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Artificial Intelligence at Human Level by 2029?
Posted by
Zonk
on Sat Feb 16, 2008 11:24 PM
from the i-need-me-an-implanted-robot-buddy dept.
from the i-need-me-an-implanted-robot-buddy dept.
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."
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Science: Ray Kurzweil On IT And The Future of Technology 450 comments
Roland Piquepaille writes "In this interview with CIO Magazine, Ray Kurzweil says that one day, software and computers will reside inside us. He adds that by 2020, "we will be placing millions or billions of nanobots -- blood cell-size devices -- inside our bloodstream to travel into our brains and interact with our neurons." He also says that if we're not enhanced by machines, they will surpass us. But he doesn't think it will happen. According to him, machines and humans will merge. In the mean time, he's pursuing his anti-aging quest and takes about 250 supplements to his diet every day! With this regime, he says his biological age is 40 while he's 56 years old. By 2030, there will be very little difference between 30-year-old and 120-year-old people, says Kurzweil. He's certainly a bright person, but I'm not sure that I agree with someone taking daily such an amount of pills. What do you think? This summary contains some selected -- and biased -- excerpts to help you forge your opinion."
Firehose:Artificial Intelligence at Human Level by 2029? by Anonymous Coward
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Oblig. (Score:5, Funny)
Re:Oblig. (Score:5, Insightful)
Speaking as an engineer and a (~40-year) programmer:
Odds are extremely good for beyond human AI, given no restrictions on initial and early form factor. I say this because thus far, we've discovered nothing whatsoever that is non-reproducible about the brain's structure and function, all that has to happen here is for that trend to continue; and given that nowhere in nature, at any scale remotely similar to the range that includes particles, cells and animals, have we discovered anything that appears to follow an unknowable set of rules, the odds of finding anything like that in the brain, that is, something we can't simulate or emulate with 100% functional veracity, are just about zero.
Odds are downright terrible for "intelligent nanobots", we might have hardware that can do what a cell can do, that is, hunt for (possibly a series of) chemical cues and latch on to them, then deliver the payload -- perhaps repeatedly in the case of disease-fighting designs -- but putting intelligence into something on the nanoscale is a challenge of an entirely different sort that we have not even begun to move down the road on; if this is to be accomplished, the intelligence won't be "in" the nano bot, it'll be a telepresence for an external unit (and we're nowhere down *that* road, either -- nanoscale sensors and transceivers are the target, we're more at the level of Look, Martha, a GEAR! A Pseudo-Flagellum!)
The problem with hand-waving -- even when you're Ray Kurzweil, whom I respect enormously -- is that one wave out of many can include a technology that never develops, and your whole creation comes crashing down.
I love this discussion. :-)
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Re:Oblig. (Score:5, Interesting)
Maybe they aren't. But when you say a few centuries, I can't agree anymore. Let's imagine one century. Now we're hitting 1.12589991 × 10^15 times. A human brain is CERTAINLY within that complexity range. The caveat here is can we maintain the doubling rate for a full century? Well...Ray thinks we'll do far better than that (his "law of accelerating returns"), I'm not convinced we'll even be able to sustain the rate -- I think honestly we're looking at a plateau maybe 10, 20 years down the line, and will look back at computing as an S-curve until the next big breakthrough which nobody can predict. In my view the last couple "next big breakthrough"s happened at convenient times to make it look like we weren't following an S curve but we're just getting sharper and sharper, but I don't see any reason why the next one should happen just as conveniently. But since it's unpredictable, I could blindsided by it and it could happen next week.
Language isn't far off at all, we just about have it already. Emotions are nebulous and some people will move the goalposts forever, while some may prematurely be convinced by a video game character. I'm not necessarily convinced they are the hardest part of this. I don't know how to make them, but I don't know how to do the rest of this too. I just often see emotion being listed as the be all and end all most difficult task and I've never seen any reason to believe that to be so.
