An Argument For The Singularity


Earlier this week, Charles Stross posted his thoughts on why he doesn’t think the Technological Singularity will happen. If you’re not familiar with the concept, the Technological Singularity is defined as that point in the future when a self-improving artificial general intelligence (AGI) achieves superintelligence. As Stross recommends, if you’re not familiar with the topic, you’d be wise to read the following first:

I’m going to take it as read that you’ve read Vernor Vinge’s essay on the coming technological singularity (1993), are familiar with Hans Moravec’s concept of mind uploading, and know about Nick Bostrom’s Simulation argument. If not, stop right now and read them before
you continue with this piece. Otherwise you’re missing out on the fertilizer in which the whole field of singularitarian SF, not to mention posthuman thought, is rooted. It’s probably a good idea to also be familiar with Extropianism and to have read the posthumanism FAQ, because if you haven’t you’ll have missed out on the salient social point that posthumanism has a posse.

First, let me say that Stross is a first-class writer who brings serious thought to bear on a complex and controversial subject. I completely agree with many of his points and I can definitely see how the Singularity may never happen. But as I was reading his arguments, one thought popped out at me. To sum up why I think there’s a reasonable chance the Singularity will happen:

“Human-equivalent AI does not equal human-level AI.”

Early on, Stross makes the argument against human-equivalent AI, the building of an intelligence that thinks like us. This is an accomplishment that may never happen due to a number of issues I won’t repeat. Re-read Stross. But then, based on my reading of it anyway, he proceeds with his argument as though human-equivalent AI were the same as human-level AI and they’re not.

We stand on the cusp of a technological explosion that may (or may not) be unprecedented in the history of the universe. Authors such as Ray Kurzweil (The Singularity is Near), James M. Gardner (Biocosm) and Kevin Kelly (What Technology Wants) have discussed this at length. Read them. Based on the long history of self-organizing principles in the universe – what Kelly refers to as exotropy and Max More calls extropy – this technological explosion may well result in an explosion of intelligence as well. Now this may not occur as early as 2045, as Kurzweil has forecast. And potentially, it could happen in the next decade, though I’m skeptical of that time frame. But in geological and cosmological terms, if it happens, it will be in a relative eye blink from now. The resulting growth of intelligence would be comparable to the Cambrian Explosion, that era in Earth’s history when complex life underwent rapid and extensive diversification into many of the morphologies we see today.

My point is that technology needn’t emulate humans in order to be intelligent. We’re one accident in the history of evolution that managed to get it right. (From our perspective, anyway.) Technology is different. Unlike the long, arduous, vertical exchange of information that occurs through sexual recombination, technology moves its best solutions around in a much more free-form, horizontal manner. It’s not unlike the idea of horizontal gene transfer (HGT) which preceded complex life on Earth, explored by microbiologist Carl Woese and others. Historically, this process of technological recombination has required human intelligence as an intermediary, but recently this has started to change.

This, I believe, will eventually lead to a vast array of intelligences. Some will be smarter than us in certain ways, some won’t. Some might seem vaguely familiar; others will be utterly unfathomable. But ultimately these many intelligences will span the domain of possible intelligences to fill every niche in the information ecosphere. The extent of this domain is potentially very large and in it, human intelligence would be a very, very small subset.

Does this mean the Singularity will happen? I don’t know. The Singularity has come to represent different things to different people. Some who need it to fulfill some religious circuit in their brains, see it in quasi-spiritual terms – the so-called “Rapture of the Nerds.” Others believe it will result in a “fast take-off”, leading to an uplift of our own species (and potentially others as well). To myself and others, it’s “just” the development of a superintelligence which may possibly be followed by an explosion of intelligences within our light-cone of the universe. Ultimately, there’s no reason to expect it will result in anything like an entity that cares one iota about us. This is why ideas such as Eliezer Yudkowsky’s “Friendly AI” are really important. Within this domain of possible intelligences, whether vast monolithic superintelligences or distributed networked intelligences or bio-digital amalgams, some will inevitably have enough volition to present an existential threat to the human race unless safeguards are put in place. And even these are no guarantee.

