The Singularity is Far: A Neuroscientist’s View
July 21, 2011 by David J. Linden
David J. Linden is the author of a new book, The Compass of Pleasure: How Our Brains Make Fatty Foods, Orgasm, Exercise, Marijuana, Generosity, Vodka, Learning, and Gambling Feel So Good. He is a Professor of Neuroscience at The Johns Hopkins University School of Medicine and Chief Editor of the Journal of Neurophysiology.
It should be noted that many of the criticisms in this blog post were addressed in Ray Kurzweil’s book, The Singularity is Near, and elsewhere — see editorial comments below. Also, the Singularity concept is not limited to neuromorphic models. — Ed.
Ray Kurzweil, the prominent inventor and futurist, can’t wait to get nanobots into his brain. In his view, these devices will be equipped with a variety of sensors and stimulators and will communicate wirelessly with computers outside of the body. In addition to providing unprecedented insight into brain function at the cellular level, brain-penetrating nanobots would provide the ultimate virtual reality experience. In an interview with GOOD magazine, Kurzweil says:
“By the late 2020s, nanobots in our brain, that will get there noninvasively, through the capillaries, will create full-immersion virtual-reality environments from within the nervous system. So if you want to go into virtual reality the nanobots shut down the signals coming from your real senses and replace them with the signals that your brain would be receiving if you were actually in the virtual environment. So this will provide full-immersion virtual reality incorporating all of the senses.”
Of course, there’s no reason why these nanobots must be restricted in their manipulations to the sensory portions of the brain. In Kurzweil’s scenario, brain nanobots could just as easily manipulate motor functions, cognitive processes, memories, emotions, and basic drives.
But nanobot-mediated virtual reality, virtual emotion, and modulated cognition are only the beginning. Kurzweil predicts that by the late 2030s, we will be able to routinely scan an individual’s brain with such molecular precision and with such a complete understanding of the rules underlying neuronal function and plasticity that we will be able to “upload” our mental life into a vastly powerful and capacious future computer. As Kurzweil describes it his book The Singularity is Near, “This process would capture a person’s entire personality, memory, skills and history.”
At that point, boundaries between brain, mind, and machine would fall away. Once our individual mental selves are instantiated in machine form, manipulations of mental function, perception, and action just become software modules. Want to improve your mood? Want to preserve all your experiences in memories with perfect fidelity? Want to have the mother of all orgasms? There’s an app for that.
As much as I respect Ray Kurzweil and appreciate his willingness to make predictions about and argue for specific future events, I take issue with his timetables for both the introduction of brain-nanobots and the ability to upload the contents and meaning of a brain.
I am a neurobiologist and I have spent the past 28 years engaged in studies of the cellular and molecular basis of memory and cognition. I am an optimist and a technophile, but I believe that I speak for the vast majority of brain researchers when I express serious doubts about Kurzweil’s timetable.
The central premise underlying his predictions is that enabling technologies like computer processors, computer memory, microscopes, brain scanners, and DNA sequencing machines have been on an exponential rather than a linear trajectory in terms of their capacity, speed, resolution, and real-world cost, and that it is reasonable to imagine that this exponential trend will continue. Kurzweil also assumes that the human mind resides entirely in the brain (or at least in the nervous system): There is no immortal soul, collective energy, or other nonbiological component that encodes our individual mental selves. At this point in his argument I’m still on board.
However, Kurzweil then argues that our understanding of biology — and of neurobiology in particular — is also on an exponential trajectory, driven by enabling technologies. The unstated but crucial foundation of Kurzweil’s scenario requires that at some point in the 2020s, a miracle will occur: If we keep accumulating data about the brain at an exponential rate (its connection maps, its activity patterns, etc.), then the long-standing mysteries of development, consciousness, perception, decision, and action will necessarily be revealed. Our understanding of brain function and our ability to measure the relevant parameters of individual brains (aided by technologies like brain nanobots) will consequently increase in an exponential manner to allow for brain-uploading to computers in the year 2039.
That’s where I get off the bus.
I contend that our understanding of biological processes remains on a stubbornly linear trajectory. In my view the central problem here is that Kurzweil is conflating biological data collection with biological insight.
A Lake of Data, A Puddle of Knowledge
Let’s take genetic sequencing as an example. Yes, we have now sequenced quite a few human genomes and, yes, the speed and cost of doing so are improving exponentially. The human genome sequence — and those of the rat, mouse, fly, zebrafish and rhesus monkey — are an invaluable tool for biologists. That said, while the fundamental insights that have emerged to date from the human genome sequence have been important, they have been far from revelatory.
For example, we have learned that gene duplication is more common than we originally thought. It’s not all that rare for regions of chromosomes to repeat themselves. We have also learned that humans have fewer genes, but that those genes have more complex modes of regulation and more splice-forms than we had initially predicted.
That’s all useful information, but it doesn’t represent a game-changing, exponential transformation in our understanding of genetics. When the human genome sequence was finished, no one was able to look at it and say, “A-ha, now I can understand what makes us uniquely human,” or “A-ha, now I see how a fertilized egg becomes a newborn during the course of gestation.”
