There are no points or score of anything like that, because this is too complex to fit on a linear scale. Just write down your answers, list them in the comments, and waste a bunch of time arguing about them.

Here we go.

1) The military is…

A) Necessary to defend our country, but much larger than it needs to be. Its funding should be cut by 10-30% to help fund health care, infrastructure, and programs for the needy.

B)  Essential. The world is a dangerous place, and we need a large military to assert our interests globally. Funding should be kept roughly where it is, though it would be nice if there were less waste and more efficiency. Maybe the military should even be a little larger than it is now.

C) An overblown military-industrial complex perpetuated by bloodthirsty hawks, killer of ...]]> I thought it would be fun to write up some poll questions and see the answers. These answers reflect what I hear all the time. They aren’t strawmen because I’ve heard people who sincerely espouse them all.

There are no points or score of anything like that, because this is too complex to fit on a linear scale. Just write down your answers, list them in the comments, and waste a bunch of time arguing about them.

Here we go.

1) The military is…

A) Necessary to defend our country, but much larger than it needs to be. Its funding should be cut by 10-30% to help fund health care, infrastructure, and programs for the needy.

B)  Essential. The world is a dangerous place, and we need a large military to assert our interests globally. Funding should be kept roughly where it is, though it would be nice if there were less waste and more efficiency. Maybe the military should even be a little larger than it is now.

C) An overblown military-industrial complex perpetuated by bloodthirsty hawks, killer of women and children, provoking the entire world to rebel against our imperialist designs. Say no to War, say no to Imperialism.

D) Too large, and indicative of the runaway expansionary policies of big government. The military should be cut, just like every other sector of government.

E)  Uhhh… I think that we should really consider… (waffling, bringing up complex tangential points, hyper-analysis and hesitation that doesn’t amount to a concrete answer.)

F) I don’t know.

2) The best way to deal with the economy is…

A) To put the bankers in jail, where they belong, let banks fail, and give the people their due share. We need to tax and regulate out-of-control corporations so that the ultra-rich don’t exploit us. The People are tired of being kicked around by the ultra-wealthy.

B) Carefully manage the economy by lowering interest rates and increasing the money supply to stimulate growth.

C) Stop lowering interest rates, let the market do its thing, and get the government’s grubby hands out of the economy. And oh, we obviously need to go back to the gold standard. Impose “austerity” (they used to call it “economy”) to lower our debt, which is unsustainable.

D) Keep an eye on the economy, don’t artificially lower interest rates, regulate corporations with a light hand if need be, neither let the free market have total free rein nor print money like it’s going out of style. Impose some degree of austerity to lower our public debt, which is unsustainable.

E) None of these answers describe me. My real answer is special and very lengthy, but doesn’t make any concrete policy suggestions. Basically, I don’t know anything about economics but want to sound smart.

F) I’m not an economist, and I admit I don’t know much about economics. Next question.

3) Democracy…

A)  Is dominated by corrupt politicians. These scumbags are controlled by lobbyists and the elite. Their choices don’t reflect the common good. “Democracy” in America is not very democratic at all. The people need our say, and we aren’t getting it. Get rid of these clowns and replace them with direct democracy.

B) Is more or less fine as it is. It’s not perfect, and I have some quibbles with it, but by and large the system works, as it has for hundreds of years.

C)  Is a wonderful thing. We should appreciate the benefits of democracy, enjoy how free our country is, and stop taking democracy for granted.

D) Is dominated by the rich and powerful and is therefore unjust. We need a strong leader who really cares about the People and does what it takes to ensure that the plutocracy stops exploiting the working class, even if it means suspending democratic processes for the good of the proletariat.

E) Threatens freedom. We need to place a priority on freedom and not so much on mob rule. Popular rule often compromises individual freedoms. Government needs to be much smaller, to let us live in peace, and observe the non-aggression principle. We also need much lower taxes.

F) Was a terrible idea from the start. The whole thing should be thrown into the wood chipper.

G) No idea.

4) Equality…

A)  Is the bedrock on which our nation was built. Social justice should be our major priority. All people are created equal. I particularly think we should be concerned about equal rights for minorities, women, the disabled, transgendered, and other oppressed groups. The world the way it is now is just plain unfair.

B) Should be maintained under the law, but we shouldn’t get too carried away. I believe in equality of opportunity, but not in social engineering everyone to be perfectly equal at all times. Redistributing too much wealth would just turn us into Communists, but I think that the rich can afford to fork over enough cash for some basic services like health care.

C)  What is this equality you speak of?

D) This poll seems so serious. I would rather make a funny joke to lighten the mood. Why are you so obsessed with all this stuff?

5) Drones…

A) Are pretty handy for blowing up terrorists. Terrorists go boom.

B) Demonstrate how our freedoms are being compromised by leaders with no checks on their power. You could be next.

C) Seem like a useful tool for surveillance and precision strikes, but we need to carefully introduce legislation to ensure that these technologies aren’t abused in the future.

D) I don’t care.

6) Firearms…

A) Are lawful and should not be restricted.

B) Are used to kill dozens of people in blood-soaked shooting sprees. We need to introduce new gun legislation that puts restrictions on high-capacity magazines and assault rifles. Assault rifles have no reasonable use in hunting. It doesn’t take an AK-47 to take down a deer.

C) Can be dangerous, but due to the 2nd Amendment, we shouldn’t regulate them too harshly. Instead of focusing on the guns themselves, we need to be a more responsible society and reach out to people who seem disturbed.

D) Don’t care.

7) Immigration…

A) Contributes valuable diversity and human capital to our country. Everyone has the right to be here. The right to choose where you live is a fundamental human right.

B) Is necessary because there are so many jobs Americans won’t do. Who is going to work in the fields, if we don’t have immigrants?

C) Is destroying our culture.

D) Is too contentious. I don’t have an opinion.

8) Marijuana…

A) Is good for getting you high. Rolling technique is important.

B) I’m alright with it, but don’t usually smoke.

C) Is a drug that turns people into lazy slobs.

D) Is a dangerous drug and needs to remain illegal.

E) I’ve never seen a marijuana before.

9) Gays…

A) Deserve the same rights as everyone else, including the right to marry and to adopt.

B) Should not be married. Marriage is between a man and a woman.

C) Why does the government have the authority to grant marriage licenses, again?

D) It makes no difference to me.

10) The death penalty…

A) Is barbaric and should be banned.

B) Costs too much to carry out, and should be suspended for that reason.

C) Is a necessary deterrent. It can be expensive, but it’s worth it.

D) We need to remove the barriers that make it so expensive for the state to execute the worst criminals. Let ‘em fry.

E) I’m not sure.

11) “Human biodiversity”…

A) Never heard of it.

B) Is a code-word for racism.

C) Is a fact. Different ethnicities have different relative qualities, and varying averages make an aggregate difference.

D) The idea makes me uncomfortable.

12) Evolutionary psychology…

A) Don’t know much about it.

B) Makes some good points, but there’s a lot more to people than evpsych just-so stories. Look deeper.

C) Determines the primary contours of our psychology, and the rest of just icing on the cake.

D) More research is needed before we can say anything conclusive.

13) Authority and hierarchy…

A) Is a way for a bunch of old white men to masturbate in the mirror.