As Vernor Vinge stated in an interview I did with him a few years ago, he thinks “the Singularity is the most likely non-catastrophic event for this century and [he’d] be surprised if it doesn’t happen by 2030.” But whether the Singularity happens or not, I think we have to be prepared for a world in which we are far from the only form of advanced, tool-using, concept-manipulating intelligence. As I stated earlier, “Human-equivalent AI does not equal human-level AI.”

To which I would add: “Nor does it need to be.”

Watson: The New Jeopardy Champion


I consider myself a techno-optimist, but Watson’s performance in Jeopardy’s IBM Challenge has definitely exceeded my expectations. While I did predict Watson would win the competition, I didn’t think it would be so dominant. This was a contest I thought machine intelligence might win by a narrow margin, but the three-day, two game match wasn’t even close. AI has come a long way, baby.

As impressive as Watson’s final cash score was, what I think was more remarkable was it’s answer success rate. In the first match, out of sixty clues, Watson rang in first and answered 38 correctly, with five errors. This is an 88.4% success rate. If only the 30 questions in the Double Jeopardy portion are considered, this jumps to a whopping 96%. You’ll notice I’ve left the Final Jeopardy question out of these calculations. This is because this question had to be answered regardless of the machine’s low confidence level of 14%. It’s important to the competition, but actually indicates the success of the machine’s algorithms.

While the second game (Day 3) wasn’t quite as impressive as the first, Watson still won by a significant margin. Considering it was competing against the two best human Jeopardy players of all time, it’s safe to say IBM met its goal and then some.

Some of the more intriguing (some would rightly say, concerning) moments in the contest were those in which Watson arrived at unfathomably wrong answers. As the lead on the project, Watson Principal Investigator, Dr. David Ferrucci commented:

“Watson absolutely surprises me. People say: ‘Why did it get that one wrong?’ I don’t know. ‘Why did it get that one right?’ I don’t know.”

The fact is, even Watson’s developers often can’t fathom how it arrives at the answers it does. Parsing through millions of stored documents, Watson applies hundreds of algorithms to arrive at the answer with the highest confidence rating. (While this bears a passing resemblance to Minsky’s “society of mind” concept, it still remains very different from the way humans think.) The incredible complexity of the process means we can’t fully understand it. This is the nature of emergent systems – they generate outcomes that can’t be accurately predicted a lot of the time. They follow an internal logic of their own, one we can’t possibly follow.

In Watson, we’re seeing the barest hints, the merest beginnings of this. The potential domain of future intelligences is vast. It’s possible that one day there will be as many different kinds of machine intelligence as there are biological species. And in all likelihood, we won’t understand the behaviors and motivations of a single one.

Watson is a long way from being an artificial general intelligence. It isn’t HAL-3000. But it is a huge step forward. A step that should be making us ask serious questions about the future of AI. We face a future full of machine intelligences as smart or smarter than we are. Some experts even speculate recursive self-improvement will yield superintelligences vastly more intelligent than the entire human race combined. There should be no question we’ll be incapable of grasping the motivations of such a machine. And there’s no reason to expect it’s objectives will mesh with our own. Obviously, this could have results that are disastrous, potentially even existentially catastrophic.

We aren’t going to stop the advance of artificial intelligence or the eventual development of an artificial general intelligence. Therefore, steps will need to be taken that ensure these machines remain as benevolent as possible and not because they will necessarily be malevolent otherwise. An indifferent superintelligence would be just as big a threat to humanity because it could be capable of taking potentially world-altering actions without considering what they mean for us. Arguments for creating rules-based safeguards, such as Asimov’s “Three Laws of Robotics” will likely fail, simply because rules can be misinterpreted or circumvented given sufficient motivation.

