my heavy and bulky monitor. I hope Sony is listening.
Here is a good explanation of the technology,
6 to 12 second delay? That sounds awfully high. Are you sure that's not an exaggerated figure? If you recall the failed motorola iridium satellite phone system. The system failed not because of such high delays, but because of the high price/bulky equipment.
Well, we've all seen the six-second delay with the SAT phones used by news reporters during the war coverage of Afghanistan. The reason, I believe, mainly has to do with the high geosynchronous orbits of the satellites used. If I recall correctly, the Motorola Iridium satellites were positioned at much lower, non-geosynchronous orbits.
If you figure out what that Universal Syntax is (sorry, I forgot the exact term he used - it's been a while, and my university education was in Dutch), you can feed that into a computer and teach it to reduce all phonemes from a given language to it.
Syntax is not nearly enough. Unless you know what the individual words mean, you're shit out of luck. As an example, linguists pretty much had the syntax of Egyptian hieroglyphics figured out, but it took the discovery of the Rosetta stone for them to begin to understand what they were reading. Even then, there is still stuff they can't figure out. Something similar (old Spanish/Maya lexicons compiled by missionaries) was required to decipher Mayan symbols.
but what about a central supercomputer communicating via satellite with handheld devices?
An excellent idea. That would be fine if you can multitask several AIs on the same supercomputer because a huge number of people may want to access the same system simultaneously. Best bet might be to give everybody satellite access to his or her own supercomputer at home. Another twenty years or so should make that possible. Assuming, of course, that Moore's law continues unabated.
Of course, then it'd have to deal with noise as well
Digital communication is pretty much noiseless. A bigger problem is the annoying six to twelve seconds delay inherent in satellite communition.
Does this proposal in any way sound achievable?
Not unless they solve the general AI problem first. In other words, no. You need a machine with a HAL-like intelligence to do good real-time translation. The machine will need to go to school for a few years to learn the idiosynchracies of each language and culture. No way this technology can fit into a portable translator given the current state of computer technology. Besides, even human beings have a hard time interpreting languages that they are fluent in.
This scandal will pale in comparison to quantum computing. The writing is one the wall.
He's just trying to sell more copies of Mathematica!
That's not it. Wolfram is saying the exact opposite. He is saying the universe uses very little math. Just a few simple rules. Fancy math is a red herring, in my opinion. It explains nothing. On the contrary, it is our equations that are in dire need of an explanation, from Newton's gravity equation to Einstein's GR/SR equations. They only describe the evolution of matter but do not explain the causal mechanisms.
Real science is about causal mechanisms at the fundamental level where simple rules rule! This is where Wolfram's ideas are revolutionary. They will not be well received in academic circles. Academics hate simplicity because they can't show off with it.
Third, speech is a very low bandwidth output compared to other solutions
It may be low bandwith but that does not mean it's not a powerful way of communicating. If I can say to my computer, "book me a flight to Miami for the weekend," it sure is a lot more powerful than going to Travelocity's or Yahoo' Travel's sites and make the travel arrangements on my own. That's the main difference between intelligent machines that truly understand natural languages and simple speech recognizers used for dictation and simple commands.
Hal was more than just speech recognition, it was more like a very clever secretary.
You are 100% right. If computers were smart enough to understand what we are saying, I certainly would not be sitting at work enunciating simple commands to my desktop machine to "scroll this page down a bit" or "load that document." Heck, I would not be working at all.
ESP, psychic power, and alien abduction
These things don't look any more superstitious to me than time travel, wormholes, black holes, parallel universes, quantum computing and the like. If scientists can openly believe in a bunch of crap without fear of being labeled crackpots, why can't the general public?
If a scientist has a theory for a natural phenomenon and yet cannot explain it in simple language that the average lay-person can understand, there is a 90% probability that it's crap. Furthermore, if the scientist claims that one needs math to understand the theory, then there is a 100% probability that he or she is as clueless about the phenomenon as everyone else.
Your argument that biological intelligence does not use symbolic manipulation is hollow. It's like saying computer intelligence does not use symbolic manipulation because it's just a bunch of transistors.
Thanks for making my point for me.
There has been plenty of great research in reinforcement learning. Isn't that what you mean by rewards and punishments?
If it is not done within the context of signal processing, i.e., by using spiking neural networks, it's crap. Spiking or pulsed networks did not come from the AI community, BTW. They came from the computational neuroscience community which is part of neurobiology. So don't try to get credit for something the GOFAI community has pretty much ignored for fifty years.
People have also tried to make neurologically inspired models for AI. Note that the transistor is even abstractly similar to the neuron.
The only neural networks that came from the GOFAI community (after Marvin Minsky and Seymour Papert tried to put a monkey wrench in neural network research) are the so-called ANNs. ANNs are a pathetic joke. They bear little ressemblance to biological neurons. Unless an AI researcher realizes that intelligence has to do with the temporal relationships between discrete signals, he or she is not doing AI. He or she is just spitting against the wind.
