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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'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?
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.
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.
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
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.
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
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
Back in 1978 Robert Anton Wilson predicted that we'd be "immortal" by now. Boy, did he get a serious reality-fucking about a year ago.
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
Let's imagine that computer "processing power" doubles every 2 years for the next 20 years, from a combination of hardware advances and software algorithm innovation. That's not quite Moore's law, and it's not really likely to work smoothly just like that, but just take it as one possibility. In that case, computers of 2029 will be 1024 times as powerful as today. So the question is, are human brains = 1000 times as powerful as a mouse's brain?
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.
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
I've fallen off your lawn, and I can't get up.
Note that it is not necessary to build 'perfect robots'. People think, and yet they are not perfect. They make mistakes, yet navigate through life. So we do not have to make flawless logic brains. The way people work is that we try to find good if not optimal solutions to problems, but we do not always exhaustively search for the perfect solution. Thus many problems in life can be solved in different ways than you would expect. We do not have to build a machine that finds the optimal solution to a traveling salesman problem in order to make a system that can walk from the kitchen to the front door. It just has to be able to get there reasonably optimally. Also, we do not have to replicate the human brain in order to think much like a human, we merely have to come up with functional systems that can provide similar functions. For instance, the human brain has the amygdala, which can be likened to an interrupt controller for emotional responses. Well, that functionality can be done in a hardware-software system that reasons about priorities of tasks and goals depending on their current 'value' of urgency to the 'brain'.
Many current researchers in many cases are missing the mark. For example, as good as it is, the widely-used AI textbook by Stuart Russell and Peter Norvig (who heads research at Google) has major omissions. It does not dwell on key things needed to bridge between AI and human psychology. Other things like the OCC model of emotion used in AI is incomplete and incorrect in parts. A new approach has been needed, and one I've been developing for decades in stealth mode. I'm writing a 5-volume book set on it. I want it to be the Knuth set of AI.
I'm in the process of patenting the mechanizations of my underlying technologies, and trying to cut deals with companies making multicore processors so their architectures support the thread swapping needed to make virtual neural nets practical. Once we get a 1024 simplified-core processor that supports virtual NNs, it'll be a lot easier to build a machine with many of these that does for NNs what disk swapping does for OSs, than to build a billion-neural processor hardwired machine. And easier to do visual perception systems properly too. So Ray is right. If I can drive certain companies to build the right silicon, we can get there by or before 2029. My current software does what I said, but it's too slow on current hardware. Needs new processors and new system architectures, and it will take 20 years to get the infrastructure all built up. But not a lot more.
That's a *huge* claim; if it is true, you have AI now. Because -- as I explained in a previous post in this thread -- speed is absolutely irrelevant. If you can demonstrate your claim that your software operates now, no matter *how* slowly it operates, you are at the end of your funding issues, not to mention any other issues you may face in life. Which -- to be frank -- is why I doubt your claim. At the point you explicitly claim to be at, I'd already own a mega-yacht and be pulling up next to a lot of potential love.
But good luck, and I really mean that. I'd much rather be wrong and see you bring this right to the table, even if you have completely blown the financial potentials of the development process.
I've fallen off your lawn, and I can't get up.
Wasn't there a simulation of a mouse's brain, or a few cells of it, for a few seconds with the help of a modern supercomputer, we can barley manage to do that.
We already know that it's possible to contain 100% of real-time human brain functions in a casing 10cm by 10cm by 10cm and weighing under five pounds. Now we have to build one from the ground up with potentially slower, yet better understood technology. The problem, unfortunately, isn't related to hardware. I have no doubt that processor power will soon be sufficient for our needs, but without software that can think on the level of a human, it's just another personal platform to play Duke Nukem Forever on.
The good news is that Kurzweil put cash money down to back his opinion. In this case, Mitch Kapor (of Lotus and OSAF fame) is betting against him.
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).