B) Has its place, especially in the workplace, but it shouldn’t be omnipresent in society. Many bosses are assholes.

C) Is the foundation on which a healthy society should be built.

D) No opinion.

14) IQ…

A) Is a meaningless measure. It only measures how well you can do on IQ tests, and that’s it.

B) Probably matters somewhat, but it makes me uncomfortable to think about.

C) Exists, and makes a huge difference in all areas of life.

D) I don’t know.

15) Government…

A) Should stay out of society’s business. Freedom is the most important.

B) Should stay out of our private lives, but provide a social safety net.

C) Done well, can lead a country to greatness.

D) I’m not sure.

Doctors, like other experts, have limited domain knowledge. The average primary care visit is only 11 minutes, a figure which hasn’t changed since the 1930s, with four minutes of that being the patient talking. Doctors often lack the time to evaluate up-to-date research relevant to specific patients or diseases. In a widely cited and approved study, one researcher, John P. A. Ioannidis, even argued that up to 80% of medical research findings doctors rely on are flawed.

Many doctors and medical professionals lack a basic understanding of statistics. For instance, in one study, sixteen out of twenty HIV counselors said that there was no such thing as a false positive HIV test (Gigerenzer et al 1998). Another study found that British general practitioners rarely change their prescribing patterns, and when they do, it’s not in response to evidence (Armstrong et al 1996). Gigerenzer and others have shown that statistical illiteracy is ubiquitous among patients and doctors. Many confuse sensitivity and specificity, and most physicians do not understand how to compute the positive predictive value of a ...]]>

Doctors, like other experts, have limited domain knowledge. The average primary care visit is only 11 minutes, a figure which hasn’t changed since the 1930s, with four minutes of that being the patient talking. Doctors often lack the time to evaluate up-to-date research relevant to specific patients or diseases. In a widely cited and approved study, one researcher, John P. A. Ioannidis, even argued that up to 80% of medical research findings doctors rely on are flawed.

Many doctors and medical professionals lack a basic understanding of statistics. For instance, in one study, sixteen out of twenty HIV counselors said that there was no such thing as a false positive HIV test (Gigerenzer et al 1998). Another study found that British general practitioners rarely change their prescribing patterns, and when they do, it’s not in response to evidence (Armstrong et al 1996). Gigerenzer and others have shown that statistical illiteracy is ubiquitous among patients and doctors. Many confuse sensitivity and specificity, and most physicians do not understand how to compute the positive predictive value of a test. This can cause them to overestimate the probability of someone having a disease, say breast cancer, by an order of magnitude or more.

For someone with a persistent medical problem, going to their usual doctor may not be enough. They need to know more than their physician can tell them, and they need to know it soon. That is the idea behind the new company MetaMed, a personalized medical research service backed by Peter Thiel and Jaan Tallinn. In an interview with VentureBeat, CEO Michael Vassar aims to improve the U.S. medical system by demonstrating a “product that works better than the system.” By using doctors and researchers who understand statistics and how to evaluate the relative importance of research findings, MetaMed provides a diagnosis, treatment recommendations, and referrals to doctors in their network who can provide the treatments best for a specific patient. MetaMed provides “evidence based medicine” rather than vague suggestions solely based on domain-general knowledge.

MetaMed is not cheap — $200/hr — but for those who deeply need a solution and aren’t confident that their doctor is providing it, it may be the best option. For some problems, our health care system doesn’t cut it. 1 out of 3 patients are actually harmed during their hospital stay, and 7% are harmed permanently or even die as a result of medical errors. Autopsies have proven that doctors misdiagnose fatal illnesses as much as 20% of the time. Given all this uncertainty and error, it’s clear that there’s a market for personalized medical advice based on serious research and statistical understanding.

Holding back the implementation of ideal medical treatments is a lack of knowledge. More than half a million medical articles are published annually. It’s impossible to keep up to date on everything. One woman, Deepa Kulkarni, had the tip of her pinky amputated when a door closed on it. Doctors said there was nothing she could do. Instead of giving up, she persisted and was able to find a regenerative medicine therapy  that completely grew back her pinky, fingernail and all! This makes one wonder, how many other serious diseases are going uncured and untreated because doctors are not up on the latest research and therapies?

Here are some unfortunate statistics on error in medical care:

If you or someone you know has a medical problem you want to get figured out for good, and you can afford the price, you should consider contacting MetaMed. A lot of money is spent on health care in the USA, and much of it goes to waste. Though $5,000 might seem like a lot, there are many diseases where the opportunity cost of a misdiagnosis or poor treatment greatly exceeds that number. For these cases, making use of MetaMed may be a good choice.

Kelly begins his blog post by stating that “thinkism doesn’t work”, specifically meaning that he doesn’t believe that a smarter-than-human Artificial Intelligence could rapidly develop infrastructure to transform the world.  After using the Wikipedia definition of the Singularity, Kelly writes that Vernor Vinge, Ray Kurzweil and others view the Singularity as deriving from smarter-than-human Artificial Intelligences (superintelligences) developing the skills to make themselves smarter, doing so at a rapid rate. Then, “technical problems are quickly solved, so that society’s overall progress makes it impossible for us to imagine what lies beyond the Singularity’s birth”, Kelly says. Specifically, he alludes to ...]]> In late 2008, tech luminary Kevin Kelly, the founding executive editor of Wired magazine, published a critique of what he calls “thinkism” — the idea of smarter-than-human Artificial Intelligences with accelerated thinking and acting speeds developing science, technology, civilization, and physical constructs at faster-than-human rates. The argument over “thinkism” is important to answering the question of whether Artificial Intelligence could quickly transform the world once it passes a certain threshold of intelligence, called the “intelligence explosion” scenario.

Kelly begins his blog post by stating that “thinkism doesn’t work”, specifically meaning that he doesn’t believe that a smarter-than-human Artificial Intelligence could rapidly develop infrastructure to transform the world.  After using the Wikipedia definition of the Singularity, Kelly writes that Vernor Vinge, Ray Kurzweil and others view the Singularity as deriving from smarter-than-human Artificial Intelligences (superintelligences) developing the skills to make themselves smarter, doing so at a rapid rate. Then, “technical problems are quickly solved, so that society’s overall progress makes it impossible for us to imagine what lies beyond the Singularity’s birth”, Kelly says. Specifically, he alludes to superintelligence developing the science to cure the effects of human aging faster than they accumulate, thereby giving us indefinite lifespans. The notion of the Singularity is roughly that the creation of superintelligence could lead to indefinite lifespans and post-scarcity abundance within a matter of years or even months, due to the vastly accelerated science and robotics that superintelligence could develop. Obviously, if this scenario is plausible, then it might be worth devoting more resources to developing human-friendly Artificial Intelligence than we are currently. A number of eminent scientists are beginning to take the scenario seriously, while Kelly stands out as an interesting critic.

Kelly does not dismiss the Singularity concept out of hand, saying “I agree with parts of that. There appears to be nothing in the composition of the universe, or our minds, that would prevent us from making a machine as smart as us, and probably (but not as surely) smarter than us.” However, he then rejects the hypothesis, saying, “the major trouble with this scenario is a confusion between intelligence and work. The notion of an instant Singularity rests upon the misguided idea that intelligence alone can solve problems.” Kelly quotes the Singularity Institute article, “Why Work Towards the Singularity”, arguing it implies an “approach [where] one only has to think about problems smartly enough to solve them.” Kelly calls this “thinkism”.

