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Artificial Intelligence at Human Level by 2029?
Gerard Boyers writes "Some members of the US National Academy of Engineering have predicted that Artificial Intelligence will reach the level of humans in around 20 years. Ray Kurzweil leads the charge: 'We will have both the hardware and the software to achieve human level artificial intelligence with the broad suppleness of human intelligence including our emotional intelligence by 2029. We're already a human machine civilization, we use our technology to expand our physical and mental horizons and this will be a further extension of that. We'll have intelligent nanobots go into our brains through the capillaries and interact directly with our biological neurons.' Mr Kurzweil is one of 18 influential thinkers, and a gentleman we've discussed previously. He was chosen to identify the great technological challenges facing humanity in the 21st century by the US National Academy of Engineering. The experts include Google founder Larry Page and genome pioneer Dr Craig Venter."
I for one welcome our broadly supple, emotionally intelligent overlords.
I mean it could happpen but this is so far from the current state of the art, I think we're talking 50-100 years forward in time. We have the brute powers of computers but nowhere near the sophistication in software or neural interfaces to do anything like this.
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I'll be meeting with Kurzweil in April.... Speaking as a neuroscientist who is doing complex neural reconstructions, I think he's off his timeline by at least two decades. Note that we (scientists) have yet to really reconstruct an actual neural system outside of an invertebrate and are finding that the model diagrams grossly under-predict the actual complexity present.
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If artificial intelligence ever gets to the point where it is greater than humans, won't it be capable of producing even better AI, which would in turn create even better AI, and so on? If AI does reach the level of human intelligence, and eventually surpasses it, can we expect an explosion in technology and other sciences as a result?
The farther out you make a projection, the less likely it is to be true. With this one in particular, I just don't see it being a focus of research. Yes we will have increase levels of intelligence in cars toasters and ball point pens, but the intelligence will be in a supporting role to make the devices more useful to us. There isn't a need for a human like intelligence inside a computer. We have enough ones inside human bodies.
Also, I will not be ingesting nano bots to interact with my neurons, I'll be injecting them into my enemies to disrupt their thinking. Or possibly just threatening to do so to extract large sums of money from various governmental organisations.
Well.. maybe. Or Maybe not. But Definitely not sort of.
How are we so sure that advances in computers will continue at such a rapid pace. Computer miniaturization is hitting against fundamental quantum-mechanical limits and it's crazy to expect 2008-2028 to have progress quit as rapid as 1988-2008.
Short of major breakthroughs on the software end, I don't expect AI to be able to pass a generalized Turing Test anytime soon, and I'm pretty certain the hardware end isn't going to advance enough to brute-force our way through.
Artificial intelligence would be a nice tool to use to reach towards, or to use to understand ourselves... but rare is there a circumstance that demands, or is worth the risks involved with making a truly intelligent agent.
The real implication to me, is that it will be possible to have machines capable of running the same 'software' that runs in our own minds. To be able to 'back up' people's states and memories, and all the implications behind that.
Artificial intelligence is a nice goal to reach for - but it is nothing compared the the siren's call of memories being able to survive the traditional end of existence, cellular death.
Ryan Fenton
So far _not one_ of those claims has come true, with the possible exception of the the much-vaunted "robotic snake".
So ... I'd say: less claims, fewer predictions, and more work. Let me know when you've got anything worthwhile to show.
Not to be outdone by forecasters, I have a forecast of my own to make: before the term is us it will transpire out that all this fanfare and this announcement were only ever meant as means to attract research grants.
Good news: This could herald a lot of good stuff, increased unemployment, greater reliance on computers, newer divides in the class strata of society, further confusion on what authority is and who controls it, as well as greater largess in the well meaning 'we are here to help' phrase department.
Bad news: After reviewing the latest in the US political scene, getting machines smarter than humans isn't going to take so much as we thought. My toaster almost qualifies now. 'You have to be smarter than the door' insults are no longer funny. Geeks will no longer be lonely. Women will have an entire new group of things to compete with. If you think math is hard now, wait till your microwave tells you that you paid too much for groceries or that you really aren't saving money in a 2 for 1 sale of things you don't need. Married men will now be third smartest things in their own homes, but will never need a doctor (bad news for doctors) since when a man opens his mouth at home to say anything there will now be a wife AND a toaster to tell him what is wrong with him.
oh god, this list goes on and on.
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As soon as they make robots that can have sex like humans...what's the point in inventing anything else? All scientists will be busy "researching" their robots.
Just in time for AI to help me drive my new fusion-powered flying car!
O.
He obviously hasn't been paying attention to AI developments. The story of AI is largely a story of failure. There have been many dead ends and unfulfilled predictions. This will be another inaccurate prediction.
Computers can't even defeat humans at go, and go is a closed system. We are not twenty years away from a human level of machine intelligence. We may not even be *200 years* away from a human level of machine intelligence. The technology just isn't here yet. It's not even on the horizon. It's nonexistent.