There have been a number of genuine paradigm-shifting insights in genetics in recent years. For example, we now know that chemical modification of DNA through a process called methylation can alter its structure and the way in which it interacts with a set of regulatory/structural proteins called histones, thereby silencing the expression of certain genes. This is one of several mechanisms that controls the regulation of gene expression or “epigenetics.” Such insights have explained a whole set of puzzles and are a major step forward in our understanding of genetics.
But these discoveries, and most of the other key conceptual breakthroughs in this field, have come slowly, the result of stubbornly linear small science, and not of the huge technology-driven data sets that Kurzweil describes.
In “The new era of health and medicine as an information technology is broader than individual genes,” published Feb. 4, 2011 on KurzweilAI, Ray Kurzweil further responds to these criticisms. Summing up, he states: “Our knowledge is still very incomplete, but our knowledge of these processes is growing exponentially and that is feeding into medical research which is already bearing fruit. To focus just on the narrow concepts that were originally associated with “genomics” is as limited a view as the old idea of AI being just expert systems.” — Ed.
This linear progress also holds true for the growth in our knowledge of brain function. For example, we now have a map called the Allen Brain Atlas that shows the expression pattern of almost every gene in the mouse brain, detailed in a huge series of microscopic images. This resource, which is available to everyone on the Internet, is a wonderful tool for brain researchers, but it has produced few “Eureka!” moments. The temporal and spatial resolution of our brain scanners is also improving, but these improvements have likewise yielded fundamentally linear insights.
Kurzweil’s ideas about nanobots in the brain are problematic, as well.
He says his nanobots will measure seven microns across—about half the diameter of a typical neuronal cell body—and their job will be to maneuver through brain tissue and deploy microsensors and stimulators to evaluate normal brain function.
You might imagine the nanobot as a car, something the size of a Volkswagen Beetle. It drives down the road, until it finds something the size of an SUV (a neuron). Here is the first of many problems in Kurzweil’s scenario: The brain is composed of neurons and glial cells—non-neuronal cells that outnumber neurons 10-to-1 and provide metabolic support and slow forms of information processing in the brain. These cells are packed together very tightly, leaving only miniscule gaps between them.
It is easy to look at the left panel of the figure that shows a computer-based reconstruction of the tip of a growing axon in the brain and imagine that there is plenty of space around it. However, the complete view of this same growing axon tip is shown in the panel on the right. This image is made with a transmission electron microscope and it shows how the same growing axon (marked with asterisks) is packed into a dense and complex matrix of tissue containing other neurons and glial cells. The scale bar in the left panel is 0.5 microns long, about 1/160th of the diameter of a human hair.
So you can imagine Kurzweil’s brain nanobot, a structure about fourteen times larger in diameter than the scale bar, crashing through this delicate web of living, electrically active connections.
Actually, the nanobots described by Kurzweil in The Singularity Is Near (p. 164) would only travel through capillaries, and would access tissue outside the capillary via various possible future strategies, including a biocompatible robotic arm under 20 nanometers (.02 microns) in width and non-invasive scanning methods. Kurzweil’s nanobot concepts are explicitly based on medical nanotechnologist Robert A. Freitas Jr.’s classic reference book, Nanomedicine, Volume IIA: Biocompatibility (full text is available free online). Chapter 15.6, “Nanorobot Volumetric Intrusiveness,” specifically addresses tissue intrusion issues in detail. — Ed.
What’s more, the tiny spaces between these cells are filled not just with salt solution, but with structural cables built of proteins and sugars, which have the important function of conveying signals to and from neighboring cells. So let’s imagine our nanobot-Volkswagen approaching the brain, where it encounters a parking lot of GMC Yukon SUVs stretching as far as the eye can see. The vehicles are all parked in a grid, with only one half-inch between them, and that half-inch is filled with crucial cables hooked to their mechanical systems. (To be accurate, we should picture the lot to be a three-dimensional matrix, a parking lot of SUVs soaring stories into the sky and stretching as far as the eye can see, but you get the idea).
Even if our intrepid nanobot were jet-powered and equipped with a powerful cutting laser, how would it move through the brain and not leave a trail of destruction in its wake?
The nanobot also needs its own power source.
And it needs to evade reactive microglia, specialized brain cells that attack and engulf foreign bodies.
And all of this has to happen in a way that does not compromise the physiology that the nanobot is trying to measure. These problems are not fundamentally or philosophically unsolvable, but they are enormous. The 2020s are coming up fast, and so there’s a lot that would need to be accomplished in a very short time to keep Kurzweil’s nanobot timetable on track.
Don’t get me wrong. I do believe that the fundamental and long-standing mysteries of the brain will ultimately be solved. I don’t hold with those pessimists who claim that we can never understand our minds by using our brains. I also share Kurzweil’s belief that technological advancement will be central to unlocking the enduring mysteries of brain function. But while I see an exponential trajectory in the amount of neurobiological data collected to date, the ploddingly linear increase in our understanding of neural function means that an idea like mind-uploading to machines being usefully deployed by the 2020s or even the 2030s seems overly optimistic.