Kelly brings up concrete examples, such as curing cancer and prolonging life, stating that these problems cannot be solved by “thinkism.” “No amount of thinkism will discover how the cell ages, or how telomeres fall off”, Kelly writes. “No intelligence, no matter how super duper, can figure out how human body works simply by reading all the known scientific literature in the world and then contemplating it.” He then highlights the necessity of experimentation in deriving new knowledge and working hypotheses, concluding that, “thinking about the potential data will not yield the correct data. Thinking is only part of science; maybe even a small part.”

Part of Kelly’s argument rests on the idea that there are fixed-rate external processes, such as the metabolism of a cell, which cannot be sped up to provide more experimental data than they would otherwise. He explains, that “there is no doubt that a super AI can accelerate the process of science, as even non-AI computation has already sped it up. But the slow metabolism of a cell (which is what we are trying to augment) cannot be sped up.” He also uses physics as an example, saying “If we want to know what happens to subatomic particles, we can’t just think about them. We have to build very large, very complex, very tricky physical structures to find out. Even if the smartest physicists were 1,000 smarter than they are now, without a Collider, they will know nothing new.” Kelly acknowledges the potential of computer simulations but argues they are still constrained by fixed-rate external processes, noting, “Sure, we can make a computer simulation of an atom or cell (and will someday). We can speed up this simulations many factors, but the testing, vetting and proving of those models also has to take place in calendar time to match the rate of their targets.”

Continuing his argument, Kelly writes: “To be useful artificial intelligences have to be embodied in the world, and that world will often set their pace of innovations. Thinkism is not enough. Without conducting experiements, building prototypes, having failures, and engaging in reality, an intelligence can have thoughts but not results. It cannot think its way to solving the world’s problems. There won’t be instant discoveries the minute, hour, day or year a smarter-than-human AI appears. The rate of discovery will hopefully be significantly accelerated. Even better, a super AI will ask questions no human would ask. But, to take one example, it will require many generations of experiments on living organisms, not even to mention humans, before such a difficult achievement as immortality is gained.”

Concluding, Kelly writes: “The Singularity is an illusion that will be constantly retreating — always “near” but never arriving. We’ll wonder why it never came after we got AI. Then one day in the future, we’ll realize it already happened. The super AI came, and all the things we thought it would bring instantly — personal nanotechnology, brain upgrades, immortality — did not come. Instead other benefits accrued, which we did not anticipate, and took long to appreciate. Since we did not see them coming, we look back and say, yes, that was the Singularity.”

This fascinating post of Kelly’s raises many issues, the two most prominent being:

1) Given sensory data X, how difficult is it for agent Y to come to conclusion Z?
2) Can experimentation be accelerated past the human-familiar rate or not?

These will be addressed below.

Can We Just Think Our Way Through Problems?

There are many interesting examples in human history of situations where people “should” have realized something but didn’t. For instance, the ancient Egyptians, Greeks, and Romans had all the necessary technology to manufacture hot air balloons, but apparently never thought of it. It wasn’t until 1783 that the first historic hot-air balloon flew. It is possible that ancient civilizations did build hot-air balloons and left no archeological evidence of their remains. One hot air balloonist thinks the Nazca lines were viewed by prehistoric balloonists. My guess would be that the ancients might have been clever enough to manufacture hot air balloons, but probably not. The point is that they could have built them, but didn’t.

Inoculation and vaccination are another relevant example. A text from 8th century India included a chapter on smallpox and mentioned methods of inoculating against the disease. Given that the value of inoculation was known in India c. 750 BC, it would seem that the modern age of vaccination should have arrived prior to 1796. Besides safe water, vaccines reduce mortality and increase population growth more than any other means. Aren’t 2,550 years enough time to go from the basic principle of inoculation to the notion of systematic vaccination? It could be argued that the discovery of cell theory (1665) was a limiting factor; if cell theory had been introduced to 8th century Indians, perhaps they would have been able to develop vaccines and save the world from hundreds of millions of unnecessary deaths.

Lenses, which are no more than precisely curved pieces of glass, are fundamental to scientific instruments: the microscope and the telescope and are at least 2,700 years old; the Nimrud lens, discovered at the Assyrian palace of Nimrud in modern-day Iraq, demonstrates their antiquity. The discoverer of the lens noted that he had seen very small inscriptions on Assyrian artifacts that made him suspect that a lens was used to create them. There are numerous references to and evidence of lenses in antiquity. The Visby lenses found in a 11th to 12th century Viking town are sophisticated aspheric lenses with angular resolution of 25–30 µm. Even after lenses became widespread in 1280, it took microscopes almost 500 years to develop to the point of being able to discover cells. Given that lenses are as old as they are, why did it take so incredibly long for our ancestors to develop them to the point of being able to build microscopes and telescopes?

A final example that I will discuss regards complex gear mechanisms and analog computers in general. The Antikythera mechanism, dated to 100 BC, consists of about 30 precisely interlocked bronze gears designed to display the locations in the sky of the Sun, Moon, and the five planets known at the time. Why wasn’t it until more than 1,400 years later that mechanisms of similar complexity were constructed? At the time, Greece was a developed civilization of about 4-5 million people. It could be that a civilization of sufficient size and stability to produce complex gear mechanisms did not come into existence until 1,400 years later. Perhaps a simple lack of ingenuity is to blame. The exact answer is unknown, but we do know that the mechanical basis for constructing bronze gears of similar quality existed for a long time, it just wasn’t put into use.

It apparently takes a long time for humans to figure some things out. There are numerous historic examples where all the pieces of a puzzle were on the table, there was just no one who put them together. The perspective of “thinkism” suggests that if the right genius were alive at the right time, he or she would have put the pieces together and given civilization a major push forward. I believe that this is borne out by contrasting the historical record with what we know today.

Value of Information

It takes a certain amount of information to come to certain conclusions. There is a minimum amount of information required to identify an object, plan a winning strategy in a game, model someone’s psychology, or design an artifact. The more intelligent or specialized the agent is, the less information it needs to reach the conclusion. Conclusions may be “good enough” rather than perfect, in other words, “ecologically rational”.

An example is how good humans are at recognizing faces. The experimental data shows that we are fantastic at this; in one study, half of respondents correctly identified this image as being a portrait of Napoleon Bonaparte, even though it is only a mere 6×7 pixels.

MIT computational neuroscientist Pawan Sinha found that given 12 by 14 pixels worth of visual information, his experimental subjects could recognize 75-percent of the face images in a set accurately, where the set had a mix of faces and other objects. Sinha also programmed a computer to identify face images, with a high success rate. A New York Times article quotes Dr. Sinha: “These turn out to be very simple relationships, things like the eyes are always darker than the forehead, and the mouth is darker than the cheeks,” Dr. Sinha said. “If you put together about 12 of these relationships, you get a template that you can use to locate a face.” There are already algorithms that can identify faces from databases which only include a single picture of an individual.