We may break through the barrier someday, and I certainly believe the research is worthwhile, for what we have learned. Right now, however, computers are good in some areas and humans are good in others. We should spend more research dollars trying to find ways for humans and computers to efficiently work together.
If you had super powers, would you use them for good, or for awesome?
Yes, I remember well my youth, reading Goedel Escher Bach and Winograd, etc., thinking that the next scientific revolution was coming. Things never got any better than Eliza. Now as a hard scientist, I strongly feel that the problem is far far off.
(most) People can go out to get more education to advance from a menial job to a more skilled one when taken over by a robot but wtf do we do if the machines are as smart as we are? Who is going to hire any people to do even the most advanced thinking jobs when the machine that works for electricity 24/7 can do it? This kind of thing will bring on the luddite revolution in a hurry.
" Artificial Intelligence will reach the level of humans"
Buddy,I've been around more than four decades.I've yet to see more than a superficial level of intelligence in humans.
Send your coders back to the drawing board with a loftier goal.
*Repent!Quit Your Job!Slack Off!The World Ends Tomorrow and You May Die!
It might seem like the lack of AI development is a temporary problem and altogether a peripheral issue. It is however neither - it is a fundamental problem and it affects all software development.
Early in the history of computing, software and hardware development progressed at a similar pace. Today there is a giant and growing gap between the rate of hardware improvements and software improvements. As most people involved in the study of the field of software engineering are aware of, software development is in a deep crisis.
The problem can be summarized in one word: complexity. The approach to building software has largely been based on traditional engineering principles and approaches. Traditional engineering projects never reached the level of complexity that software projects have. As it turns out humans are not very good at handling and predicting complex system.
A good example of the problems facing software developers is Microsoft's new operating system Windows Vista. It took half a decade to build and cost nearly 10 billion dollars. At two orders of magnitude higher costs than the previous incarnation it featured relatively minor improvements - almost every single new radical feature (such as a new file system) that was originally planned was abandoned. The reason for this is that the complexity of the code base had become unmanageable. Adequate testing and quality assurance proved to be impossible and the development cycle became painfully slow. Not even Microsoft with its virtually unlimited resources could handle it.
At this point, it is important to note that this remains an unsolved problem. It would have not been solved by a better structured development process or directly by better computer hardware. The number of free variables in such a system are simply too great to be handled manually. A structured process and standardized information transfer protocols won't do much good either. Complexity is not just a quantitative problem but at a certain level you'll get emergent phenomena in the system.
Sadly artificial intelligence research which is supposed to be the vanguard of software development is facing the same problems. Although complexity is not (yet) the primary problem there manual design has proved very inefficient. While there are clever ideas that move the field forward on occasion there is nothing to match the relentless progress of computer hardware. There exists no systematic recipe for progress.
Software engineering is intelligent design and AI is no exception. The fundamental idea persists that it takes a clever mind to produce a good design. The view, that it takes a very intelligent thing to design a less intelligent thing is deeply entrenched on every level. This clearly pre-Darwinian view of design isn't based on some form of dogma, but a pragmatism and common sense that aren't challenged where they should be. While intelligent design was a good approach while software was trivial enough to be manageable, it should have become blindingly obvious that it was an untenable approach in the long run. There are approaches that take the meta level - neural networks, genetic algorithms etc, but it is thoroughly insufficient. All these algorithms are still results of intelligent design.
So what Darwinian lessons should we have learned?
We have learned that a simple, dumb optimization algorithm can produce very clever designs. The important insight is that intelligence can be traded for time. In a short in
It's one thing to predict when a building project will be finished or when we'll reach a certain level of raw processing power because these things proceed by predictable means. But strong AI requires us to make theoretical advances. Theoretical advances don't proceed like a building project--someone has to have a clever idea, fully develop and understand it himself and convince others of it. And it won't occur to someone all at once, so we'll need incremental advances, all of which will happen unpredictably.
In Repressive Burma, it's not just your connection that dies. slashdot.org/comments.pl?sid=314547&cid=20819199
If you read a Kurzweil book, it's as if he understands hope and has no concept of problems. The man is so good at glossing over difficulties he should patent his methods and join the magazine industry.
we aren't even close to the processing power of the human brain.
We aren't that far off. Estimates for the computational power of the human brain are around 10**16 operations per second. Supercomputers today do roughly 10**14, and Moore's Law increases the exponent by 1 every 5 years. Even if we have to simulate the brain's neurons by brute force and the simulation has 99% overhead, we'll be there in 20 years. (Assuming Moore's Law doesn't hit physical limits).
How to solve most of our problems: 1.Lots of nuclear plants. 2.Cure aging.
As an party "outside" the field but interested, I agree with all of you here so far, except that of course you disagree on timelines. :o)
"Artificial Intelligence" in the last few decades has been a model of failure. The greatest hope during that time, neural nets, have gone virtually nowhere. Yes, they are good at learning, but they have only been good at learning exactly what they are taught, and not at all at putting it all together. Until something like that can be achieved (a "meta-awareness" of the data), they will remain little more than automated libraries. And of course at this time we have no idea how to achieve that.
