Technology

Back in November I received an unusual request: to take part in a conversation at the Discover expo in London, an event put on by Hewlett Packard Enterprise (HPE) to showcase their new technologies. The occasion was a project called simply The Machine — a step forward in what’s known as “memory-driven computing.” On the one hand, I am not in any sense an expert in high-performance computing technologies. On the other hand (full disclosure alert), they offered to pay me, which is always nice. What they were looking for was simply someone who could speak to the types of scientific research that would be aided by this kind of approach to large-scale computation. After looking into it, I thought that I could sensibly talk about some research projects that were relevant to the program, and the technology itself seemed very interesting, so I agreed stop by London on the way from Los Angeles to a conference in Rome in honor of Georges Lemaître (who, coincidentally, was a pioneer in scientific computing).

Everyone knows about Moore’s Law: computer processing power doubles about every eighteen months. It’s that progress that has enabled the massive technological changes witnessed over the past few decades, from supercomputers to handheld devices. The problem is, exponential growth can’t go on forever, and indeed Moore’s Law seems to be ending. It’s a pretty fundamental problem — you can only make components so small, since atoms themselves have a fixed size. The best current technologies sport numbers like 30 atoms per gate and 6 atoms per insulator; we can’t squeeze things much smaller than that.

So how do we push computers to faster processing, in the face of such fundamental limits? HPE’s idea with The Machine (okay, the name could have been more descriptive) is memory-driven computing — change the focus from the processors themselves to the stored data they are manipulating. As I understand it (remember, not an expert), in practice this involves three aspects:

1. Use “non-volatile” memory — a way to store data without actively using power.
2. Wherever possible, use photonics rather than ordinary electronics. Photons move faster than electrons, and cost less energy to get moving.
3. Switch the fundamental architecture, so that input/output and individual processors access the memory as directly as possible.

Here’s a promotional video, made by people who actually are experts.

The project is still in the development stage; you can’t buy The Machine at your local Best Buy. But the developers have imagined a number of ways that the memory-driven approach might change how we do large-scale computational tasks. Back in the early days of electronic computers, processing speed was so slow that it was simplest to store large tables of special functions — sines, cosines, logarithms, etc. — and just look them up as needed. With the huge capacities and swift access of memory-driven computing, that kind of “pre-computation” strategy becomes effective for a wide variety of complex problems, from facial recognition to planing airline routes.

It’s not hard to imagine how physicists would find this useful, so that’s what I briefly talked about in London. Two aspects in particular are pretty obvious. One is searching for anomalies in data, especially in real time. We’re in a data-intensive era in modern science, where very often we have so much data that we can only find signals we know how to look for. Memory-driven computing could offer the prospect of greatly enhanced searches for generic “anomalies” — patterns in the data that nobody had anticipated. You can imagine how that might be useful for something like LIGO’s search for gravitational waves, or the real-time sweeps of the night sky we anticipate from the Large Synoptic Survey Telescope.

The other obvious application, of course, is on the theory side, to large-scale simulations. In my own bailiwick of cosmology, we’re doing better and better at including realistic physics (star formation, supernovae) in simulations of galaxy and large-scale structure formation. But there’s a long way to go, and improved simulations are crucial if we want to understand the interplay of dark matter and ordinary baryonic physics in accounting for the dynamics of galaxies. So if a dramatic new technology comes along that allows us to manipulate and access huge amounts of data (e.g. the current state of a cosmological simulation) rapidly, that would be extremely useful.

Like I said, HPE compensated me for my involvement. But I wouldn’t have gone along if I didn’t think the technology was intriguing. We take improvements in our computers for granted; keeping up with expectations is going to require some clever thinking on the part of engineers and computer scientists.

My answer to this year’s Edge Question, “What Do You Think About Machines That Think?”

Julien de La Mettrie would be classified as a quintessential New Atheist, except for the fact that there’s not much New about him by now. Writing in eighteenth-century France, La Mettrie was brash in his pronouncements, openly disparaging of his opponents, and boisterously assured in his anti-spiritualist convictions. His most influential work, L’homme machine (Man a Machine), derided the idea of a Cartesian non-material soul. A physician by trade, he argued that the workings and diseases of the mind were best understood as features of the body and brain.

As we all know, even today La Mettrie’s ideas aren’t universally accepted, but he was largely on the right track. Modern physics has achieved a complete list of the particles and forces that make up all the matter we directly see around us, both living and non-living, with no room left for extra-physical life forces. Neuroscience, a much more challenging field and correspondingly not nearly as far along as physics, has nevertheless made enormous strides in connecting human thoughts and behaviors with specific actions in our brains. When asked for my thoughts about machines that think, I can’t help but reply: Hey, those are my friends you’re talking about. We are all machines that think, and the distinction between different types of machines is eroding.

