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[The following is tentative material for my presentation at the Society for Philosophy & Technology Conference this summer.]
Do Posthumanists Dream of Pixilated Sheep?
Bostrom and Sandberg's Brain Emulation,
Examined and Critiqued
Section 5:
Even While Men’s Minds are Wild?
Neural noise can result from external interferences like magnetic fields. Or internal random fluctuations might make the signals unpredictable. (Ward 116-117) In both cases, chance & chaos reign our brains. According to Steven Rose, our brain is an “uncertain” system on account of “random, indeterminate, and probabilistic” events that are essential to its functioning (Rose 93). Alex Pouget and his research team recently found that the mind's ability to compute complex calculations has much to do with its noise. The word noise is misleading, he says, because it implies something goes wrong. But these unpredictable irregularities are the mind’s way of running at optimum performance. Our mind produces noisy signals to represent the uncertainty of the world around us. Pouget explains,
if we want to do something, such as jump over a stream, we need to extract data that is not inherently part of that information. We need to process all the variables we see, including how wide the stream appears, what the consequences of falling in might be, and how far we know we can jump.
In this way, the brain is flooded with countless variables. And the neurons transmit various signal patterns for the same stimulus. This allows us to estimate margins of error. We then use a probabilistic inference to make what is most likely to be the best decision. (Pouget, interview with Science Daily) So we might jump the stream, if probably we can cross it, even though we can never be certain about such matters.
Some also theorize that noise is essential to the human brain’s creativity. Johnson-Laird claims that creative mental processes are never predictable. (Johnson-Laird, The Computer and the Mind 256) He hypothesizes that we could make a machine creative by programming it to alter its own functioning according to generated random variations. (Human and Machine Thinking, 119-120) This would produce what Ben Goertzel refers to as “a complex combination of random chance with strict, deterministic rules.” (Goertzel 119) And according to Daniel Dennett, this indeterminism is precisely what endows us with what we call free will. (Dennett 295, cited in Dartnall 37) Likewise, Bostrom & Sandberg suggest we introduce random noise into our simulation by using pseudo-random number generators. They are not truly random, because eventually the pattern will repeat. But if it takes a very long time before the repetitions appear, then probably it would be sufficiently close to real randomness (Bostrom & Sandberg 38-39). Also, there might be random variations that are hidden to our observations, and thus would not be properly represented in the simulation. They recognize the profound difficulty in incorporating true randomness or hidden variables into the simulation. Yet they believe these sorts of randoms most likely will be unnecessary for whole brain emulation.
But perhaps there is more to consider. Lawrence Ward reviews findings that demonstrate neural noise is pink noise, or what is called 1/f noise. (Ward 145-153, citing research by Lundström and McQueen, and Novikov, Shannonhoff-Khalsa, Schwartz, and Wright) On account of its fractal nature, 1/f noises are always parts of similar larger-orders of variation happening on much longer time-scales. We might have to wait weeks or months to see larger-scale variations that were varying the randomness of the more local noisy events. (Anderson & Mandell 78-79) These are what Gregory Bateson calls metarandom variables. They are hidden to us, because we never see the whole picture (Bateson Steps to an Ecology 418). It’s why live lobsters never notice their cooking water gradually increase to boil (Mind and Nature 109). They only notices alterations on a local level, so nothing really seems to be changing. In a similar way, if all we are observing is randomness on a smaller scale, we might be missing the larger scale variations. It would be like randomly adjusting a radio to pick up different bands of radio static. If all we knew was the randomness of radio static at each moment, we might not also notice the higher order randomness that varies the lower one that we are listening-to. Because these uncontrollable unpredictabilities are essential to all the random changes happening around us, Bateson calls them wild variables. (Mind and Nature 49-50)
Perhaps it is for similar reasons that Benoit Mandelbrot classifies 1/f noise under what he terms “wild randomness” and “wild variation.” (Mandelbrot The (mis)Behavior of Markets 39-41) This sort of random might not be so easily simulated. Mandelbrot gives two reasons for this.
1) In wild randomness, there are events that defy the normal random distribution of the bell curve. He cites a number of stock market events that are astronomically improbable. But such events in fact happen quite frequently in natural systems despite their seeming impossibility. There is no way to predict when they will happen or how drastic they will be. (The (mis)Behavior of Markets 4)
2) Each event is random and yet it is not independent from the rest, like each toss of a coin is. One seemingly small anomalous event will echo like reverberations at unpredictable intervals into the future. (The (mis)Behavior of Markets 181-185)
For these reasons, he considers wild variation to be a qualitatively different state of indeterminism than the usual mild variations we encounter at the casino. For, there is infinite variance in the distributions of wild randomness. Anything can happen at any time (Mandelbrot, Fractals and Scaling 128). He says, “the fluctuation from one value to the next is limitless and frightening.” (Mandelbrot (mis)Behavior of Markets 39-41) This is the wildness of our brains.