Work toward “Friendly AI”, as proposed by AI researcher, Eliezer Yudkowsky, stands a much better chance of a human-positive outcome. Instilling a machine equivalent of morality not only protects us from the actions of a superintelligence, but from its self-improved progeny as well. Creating “Friendly” safeguards that motivate such a machine to do everything in its power to ensure humans do not come to harm now or in the future may be our best bet. As Yudkowsky states:

“Gandhi does not want to commit murder, and does not want to modify himself to commit murder.”

We can hope that a superintelligence comes to the same conclusion. But we can do more than just hope; we can work to ensure it happens.

The capabilities Watson has demonstrated using deep analytics and natural language processing are truly stunning. The technologies that will develop from this will no doubt help the world with many of its significant problems. Not least of these is dealing with the vast, escalating volumes of data our modern world generates. But there is the potential for significant dangers to arise from such technology too. I feel certain though we can overcome these threats and continue the long legacy of building a better world with the help of our technology.

How’s that for techno-optimism?

What is a Milestone in Artificial Intelligence?

On January 13, 2011, IBM’s Watson supercomputer competed in a practice round of Jeopardy, the long-running trivia quiz show. Playing against the program’s two most successful champions, Ken Jennings and Brad Rutter, Watson won the preliminary match. Is this all a big publicity stunt? Of course it is. But it also marks a significant milestone in the development of artificial intelligence.

For decades, AI – artificial intelligence – has been pursued by computer scientists and others with greater and lesser degrees of success. Promises of Turing tests passed and human-level intelligence being achieved have routinely fallen far short. Nonetheless, there has continued to be an inexorable march toward more and ever more capable machine intelligences. In the midst of all this, IBM’s achievement in developing Watson may mark a very important turning point.

Early attempts at strong AI or artificial general intelligence (AGI) brought to light the daunting complexity of trying to emulate human intelligence. However, during the last few decades, work on weak AI – intelligence targeted to very specific domains or tasks – has met with considerably more success. As a result, today AI permeates our lives, playing a role in everything from anti-lock braking systems to warehouse stocking to electronic trading on stock exchanges. Little by little, AI has taken on roles previously performed by people and bested them in ways once unimaginable. Computer phone attendants capable of routing hundreds of calls a minute. Robot-operated warehouses that deliver items to packers in seconds. Pattern matching algorithms that pick out the correct image from among thousands in a matter of moments. But until now, nothing could compete with a human being when it came to general knowledge about the world.

True, these human champions may yet best Watson, a product of IBM’s DeepQA research project. (The three day match will air February 14-16.) But we only need to think back to 1997 when IBM’s Deep Blue defeated world chess champion Garry Kasparov to understand that it doesn’t really matter. Kasparov had handily beaten Deep Blue only a year earlier, though the 1996 match did mark the first time a computer won a single game in such a match. Today, just as then, the continuing improvements in computer processing speed, memory, storage and algorithms all but ensure that any such triumph would be fleeting. We have turned a page on this once most human of intellectual feats and the world won’t be the same again.

So what can we look ahead to now that we’ve reached this milestone? In the short term, IBM plans to market their technology and profit by their achievement. Initially, the system price will be high, probably in the millions of dollars, but like so much computer technology, the price will plummet over the coming decade. As the technology becomes more widely used, a range of tasks and jobs previously considered safe from AI will no longer be performed by human workers. Protectionist regulations may attempt to save these jobs but these efforts will probably be short-lived. The resulting large-scale unemployment will require a rethinking of government institutions and safety nets, as well as corporate business models.

At the same time, this type of general knowledge AI (it’s far too early to call it AGI) will contribute to greater and more rapid advances in machine intelligence. Such technology could bootstrap the Semantic Web into broad usage. In all likelihood, it will be used to create personal intelligent agents, giving users the virtual equivalent of a staff of assistants. And eventually, it could facilitate the development of a true artificial general intelligence or at least contribute to the education of such an AGI.

Will such an intelligence be conscious? Will it be self-improving, leading to a positive feedback loop that brings about a powerful and hopefully benign superintelligence? Only time will tell. But perhaps one day, on a future holographic version of Jeopardy, we’ll be presented with clues to which the correct response will be, “What was the Singularity?”