The AI community is in much better shape than you think.
I doubt that very much. Only a blind fool would believe in the AI community after fifty years of abject failure. They still don't have a clue and they don't seem interested in getting one either. Just recently, MIT Technology Review publisehed an article titled "AI Reboots" which is a PR piece for GOFAI guru Doug Lenat and that database, symbolic representation monstrosity of his called Cyc.
While you may not consider that to be a contribution, the same model may be used for more complicated things, such as piloting a spacecraft. I saw a talk the other day who trained a program to hover a remote-controlled helicopter in place. It performed better than the leading human controllers.
None of it has anything to do with intelligence. Again, to be intelligent, a program must not only be able to learn but it must learn anything and everything, not just a limited domain environment. But that is not all. It must be motivated and adapt to reward and punishment.
There's a lot more to AI than symbol manipulation. Knowledge representation is a very small subset of the field.
Neither symbol manipulation nor knowledge representation has anything to do with intelligence since the only intelligence we know of (biological intelligence) doesn't use any of it. First and foremost, intelligence has to do with discrete, temporal signal processing. The biological evidence is clear on this issue: neurons generate discrete signals or spikes. Second, intelligence has to do with motivation, i.e., it must react properly to reward and punishment. This is what psychology has taught us for the last one hundred years. The GOFAI crowd is not listening.
Calling AI researchers clueless for ignoring these areas is just revealing your ignorance about AI and your fanatacism against AI..
They're worse than clueless. They have wasted the taxpayer's money for fifty years. And, for the record, I am not against AI. Why lie about it when I have a site that promotes AI? On the contrary, I am trying to wake people up to the fact that they are being taken to cleaners by a bunch of clueless career propagandists. If you don't like my opinion on the matter, all can say to you is that it's that's too bad. I exercise my freedom of speech the way I see fit.
I know very few programmers outside of AI or mechanical engineering who can write a program to perform optimal cruise control given sensor/motor noise and unknown road conditions. There's more to it than "Read the speedometer. Accelerate if too slow, decelerate if too fast. Repeat."
There is not much more to it than that. So don't try to make it look like it's a harder problem than it actually is. Any junior programmer can write a good cruise control program through trial and error.
What if "optimal" were defined as some weighted combination making the ride smooth and conserving gas? Is that so trivial a program to write?
Yes it is. I've seen self-taught programmers do amazing things with a 1Mhz Apple II back in the early eighties. Things that are orders of magnitude more complex and clever than cruise control. They never claimed they were doing AI.
AI researchers have solved many important problems over the years, and I don't consider it at all a disappointment that we are not even close to approaching human intelligence.
It's worse than a disappointment. It's a pathetic failure. Many of us remember people like Minsky and others making outrageous claims about their ability to create human-level AI by the end of the last century. We all know about their promotion of the symbol manipulation and knowledge representation nonsense. It all turned out to be mostly worthless crap with little to do with intelligence. Those guys made it a point to ignore every significant advance in neurobiology and psychology over the last one hundred years. Talk about clueless!
Cruise control could be very well formulated as an AI problem.
One does not have to major in artificial intelligence at a university to write a cruise control program. Any competent programmer can write a program that accomplish safe and effective cruise control. It's a simple coding problem that does not require fifty years of DARPA funding to figure out. The only intelligence we know is animal intelligence. Biological intelligence is what we should be trying to emulate. Human intelligence is general, scalable and adaptive. I have seen nothing from the AI community that qualifies as true intelligence. All they have is hype and propaganda.
I'm sick of people asking "When will we see widespread commercial application of AI". AI researchers often quote the so-called "moving frontier" problem, that is, as soon as an AI application becomes useful enough to solve real-world problems, it ceases to be known as AI and looks a whole lot more mundane.
Could it be because it was never AI to begin with? I am sick and tired of the GOFAI (good old fashioned AI) community pasting the AI label on every clever computer application out there so they can cover up their failure to come up with human-level AI. People are not stupid. They can tell the difference between automatic cruise control and HAL. The former is not AI, it's just a clever hack. The latter has real intelligence. Let's face it. The GOFAI research community has failed. They had no clue as to what intelligence is about when they started the field fifty years ago and they have no clue now. We need new blood and new ideas in AI research.
Remember "connectionism"?
The problem with connectionism is that it came from the same GOFAI crowd that gave us symbol manipulation and knowledge representation. Those guys made it a point to ignore every significant advance that happened in neurobiology and psychology over the last 100 years. ANNs are a joke. They have as much to do with animal intelligence as an alpha-beta tree-searching algorithm. Temporal, spiking neural networks are where it's at in the new AI, AKA computational neuroscience. Everything else is a joke. Like I said, we need new blood in AI. The old school has got to go.