Paul Shepard considers our minds to be wild in an even more literal sense: we are wild animals. He distinguishes tameness from domestication. Cows are domesticated. They have been bred to suit our needs. And now their genes would probably not prepare them to live in the wild without human protections. But the human species has merely been tamed by culture and not domesticated like cows. Genetically, we are still the same wild creatures who hunted the Pleistocene savannas. So to emulate the human brain is to simulate the workings not of a rational machine, but of a wild animal. (Shepard 132-133) He writes, “The savage mind is ours! ... as a species we have in us the call of the wild.” (143)
Shepard’s characterizes the wild, like Bateson and Mandelbrot do, as being too complex for any simulation. But he offers his own theory to explain why. He notes the fractal nature of reality. Within every scale is another smaller scale, and so on to infinity. He says that every layer of complexity operates according to deterministic principles. But, there is no lowest level of complexity. Hence there is no way to get to the bottom of what is happening now. It’s turtles all the way down. Thus there is no way to fully understand why things are the way they are now. And thus we can never know how things will be in the future. (146-147)
But let’s suppose that the brain’s wild randomness can be adequately simulated. Will brain emulation still attain its fullest success of perfectly replicating a specific person’s own identity? Bostrom & Sandberg recognize that neural noise will prevent precise one-to-one emulation. However, they think that the noise will not prevent the simulation from producing meaningful brain states (Bostrom & Sandberg 7). But to pursue further the personal identity question, let’s imagine that we want to emulate a certain slot machine. A relevant property is its unpredictability. Consider these two possibilities. 1) We set the original and the simulation to the same starting position. We give both handles a number of pulls. Each time, they both show the same outcomes, because we replicated the mechanics perfectly. But then, we cannot say that we have preserved its relevant essential property of being unpredictable. For, we can just run the simulator by itself and that will predict the original’s future outcomes. Or, 2) instead the emulation produced its own different random series of outcomes. Then in fact we would be replicating the original’s property of unpredictability.
The problem is that the brain’s 1/f noise is wildly random. So suppose we emulate some person’s brain perfectly. And suppose further that the original person and her emulation have an identity merger where each one thinks they are talking to their very own selves when really they are talking to the other. They confuse themselves with one another. They are completely aware of what is in the other’s mind at that first moment, because they can tell it is the same as what is in their own mind. And suppose further that the original person loses her fear of death, knowing that something she cannot distinguish from himself will carry on after her body dies. But if both minds are subject to wild variations, then their consciousness and identity might come to differ more than just slightly. They could veer-off wildly. The original person and her emulation might become so mistrustful of each other, that they want to end the other’s existence.
So we might need to emulate this wild neural randomness. But that seems to remove the possibility that the emulation will continue on as the original person. Perhaps our very effort to emulate a specific human brain results in our producing an entirely different brain altogether.
Anderson, Carl M. & Arnold J. Mandell. Fractal Time and the Foundations of Consciousness: Vertical Convergence of 1/fPhenomena from Ion Channels to Behavior States. in Fractals of Brain, Fractals of Mind.
Bateson, Gregory. "Effects of Conscious Purpose on Human Adaptation." in Steps to an Ecology of Mind.
Bateson, Gregory. Mind and Nature: A Necessary Unity.
Sandberg, A. & Bostrom, N. (2008): Whole Brain Emulation: A Roadmap, Technical Report #2008‐3, Future of Humanity Institute,
Dartnall, Terry. "Introduction: On Having a Mind of Your Own." in Artificial Intelligence and Creativity: An Interdisciplinary Approach. Ed. Terry Dartnall.
Dennett, Daniel. Brainstorms. Hassocks: Harvester Press, 1978. More information available at: http://books.google.be/books?id=s3V-AAAAMAAJ&hl=en&pgis=1
Goertzel, Ben. Chaotic Logic: Language, Thought, and Reality from the Perspective of Complex Systems Science.
Johnson-Laird, R. N. The Computer and the Mind: An Introduction to Cognitive Science.
Johnson-Laird, Philip. Human and Machine Thinking.
Mandelbrot, Benoit B., & Richard L. Hudson. The (mis)Behavior of Markets: A Fractal View of Risk, Ruin, and Reward.
Science Daily. "Mysterious 'Neural Noise' Actually Primes Brain For Peak Performance."
Shepard, Paul. Coming Home to the Pleistocene.
Ward, Lawrence M. Dynamical Cognitive Science.
Also mentioned:
(Lundström and McQueen, 1974, "A proposed 1/f noise mechanism in nerve cell membranes," Journal of Theoretical Biology, 45, 405-409).
(Novikov E., A. Novikov, Shannonhoff-Khalsa, Schwartz, and Wright, 1997, "Scale-similar activity in the brain," Physical Review E, 56, R2387-R2389,)
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