"Genetic algorithms" have enormous potential for solving problems. Just for example, recently a genetic algorithm improved on something that humans had not improved in over 40 years... the Quicksort algorithm. We now have an improved Quicksort that is only marginally larger in code size, but runs consistently faster on datasets that are appropriate for Quicksort in the first place.
But genetic algorithms are not intelligent, either. In fact, they are something of the opposite: they must be carefully designed for very specific purposes, require constant supervision, and achieve their results through the application of "brute force" (i.e., pure trial and error).
I will start believing that something like this will happen in the near future, only when I see something that actually impresses me in terms of some kind of autonomous intelligence... even a little bit. So far, no go. Even those devices that were touted as being "as intelligent as a cockroach" are not. If one actually were, I might be marginally impressed.
The comedian Emo Philips once remarked that "I used to think my brain was the most important organ in my body until I realized what was telling me this."
We have tendency to use human intelligence as a benchmark and as the ultimate example of intelligence. There is a mystery surrounding consciousness and many people, including prominent philosophers such as Roger Penrose, ardently try to keep it that way.
Given however what we through biological research actually know about the brain and the evolution of it there is essentially no justification for attributing mystical properties to our data processing wetware. Steadily with increased capabilities of brain scanning we have been developing functional models for describing many parts of the brain. For other parts that need still more investigation we do have a picture, even if rough.
The sacred consciousness has not been untouched by this research. Although far from a final understanding we have a fairly good idea, backed by solid empirical evidence that consciousness is a post-processing effect rather than being the first cause of decision. The quantity of desperation can be seen in attempts to explain away the delay between conscious response and the activations of other parts of the brain. Penrose for instance suggests that yes, there is an average 500 ms delay, but that is compensated by quantum effects that are time symmetric - that the brain actually sees into the future, which then is delayed to create a real-time decision process. While this is rejected as absurd by a majority of neuroscientists and physicists, it is a good example of how passionately some people feel about the role of the brain. It is however painstakingly clear that just like we were forced to abandon an Earth-centered universe we do need to abandon the myth of the special place of human consciousness. The important point here is that once we rid ourselves of the self-imposed veil of mystery of human intelligence we can have a sober view on what artificial intelligence could be. The brain has developed through an evolutionary optimization process and while getting a lot of benefits it has taken the full blow of the limitations and problems with this process and also its context.
Evolution through natural selection is far from the best optimizing method imaginable. One major problem with it is that it is a so called "greedy" algorithm - it does not have any look ahead or planning capabilities. Every improvement, every payoff needs to be immediate. This creates systems that carry a lot of historical baggage - an improvement isn't made as a stand-alone feature but as a continuation of the previous state. It is not a coincidence that a brain cell is a cell like any other - nucleus and all. Nor is it a cell because it is the optimal structure for information processing. It was what could be done by modifying the existing wetware. It is not hard to imagine how that structure could be improved upon if not limited by the biological building blocks that were available to the genetic machinery.
Another point worth making is that our brains are optimized not for the modern type of information processing that humans engage in - such as writing software for instance. Humans have changed little in the last 50,000 years in terms of intellectual capacity but our societies have changed greatly. Our technological progress is a side effect of the capabilities we evolved that increased survivability when we roamed the plains of Africa in small family hunter-gatherer groups. To assume the resulting information processing system (the brain) would the ultimately optimal solution for anything else is not justifiable.
There has been since the 1950's ongoing research to create biologically inspired computer algorithms and methods. Some of the research has been very successful with simplified models that actually did do something useful (artificial neural networks for instance). Progress has however been agonizi
Predictions like this have been made in past, and not even come close. This one is no different.
The difference is that in 20 years we may have sufficiently powerful hardware that the software can be "dumb", that is, just simulating the entire physical brain.
The bottom line is that humans process some information in a non-representational way, while computers must operate representationally.
What prevents a computer from emulating this "non-representational" processing? Or is the human brain not subject to the laws of physics?
How to solve most of our problems: 1.Lots of nuclear plants. 2.Cure aging.
I'm no expert on AI, so for all I know, the technology could reach human intelligence by 2029. But nanobots that crawl through your brain? That I can comment on. Bone Morphogenic Protein (BMP) was discovered by Urist and Reddi in the 1970's, and it took 30 years just to make that product, a simple growth factor, go from bench top to human clinical product. You're telling me that nanobots, a medical device never before seen by the FDA so far, can be approved and ready and in use in humans by then? Let me set the record straight. Even if artificial intelligence reached human level TODAY, there would be no nanobots crawling through our brains by 2029... maybe by 2039 or 2049. Possibly. So whatever year AI reaches human intelligence level, add 30 to 40 years onto that and you'll have your year for a medical product of that magnitude. Remember, the FDA does not care what science and engineering can do, only that they can do it safely and effectively, which is a lot more difficult to show than a simple experiment proving a concept.