These results are relevant because they are examples where humans or software programs are able to make correct judgments with extremely small amounts of information, less than we would intuitively think is necessary. The picture of Napoleon above can be specified by about 168 bits. Who would imagine that hundreds of people in an experimental study could uniquely identify a historic individual based on a photo containing only 168 bits of information? It shows that humans have cognitive algorithms that are highly effective and specialized at identifying such information. Perhaps we could make huge scientific breakthroughs if we had different cognitive algorithms specialized at engaging unfamiliar, but highly relevant data sets.

The same could apply to observations and conclusions of all sorts. The amount of information needed to make breakthroughs in science could be less than we think. We do know that new ways of looking at the world can make a tremendous difference in uncovering true beliefs. A civilization without science might exist for a long time without accumulating significant amounts of objective knowledge about biology or physics. For instance, the Platonic theory of classical elements persisted for thousands of years.

Then, science came along. In the century following the development of the Scientific Method by Francis Bacon in 1620, there was rapid progress in science and technology, fueled by this new worldview. By 1780, the Industrial Revolution was in full swing. If the Scientific Method had been invented and applied in ancient Greece, progress that would have seemed mind-boggling and impossible at the time, like the Industrial Revolution, could have potentially been achieved within a century or two. The Scientific Method increased the marginal usefulness of each new piece of knowledge humanity acquired, giving it a more logical and epistemologically productive framework than was accessible in the pre-scientific haze.

Could there be other organizing principles of effective thinking analogous to the Scientific Method that we’re just missing today? It seems hard to rule it out, and quite plausible. The use of Bayesian principles in inference, which has led to breakthroughs in Artificial Intelligence, would be one candidate. Perhaps better thinkers could discover such principles more rapidly than we can, and make fundamental breakthroughs with less information than we would currently anticipate being necessary.

The Essence of Intelligence is Surprise

A key factor defining feats of intelligence or cleverness is surprise. Higher intelligence sees the solution no one else saw, looks past the surface of a problem to find the general principles and features that allow them to understand and resolve it. A classic, if cliché example is Albert Einstein deriving the principles of special relativity working as a patent clerk in Bern, Switzerland. His ideas were considered radically counterintuitive, but proved correct. The concept of the speed of light being constant for all observers regardless of their velocity had no precedent in Newtonian physics or common sense. It took a great mind to think about the universe in a completely new way.

Kelly rejects the notion of superintelligence leading to immortality when he says, “this super-super intelligence would be able to use advanced nanotechnology (which it had invented a few days before) to cure cancer, heart disease, and death itself in the few years before Ray had to die. If you can live long enough to see the Singularity, you’ll live forever [...] The major trouble with this scenario is a confusion between intelligence and work.” Kelly highlights “immortality” as being very difficult to achieve through intelligence and its fruits alone, but this understanding is relative. Medieval peasants would see rifles, freight trains, and atomic bombs as very difficult to achieve. Stone Age man would see bronze instruments as difficult to achieve, if he could imagine them at all. The impression of difficulty is relative to intelligence and the tools a civilization has. To very intelligent agents, a great deal of tasks might seem easy, including vast categories of tasks that less intelligent agents cannot even comprehend.

Would providing indefinite lifespans (biological immortality) to humans be extremely difficult, even for superintelligences? Instead of saying “yes” based on the evidence of our own imaginations, we must confess that we don’t know. This doesn’t mean that the probability is 50% — it means we really don’t know. We can come up with a tentative probability, say 10%, and iterate based on evidence that comes in. But to say that it will not happen with high confidence is impossible, because a lesser intelligence cannot place definite limits (outside of, perhaps, the laws of physics) on what a higher intelligence or more advanced civilization can achieve. To say that it will happen with high confidence is also impossible, because we lack the information.

The general point is that one of the hallmarks of great intelligence is surprise. The discovery of gunpowder must have been a surprise. The realization that the earth orbits the Sun and not vice versa was a surprise. The derivation of the laws of motion and their universal applicability was a surprise. The creation of the steam engine led to surprising results. The notion that we evolved from apes surprised and shocked many. The idea that life was not animated by a vital force but in fact operated according to the same rules of chemistry as everything else was certainly surprising. Mere human intelligence has surprised us time and time again with its results — we should not be surprised to be surprised again by higher forms of intelligence, if and when they are built.

Accelerating Experimentation

One of Kelly’s core arguments is that experimentation to derive new knowledge and the “testing, vetting and proving” of computer models will require “calendar time”. However, it is possible to imagine ways in which the process of experimentation and empirical verification could be accelerated to faster-than-human-calendar speeds.

To start, consider variance in the performance of human scientists. There are historic examples of times where scientific and technological progress was very rapid. The most recent and perhaps striking example was during World War II. Within six years, the following technologies were invented: radar, jet aircraft, ballistic missiles, nuclear power and weapons, and general-purpose computers. So, despite fixed-rate external processes limiting the rate of experimentation, innovation was temporarily accelerated anyway. Intuitively, the rate of innovation was arguably three to four times greater than in a similar period before the war. Though the exact factor is subjective, few historians would disagree that rapid scientific innovation occurred during WWII.

Why was this? Several factors may be identified: 1) increased military spending on research, 2) more scientists due to better training connected to the war effort, 3) researchers working harder and with more motivation than they otherwise would, 4) second-order effects resulting from larger groups of brilliant people interacting with one another in a supportive environment, as in the Manhattan Project.

An advanced Artificial Intelligence could employ all these strategies to accelerate its own speed of research and development. It could 1) amass a large amount of resources in the form of physical and social capital, and spend them on research, 2) copy itself thousands or millions of times using available computers to ensure there are many researchers, 3) possess perfect patience, perpetual alertness, and accelerated thinking speed to work harder than human researchers can, and 4) benefit from second-order effects by utilizing electronic communication between its constituent researcher-agents. To the extent that accelerated innovation is possible with these strategies, an Artificial Intelligence could exploit them to the fullest degree possible.

Of course, experimentation is certainly necessary to make scientific progress — many revolutions in science begin with peculiar phenomena that are artificially magnified with the aid of carefully designed experiments. For instance, the double-slit experiment in quantum mechanics emphasizes the wave-particle duality of light, a phenomenon not typically observed during everyday circumstances. Determining the details of how different chemicals intermingle to produce reaction products has required millions of experiments. Understanding biology has required many millions of experiments as well. Only strictly observational facts such as the cellular structure of life or the surface features of the Moon can be assessed through direct observation. To determine how metabolic processes actually work or what is underneath the surface of the moon requires experimentation, trial and error.

There are four concrete ways in which experimentation might be accelerated to speeds beyond the typical human level. These are conducting experiments faster, more efficiently, conducting them in parallel, and choosing the most useful experiments to begin with. Kelly argues that “the slow metabolism of a cell (which is what we are trying to augment) cannot be sped up”. But, this is not entirely clear. It should be possible to build chemical networks that simulate cellular processes operating more quickly than cellular metabolisms do. In addition, it is not clear that a comprehensive understanding of cells would be necessary to achieve biological immortality. Achieving indefinite biological lifespans could be more readily achieved by repairing cellular damage and chemical junk faster than it accumulates than constantly keeping all cells in a state of perpetual youth, which seems to be what Kelly is implying is necessary. In fact, it may be possible to develop therapies for repairing the damages of aging with our current biological knowledge. Since we aren’t superintelligences, it is impossible to tell. But Kelly makes an error when he assumes that keeping all cells in a state of perpetual youth, or total understanding, is required for indefinite lifespans. This shows how even small differences in knowledge between humans can make an all-important difference in research targets and agendas. The difference in knowledge between humans and superintelligences will make the difference larger still.

