With 'singulatarian' Ray Kurzweil

Back to Boston.

It’s amazing how quickly you can accept international travel as work-a-day. When I started my journey a flight heralded a feeling of adventure in me. Now, it’s like getting in a car. Another thing that’s changed is my attitude to my interviewees. When I first secured an interview with my quarry in Boston I was slightly intimidated. ‘How do you talk to someone like that?’ I asked myself, the ‘that’ in question being Ray Kurzweil. Now, as I come to end of my journey and try to tie it all together I find less trepidation in myself. I’ve spent the last year meeting extraordinary people, and I’ve got used to it. Turns out extraordinary people have plenty enough ordinary about them to get hold of.

I arrive in Boston, deal with the ever rude and superior immigration staff and am picked up by Tracy Wemett, who you may remember as Konarka’s PR woman and driver of some, shall we say, reckless enthusiasm. Tracy, on hearing of my return to Boston has generously offered me her basement for the week, which makes a welcome change from hotels. Still, we’ve got to get to her apartment alive which, given her driving, is not a certainty.

Since I saw Tracy last it seems I haven’t been the only one to notice her maverick approach to the road. One speeding ticket too many and she’s been required to take a driving education course by the state of Massachusetts. The results are reassuring. She tells me, “I was told I’m the sort of person who will make a road where there isn’t one.” She pauses. “Apparently that’s not good.”

I spend the next day preparing for my interview with Ray. (I also take a visit to meet genius-entrepreneur Howard Berke at Konarka, who was, like many genius-entrepreneurs, a mixture of enthralling, socially odd and genuinely entertaining. More on him in my chapter on Solar).

Ray Kurzweil is variously an inventor, guru, madman, prophet or genius depending on who you listen to. One indisputable truth is that Ray is a very good inventor. He invented the first machine that could scan text in any font and convert it into a computer document, a technology he applied to building a reading machine for the blind (which led to him, on the side, inventing the flatbed scanner and the text-to-speech synthesizer too). Stevie Wonder was the first customer – and this in turn led to Ray inventing a new breed of electronic synthesizers that captured the nuances of traditional ones. (In a former life as a musician I coveted the ‘Kurzweil K2000’ but not being very successful musician I could never afford one). Our interview opens in much the same way as Ray’s last book The Singularity is Near (hereafter referred to as TSIN). “The philosophy of my family, the religion, was the power of human ideas and it was personalised,” he says. “My parents told me, ‘you Ray can find the ideas to overcome challenges whether they’re grand challenges of humanity, or personal challenges’ ”.

Ray’s journey to visionary genius/ techno-prophet/ crazy person (delete as appropriate depending on your prejudices) had its genesis in his attempt to work out a way to time his inventions for maximum impact. “I realized that most inventions fail not because the R&D department can’t get them to work but because the timing is wrong. Inventing is a lot like surfing: you have to anticipate and catch the wave at just the right moment,” he writes on page three of TSIN. So Ray started looking at technology trends and he saw something extraordinary – a clear, unmistakable pattern of exponential innovation, something he calls ‘the law of accelerating returns’ – a phenomenon centred around the idea that technology regularly doubles in efficiency. Such doubling is seen, for instance, in the increasing processing power of computers. Reality has kept pace with the predictions of ‘Moore’s law’ with almost unwavering allegiance, with performance per dollar doubling about every 18 months. But Ray says the effects of the law can be found, well, nearly everywhere, that the law of accelerating returns is the governing law of all creation.

To understand the implications of Ray’s idea you have to get your head around how potent a force it is if something has the propensity to double. Think of it this way. Let’s say you travel a metre with each step you take. If you take ten steps you’ll have covered ten metres. Now imagine that instead of each step progressing one metre, it somehow doubles the distance you covered with the last one. So while your first step covers one metre, your second covers two and by your third your stride is four metres. The difference between ‘normal stepping’ you and ‘doubling stepping’ you is extreme and gets ever more so. As a doubling stepper your first ten steps will cover not ten metres, but one thousand and twenty four. Instead of covering the equivalent of about 1/10^th of a football field you’ve covered over ten. And with your next step you’ll cover ten more – with the step after that covering another twenty whole pitches.