The blue brain project is already simulating a cluster of 10,000 neurons known as a neucortical column. Althought quite good already (in terms of biological realism), their simulation model is still incomplete with a few more years work to get the neurons working like in real life. With more computational power to increase neuron count and better models they will be able to one day simulate an entire mammalian brain.
The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.
Whenever I see stories like this and the usual negative rebuttals that follow, I wonder if I am the only person who read Asimov, Clarke, Crichton, Roddenberry, Heinlein and many others. I am starting to believe that it is because we feel we have "dealt" with the bogeyman of "truly aware" A.I., now that it has been confronted handily by Hollywood via The Terminator and its ilk. In the same way that it was almost comforting to embrace the dark specter of biological terrorism as a pleasant relief from the more real and closer danger of nuclear destruction; focusing on the dawn of A.I. is a relief from the true technological tsunami heading our way.
In the midst of all this talk of pure A.I. is the real steady progress being made in hooking mammalian brains to computers. So far it is in the safe yet icky domain of direct control over robots and other advanced technical based prosthetics, but it is the door to the bigger more powerful scenario that may await us compared to the "birth of A.I." to reference The Matrix. What people fail to understand is that we will make huge progress in this area, much faster than in solely silicon A.I. Why? Because we don't have to understand how the mind works to reap powerful benefits from hybrid A.I. like we do with pure A.I. Neurons by their very nature analyze and adapt to patterns and signals, they just need to be connected and protected.
The most disruptive mind-numbing change heading our way is when human brains can connect with each other over a digital conduit like the Internet. What happens when I can expand my consciousness to be able to maintain far more than the average capacity of 4 to 7 active symbols in my mind, by harnessing the brain capacity of others on a shared peer to peer neuronal network? What powerful meta-consciousness will form when your mind can directly alter a visualization held in real time by another, group dreaming as it were? Or perhaps 10 minds, or a thousand? When we unplug, if we ever do, will we feel as if we woke up from a greater more powerful and majestic dream that evaporates as soon as we disconnect because our minds, by themselves and in comparison, are too tiny to hold the more complex patterns a mind cloud can handle? Perhaps feeling like a butterfly who was dreaming that he was a man, now awake and relegated back to simple thoughts of procreation and feeding, to paraphrase Zen?
In closing, what problems which are now intractable to any single human due to their complexity and scope will fall astonishingly quickly to the power of a million minds focused like a laser on their solution? Please don't take the laser analogy lightly. Right now all of us, and any computer programmer knows this all too well, are recomputing and resolving billions of thought problems which are complete duplicates of each other. What happens when all that duplication is virtually eliminated and our minds in unison all take one small slice of a much larger problem and tear it to pieces? Heaven or hell, you decide, but coming a lot sooner than any of us think.
Robert Oschler - RobotsRule.com
You can similarly compare the temperature of the human brain and then observe that the machines have long bypassed it. Does it make machines smarter? I don't think so.
The brain is so insanely parallel and the neurons are not just digital gates, more like computers in themselves. The machines of today are a far cry from the brain in how they are built. But sure, you can compare them by some meaningless parameter to say that we're close. How about the clock frequency: neurons are 1kHz devices, and modern CPUs are in GHz now...
The Singularity is Near has a rebuttal of your first paragraph. Any sucessful part of AI research spins off into its own well-functioning discipline... optical character recognition, dictation software, text-to-speech, etc... they were sci-fi "AI" in 1980 and now they are working technologies. AI research is the umbrella under which only the unsolved problems still lie, and thus is always undone.
I predict that the Sun will become a white dwarf within 10,000,000,000 years. Predicting 10 billion years instead of 5 billion years actually makes it more likely to be true.
"Screw Sun, cross-platform will never work. Let's move on and steal the Java language." - Visual J++ Product Manager
At least not yet. I can't believe that the sort of bullshit that Ray Kurzweil keeps peddling gets taken so seriously.
There is a lot of talk about computers surpassing, or not surpassing, humans at various tasks - does it not bother anyone that computers don't actually posses any intelligence? By any definition of intelligence you'd like? Every problem that a computer can "solve" is in reality solved by a human using that computer as a tool. I feel like I'm losing my mind reading these discussions. Did I miss something? Has someone actually produced a sentient machine? You'd think I would have seen that in the papers!
What's the point of projecting that A will surpass B in X if the current level of X possessed by A is zero? There seems to be an underlying assumption that merely increasing the complexity of a computational device will somehow automatically produce intelligence. "If only we could wire together a billion Deep Blues," the argument seems to go "it would surpass human intelligence." By that logic, if computers are more complex than cars, does wiring together a billion cars produce a computer?
Repeat after me - The current state of the art in artificial intelligence research is: fuck all. We have not produced any artificial intelligence. We have not begun to approach the problems which would allow us to start on the road to producing artificial intelligence.
Before you can create something that surpasses human levels of intelligence, one would think you'd need to be able to precisely define and quantify human intelligence. Unless I missed something else fairly major, that has not been done by anyone yet.
sic transit gloria mundi
Every time I try out a new expert system, it gets more depressing -- it honestly feels like no progress is happening in that market at all. I have yet to have a conversation with a computer that has been any more compelling than my first round with WinEliza on Windows 3.1 in 1995.