We pay a lot of attention these days, with good reason, to “artificial” machines and intelligences — ones constructed by human ingenuity. But the “natural” ones that have evolved through natural selection, like you and me, are still around. And one of the most exciting frontiers in technology and cognition is the increasingly permeable boundary between the two categories.

Artificial intelligence, unsurprisingly in retrospect, is a much more challenging field than many of its pioneers originally supposed. Human programmers naturally think in terms of a conceptual separation between hardware and software, and imagine that conjuring intelligent behavior is a matter of writing the right code. But evolution makes no such distinction. The neurons in our brains, as well as the bodies through which they interact with the world, function as both hardware and software. Roboticists have found that human-seeming behavior is much easier to model in machines when cognition is embodied. Give that computer some arms, legs, and a face, and it starts acting much more like a person.

From the other side, neuroscientists and engineers are getting much better at augmenting human cognition, breaking down the barrier between mind and (artificial) machine. We have primitive brain/computer interfaces, offering the hope that paralyzed patients will be able to speak through computers and operate prosthetic limbs directly.

What’s harder to predict is how connecting human brains with machines and computers will ultimately change the way we actually think. DARPA-sponsored researchers have discovered that the human brain is better than any current computer at quickly analyzing certain kinds of visual data, and developed techniques for extracting the relevant subconscious signals directly from the brain, unmediated by pesky human awareness. Ultimately we’ll want to reverse the process, feeding data (and thoughts) directly to the brain. People, properly augmented, will be able sift through enormous amounts of information, perform mathematical calculations at supercomputer speeds, and visualize virtual directions well beyond our ordinary three dimensions of space.

Where will the breakdown of the human/machine barrier lead us? Julien de La Mettrie, we are told, died at the young age of 41, after attempting to show off his rigorous constitution by eating an enormous quantity of pheasant pâte with truffles. Even leading intellects of the Enlightenment sometimes behaved irrationally. The way we think and act in the world is changing in profound ways, with the help of computers and the way we connect with them. It will be up to us to use our new capabilities wisely.

Ready for your close-up? I mean, really close up. IBM has released the world’s highest-resolution movie: an animated short film in which what you’re seeing are individual atoms, manipulated by a scanning tunneling microscope. Here is “A Boy and His Atom”:

And here is an explanation of how it was made:

Over on Facebook, a single blog post was linked to by four different friends of mine: a physicist, a science writer/spouse, a saxophone player, and a screenwriter. Clearly something has struck a nerve!

The common thread binding together these creative people who make a living off of their creative work is the impact of technology on how we distribute intellectual property. In other words: do you ever pay for music any more?

Emily White doesn’t. She’s an intern at NPR’s All Things Considered, where she wrote a blog post saying that she “owns” over 11,000 songs, but has only paid for about 15 CD’s in her entire life. The rest were copied from various sources or shared over the internet. She understands that the people who made the music she loves deserve to be paid for their work, and she’s willing to do so — but only if it’s convenient, and apparently the click it takes to purchase from iTunes doesn’t qualify.

The brilliant (and excessively level-headed) response that my friends all linked to was penned by David Lowery. He makes the case much better than I would have, so read him. Making the case is necessary; there is a long tail of compensation in creative fields, and we’re all familiar with the multi-millionaires, so it’s easy to forget the much larger numbers of people sweating to earn a decent living. Not everyone has the ability to create work that other people are willing to pay for, of course; the universe does not owe you the right to earn money from your writing or thinking or playing. But when other people appreciate and benefit from your stuff, you do have a right to be compensated, I think.

Coincidentally, today I stumbled across a book that I didn’t know existed — one about me! Or at least, one whose title is my name. Since nobody other than my Mom thinks I deserve to have a book written about me, my curiosity was piqued.

Turns out that the book (apparently) isn’t so much about me, as a collection of things I have written, supplemented by Wikipedia pages. None of which I knew about at all. In other words, for $60 you can purchase a 160-page book of things you can find on the internet for free. There is a company, VDM Publishing, that specializes in churning such things out via print-on-demand. Turning Wikipedia pages into a book is bizarre and disreputable, but possibly legal. Taking blog posts and articles I have written and including them in the book is straight-up illegal, I’m afraid.

Fortunately, I’m not losing much value here, as only a crazy person would pay $60 for an unauthorized collection of Wikipedia articles and blog posts, and I like to think that my target audience is mostly non-crazy people. But it’s a bad sign, I would think. Stuff like this is only going to become more popular.

Don’t let that dissuade you from purchasing highly authorized collections of very good blog posts! For example The Best Science Writing Online 2012, appearing this September. No posts from Cosmic Variance this year, but I have it on good authority that the editor worked really hard to make this a standout collection.