By the time you’ve done just 27 steps you’ve traversed 67 million metres, or to put it another way, you’ve gone one and a half times round the world. Your next step? You double that distance and do another 67 million metres. At this rate you could walk to the sun and back (and be 85% of the way to Mars) in 38 steps (your last step having covered 137,438,953,000 metres). One can only imagine the trousers you’d need. Meanwhile, normal stepping you is about a third of the way down a football pitch. Now, of course, you can’t step like that but technology, says Ray, can. And he’s not wrong.

Certainly on my trip I’ve seen other examples of mankind’s exponential adventure, in the plummeting cost of genome sequencing, or the ‘cost per watt’ performance of solar technologies for example. Ray cites these examples and others. The first hundred pages of TSIN almost bludgeons the reader with graph after graph, based on historical data showing exponential growth in the number of phone calls per day, cell phone subscriptions, wireless network price-performance, computers connected to the internet, internet bandwidth and so on. These all have a computing flavour, but Ray sees exponential growth of knowledge too, citing exponential growth in nanotechnology patents as an example. What about the economy? Ray plots exponential growth in the value of output per hour (measured in dollars) in private manufacturing and in the per-capita GDP of the US. Ray quotes example after example because he want us to get past what he sees as an inherit prejudice in our human thinking.

“Our intuition is linear and I believe that’s hard-wired in our brains. I have debates with sophisticated scientists all the time, including Nobel prize winners that take a linear projection and say “it’s going to be centuries before we…” and “we know so little about…” and here you can fill in the blank depending on their field of research. They just love to say that. But they’re completely oblivious to the exponential growth of information technology and how it’s invading one field after another, health and medicine being just the latest.”

You can’t get to Mars in 39 steps wearing linear trousers (like the one’s most of our minds wear). You need exponential ones (like technology has). But because we’re hard-wired to think in linear, rather than exponential terms we fail to see when things are coming, argues Ray. We’ll be far further than we think, far quicker than we expect. Ray predicts for instance that by the middle of the century we’ll have artificial intelligence that exceeds human cognition, a game-changing explosion of intelligence that we will merge with to usher in the next stage in our evolution – a human-machine hybrid, enhanced with similar exponential bounty brought to us by entwined revolutions in nanotechnology and biotechnology. Aging will be ‘cured’ and we’ll be able to move onto a more stable platform than our frail biology. At the same time we’ll have solved the energy crisis and dealt conclusively with climate change.

“All these Malthusian concerns that we’re running out of resources are absolutely true if it were the case that the law of accelerating returns didn’t exist,” he says. “For instance, people take current trends in the use of energy and just assume nothing’s going to change, ignoring the fact that we have 10,000 times more energy that falls on the Earth from the Sun every day than we are using. So if we restrict ourselves to 19^th Century technologies, these Malthusian concerns would be correct.” In other words, the law of accelerating returns in solar energy will soon see a green energy revolution, as the technology keeps doubling its efficiency. Ray reckons five years from now solar will be taking coal to the cleaners when it comes to cost per watt. We won’t be switching to solar because we want to save the planet, we’ll be doing it to save our bank accounts.

“I just had a debate this week at a conference held by The Economist with Jared Diamond who basically sees our civilization going to hell in a hand-basket and points out various trends and makes this assumption that technology is a disaster and only creates problems and he has really no data to point to, it’s just aphorisms and scoffing at technology with no analysis. But he’s got a bestselling book because people love to read about how we’re heading to disaster.”