There's still no semblance of a short-term memory, even so much as continuity between responses. It always quickly becomes obvious that each response has been prepared verbatim beforehand by a human, that the system is still performing only a keyword-canned response routine, perhaps feeding in a few variable strings.
Today we have the same stone wheels we've had for decades, and the article suggests we'll have an internal combustion engine with antilock brakes and a hood ornament in another 20 years. We'll see.
Your mind is clear / The things that you fear / Will fade with how much you / Believe what you hear
We've had them for a long time, too.
The thing is, we don't actually *want* flying cars. Ground transport is sufficient for most situations, and it's far more economical to cluster together long range transport.
The human brain and consciousness are complex. We don't know that they are non-deterministic. And furthermore, even if it's fundamentally random on some level, can't that still be approximated with a random (on some level) algorithm? There may be other arguments as to why the brain can't be modeled ("maybe if the brain were modeled as an algorithm, it would have to be *infinitely long*"), but I don't know many / I'm not sure how I feel about them.
Consciousness is also a strange beast. What is consciousness? Why does consciousness feel continuous, when we know it isn't? (Some people even regain consciousness after they have been pronounced dead.) Why do I still think I am the same being that I was 10 years ago, when my brain was made of completely different cells? Because of the uncertainty of these questions, I think that *what consciousness is* really doesn't matter.
Consciousness may just be part of the noise that results when a thinking being becomes self aware. But no matter what it is, I think it developed as a means to an end, rather than an end in itself. If this is so, when we create computers that can parse written information and communicate effectively, it won't really matter whether they are "conscious", and it won't matter what it would mean for such a machine to be conscious.
A cat can't teach a dog to bark.
Why on earth would this advanced AI want to stay on little old earth?
Seems to me that any crazy smart AI would just beam themselves out into space to avoid us and maybe watch us from a distance occasionally for amusement.
Think of this way, when you see an anthill, it's rather curious for a while, then you get bored and go on your merry way. Unless of course you are a sociopath and want to destroy the ant hill and all the ants for fighting with other ants, or you are insane and you want to teach the ants to get along with other ants or spiders their mortal enemy or perhaps you are psychotic and want to train the ants to do your bidding. More likely you would just leave and go on to something more interesting (unless you are not that intelligent to begin with).
I fail to understand why people seem to insist that any really smart AI would want to have anything to do with us except on an occasional basis. Humans and earth aren't really that important in the bigger scheme of things (just important to us humans of course) and we'd probably not have much in common with any really advanced AI anyhow.
If humans would ever create such an AI, it would be like a bunch of ordinary joes giving birth to a super einstien. Eventually, the 'kid' would stop listening to us, go do their own thing which we would be too dumb to understand or appreciate and occasionally we'd invite it to visit to help us fix the settings on our computer because we got it messed up. It would explain to us in excruciating detail how we were using the wrong type of computer and how we needed to get up to date on technology and we'd just tell them a story about how it was in the old days, it would roll it's virtual eyes and say thanks for the tip, and go back to it's own business of which we would be blissfully ignorant...
Just think about it for a second.
I assure you that I did not make this up, but I could have been the victim of a hoax.
About a year ago, I found a link (from a reputable source, IIRC) to a site from a company that claimed to be doing significant work with genetic algorithms. As an example, they had a description (and even a graphic demo) of their modified quicksort vs. a regular quicksort. Accordng to their lit., it showed marginal improvements over quicksort by ensuring (in some non-obvious way) that each element in the dataset was only compared once. It was all very convincing. But of course I did not scrutinize their actual code.
Since you asked, I went out looking for that source, and I, too, have been unable to locate it. In the process, I found a number of references to claims (Sedgewick, et al.) that Quicksort is already optimal.
So, right now anyway, it appears that someone pulled the wool over my eyes.
Maybe that's why Google is hoarding all the remaining three digit IQ scores so that there is no shortage of IQ.
In other news, lots of flying chairs were heard swishing around Redmond Campus at Microsoft when the CEO heard google was cornering the market on Human IQs.
Abrams starts a new Serial: LOST IQ.
"Doing what i can, with what i have." ~ Burt Gummer
And I work on AI and machine learning day in and day out. I'd put the goal post at 50 years, and that's an optimistic estimate. There are scant few research centers that do "general AI" research. Even fewer actually talk to neuroscientists, thus dismissing one viable (though extremely complex and costly) avenue of research. The fact remains, however, that at this point we don't have the required sophistication in any of the areas that presumably would be required to build a "thinking" machine. We can't process human language well enough (and therefore speech recognition and textual information sources are pretty much useless), we can't process visual information well enough either (segmentation, recognition, prediction, handling a continuous visual stream), we don't know the cognitive mechanisms below high level abstract reasoning, and even at a high level our abilities are weak (try to build a classifier that will recognize sarcasm, for example), finally even if we could do all that, we wouldn't be able to store the resulting data efficiently enough (in terms of required space and retrieval speed), because we have no idea how to do it.