Considering these factors highlights the earlier point that the perceived difficulty of a given advance, like biological immortality, is strongly influenced by the framing of the necessary prerequisites to achieve that advance, and the intelligence doing the evaluation. Kelly’s framing of the problem is that massive amounts of biological experimentation would be necessary to derive the knowledge to repair the body faster than it breaks down. This may be the case, but it might not be. A higher intelligence might be able to achieve equivalent insights with ten experiments that a lesser intelligence would require a thousand experiments to uncover.

The rate of useful experimentation by superhuman intelligences will depend on factors such as 1) how much data is needed to make a given advance and 2) whether experiments be accelerated, simplified, or made massively parallel.

Research in biology, medicine, and chemistry have exploited highly parallel robotic systems for experiments. This field is called high-throughput screening (HTS). One paper describes a machine that simultaneously introduces 1,536 compounds to 1,536 assay plates, performing 1,536 chemical experiments at once in a completely automated fashion, determining 1,536 dose-response curves per cycle. Only 23 nanoliters of each compound is transferred. This highly miniaturized, highly parallel, high-density mode of experimentation has only begun to be exploited due to advances in robotics. If robotics could be manufactured on a massive scale more cheaply, one can imagine warehouses full of such machines conducting many hundreds of millions of experiments simultaneously.

Another method of accelerating experimentation would be to improve microscale manufacturing and to construct experiments using the minimum possible quantity of matter. For instance, instead of dropping weights off the Leaning Tower of Pisa, construct a microscale vacuum chamber and drop a cell-sized diamond grain in that chamber. Thousands of physics experiments could be conducted in the time it would require to conduct one experiment by the traditional method. With better sensors, you can conduct an experiment on ten cells that with inferior sensors would necessitate a million cells. More fine-grained control of matter can allow an agent to extract much more information from a smaller experiment that costs less and can be run faster and massively parallel. It is conceivable that an advanced Artificial Intelligence could come up with millions of hypotheses and test them all simultaneously in one small building.

Between-Species Comparisons

In his 1992 paper defining the Singularity, Vernor Vinge called the hypothetical post-Singularity world “a regime as radically different from our human past as we humans are from the lower animals”. Kelly, meanwhile, said that for artificial intelligences to amass scientific knowledge and make breakthroughs (like biological immortality) would require detailed models, and the “testing, vetting and proving of those models” requires “calendar time”. These models will “take years, or months, or at least days, to get results”. Since the comparison between different species is sometimes seen as a model for plausible differences between humans and superintelligences, let’s apply that model to the context of experiments that Kelly is referring to. Do humans create effects in the world faster than squirrels? Yes. Are humans qualitatively better at working towards biological immortality than squirrels? Yes. Do humans have a fundamentally superior understanding of the universe than squirrels do? It would be safe to say that we do.

The comparison with squirrels sounds absurd because concepts like biological immortality and “understanding the universe” are fuzzy at best from the perspective of a squirrel. Analogously, there may be stages in comprehension of reality that are fundamentally more advanced than our own and only accessible to higher intelligences. In this way, the “calendar time” of humans would have no more meaning to a superintelligence than “squirrel time” has relevance to human life. It’s not so much a factor of time — though higher intelligences can do much more in much less time — but also the general category of thoughts which can be processed, objectives which can be imagined, and plans which can be achieved. The objectives and methods of a higher intelligence would be on a completely different level than those of a lower intelligence and are different in kind, not degree.

There are several reasons why it makes sense to assume that qualitatively smarter-than-human intelligence, that is, qualitative differences on the order of difference between humans and squirrels or greater, should be possible. The first reason concerns the relative speed of human neurons relative to artificial computing machinery. Modern computers operate at billions of serial operations per second. Human neurons operate at only a couple hundred serial operations per second. Since most acts of cognition must occur within one second to be evolutionarily useful, and must include redundancy and fault tolerance, the brain is constrained to problem solutions involving 100 serial steps or less. What about the universe of possible solutions to cognitive tasks that require more than 100 serial steps? If the computer you are using had to implement every meaningful operation in 100 serial steps, the vast majority of common algorithms used today would have to be thrown out. In the space of possible algorithms, it quickly becomes obvious that constraining a computer to 100 serial steps is an onerous limitation. Expanding this space by a factor of ten million seems likely to lead to significant qualitative improvements in intelligence.

The reason that qualitatively smarter-than-human intelligence is possible is about neurological hardware and software. There are relatively few hardware differences between humans and chimpanzee brains. The evidence actually supports the notion that primate brains are more distinct from non-primates than humans are from other primates, and that the human brain is merely a primate brain scaled up for a larger body and with an enlarged prefrontal cortex. One quantitative study of human vs. chimpanzee brain cells came to this conclusion:

Despite our ongoing efforts to understand biology under the light of evolution, we have often resorted to considering the human brain as an outlier to justify our cognitive abilities, as if evolution applied to all species except humans. Remarkably, all the characteristics that appeared to single out the human brain as extraordinary, a point off the curve, can now, in retrospect, be understood as stemming from comparisons against body size with the underlying assumptions that all brains are uniformly scaled-up or scaled-down versions of each other and that brain size (and, hence, number of neurons) is tightly coupled to body size. Our recently acquired quantitative data on the cellular composition of the human brain and its comparison to other brains, both primate and nonprimate, strongly indicate that we need to rethink the place that the human brain holds in nature and evolution, and to rewrite some basic concepts that are taught in textbooks. The human brain has just the number of neurons and nonneuronal cells that would be expected for a primate brain of its size, with the same distribution of neurons between its cerebral cortex and cerebellum as in other species, despite the relative enlargement of the former; it costs as much energy as would be expected from its number of neurons; and it may have been a change from a raw diet to a cooked diet that afforded us its remarkable number of neurons, possibly responsible for its remarkable cognitive abilities.

In other words, it appears as if our exceptional cognitive abilities are the direct result of having more neurons rather than neurons in differing arrangements or relative quantities. If this continues to be confirmed in subsequent analyses, it implies, all else equal, that scaling up the number of neurons in the human brain could lead to similar intelligence differentials as those between humans and chimps. Given the evidence above, this should be our default assumption — we would need specific reasoning or evidence to assume otherwise.

A more detailed reason for why qualitatively smarter-than-human intelligence seems possible is that the higher intelligence of humans and primates appears to have something to do with self-awareness and complex self-referential loops in thinking and acting. The evolution of primate general intelligence appears correlated with the evolution of brain structures that control, manipulate, and channel the activity of other brain structures in a contingent way. For instance, a region called the pulvinar was called the brain’s “switchboard operator” in a recent study, though there are dozens of brain areas that could be given this description. Of 52 Brodmann areas in the cortex, at least seven are “hub areas” which lie near the top of a self-reflective control hierarchy: areas 8, 9, 10, 11, 12, 25, and 28. Given that these areas obviously play important roles in what we consider higher intelligence, yet only evolved relatively recently in evolutionary terms and are comparatively poorly developed, it is quite plausible to suggest that there is a lot of room for improvement in these areas and that qualitative intelligence improvements could result.