Part of understanding what Ray is getting at requires you to understand that he sees all creation as an exercise in information processing. Everything can be expressed as data coming in, some kind of manipulation or interaction, and some data goes out. So, two atoms collide (data in), they interact in some way (data processing) and emit light and heat (data out). This is the most boring way ever to describe fire, but it doesn’t take away from the essential premise that everything can be viewed as a manipulation of information. In other words, everything (including you) is an ‘information technology’ and therefore the law of accelerating returns becomes the fundamental law that governs all creation.

In 1999 Ray published a book called The Age of Spiritual Machines in which he applied this law to make predictions, and handily he made a bunch for the decade from 2009. Critics and advocates alike have lept on these, loudly proclaiming “Ray was right!” or “Ray was wrong!” depending, it seems, on how they view the world – and all ignoring the fact that Ray didn’t say his predictions were for one year, but for the period beginning 2009. “Most of Kurzweil’s predictions are actually astoundingly accurate,” writes one blogger, while another asserts his forecasts are “ludicrously inaccurate.” Oh dear.

My own analysis is that, with the odd caveat, Ray seems to be on the right track with his predictions and many seem extremely prescient. According to Ray 89 are correct, 13 are “essentially correct”, three are partially correct, two are ten years off, and just one is wrong (but he claims it was tongue in cheek anyway). Certainly there is some pride in Kurzweil’s response to his critics and you could argue he’s stretching the point a bit when he defends some of his predictions, massaging the semantics of the prediction to match the current situation, but, all that aside, he’s still been right more often than he hasn’t. By anybody’s reckoning that’s prediction nirvana, and a skill any investor would love to have (oh, Ray’s latest venture? A hedge fund.)

But part of the problem with Ray Kurzweil, or rather part of the problem in talking about Ray Kurzweil is that he raises strong emotions. Trying to separate reasoned debate from the howl of emotion that his work provokes is hard. Take the view of Douglas R. Hofstadter, now a cognitive scientist at Indiana University, but more famously the author of Gödel, Escher, Bach: An Eternal Golden Braid – an attempt to explain how consciousness can arise from a system, even though the system’s component parts aren’t individually conscious.  (This is a key area of study for Ray too, because it is through reverse engineering the human brain that he believes we’ll be able to unlock the mechanisms of mind, replicate them in machines and so free ourselves from the biological limitations of our brain). Here’s what Hofstader has to say about Ray’s ideas:

“I find is that it’s a very bizarre mixture of ideas that are solid and good with ideas that are crazy. It’s as if you took a lot of very good food and some dog excrement and blended it all up so that you can’t possibly figure out what’s good or bad. It’s an intimate mixture of rubbish and good ideas, and it’s very hard to disentangle the two…”

That’s like Stevie Wonder saying, “I can’t work out if Paul McCartney is a genius or a wanker”. Such is the trouble with talking about Ray. (You can see the full text of the interview this comes from here)

As I comment throughout An Optimist’s Tour of the Future, the advance of new technologies, particularly biotechnology, make many people (including me) uncomfortable – and then Ray comes along and says, ‘belt up, things are going way faster than you thought, and by the way, that means I’m not going to die. Would you like to transcend your biology with me? Hurry now’. It’s no wonder our linear-trousered brains are stretched to the limit, no wonder some people find Ray just too difficult to engage with. And on the other side of the coin are those who do see Ray as some kind of prophet, whose ideas save them from the sticky issue of their mortality. Ray’s ‘Singularity’ – the moment at which ‘strong AI’ arrives and we merge with it – has been called “the Rapture of the nerds” (a phrase coined by science fiction author Ken MacLeod). These Utopian-techno-nerds don’t really help Ray’s cause. I advocate the approach of Juan Enriquez, the founder of Harvard Business Schools’ Life Science Project, and another Boston resident, who told me, “Do I always agree with Ray? No. Does he make me think? Always.”

It seems to me (from my linear trousered perspective) that progress in robotics, AI, synthetic biology and genomics brings philosophical questions such as “what does it mean to be human?” into your living room, and not in an ‘interesting-debate-over-a-glass-of-wine’ sort of way, but in a ‘right-in-your-face-what-are-you-going-to-do-about-it?’ sort of way.