That said, a lot of stuff can happen in 50 years, and I bet that once some of the major problems get solved, there will be an insane stream of money pouring into this field to accelerate the research. Just imagine the benefits an "omniscient" AI trader would bring to a bank. The question is, do we want this to happen? This will be far more disruptive a technology than anything you've ever seen.
That's enough. Err ... frankly your reply has given me pause. Seriously. It betrays a wealth of misunderstanding about AI and computing in general, and I have been wondering if I my reply should be a sarcastic one or just an explanatory one. Given the nature and the depth of the misunerstanding displayed here, I have settled on an explanatory one.
What you call "Automated scheduling" is part of a branch of applied mathematics known as "Operations Research". Basically it's the art and science of formulating a practical, real-world problem (such as air-crew scheduling, devising FedEx routes, loading aircraft, routing goods flows through transport networks as efficiently as possible, finding optimal stock portfolios, finding optimal ways of running an oil refinery, etc. etc.) into a mathematical problem, (usually a so-called "optimisation problem; see http://en.wikipedia.org/wiki/Category:Optimization_algorithms) and then devising appropriate solution algorithms that can be executed by a computer (usually a digital one) to give exact or approximate optimal solutions to said problem. See also: http://en.wikipedia.org/wiki/Operations_research
Such problems can be quite large ... e.g. with thousands of variables and tens of thousands of constraints. Now I'm confident that you would be quite unable to solve a 2x2 LP problem (i.e. a Linear Programming Problem, one of the most basic Operations research problems) in your head, or a 3x3 problem using pen and paper. Any PC can run a program that solves such problems in microseconds. This however has nothing to do with the question of whether solving an LP problem is to be classified as AI or not. As a matter of fact, solving LP problems is not, and has never been, considered part of AI. The same holds for all the other OR problems I mentioned.
Now it turns out that many of the problems I mentioned don't have what are known as "efficient" solution algorithms. Meaning we don't know of any exact solution algorithm that has polynomial run-time on a digital computer; instead all known algorithms have *exponential* run time on a digital computer. In such cases one resorts to what are known as "heuristics" (see http://en.wikipedia.org/wiki/Heuristics#Computer_science ), being algorithms that aren't guaranteed to find an optimal solution, but which sometimes *can* be guaranteed to come within say p% of the optimum, or at least to come up with a fairly decent solution. Some of the heuristics used, e.g. what are known as "branch-and-bound" algorithms (see http://en.wikipedia.org/wiki/Branch_and_bound) are based on questions that were (also) encountered or raised in the study of AI.
The important thing to note is that in general this has nothing whatsoever to do with Artificial Intelligence per se. Artificial Intelligence (AI) research on the other hand deals with problems like: "How can we induce computers to exhibit behaviour mimicking the Human Mind, or the Human body" (see: http://en.wikipedia.org/wiki/Artificial_intelligence))
Note the lack of overlap between Operations Research (OR) and Artificial Intelligence (AI) problems. The m
I wrote the parent comment. Since I posted it, I've been trying to understand how Ray Kurzweil could say something so foolish as "We'll have intelligent nanobots go into our brains through the capillaries and interact directly with our biological neurons."
Not only is he saying that there will be artificial intelligence in only 21 years, but he is saying that the computers on which the new AI runs will be so small they can travel like cells in our bloodstream, and do useful work based on an extremely advanced understanding of biochemistry and an ability to interact on a molecular level.
There is no evidence that anything like that is happening. It is wild imagining.
I'm guessing that Ray Kurzweil understood correctly that the National Academy of Engineering Grand Challenges for Engineering is a publicity gimmick, and that the committee is a social group. Maybe Mr. Kurzweil decided to try to outdo everyone else in getting publicity. So, he put together the popular prefix nano- and the hot words robots, medicine, and AI. And he was successful. He tricked the BBC into quoting a prediction he himself doesn't believe.
Apparently, Ray Kurzweil interpreted the event as a socially backward macho male competition, and, given that, he won.
The National Academy of Engineering web page, Reverse-engineer the brain, is also wildly nonsensical, but somewhat more restrained, saying: "... further advances are needed...", and "Because each nerve cell receives messages from tens of thousands of others, and circuits of nerve cells link up in complex networks, it is extremely difficult to completely trace the signaling pathways."
There is lying, and then there is creative, energetic pseudo-scientific lying. There is treating other people badly, and then there is using a knowledge of science to take advantage of the shortcomings and weaknesses of other people. I suppose Ray Kurzweil was only getting into the mood of the baloney artistry the National Academy of Engineering created for him. But using baloney artistry to get attention is not only infantile, it is FRAUD.
This is all my opinion. If you can find a more positive interpretation of it, I'm interested.
Ray Kurzweil gave another interview about his imaginings that was rather uninformative, but not so nutty: Interview with Ray Kurzweil about the engineering challenges of the 21st Century (MP3, 6 minutes).