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Re:Oblig. (Score:5, Interesting)
Well, let's look at the rate of general progress in computing. In 1971, we were putting 2300 transistors on a chip. They ran at a few hundred KHz. In a fairly smooth progression, we've gotten to 3 GHz, where we're likely to stay, and today, we're at about two billion transistors on a chip, with no end in sight as to how far that can go. This is not Moore's law; Moore's law is about how many fit into a particular space; this is about how many can be integrated into a functional unit. That's 36 years. Thirty six years from now, that ability to "simulate a few cells" should grow just in the *normal* scheme of things into an ability to simulate a billion or so cells without any trouble. But there's more to this. Not everything in a cell needs to be simulated; for instance, metabolic processes such as waste generation and removal don't, nor do breakdown, aging, impacts by free radicals, all of that. Part of what needs to be done between here and the goal is streamline the simulation so that it is operating in the zone of mentation and not biological imperatives. I suspect, and yes indeed this is just my opinion, that the simulation will be much easier when we understand just what it is we need to simulate.
This all leaves out the issue of non-simulating intelligence, where the thinking is not patterned after human mechanisms; this could arise from evolutionary software or something along those lines. And of course, one of the reasons that all this is kind of a holy grail anyway, only the first intelligence is difficult; the second... Nth is just a matter of copying a machine state.
As for language, that's solved in the I/o sense -- synthesis and "listening" are both satisfactorily complete. Intelligent discussion can only be expected from an intelligent machine, so that's only as far away as machine intelligence is.
Small animals, I'm of the opinion, are a lot more intelligent than most people give them credit for. They just have a different intelligence. I am sure that we will go through the small animal level on the way to our level, and beyond; the thing is, if you can do the one, you can do the other. There's no indication of a significant difference in the wetware, there's just more of it and it is arranged somewhat differently. No reason to expect anything different from hardware designed to do the same job.
Why? Small animals do both. Those aren't even the hard things. The hard things are introspection and self-awareness. Those are the ones we have not even a theory for, today. In any case, your ideas are certainly in with a lot of good company; but not me. I think we're only one discovery - algorithmic in nature - from AI. Self-awareness may turn out to be a property that self-organizes and arises without any special prodding from us; that would be marvelous, not to mention fortuitous, but hardly impossible - again, that's how nature did it.
Here's why I think we're just an algorithm away. If you left a question that absolutely required intelligence on a counter, and went back to pick it up the next day, and the answer was there -- you would agree that an intelligence had answered the question. If a human could answer it in one second, or an AI could answer it in 23 hours, it's still just as intelligent an answer when you pick it up. The point is that speed really isn't the issue. The issue is the process, that is, the algorithm. So it turns out that in terms of speed, number of transistors, etc, that's really not the limiting factor for developing intelligen
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Re:Oblig. (Score:5, Insightful)
Most of us know jack about the algorithms that allow us to catch a baseball in flight, yet we can still do it. Furthermore, a person from 10000 BC with no math at all by today's standards could do it just as well as we can. Implementing solutions does not always require a complete understanding of what you've done. You can even be wrong and it'll still work for other reasons. So hard-pegging this to what we "know" could be a severe error.
That's a very bold statement, especially since (a) that's the way nature does it for all its intelligences, high and low, so we know the process works in the general case, and (b) as you say, we don't know many things yet, so claiming that we "know" what won't work seems to be disingenuous or at the very least not well thought out.
I think it is important not to conflate the fact that we don't understand something with the idea that it will be difficult once figured out or discovered as a consequence of some fortuitous sequence of events. That's been shown again and again not to be the case. It *may* be so, but it is by no means certain to be so, and for that matter, it isn't indicated by the complexity of the brain's hardware. The brain is considerably more formidable as a mass of immensely complex moderated connectivity than it is as a collection of cellular-level mystery machines, and a good deal of the complexity at the cellular level is almost certainly irrelevant to the task of thought -- keeping the cell alive is probably in no way related to non-pathological mental operation, yet there's a lot of hardware and systems involved in the task.
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Exponential AI? (Score:5, Interesting)
Re:Exponential AI? (Score:5, Informative)
That's the popular hypothesis [wikipedia.org].
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To heck with Artificial Intelligence! (Score:5, Insightful)
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
This is good news and bad news... (Score:5, Funny)
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.
wrong (Score:5, Insightful)
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.
Whatever Could They Mean? (Score:5, Funny)
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.
The End of Intelligent Design (Score:5, Interesting)
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
The sacred brain and other myths (Score:5, Interesting)
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
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Kurzweil's rebuttal from his book... (Score:5, Insightful)
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There is no such thing as Artificial Intelligence (Score:5, Insightful)
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.
