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."

Comments

Oblig.

[Anonymous Coward]

I for one welcome our broadly supple, emotionally intelligent overlords.

Re:Oblig.

[fyngyrz]

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. :-)

Re:Oblig.

[fyngyrz]

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.

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.

Even if an intelligent computer was somehow created it would be an enormous accomplishment to have it be as intelligent as a bug or a small animal.

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.

Emotions and language seem very far off, I'd say such a thing is centuries away.

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

Re:Oblig.

[fyngyrz]

But sadly, we still know jack shit about how the brain works

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.

And no, simply copying the brain structure will not the answer.

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.

Exponential AI?

[TheGoodSteven]

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?

Re:Well I'm not holding my breath

[2.7182]

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.

Whatever Could They Mean?

[flyneye]

" 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.

The End of Intelligent Design

[denoir]

It is not too much of an overstatement to say that the field of AI has not significantly progressed since the 1980's. The advancements have been largely superficial with better and more efficient algorithms being created but without any major insights and much less a road map for the future. While methods that originated as AI research are more common in real-world applications, the research and development of new concepts has made a grinding halt - not that it was ever a question of smooth continuous progress.

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

[denoir]

This is a sort of continuation of the parent post.

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

Kurzweil's rebuttal from his book...

[doug141]

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.

Re:Hrmmmm

[teh moges]

What does a human do to read a bluff? He observes his opponent, takes inputs such as bet size and heart rate, applies them to known patterns of bluffers and looks for a match. Sure a human does this without realizing, but little of how this happens is a mystery. Also, how do humans bluff? They just bet at a negative EV play*, and bluffing properly is a matter of knowing the probability that the opponent will call. I am researching applying AI to poker (look out in June for a lot of high quality research from the AAAI Computer Poker Championship) and this sort of argument, "Computers can't bluff, they just run numbers", is both understating what has been achieved in AI in this field and also overstates what humans do. Yes, computer programs aren't quite up to the standard of world class players (Limit poker has achieved this, but not No-Limit), but this game has only a couple of years to go before this milestone is reached. I predict that by the end of the year, we will have high quality bots that can beat 99% of players, and by the end of 2010 No Limit Texas will be a computer dominated game.

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.