AND BY 2029, YOU'LL BE ABLE TO DOWNLOAD RICE.
I've rtfm, there is NO science at work here, just some bloke making HUGE unsubstantiated claims. They cite no research, they don't even make any concrete claims apart from "at a human level". I've seen more technical and in depth discussions between piss heads on a parkbench.
Finally at the bottom, they namedrop a google founder to try and make this sound more believable.
Shame on the BBC covering nothing, and shame on Slashdot for posting filler.
You feel sleepy. Close your eyes. The opinions stated above are yours. You cannot imagine why you ever felt otherwise.
The problem I see is not the computing power, neither things like wetware like neural simulations where we have come far. The main problem is the architechture, to glue all this power together. I work with unsupervised pattern recognition which is one of the weak AI methods and from my view a key to AI. My strong belief about strong AI is that we need to design it using an ethical, hybrid reasoning, recursive approach.
The algorithm below may need some improvements, it's only conceptual, but within 10 years I believe that this can be implemented and as such work at any abstraction level within a system.
BEGIN
Axioms
Goals
Priors
Questions
REPEAT
Data
Patterns
IF (Answered(Patterns,Goals)) (* Deduce goals *)
AND (Answered(Patterns,Questions)) (* Deduce questions *)
AND NOT Contradiction(Patterns,Questions,Goals,Axioms);(* Resolution! *)
THEN BEGIN
Proofs
Apply(OccamsRazor,Proofs); (* In case multiple solutions, simplest! *)
RealWorldReport(Proofs); (* Report/use results *)
END
UNTIL forever;
END
As we want these AI to serve us, without really being dependent of us, if we, or they choose to escape this universe, I suggested this as the modified ethical laws:
That is, these creatures would have no choice but to love us, thus they wouldn't have free will. To create an AI that would learn to love and respect others, I consider a much too hard (and risky) problem that may take thousands of years to solve.
This may not create human like intelligence, even though it is insipired by introspection of my own thinking, but would we really want to create a creature mimicking our problems, taking into consideration that a large part of the human population have different problems with themselves, as power-hungriness, paranoia, anxiety, depression etc...? I think we create AI because we need assistance and to simply relieve us from tasks we consider too hard or too boring.
In our own case we are using a subset of this type of reasoning to implement a (patent applied) business method for AI-assisted customer driven innovation, but then we still speak about weak AI of course.
Talking here about predictions of Artificial Intelligence and its state 20 years from now... have you read any of the works by Marvin Minsky and his predictions in the early 1970's? He also made similar predictions that human-like intelligence would be achievable "20 years from now". The 1990's came and went without human-like AI, and here is yet again somebody making almost the same kind of prediction.
And this isn't to completely mark as irrelevant anything that Minsky said about AI in the 1970s or what he has done since then, but to note that the study of intelligence, whether from the perspective of a nano-technology/biology perspective or from a software engineering approach, is still trying to uncover the basic ground rules and understand even the sheer domain of the problem.
If you don't understand the domain... or if the size of the domain keeps expanding... you really don't even know where to begin to solve the problem. I challenge any of the researchers in this field to clearly define even what it means to have human intelligence or what even the intelligence of an earthworm really is. Let's just say that Charles Darwin was sufficiently impressed at the intelligence of an earthworm that he choose to use that species as the foundation block for his study of intelligence. (Yes, I know there are multiple species of earthworms.) And only recently is this aspect of intelligence even being reconsidered.
I do think that a proclamation that we might be able to reach the computational processing level of an earthworm in the next 20 years is reasonable, but even then you had better be extra sure that you understand even the scope and domain of that problem before you claim it is "solved". I for one am still not convinced, in spite of some pretty incredible research about the issue.
Not only that, the GP (as many AI enthusiasts do) forgets that the synaptical connections are electro-chemical, not just purely electrical, and thus a whole new dimension of chemical communication enters the fray, complete with different functions of different neuro-transmitters at different synapses of the same cell, which can alter the functions of the said cell both short-term and long-term.
The more fair comparison to a neuron is not that of a transistor or even a logic gate but to a whole complete embedded microprocessor with up to 50 thousands I/O channels!
Each cell!
Now multiply times 100 billion...
Any aircraft the size of a barn swallow.
Your question displays a lack of understanding. Not of biology, but of physics. Square cube law specifically. Aircraft don't corner as fast as small birds. the reason isn't any magic of biology, it's simple momentum.
The larger any object is, the more it weighs. Make it twice as big, it weighs eight times as much. packs eight times as much momentum. A large bird doesn't turn s fast as a small bird. Same is true of planes. Same is true of ships. A buss won't corner as fast as sports cars either.
A typical aircraft is 1000 times bigger than a swallow. It's a million times heavier. It packs a million times the momentum. It's not that the swallows design is better, or that there is some biological magic. It's just a question of size. It's true the other way too. A mosquito can turn a lot quicker than a barn swallow. Barn swallows catch mosquitoes because they can fly faster. Guess what, the aircraft you were so dismissive of can fly a lot faster than that barn swallow too. Visit a large airport. Swallows get killed by aircraft every day. They can't get out of the way in time. A barn swallow that was as large as a chicken would be ripped apart by the stresses if it were able to corner as fast as a real barn swallow. That's the real reason that chickens don't turn well in flight. (Yes, chickens can fly for short distances.) Momentum.