When the possibility that the hand your mate Robin lost to cancer three years ago can be replaced by a robotic one with a sense of touch becomes a real option we begin to ask ourselves, ‘Is that hand really part of Robin? If I shake that hand am I really shaking Robert’s hand? Gee I don’t know. I feel kinda weird’. (By the way, Robin isn’t fictional, he’s Robin af Ekenstam and you can watch a video of his new hand being attached here). And just as we can start to engineer robot hands and merge them with humans, we will soon, thanks to the law of accelerating returns, be able to engineer to genuine robot intelligence and merge it with our brains, argues Ray.

“The basic principles of intelligence are not that complicated, and we understand some of them, but we don’t fully understand them yet. When we understand them we’ll be able to amplify them, focus on them – we won’t be limited to a neo-cortex that fits into a less than one cubic foot skull and we certainly won’t run it on a chemical substrate that sends information at a few hundred feet per second, which is a million times slower than electronics. We can take those principles and re-engineer them and we’re going to merge them with our own brains”.

It’s statements like this that bring Ray into conflict with many scientists who think he’s not so much running before he can walk, as getting in jet fighter straight out of the crib. Although, for Ray, that’s kind of the point. Crib to jet fighter is really just a few doublings after all, the law of accelerating returns in action. But for some, Ray is a bit like Tracy. He makes a road where there isn’t one, they say.

One thing is certain. If a conscious human-like intelligence is ‘computable’ (i.e. it can be run on a machine substrate) the processing power to compute it will be within reach of the even your desktop very soon. Hans Moravec wondered, “what processing rate would be necessary to yield performance on par with the human brain?” and came up with the gargantuan figure of 100 trillion instructions per second, which is one of those numbers that generally makes most of us go “hmmm, I think I’ll make a cup of tea now.” To put this number in context, as I was ushered into the world in the early seventies IBM introduced a computer that could perform one million instructions per second. This is one millionth of Moravec’s figure. By the dawn of the millennium chip-maker, AMD, were selling a microprocessor over three and half thousand times quicker (testament to a technological journey that had been populated with continual exponential leaps in processing power throughout the intervening period). This yielded a chip that is still 280 times less powerful than the brain’s computational prowess (by Moravec’s reckoning) but is a staggering upswing in power nonetheless. Intel have just released their ‘Core i7 Extreme’ chip which is forty times faster than the AMD device from 2000 and computes at the mind-numbing speed of 147,600,000,000 instructions per second – or about one seventh of Moravec’s figure. At this rate your new laptop will achieve the same computational speed as the human brain before the decade is out. Soon after that, if the exponential trend continues, your laptop (or whatever replaces it) will have more hard processing muscle than all human brains put together. This will happen sometime around the middle of the century according to Kurzweil.

Supercomputers have passed Moravec’s milestone and it’s therefore no surprise to find various projects using them to try to simulate parts of animal and human brains, merging neuroscience and computer science in an attempt to get to the bottom of what’s really going on in that skull of yours. It’s important to realise that simulating something often takes more computing power than being something (aircraft simulators have more computers than actual aircraft for instance) and a complete simulation of an entire human brain running in real-time is still beyond the reach of even the most powerful computers. But not for long. Henry Markram’s Blue Brain project (which works by simulating individual brain cells on different processors and then linking them together) believes “It is not impossible to build a brain, and we can do it in ten years.” He’s even joked (or not, depending on how seriously you take the claim) he’ll bring the result to talk at conferences. Markram has similarly upset more conservative voices in the AI field. Even Ray thinks he’s over-optimistic. (The prediction falls outside the curve predicted by Ray’s graphs by a hefty margin).

You can see Markram’s TED talk (where he suggests he’ll be bringing the Blue Brain back to the conference as a speaker within a decade) below.