Imagine a brain that has “hub areas” which can completely reprogram other brain modules on a fine-grained level, the sort of reprogramming and flexibility only currently available in computers. Instead of only being able to reprogram a few percent of the information content of our brains, like we have now, a mind that can reprogram 100 percent of its own information content would allow limitless room for fast, flexible cognitive adaptation. Such a mind could quickly reprogram itself to suit the task at hand. Biological intelligences can only dream of this kind of adaptiveness and versatility. It would open up a vast new space not only for functional cognition but also appreciation of aesthetics and other higher-order mental traits.

Superior Hardware and Software

Say that we could throw open the hood of the brain and enhance it. How would that work?

To understand how “smarter than human intelligence” would work requires overviewing how the brain works. The brain is a very complicated machine. It operates entirely according to the laws of physics, and includes specific modules designed to handle different tasks. Look at our capabilities of identifying faces; it is clear that our brains have specific neural hardware adapted to rapidly identifying human faces. We don’t have the same hardware for rapidly identifying lizard faces — every lizard is just a lizard. To a lizard, different lizard faces might intuitively appear highly distinct, but to us humans, a species wherein there is no adaptive value in differentiating lizard faces, they all look the same.

The paper “Intelligence Explosion: Evidence and Import” by Luke Muehlhauser and Anna Salamon reviews some features of what Eliezer Yudkowsky calls the “AI Advantage” — inherent advantages that an Artificial Intelligence would have over human thinkers as a natural consequence of its digital properties. Because many of these properties are so key to understanding the “cognitive horsepower” behind claims of “thinkism”, I’ve chosen to excerpt the entire section on “AI Advantages” here, minus references (you can find those in the paper):

Below we list a few AI advantages that may allow AIs to become not only vastly more intelligent than any human, but also more intelligent than all of biological humanity. Many of these are unique to machine intelligence, and that is why we focus on intelligence explosion from AI rather than from biological cognitive enhancement.

Increased computational resources. The human brain uses 85–100 billion neurons. This limit is imposed by evolution-produced constraints on brain volume and metabolism. In contrast, a machine intelligence could use scalable computational resources (imagine a “brain” the size of a warehouse). While algorithms would need to be changed in order to be usefully scaled up, one can perhaps get a rough feel for the potential impact here by noting that humans have about 3.5 times the brain size of chimps, and that brain size and IQ correlate positively in humans, with a correlation coefficient of about 0.35. One study suggested a similar correlation between brain size and cognitive ability in rats and mice.

Communication speed. Axons carry spike signals at 75 meters per second or less. That speed is a fixed consequence of our physiology. In contrast, software minds could be ported to faster hardware, and could therefore process information more rapidly. (Of course, this also depends on the efficiency of the algorithms in use; faster hardware compensates for less efficient software.)

Increased serial depth. Due to neurons’ slow firing speed, the human brain relies on massive parallelization and is incapable of rapidly performing any computation that requires more than about 100 sequential operations. Perhaps there are cognitive tasks that could be performed more efficiently and precisely if the brain’s ability to support parallelizable pattern-matching algorithms were supplemented by support for longer sequential processes. In fact, there are many known algorithms for which the best parallel version uses far more computational resources than the best serial algorithm, due to the overhead of parallelization.

Duplicability. Our research colleague Steve Rayhawk likes to describe AI as “instant intelligence; just add hardware!” What Rayhawk means is that, while it will require extensive research to design the first AI, creating additional AIs is just a matter of copying software. The population of digital minds can thus expand to fill the available hardware base, perhaps rapidly surpassing the population of biological minds. Duplicability also allows the AI population to rapidly become dominated by newly built AIs, with new skills. Since an AI’s skills are stored digitally, its exact current state can be copied, including memories and acquired skills—similar to how a “system state” can be copied by hardware emulation programs or system backup programs. A human who undergoes education increases only his or her own performance, but an AI that becomes 10% better at earning money (per dollar of rentable hardware) than other AIs can be used to replace the others across the hardware base—making each copy 10% more efficient.

Editability. Digitality opens up more parameters for controlled variation than is possible with humans. We can put humans through job-training programs, but we can’t perform precise, replicable neurosurgeries on them. Digital workers would be more editable than human workers are. Consider first the possibilities from whole brain emulation. We know that transcranial magnetic stimulation (TMS) applied to one part of the prefrontal cortex can improve working memory. Since TMS works by temporarily decreasing or increasing the excitability of populations of neurons, it seems plausible that decreasing or increasing the “excitability” parameter of certain populations of (virtual) neurons in a digital mind would improve performance. We could also experimentally modify dozens of other whole brain emulation parameters, such as simulated glucose levels, undifferentiated (virtual) stem cells grafted onto particular brain modules such as the motor cortex, and rapid connections across different parts of the brain. Secondly, a modular, transparent AI could be even more directly editable than a whole brain emulation—possibly via its source code. (Of course, such possibilities raise ethical concerns.)

Goal coordination. Let us call a set of AI copies or near-copies a “copy clan.” Given shared goals, a copy clan would not face certain goal coordination problems that limit human effectiveness. A human cannot use a hundredfold salary increase to purchase a hundredfold increase in productive hours per day. But a copy clan, if its tasks are parallelizable, could do just that. Any gains made by such a copy clan, or by a human or human organization controlling that clan, could potentially be invested in further AI development, allowing initial advantages to compound.

Improved rationality. Some economists model humans as Homo economicus: self-interested rational agents who do what they believe will maximize the fulfillment of their goals. On the basis of behavioral studies, though, Schneider (2010) points out that we are more akin to Homer Simpson: we are irrational beings that lack consistent, stable goals. But imagine if you were an instance of Homo economicus. You could stay on a diet, spend the optimal amount of time learning which activities will achieve your goals, and then follow through on an optimal plan, no matter how tedious it was to execute. Machine intelligences of many types could be written to be vastly more rational than humans, and thereby accrue the benefits of rational thought and action. The rational agent model (using Bayesian probability theory and expected utility theory) is a mature paradigm in current AI design.

It seems likely to me that Kevin Kelly does not really understand the AI advantages of increased computational resources, communication speed, increased serial depth, duplicability, editability, goal coordination, and improved rationality, and how these abilities could be used to accelerate, miniaturize, parallelize, and prioritize experimentation to such a degree that the “calendar time” limitation could be surpassed. The calendar of a powerful AI superintelligence might be measured in microseconds rather than months. Different categories of beings have different calendars to which they are most accustomed. In the time it takes for a single human neuron to fire, a superintelligence might have decades of subjective time to contemplate the mysteries of the universe.

Nos es a Polim

Part of the initial insight that prompted the perspective that Kelly calls “thinkism” was that the brain is a machine which can be accelerated by porting the crucial algorithms on a different substrate, namely a computer, and running them faster. The brain works through algorithms — that is, systematic procedures. For an example, take the visual cortex, the part of the brain that processes what you see. This region of the brain is actually relatively well understood. The first layers capture surface features such as lines, darkness, and light. Deeper layers make out shapes, then motion, then specifics such as which face belongs to which person. It gets so specific that scientists have measured individual neurons that recognize celebrities like Bill Clinton or Marilyn Monroe.