Your problem appears to be that you just don't understand scale. It is a wonderful thing when you do. You see reasons all around us, for all kinds of things.
So, yes, we should study biology. But, we should also remember the physics. The tricks the mosquito uses just won't work for a passenger jet. Nor will the barn swallows turns be good for the passengers on that jumbo jet. Still, some things will be useful. We just don't know what. Who would have thought that studying a sharks skin would help racing yachts. Personally, I hope that we get a lot of surprises. That's where the fun in science is.
I don't expect AI research to give us human type intelligence in a machine. Ever. That doesn't mean we shouldn't try. We don't know what we will get, or what it will make possible. We can't know before the fact. Studying birds didn't give us aircraft that can corner in a second or two, it did give us jumbo jets that can take us half way around the world in an easy chair. That took a lot of other things too.
The Wright brothers succeeded where Lilenthal failed. Not because they understood birds better, but because in the meantime the internal combustion engine was developed. AI will be the same. Right now, we don't even know what we need in order to make this work. There will be surprises.
Everybody knows 3 people with my name.
Warning: rambling post ahead.
My gut feeling is that, from strictly a hardware perspective, we're already capable of building a human-level AI. The problem is that, from a software perspective, we've focused too much on approaches that will never work.
As far as I'm concerned, the #1 problem is the Big Damn Database approach, which is basically a cargo cult in disguise. Though expert systems are useful in their niches, "1. Expert system 2. ??? 3. AI!" is not a workable roadmap to the future. I'm certain that it's far easier to start with an ignorant AI and teach it a pile of facts than it is to start with a pile of facts and teach it to develop a personality.
The #2 problem is the Down To The Synapse approach. This, unlike BDD, could quite possibly create "A"I if given enough hardware. But I think that, while DTTS will lead to a better understanding of medicine, it won't advance the AI field. It won't lead to an improved understanding of how human cognition works — it certainly won't teach us anything we didn't already know from Phineas Gage and company.
Even if we go to all the trouble of developing a supercomputer capable of DTTS emulation of a human brain — so what? If we ask this emulated AI to compute 2+2, millions of simulated synapses will fire, trillions of transistors will flip states, phenomenal amounts of electricity will pour into the supercomputer, just for the AI to give the very same answer that a simple circuit consisting of a few dozen transistors could've answered in a tiny fraction of the time, using the amount of electricity stored on your fingertip when you rub your shoes on the carpet during winter. And that's not even a Strong AI question. That's not to say that working DTTS won't be profound in some sense, but we know we can build it better, yet we won't have the faintest idea of where to go next.
That brings me to my core idea — goals first, emotions close behind. Anyone who's pondered the "is/ought" problem in philosophy already knows the truth of this, even if they don't know they know the truth of it. The people building cockroach robots were on the right track all along; they're just thinking too small. MIT's Kismet, for instance, gives an idea of where AI needs to head.
That said, I think building a full-on robot like Kismet is premature. A robot requires an enormous number of systems to process sensory data, and those processing systems are largely peripheral to the core idea of AI. If we had an AI already, we could put the AI in the robot, try a few things, and ask the AI what works best. So, ideally, I think we need to look at a pure software approach to AI before we go off building robot bodies for them to inhabit.
And how to do that? I think Electric Funstuff's Sim-hilarities captures the essence of that. If we give AIs a virtual world to live in — say, an MMO — then that removes a lot of the need for divining meaning from sensory input, allowing a sharper focus on the "intelligence" aspect of AI. Start with that, grow from there, and I can definitely see human-level AI by 2029.
Range Voting: preference intensity matters
For one thing, it does not compete with religion, and many strongly religious people (in every major religious tradition) have the same humanistic convictions and take their religion to support their humanism (and vice versa). The same goes for a belief in the results and methods of science: This belief does not crowd out religious belief, and most educated religious people in the West believe in science just as much as atheists do. Ditto for environmentalism and all the other ism's you mention.
You're right that various humanistic movements are organized, but so are chess clubs, national elections and universities. Belonging to an organized religion prevents membership in another organized religion (unless you're Japanese, who seem to have no problem with accepting several religions simultaneously), but it certainly does not prevent membership in another, non-religous organized movement.
I just want it to be clear that humanistic endeavors like the fight against poverty, for environmental conservation, for global justice, etc. are nothing like religions. Religion is a different sort of thing.
Atheists simply don't have a religion. What makes them atheists is that in them, any belief that gods of any sort exist, is absent. This does not force them to put their "faith" in any other movement in particular. I mean, to some extent, every human being with normal, human compassion has some sort of humanistic ideals. But again, that's just a result of being a moral and empathetic person, and it happens to moral people whether or not they have any faith in various gods.