First, men wanted to find the Garden of Eden, where milk, honey, spice and everything nice was to be found. It didn't happen.
Then men wanted to reach the Kingdom of God, where sins would be forgotten and peace and love reigned forever. Didn't happen.
Lately men want to achieve the Singularity, where Skynet does all the work and allow us to be free and idle to engage in polyamorous poetry readings with fat transexuals and a token negro here and there. This may or may not involve having our bodies hooked to the Matrix.
Probably not going to happen.
Don't take my word about it though. Edge Magazine, which is about one of the best places out there for Academics to actually debate each other and reach the public, asked this year in their annual question about intelligent machines. Which is just code for the AI singularity.
Understandably, 80% of the articles contributed were total fluff, as most people don't know crap about stuff besides their own discipline, and few of the people invited actually has any expertise on how the human brain works or if computers can ever do the same things.
A few academics though do know something about the human brain, and they had this to say:
The Singularity—the fateful moment when AI surpasses its creators in intelligence and takes over the world—is a meme worth pondering. It has the earmarks of an urban legend: a certain scientific plausibility ("Well, in principle I guess it's possible!") coupled with a deliciously shudder-inducing punch line ("We'd be ruled by robots!"). Did you know that if you sneeze, belch, and fart all at the same time, you die? Wow. Following in the wake of decades of AI hype, you might think the Singularity would be regarded as a parody, a joke, but it has proven to be a remarkably persuasive escalation.
What I think about machines thinking is that it won't happen anytime soon. I don't imagine that there is any in-principle limitation; carbon isn't magical, and I suspect silicon will do just fine. But lately the hype has gotten way ahead of reality. Learning to detect a cat in full frontal position after 10 million frames drawn from Internet videos is a long way from understanding what a cat is, and anybody who thinks that we have "solved" AI doesn't realize the limitations of the current technology.
To be sure, there have been exponential advances in narrow-engineering applications of artificial intelligence, such as playing chess, calculating travel routes, or translating texts in rough fashion, but there has been scarcely more than linear progress in five decade of working towards strong AI. For example, the different flavors of "intelligent personal assistants" available on your smart phone are only modestly better than ELIZA, an early example of primitive natural language processing from the mid-60s.
We still have no machine that can, say, read all that the Web has to say about war and plot a decent campaign, nor do we even have an open-ended AI system that can figure out how to write an essay to pass a freshman composition class, or an eighth-grade science exam.
Why so little progress, despite the spectacular increases in memory and CPU power? When Marvin Minksy and Gerald Sussman attempted the construction a visual system in 1966, did they envision super-clusters or gigabytes that would sit in your pocket? Why haven't advances of this nature led us straight to machines with the flexibility of human minds?
Consider three possibilities:
(a) We will solve AI (and this will finally produce machines that can think) as soon as our machines get bigger and faster.
(b) We will solve AI when our learning algorithms get better. Or when we have even Bigger Data.
(c) We will solve AI when we finally understand what it is that evolution did in the construction of the human brain.
Ray Kurzweil and many others seem to put their weight on option (a), sufficient CPU power. But how many doublings in CPU power would be enough? Have all the doublings so far gotten us closer to true intelligence? Or just to narrow agents that can give us movie times?
In option (b), big data and better learning algorithms, have so far gotten us only to innovations such as machine translations, which provide fast but mediocre translations piggybacking onto the prior work of human translators, without any semblance of thinking. The machine translation engines available today cannot, for example, answer basic queries about what they just translated. Think of them more as idiot savants than fluent thinkers.
My bet is on option (c). Evolution seems to have endowed us with a very powerful set of priors (or what Noam Chomsky or Steven Pinker might call innate constraints) that allow us to make sense of the world based on very limited data. Big Efforts with Big Data aren't really getting us closer to understanding those priors, so while we are getting better and better at the sort of problem that can be narrowly engineered (like driving on extremely well-mapped roads), we are not getting appreciably closer to machines with commonsense understanding, or the ability to process natural language. Or, more to the point of this year's Edge Question, to machines that actually think.
All the while Yudkowsky, who has made a good living out of claiming that we need to give him money RIGHT NOW or Skynet is gonna be sexist and discriminate against your favorite porn genders, goes off on a tangent and doesn't talk about whether AI is actually feasible or not.
Speaking of which, I wanna give bonus points to this guy who doesn't have any credentials, but I like how he thinks.
Smart people often manage to avoid the cognitive errors that bedevil less well-endowed minds. But there are some kinds of foolishness that seem only to afflict the very intelligent. Worrying about the dangers of unfriendly AI is a prime example. A preoccupation with the risks of superintelligent machines is the smart person’s Kool Aid.
This is not to say that superintelligent machines pose no danger to humanity. It is simply that there are many other more pressing and more probable risks facing us this century. People who worry about unfriendly AI tend to argue that the other risks are already the subject of much discussion, and that even if the probability of being wiped out by superintelligent machines is very low, it is surely wise to allocate some brainpower to preventing such an event, given the existential nature of the threat.
Not coincidentally, the problem with this argument was first identified by some of its most vocal proponents. It involves a fallacy that has been termed "Pascal’s mugging," by analogy with Pascal’s famous wager. A mugger approaches Pascal and proposes a deal: in exchange for the philosopher’s wallet, the mugger will give him back double the amount of money the following day. Pascal demurs. The mugger then offers progressively greater rewards, pointing out that for any low probability of being able to pay back a large amount of money (or pure utility) there exists a finite amount that makes it rational to take the bet—and a rational person must surely admit there is at least some small chance that such a deal is possible. Finally convinced, Pascal gives the mugger his wallet.
This thought experiment exposes a weakness in classical decision theory. If we simply calculate utilities in the classical manner, it seems there is no way round the problem; a rational Pascal must hand over his wallet. By analogy, even if there is there is only a small chance of unfriendly AI, or a small chance of preventing it, it can be rational to invest at least some resources in tackling this threat.
It is easy to make the sums come out right, especially if you invent billions of imaginary future people (perhaps existing only in software—a minor detail) who live for billions of years, and are capable of far greater levels of happiness than the pathetic flesh and blood humans alive today. When such vast amounts of utility are at stake, who could begrudge spending a few million dollars to safeguard it, even when the chances of success are tiny?
Why do some otherwise very smart people fall for this sleight of hand? I think it is because it panders to their narcissism. To regard oneself as one of a select few far-sighted thinkers who might turn out to be the saviors of mankind must be very rewarding. But the argument also has a very material benefit: it provides some of those who advance it with a lucrative income stream. For in the past few years they have managed to convince some very wealthy benefactors not only that the risk of unfriendly AI is real, but also that they are the people best placed to mitigate it. The result is a clutch of new organizations that divert philanthropy away from more deserving causes. It is worth noting, for example, that Give Well—a non-profit that evaluates the cost-effectiveness of organizations that rely on donations—refuses to endorse any of these self-proclaimed guardians of the galaxy.
But whenever an argument becomes fashionable, it is always worth asking the vital question—Cui bono? Who benefits, materially speaking, from the growing credence in this line of thinking? One need not be particularly skeptical to discern the economic interests at stake. In other words, beware not so much of machines that think, but of their self-appointed masters.