I define consciousness and awareness within a pre-determined architecture, not entirely self-organizing from scratch. The visual front-end in particular is very rigid, but I think it is okay because there is little need for an organism to self-evolve completely new architectures, but rather to be able to run cascaded pattern-recognizers. See the work of Biederman for examples of this. The VFE feeds deeper processing doing cognition, and there is feedback from that to the VFE for training. Just as a baby learns to see, and recognize shapes, and build up from that. The front end is trained as the cognitive end learns and grows too.
AI does not spontaneously rise alone from massive databases, either. I view that approach as useless and a false trail. However, human intelligence does depend on belief systems and knowledge, and those continually grow as we mature from infancy. But to create the equivalent of an 18 year old, you have to have what amounts to 18 years of accumulation of knowledge about the world, and draw upon that. And there is a key but proprietary subtlety about that I'm devoting an entire volume to, that is the key to humanlike AI. That volume is essentially a doctoral thesis about consciousness reworked for use by a design staff. As for funding, no yachts yet. But I'm real, sane, and not a charlatan, and have explained my technology to my patent attorney. I expect to be hiring staff within two years. I posted on Slashdot not for glory but to counter all the nay-sayers who haven't a clue what is achievable.
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.
Yes, that's about right. Human brain contains about 1.5kg of brain matter (and it's estimated that around 700g is necessary for high-order brain functions), and mouse brain contains about 1g.
:)
I have made the same estimation personally few years ago
I know this is very much off topic from the main point of the story, but I can't let this stay here unanswered.
I realize that those who are atheistic in nature don't have religion and gods in their lives, and it is reasonable to presume that somebody without "faith" in a higher being of any kind can still have guiding morals that govern their life.
Still, I have to argue here that there still is a "religion" of atheism, complete with "prophets/oracles", "priests", "congregations", "sacred literature" and other trappings of religion. That it takes other forms and is usually not so formally organized may be true, and even defining "orthodox atheism" can be a bit of a struggle, but all of these do exist. It certainly takes on a philosophical niche that often takes the place of other religious philosophies in terms of guiding principles in your life.
There are also multiple forms of atheism, ranging from "environmentalists" (devotion to environmental causes as a religion), "universalists" (that somehow the whole universe will make sense ultimately), "scientists" (a solid belief that science alone can solve life's problems), "anti-theologists" (opposing any form of organized religion of any kind), and many others. It is very difficult to take such an emotionally charged term like atheism and force any sort of hard stereotypes. But I do argue that you can identify atheism as a religion, including its establishment as a state religion in many cases, and concerns about how it has entered into public institutions forcing out other philosophical viewpoints.
To tie this back to artificial life/intelligence research, I do believe (there is that word somehow showing up) that some sort of religious philosophy will eventually show up in terms of identifying and working with a "soul". I'm defining that "soul" to be the consciousness or intelligence that has an independently operable sphere of influence that can relate with other intelligences, including at a human level of interaction. This isn't to say that such a "soul" can't have a purely scientific explanation either. I also believe that once such artificial intelligences are developed that it will have a tremendous impact on human religion in a large number of ways, including everything ranging from human fealty and devotion to an AI lifeform approaching god-like standing, modification of theological doctrines, to even a view that working with AI lifeforms is a type of blasphemy that should be rooted out of human society (and the source of future wars).
Copying the structure of the brain in all particulars would produce a brain and so we would still have failed at producing AI. At best we could claim is to have copied natural intelligence artificially.
I agree with your claim of disingenuity or at least a lack of forethought, though. In theory, if we understood the brain better, we would have a better understanding of the problem. We are currently finding quantum mechanisms for various things in our body (including smell and hearing!) and we still haven't identified any mechanism complex enough to account for the process of memory. If that turns out to be a quantum function it might very well be nonreproducible, at least in a familiar form.
"You're right," Fisheye says. "I should have set it on 'whip' or 'chop.'"
Actually, Eugene Izhikevich run a successful simulation of the entire human brain in 2005:
http://vesicle.nsi.edu/users/izhikevich/human_brain_simulation/Blue_Brain.htm#Simulation%20of%20Large-Scale%20Brain%20Models
Simulation of 1 second took 50 days on a cluster of 27 PCs (~4.3M times slower than realtime). Eugene is a pretty smart guy, except not as prominent as Kurzweil. Here is Eugene's estimate for AI timeline:
http://vesicle.nsi.edu/users/izhikevich/human_brain_simulation/why.htm
You may also want to google for Henry Markram and his current project.
