My latest article, “Are You Smarter Than a Sixth-Generation Computer?” leads off this month’s issue of The Futurist magazine. (Sept/Oct 2012) The article explores the need for a standard metric for AIs and is based on my paper, “Toward a Standard Metric of Machine Intelligence”, which I recently published in the World Future Review. The purpose of the article (and the paper) can be summed up as follows:

As our world becomes increasingly filled with technological intelligence, it will serve us well to know exactly how smart our machines are and in what ways. Given that we try to measure almost every other aspect of our world, it seems only prudent that we accurately measure the intelligence of our machines as well — especially since, by some projections, they’re expected to surpass us in the coming decades.

During the next few decades we’re going to see significant gains in the field of strong AI, potentially giving rise to artificial general intelligences (AGIs). Universal Intelligence tests such as those described here would seem a crucial tool as we prepare for the changes this will bring.

I’m beginning a short series at Psychology Today about the ongoing advances being made in machine intelligence. I’d originally thought about using “The Ascent of Machine” for the series title — after Jacob Bronowski’s wonderful work, “The Ascent of Man”, which I found so inspiring when it first came out. But I thought it sounded a bit kludgy and so I ultimately decided on the Cameron-esque “Rise of the Intelligent Machines”.

Step by step, we’re being equaled and more importantly, surpassed. As we’ve seen in both biology and technology, this is a march that is taking place with ever more rapid strides. Complexity breeds still further complexity, cross-fertilizing itself into previously unconceived of combinations. The world is quickly approaching a time when humanity may come to share the world with an equal or greater intelligence. One that will have been of our own making.

This multi-part series will explore the rise of machine intelligence, research and advances that will impact its development and what this may mean for the future of human intelligence. Check back at Psychology Today for future installments. Next in Part 2: How to Build a Brain.
 

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.”

Nine months ago, I wrote a post called “The Supercomputer Race” about China’s then top-ranked supercomputer, the Tianhe-1A and what it meant for the U.S. The Tianhe-1A achieved 2.57 petafolps (10¹⁵ floating point operations per second) on LINPACK, a benchmark based on a series of complex linear equations. In comparison, the world’s next fastest system at the time was Oak Ridge National Laboratory’s Jaguar, clocking in at 1.76 petaflops. (Based on TOP500′s semi-annual ranking of the world’s five hundred fastest supercomputers.)

Today TOP500 released their latest rankings, which put Japan’s K Computer in the number one spot with 8.162 petaflops (PFLOPS), a jump of more than three times the performance of the now number two Tianhe-1A. How was such a sharp increase realized and what does it mean for supercomputing in the future?

A little history: TOP 500 has been ranking the world’s supercomputers since 1993. During this time individual and cumulative performance has followed a steady pattern of exponential growth. As with this latest ranking, individual rankings have shown a significant jump in some years (e.g., 1997, 2002), followed by years of more modest improvement. On the other hand, cumulative totals have been very consistent due to the broader data set and are probably a better indicator of where the trend stands overall. (Interestingly, RIKEN’s K Computer represents a jump not seen since Japan’s last number one, the Earth Simulator in 1992.) Not surprisingly, the plot points for the performance growth of the number one spot approximate a classic series of sigmoid growth curves, as technologies reach their limits and are superseded by others.

The substantial leap forward last year by the Tianhe-1A can mostly be attributed to one significant improvement: the implementation of radically faster interconnects. Rather than focusing on the latest step up in CPU technology, the designers of the Tianhe-1A focused on the biggest bottleneck in the system. Interconnects are networking chipsets that coordinate the data continually being moved between processors, in this case thousands of Intel Westmere and Nvidia Fermi processors. China’s homegrown Galaxy interconnects were a huge improvement in performance at double the speed of the Infiniband interconnects used in many other systems.

This latest ranking saw improvements that are due to a related trend: the transition away from monolithic CPU-based systems to heterogeneous platforms. (Heterogeneous platforms utilize a variety of different types of computational units, including CPUs, GPUs, interconnects, etc.) Looking at the trend line, the Tianhe-1A represented a 50% increase over Oak Ridge’s Jaguar. Japan’s K Computer improves on the Tianhe-1A by almost 200%. During this next year, two U.S. systems are slated to become operational with peak performances in the 20 PFLOP range or a further gain of 150%.

So does this point to a long-term increase in the rate of improvement in supercomputing performance? I’d say, probably not. The elimination of bottlenecks and the transition to new approaches will likely be a blip on the trend line. As the industry moves toward the target of exascale supercomputing later this decade, we’re likely to see improvements slow at various points as we deal with some very considerable challenges of scale. It’s been said that while the move from terascale to petascale computing was evolutionary, the leap from petascale to exascale will be revolutionary. The solutions used in the earlier systems simply won’t scale up without significant changes being made.

A common question among the general public is “why do we even need more powerful supercomputers? Can’t we get by with what we have already?” The simple answer is ‘No’. If the U.S. wants to remain a leading technological and economic force in the world, it will be necessary to invest in a future in which supercomputers play a central role. If we’re to see the nascent technologies of the 21st century realized, we’ll need the vast processing power of exascale systems and beyond. Likewise, we’ll need next-generation supercomputers if we’re to overcome many of the challenges the world now faces. Our digital world is generating enormous quantities of data, data that is itself growing exponentially. Bioinformatics, proteomics and brain simulation are but a few of the fields that will require continuing improvements in supercomputing to deal with their immense data sets. For similar reasons, we’ll need these computers for complex analytic systems such as IBM’s DeepQA Project, more commonly known as Watson. The ability to create tremendously detailed climate models will also be essential as we deal with human-caused climate change, whether to predict its consequences or to implement solutions. In short, to abandon advances in supercomputing is to abandon our place in the future.

(The future of information management is explored in my recent article, “Treading in the Sea of Data”, in the July/August 2011 issue of The Futurist. The article is an abridged version of my paper which will be published this summer in the WorldFuture 2011 conference volume, “Moving From Vision to Action,” editor, Cynthia G. Wagner.)

I’m excited to announce I was recently invited to write a blog for Psychology Today. As with this blog, “The Intelligence Report” will explore the evolving relationship between intelligence and technology. It will include new posts as well as some cross-posting from this blog when the topic is a good fit. As an introduction, the initial post, “Can Machines Be Intelligent?” explores the possibility that computers may be closer to achieving true intelligence than many people think. Be sure to check out both blogs regularly for the latest postings.