The algorithms that underlie our processing of visual information are understood on a basic level, and it is only a matter of time until all the other cognitive algorithms are understood as well. When they are, they will be implemented on computers and sped up by a factor of thousands or millions. Human neurons fire 200 times every second, computer chips fire 2,000,000,000 times every second.

What would it be like to be a mind running at ten million times human speed? If your mind is really really fast, events on the outside would seem really really slow. All the elapsed time from the founding of Rome to the present day could be experienced subjectively in about two hours. All the time from the emergence of Homo sapiens to the present day could experienced in a week. All the time from the dinosaurs to the present day could be experienced in a mere 2,400 years. Imagine how quickly a mind could accrue profound wisdom running at such an accelerated speed; the “wisdom” of a 90-year old would seem childlike by comparison.

To visualize concretely the kind of arrangement in which these minds could exist, imagine a computer a couple hundred feet across made of dense nanomachinery situated at the bottom of the ocean. Such a computer would have far more computing power than the entire planet today, similar to the way that a modern smartphone has more computing power than the entire world in 1960. Within this computer would exist virtual worlds practically without end; their combined volume far exceeding that of the solar system, or perhaps even the galaxy.

In his post, Kelly seems to acknowledge that minds could be vastly accelerated and magnified in this way: remarkably, he just doesn’t think that this would translate to increased wisdom, performance, ability, or insight significantly beyond the human level. To me, at first impression, the notion that a ten million times speedup would have a negligible effect on scientific innovation or progress seems absurd. It appears obvious that it would have a world-transforming impact. Let’s look at the argument more closely.

The disagreement between Singularitarians such as Vinge and Kurzweil and skeptics such as Kelly seems to be about what sorts of information-acquisition and generation procedures can be imported into this vastly accelerated world and which cannot. In his hard sci-fi book Diaspora, author Greg Egan calls the worlds of these enormously accelerated minds “polises”, which make up the vast bulk of humanity in 2975. Vinge and Kurzweil see the process of knowledge acquisition and creation as being something that can in principle be sped up, brought “within the purview of the polis”, whereas Kelly does not.

Above, I argued how the benefits of experimentation can be accelerated through the processes of running experiments faster, parallelizing them, using less matter, and choosing the right experiments. But what about less controversial information flow from world to polis? To build the polis to begin with, you’d have to be able to emulate — not just simulate — the human mind in detail, that is, copy all of its relevant properties. Since the human brain is one of, if not the most complex object in the universe that we know of, this also implies that a vast variety of less complex objects could be scanned and inputted to the polis in a similar fashion. Trees, for instance, could be mass-inputted into the virtual environment of the polis, consuming thousands or millions of times less computing power than the sentient inhabitants. It goes without saying that nonbiological, inanimate background features such as landscapes could be input into the polis with a bare minimum of challenge or difficulty.

Once a process can be simulated with a reasonable level of computing power, it can be inputted into the polis and run at a factor of tens-of-millions speedup. Newtonian physics, for instance. Today, we use huge computers to perform molecular dynamics simulations on aggregates of a few hundred atoms, simulating a few microseconds of their activity. With futuristic nanocomputers built by superintelligent Artificial Intelligences, macro-scale systems could be simulated for hours of activity for a very affordable cost in computing power. Such simulations would allow these intelligences to extract predictive regularities, or “rules of thumb” which would allow them to avoid simulating these systems in such excruciating detail in the future. Instead of requiring full-resolution molecular dynamics simulations to extrapolate the behavior of large systems, they might resolve a set of several thousand generalities that allow these systems to be predicted and understood with a high degree of confidence. This has essentially been the process of science for hundreds of years, but the “simulations” are instead direct observations. With enough computing power, fast simulations can be “similar enough” to real-life situations that genuine wisdom and insight can be derived from them.

Though real, physical experimentation will be needed to verify the performance of models, those facets of the models that are verified will be quickly internalized by the polis, allowing it to simulate real-world phenomena at millions of times the real-world speed. Once a facet of a real-world system is internalized, understanding it instantly becomes a matter of routine, just as today the design of a massive bridge has become a matter of routine, a factor of running calculations based on the known laws of physics. Though from our current perspective, the complexities of the world of biology seem intimidating, the capability of superintelligences to quickly conduct millions of experiments in parallel and internalize knowledge once it is acquired will quickly dissolve these challenges as our recent ancestors dissolved the challenge of precision engineering.

Summary

I have only scratched the surface of the reasons why innovation and progress by superintelligences will predictably surpass the “calendar time” with which humanity has grown so accustomed. As humans routinely perform cognitive feats that bewilder the brightest squirrel or meadow vole, superintelligent scientists and engineers will leave human scientists and engineers in the dust, as if our all prior accomplishments were scarcely worth mentioning. It may be psychologically challenging to come to terms with such a possibility, but it would really just be the latest in an ongoing trend of human vanity being upset by the realities of a godless cosmos.

The Singularity is something that our generation needs to worry about — in fact, it may be the most important task we face. If we are going to create higher intelligence, we want it on our side. The benefits of success would be beyond our capacity to imagine, and will likely include the end of scarcity, war, disease, and suffering of all kinds, and the opening up of a whole new cognitive and experiential universe. The challenge is an intimidating one, but one that our best will rise to meet.

The confusion arose when Prof. Myers made incorrect assumptions about the 130-page roadmap from reading a 2-page blog post by Chris Hallquist. Hallquist wrote:

The version of the uploading idea: take a preserved dead brain, slice it into very thin slices, scan the slices, and build a computer simulation of the entire brain.

If this process manages to give you a sufficiently accurate simulation

Prof. Myers objected vociferously, writing, “It won’t. It can’t.”, subsequently launching into a reasonable attack against the notion of scanning a living human brain at nanoscale resolution with current fixation technology. The confusion is that Prof. Myers is criticizing a highly specific idea, the notion of exhaustively simulating every axon and dendrite in a live brain, as if that were the only proposal or even the central proposal forwarded by Sandberg and Bostrom. In fact, on page 13 of the report, the authors ...]]> Recently, there was some confusion by biologist P.Z. Myers regarding the Whole Brain Emulation Roadmap report of Anders Sandberg and Nick Bostrom at the Future of Humanity Institute.

The confusion arose when Prof. Myers made incorrect assumptions about the 130-page roadmap from reading a 2-page blog post by Chris Hallquist. Hallquist wrote:

The version of the uploading idea: take a preserved dead brain, slice it into very thin slices, scan the slices, and build a computer simulation of the entire brain.

If this process manages to give you a sufficiently accurate simulation

Prof. Myers objected vociferously, writing, “It won’t. It can’t.”, subsequently launching into a reasonable attack against the notion of scanning a living human brain at nanoscale resolution with current fixation technology. The confusion is that Prof. Myers is criticizing a highly specific idea, the notion of exhaustively simulating every axon and dendrite in a live brain, as if that were the only proposal or even the central proposal forwarded by Sandberg and Bostrom. In fact, on page 13 of the report, the authors present a table that includes 11 progressively more detailed “levels of emulation”, ranging from simulating the brain using high-level representational “computational modules” to simulating the quantum behavior of individual molecules. In his post, Myers writes as if the 5th level of detail, simulating all axons and dendrites, is the only path to whole brain emulation (WBE) proposed in the report (it isn’t), and also as if the authors are proposing that WBE of the human brain is possible with present-day fixation techniques (they aren’t).