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...
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.
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That is incorrect. Language is the ability to communicate feelings, goals, results. It is not "speech." Some birds do indeed have the capability of speech, that is, they can make the same sounds we can, closely enough as to make no difference. Apes, however, have demonstrated actual communications using symbols, and even dogs have recently been found to have a consistent, though very small, vocabulary. Elephants and other animals have demonstrated the ability to think in the abstract (the "recognize one's self in the mirror and operate on the information thus provided experiments.) Lemurs use calls to communicate safety and status. Don't confuse the lack of vocal apparatus with an inability to communicate. They're not the same thing at all.
As for the rest, I think you've got it, essentially, but we disagree on scales. We'll see.
I've fallen off your lawn, and I can't get up.
Although I have mod points at the moment it seems that your comments have already been modded up anyway so I guess I'll reply. We've actually argued about this before (probably last time Kurzweil was mentioned on slashdot), but as you say in a different comment this discussion is always fun :)
Slight nitpick, currently we are at one billion transistors on a chip, not two, but that doesn't really change the point you are making.
A bigger issue that I have with what you've described is that simulating a brain is not the same as "solving" AI. The problem that Kurzweil has is that he refuses to accept that there is a difference. Sure, if they are the same then strong AI is inevitable and it's merely a question of building fast enough hardware. But why assume that they are the same thing?
Twenty years from now we may have hardware that can simulate an entire human brain; and yet we may be no closer to solving any of the problems in understanding how to solve the many problems in AI. The mental sleight-of-hand that Kurzweil applies to this argument is: Once we can simulate a brain we have AI, therefore the AI can design he next generation, therefore we will reach the singularity. This argument is a logical fallacy because it assumes being able to run the system, and knowing how to design the system / how the system works are equivalent.
Everything that we know complex and dynamic systems tells us that this is not so; given a simulation of the brain it is reasonable to assume that intelligence is the ultimate emergent property of the system. Understanding this property and how to refine it is the hardest problem that mankind has ever undertaken. Currently we don't really know how to pose the question, let alone how to arrive at an answer. To assume that some kind of standard engineering methodology will solve this in 20 years is wild speculation.
As always with AI, the hardware will be available but nobody yets knows if we can write the software to run on it.
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There's no indication of a significant difference in the wetware, there's just more of it and it is arranged somewhat differently.
Do you realize how wrong that statement is? There is a tremendous difference in quality (not quantity) between a normal animal, like a young Chimpanzee, and a young human with about the same mental volume. (OK. Make it a young gorilla.) The difference is that humans are wired to communicate symbolically with GRAMMAR!! Chimpanzees can "sort of" learn grammar. But they don't become fluent in it beyond the very basic levels. (And this tells us just WHERE the software needs to be improved.)
Humans, isolated, aren't that much more intelligent than a chimpanzee. In some ways they are less intelligent. (This is an assertion that can't be checked, sorry, but the only ways of checking it are all immoral.) Chimpanzees have lots of stuff "pre-specified" that we would need to learn from scratch. The normal word for an isolated human infant (6 mo.s) in a jungle is "dead". The normal word for an isolated chimp (6 mo.s) is "scared". But humans are designed to latch onto ANY society that they find themselves in. If a human happens to get adopted by a social animal, then the human will convert itself, as nearly as possible, into a normal member of THAT society. (Rare, but examples do exist in history.) This is a hint that with proper initial programming, an AI wouldn't need to be THAT intelligent to adapt itself to simplified symbolic communication. And a society, properly structured, can learn a LOT more than an individual can. Something 1/10th the intelligence of a human, but with less self assertiveness and the ability to communicate 100 times as fast (or more?) might well be able to form societies that were more intelligent than the average human...possibly much more intelligent.
I think we've pushed this "anyone can grow up to be president" thing too far.