I find myself thinking back to my talk with George Church, Professor of Genetics at Harvard Medical School. If you accept evolution as an explanation of how humanity came to be, that the common genetic code of all living things is proof that you, I and Paris Hilton all, at some point, evolved from the same source (that source being a collection of molecules that became the first cell) then one way of looking at the human being (and therefore the human brain) is ‘simply’ as a collection of unthinking tiny bio-machines computing away – reading genetic code, and spewing out ‘computed’ proteins and the rest. We’re machines too, just wet biological ones. You are an information technology.

Robotics pioneer Rodney Brooks makes this argument as well. “The body, this mass of biomolecules, is a machine that acts according to a set of specified rules,” he writes in Robot: The Future of Flesh and Machines

Needless to say, many people bristle at the use of the word “machine”. They will accept some description of themselves as collections of components that are governed by rules of interaction, and with no component beyond what can be understood with mathematics, physics and chemistry. But that to me is the essence of what a machine is, and I have chosen to use that word to perhaps brutalize the reader a little.

In short, intelligence and consciousness are computable, because you and I are computing it right now. I compute, therefore I am. George Church was less brutal in his take on the ‘human machine’. “I think of us more and more as mechanisms,” he told me. “We’re starting to see more and more of the mechanism exposed and it just makes it more impressive to me, not less. If someone showed me a really intricate clock or computer that had emotions and self awareness and spirituality and so forth I’d be very, very impressed and I think that’s where we are heading, were we can be impressed by the mechanism.”

But something’s not sitting right with me, and it’s not that I don’t like being called a ‘machine’ (believe me, that’s nothing compared to some of the heckles I’ve had). In fact, the machine metaphor makes a kind of sense given what I found out at Harvard.

It was Cynthia Breazeal, head of the personal Robotics lab who I met last time I was in Boston that expressed it best. “The bottom line is there’s still a long way to go before we can have a simulation actually do anything. I mean they can run the simulation but what is it doing that can be seen as being intelligent? How does that grind out into real behaviour, where you show it something and have it respond to it? I still think there’s a lot of understanding that needs to be done. I do, I really do. I think we’re making fantastic strides but I think,” (she dropped to a conspiratorial whisper, smiling) “there’s a lot we still don’t know!”

Cynthia nailed the root of my discomfort. Someone can give you the best calculator in the shop, but if you’ve never learned any maths, it’s largely useless to you. If the brain is computable, it’s not that we won’t have the processing power to recreate its mechanisms, but that we’re still a long way off working out how to drive that simulation. If you’d never learned to read your eyes could take in the shape of every letter on this page, but it’d mean nothing to you, and printing it out photocopying it a hundred times (or even inventing the printer and photocopying machine in order to do so) wouldn’t help you either. Just as you had to learn to read, AI and neuroscience research, collectively, have to tease out not only what it is they’re looking at, but what it means.

Sure, there’s exponential growth in processing power, but the jury is out as to whether there is an equivalent growth in understanding how to use that power more ‘intelligently’, to create (to paraphrase one of Henry Markram’s analogies) a concerto of the mind by playing the grand piano of the brain. If there had been, maybe your new laptop would be one-seventh as smart as you are. But it isn’t. This is where the strength of projects like the Blue Brain (and Cynthia’s work) really lie – as tools to slowly help us to pose the right questions that will lead to a better understanding of intelligence, emotion and consciousness.

This is what I really want to ask Ray. “Have you got any graphs that clearly show an exponential growth in understanding? or in the ability of us to collectively make sense of the great philosophical questions, the intractable questions – ‘What is life?’, ‘What is consciousness?’” I ask. “Have we seen the law of accelerating returns in our understanding of these questions? Is our knowledge, our wisdom also keeping pace?”

“Well, I’m actually working on that in connection with my next book which is called How the mind works and how to build one,” says Ray.

Well he would be, wouldn’t he?

More of my interview with Ray will, of course, be in the book…