In fact, the report presents Whole Brain Emulation as a technological goal with a wide range of possible routes to its achievement. The narrow method that Myers criticizes is only one approach among many, and not one that I would think is particularly likely to work. In the comments section, Myers concurs that another approach to WBE could work perfectly well:

This whole slice-and-scan proposal is all about recreating the physical components of the brain in a virtual space, without bothering to understand how those components work. We’re telling you that approach requires an awfully fine-grained simulation.

An alternative would be to, for instance, break down the brain into components, figure out what the inputs and outputs to, say, the nucleus accumbens are, and then model how that tissue processes it all (that approach is being taken with models of portions of the hippocampus). That approach doesn’t require a detailed knowledge of what every molecule in the tissue is doing.

But the method described here is a brute force dismantling and reconstruction of every cell in the brain. That requires details of every molecule.

But, the report does not mandate that a “brute force dismantling and reconstruction of every cell in the brain” is the only way forward for uploading. This makes it look as if Myers did not read the report, even though he claims, “I read the paper”.

Slicing and scanning a brain will be necessary but by no means sufficient to create a high-detail Whole Brain Emulation. Surely, it is difficult to imagine how the salient features of a brain could be captured without scanning it in some way.

What Myers seems to be objecting to is a kind of dogmatic reductionism, “brain in, emulation out” direct scanning approach that is not actually being advocated by the authors of the report. The report is non-dogmatic, writing that a two-phase approach to WBE is required, where “The first phase consists of developing the basic capabilities and settling key research questions that determine the feasibility, required level of detail and optimal techniques. This phase mainly involves partial scans, simulations and integration of the research modalities.” In this first phase, there is ample room for figuring out what the tissue actually does. Then, that data can be used simplify the scanning and representation process. The required level of understanding vs. blind scan-and-simulate is up for debate, but few would claim that our current neuroscientific level of understanding suffices.

Describing the difficulties of comprehensive scanning, Myers writes:

And that’s another thing: what the heck is going to be recorded? You need to measure the epigenetic state of every nucleus, the distribution of highly specific, low copy number molecules in every dendritic spine, the state of molecules in flux along transport pathways, and the precise concentration of all ions in every single compartment. Does anyone have a fixation method that preserves the chemical state of the tissue?

Measuring the epigenetic state of every nucleus is not likely to be required to create convincing, useful, and self-aware Whole Brain Emulations. No neuroscientist familiar with the idea has ever claimed this. The report does not claim this, either. Myers seems to be inferring this claim himself through his interpretation of Hallquist’s brusque 2-sentence summary of the 130-page report. Hallquist’s sentences need not be interpreted this way — “slicing and scanning” the brain could be done simply to map neural network patterns rather than to capture the epigenetic state of every nucleus.

Next, Myers objects to the idea that brain emulations could operate at faster-than-human speeds. He responds to a passage in “Intelligence Explosion: Evidence and Import”, another paper cited in the Hallquist post which claims, “Axons carry spike signals at 75 meters per second or less (Kandel et al. 2000). That speed is a fixed consequence of our physiology. In contrast, software minds could be ported to faster hardware, and could therefore process information more rapidly.” To this, Myers says:

You’re just going to increase the speed of the computations — how are you going to do that without disrupting the interactions between all of the subunits? You’ve assumed you’ve got this gigantic database of every cell and synapse in the brain, and you’re going to just tweak the clock speed… how? You’ve got varying length constants in different axons, different kinds of processing, different kinds of synaptic outputs and receptor responses, and you’re just going to wave your hand and say, “Make them go faster!” Jebus. As if timing and hysteresis and fatigue and timing-based potentiation don’t play any role in brain function; as if sensory processing wasn’t dependent on timing. We’ve got cells that respond to phase differences in the activity of inputs, and oh, yeah, we just have a dial that we’ll turn up to 11 to make it go faster.

At first read, it almost seems in this objection as if Prof. Myers does not understand the concept that software can be run faster if it is running on a faster computer. After reading this post carefully, it doesn’t seem as if this is what he actually means, but since the connotation is there, the point is worth addressing directly.

Software is a series of electric signals passing through logic gates on computers. The software is agnostic to the processing speed of the underlying computer. The software is a pattern of electrons. The pattern is there whether the clock speed of the processor is 2 kHz or 2 GHz. When and if software is ported from a 2 kHz computer to a 2 GHz computer, it does not stand up and object to this “tweaking the clock speed”. No “waving of hands” is required. The software may very well be unable to detect that the substrate has changed. Even if it can detect the change, it will have no impact on its functioning unless the programmers especially write code that makes it react.

Speed change in software is allowed. If the hardware can support the speed change, pressing a button is all it takes to speed the software up. This is a simple point.

The crux of Myers’ objection seems to actually be about the interaction of the simulation with the environment. This objection makes much more sense. In the comments, Carl Shulman responds to Myers’ objection:

This seems to assume, contrary to the authors, running a brain model at increased speeds while connected to real-time inputs. For a brain model connected to inputs from a virtual environment, the model and the environment can be sped up by the same factor: running the exact same programs (brain model and environment) on a faster (serial speed) computer gets the same results faster. While real-time interaction with the outside would not be practicable at such speedup, the accelerated models could still exchange text, audio, and video files (and view them at high speed-up) with slower minds.

Here, there seems to be a simple misunderstanding on Myers’ part, where he is assuming that Whole Brain Emulations would have to be directly connected to real-world environments rather than virtual environments. The report (and years of informal discussion on WBE among scientists) more or less assumes that interaction with the virtual environment would be the primary stage in which the WBE would operate, with sensory information from an (optional) real-world body layered onto the VR environment as an addendum. As the report describes, “The environment simulator maintains a model of the surrounding environment, responding to actions from the body model and sending back simulated sensory information. This is also the most convenient point of interaction with the outside world. External information can be projected into the environment model, virtual objects with real world affordances can be used to trigger suitable interaction etc.”

It is unlikely that an arbitrary WBE would be running at a speed that lines it up precisely with the 200 Hz firing rate of human neurons, the rate at which we think. More realistically, the emulation is likely to be much slower or much faster than the characteristic human rate, which exists as a tiny sliver in a wide expanse of possible mind-speeds. It would be far more reasonable — and just easier — to run the WBE in a virtual environment with a speed suited to its thinking speed. Otherwise, the WBE would perceive the world around it running at either a glacial pace or a hyper-accelerated one, and have a difficult time making much sense of either.

Since the speed of the environment can be smoothly scaled with the speed of the WBE, the problems that Myers cites with respect to “turn[ing] it up to 11″ can be duly avoided. If the mind is turned up to 11, which is perfectly possible given adequate computational resources, then the virtual environment can be turned up to 11 as well. After all, the computational resources required to simulate a detailed virtual environment would pale in comparison to those required to simulate the mind itself. Thus, the mind can be turned up to 11, 12, 13, 14, or far beyond with the push of a button, to whatever level the computing hardware can support. Given the historic progress of computing hardware, this may well eventually be thousands or even millions of times the human rate of thinking. Considering minds that think and innovate a million times faster than us might be somewhat intimidating, but there it is, a direct result of the many intriguing and counterintuitive consequences of physicalism.