The simulations of Modha and Markram are about as brain-like as one of those plastic brains that neuroscientists like to keep on their desks. The plastic brain has all the parts that a real brain does, it’s roughly the same color and it has about as many molecules in it. OK, say optimists, the plastic brain doesn’t actually perceive, emote, plan or decide, but don’t be so critical! Give the researchers time! Another analogy: Current brain simulations resemble the “planes” and “radios” that Melanesian cargo-cult tribes built out of palm fronds, coral and coconut shells after being occupied by Japanese and American troops during World War II. “Brains” that can’t think are like “planes” that can’t fly.

In spite of all our sophisticated instruments and theories, our own brains are still as magical and mysterious to us as a cargo plane was to those Melanesians. Neuroscientists can’t mimic brains because they lack basic understanding of how brains work; they don’t know what to include in a simulation, and what to leave out. Most simulations assume that the basic physical unit of the brain is the neuron, and the basic unit of information is the electrochemical action potential, or spike, emitted by the neuron. A typical brain contains 100 billion cells, and each cell is linked via dendrites and synapses to as many as 100,000 others. Assuming that each synapse processes one action potential per second and that these transactions represent the brain’s computational output, then the brain performs at least one quadrillion operations per second.

Computers are fast approaching this information-processing capacity, leading to claims by artificial-intelligence enthusiast Ray Kurzweil and others that computers will soon not just equal but surpass our brains in cognitive power. But the brain may be processing information at many levels below and above that of individual neurons and synapses. Moreover, scientists have no idea how the brain encodes information. Unlike computers, which employ a single, static machine code that translates electrical pulses into information, brains may employ many different “neural codes,” which may be constantly changing in response to new experiences.

Thank you, John Horgan, for saying something that makes sense. This doesn’t mean that we shouldn’t consider A.I.s as thought experiments or understand how intelligent robots will alter our labor systems or consider the fact that our minds will likely be enhanced/augmented by minor bits of tech within the next fifty years – those ideas are still critical to developing a better system of ethics and law than we have now. What Horgan’s point does mean, however, is that the whole discussion of the Singularity and CEV and all that other nonsense is about as ethically significant as worrying about time-travel or terraforming. The technology too many steps removed for us to make reasonable guesses or assumptions, so it’s best to focus on the immanent issues instead.

But a small organism doesn’t have so many cells to control and can fit some very elaborate mental circuitry in a pinhead-sized brain.

Several hundred neurons give the ability to count. A few thousand create sentient, and perhaps even sapient, thought. If that’s really the case, then it seems that we’re barking up the wrong tree with cognitive computing concepts and AI projects.

Instead of trying to simulate huge numbers of neurons, then bragging about it as a step towards emulating real brainpower, we should focus on those individual circuits and model the brains of insects rather than mammals.

Second, cyborg astronauts. The pros are interesting, but the hearing the cons aired are a breath of fresh air:

Of course there’s a catch. Each of the procedures that would make all this possible would be a) incredibly invasive, b) exorbitantly expensive and c) require decades of highly focused research projects to make it all possible. While the benefits to those who suffered serious trauma to the brain, limbs and spine, or suffering from organ failure would be immense, there may be some serious pause about healthy individuals undergoing this sort of modification for the sake of traveling to other worlds. People who may never walk again without a prosthetic spine or mechanical legs would certainly volunteer for such procedures because being confined to a bed or a wheelchair for the rest of their lives is a far higher cost than the risks involved with the surgery.

Kaj Sotala, Roko Mijic, and Michael Annissimov all use special pleading when critiquing James Hughes piece “Liberal Democracy vs Technocratic Absolutism.” The central rebuttal for all of them can be paraphrased as “your critiques of Communism, dictatorships, and other authoritarian governments make sense for humans, but don’t apply to friendly AI because friendly AI is different than human systems and is genuinely selfless.” Hughes hears echoes of Marxist-Leninist thought in that point. Some thinkers, including the allegedly brilliant philosopher Slavoj Zizek, continue to defend Marxism using special pleading. Instead of claiming Communism isn’t based in humans, they clame Stalin and the USSR were not pure Communism, and therefore were doomed to failure because of the corrupting element of capitalism. Thus, thanks to special pleading, Stalin is not proof that Communism and authoritarianism are dangerous and bad, but that capitalism is bad and corrupts the pure motives of Communism.

The problem is that, like Communism, friendly AI, even if derived through the process described by the CEV, will ultimately fail. The reason democracy works even remotely better than authoritarian systems is because it openly admits and aims to minimize the faults in the system. These faults include both the “programming,” that is, the legislation and philosophy underpinning it, and the agents of the system, humans. Democracy, Communism, and, yes, AI based technocratic authoritarianism, are all human systems. They will be imperfect. Democracy, of the three, is the only one that sees itself as imperfect and prone to mistakes and failure. Therein lies the inherent benefits of democracy – it is a radically reflexive system.

As a final point, I think it is very interesting that those who support friendly super-AI don’t see the AI coming to the conclusion that nearly all forms of government, particularly those of an authoritarian breed, are faulty and instead advocating anarchy or a form of hyper-limited government. That the AI would want to govern at all is a further assumption I don’t understand. Assuming it’s an AI, it should be volitional, which would make forcing it to govern a restriction in its will or it would make it a program, not a genuine AI. There are just too many problems here.

h/t Anissimov

Wayne Stayskal

In what Rasskin-Gutman explains as Moravec’s Paradox, in chess, as in so many things, what computers are good at is where humans are weak, and vice versa. This gave me an idea for an experiment. What if instead of human versus machine we played as partners? My brainchild saw the light of day in a match in 1998 in León, Spain, and we called it “Advanced Chess.” Each player had a PC at hand running the chess software of his choice during the game. The idea was to create the highest level of chess ever played, a synthesis of the best of man and machine.

["The Chess Master and the Computer" - NY Review of Books, h/t MR]