Re:Well I'm not holding my breath (Score:5, Insightful)
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Eliza and the sad state of expert systems (Score:5, Interesting)
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.
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Re:Hrmmmm (Score:5, Insightful)
What exactly is the "level of humans"? Passing the Turing test? (Fatally flawed because it's not double blind, btw.) Part of human intelligence includes affective input; are we to expect intelligence to be like human intelligence because it includes artificial emotions, or are we supposed to accept a new definition of intelligence without affective input? Surely they're not going to wave the "consciousness" flag. Well, Kurzweil might. Venter might follow that flag because he doesn't know better and he's as big a media hog as Kurzweil.
I think it's a silly pursuit. Why hobble a perfectly good computer by making it pretend to be something that runs on an entirely different basis? We should concentrate on making computers be the best computers and leave being human to the billions of us who do it without massive hardware.
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Re:Hrmmmm (Score:5, Funny)
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I agree... (Score:5, Insightful)
"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.
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Re:Hrmmmm (Score:5, Interesting)
There are still many things we can learn from biology that can be translated to machines. The translations don't have to be 1:1 for us to make use of them. The way birds as well as insects make use of different shapes in surfaces during wing beats have translated into changes in some aricraft designs. They weren't directly incorporated the same way, but they taught us important lessons that we could then implement in different ways but with a similar outcome.
I think Neuroscience does have a lot to teach us about how to do AI.
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Re:Hrmmmm (Score:5, Interesting)
The only thing that humans do that AI doesn't (well) is automatically follow a few paths, rather then look at the whole picture. As an example, it has been shown (sorry no reference right now) that some chess grandmasters look only at a couple of moves and then calculate all the possible combinations from there rather then examine every possible move. This drastically speeds up the calculation, however it does miss moves that could be considered the "best". So while this act of "feeling" which is the best move is a good approximation done by humans, it isn't an optimal or maximal play.
As for the article, I don't agree with all of what he says (the idea of nanobots doing what Kurzweil says scares me and I doubt it will be legal to do this), but I do agree with the 2029 prediction, that is if proper resources are given to that particular problem. Replicating humans is a goal in AI for some researchers, but not all of them. Personally, I couldn't care less if there exists a robot that perfectly resembles a human, as long as there are intelligent computers systems that can do the problems that humans find hard (such as finding patterns in very large sets of data or solving complex mathematical equations).
*Technically, it isn't a low EV play if there is a high probability of the opponent folding. In which case, playing the highest EV play naturally involves bluffing if it can be assumed that the opponent will fold to a bet.
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Re:20 years is too long to predict (Score:5, Insightful)
That's why we even in this day and age of 2008l, we're essentially running chatbots based on Eliza since 1966. Sure, there's been refinements and the new ones are slightly better, but not by much in a grand scheme. A sign of this problem is that they are giving their answers to your questions in a fraction of a second. That's not because they're amazingly well programmed; it's because the algorithms are still way too simple and based on theories from the sixties.
If the AI researches claiming "Oh, but we aren't there yet because we haven't got hardware nearly good enough yet", why aren't we even there halfway, with at least far more clever software than chatbots working on a reply to a single question for an hour? Sure, that would be impractical, but we don't even have the software for this that uses hard with even the boundaries of our current CPU's.
So at this point, if we'd make a leap to 2029 right now, all we'd get would be super fast Eliza's (I'm restricting my AI talk of "general AI" now, not in heuristic antispam algorithms, where the algorithms are very well understood and doesn't form a hurdle). The million dollar question here is: will we before 2029 have made breakthroughs in understanding the human brain well enough in how it reasons along with constructing the machines (biological or not as necessary) to approximate the structure and form the foundation on which the software can be built?
I mean, we can talk traditional transistor-based hardware all day and how fast it will be, but it will be near meaningless if we don't have the theories in place.
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Re:Probably false alarm ... again (Score:5, Interesting)
Next consider the stock market. Many trades are now automated, meaning, computers are deciding which companies have how much money. That ultimately influences where you live and work, and the management culture of the company you work for.
We are already living well above the standard that could be maintained without computers to make decisions for us. Of course as humans we will always take the credit and say the machines are "just" doing what we told them, but the fact is we could not could not carry out these computations manually in time for them to be useful.
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Re:Don't think so (Score:5, Insightful)
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?
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