Wiener on robots

An essay by Norbert Wiener written in 1949 intended for the New York Times was recently uncovered.  He pretty much had it right 64 years ago. Below is the rather serious last section. Earlier in the piece, we find out that programming was  called “taping” at that time.

New York Times:

The Genie and the Bottle

These new machines have a great capacity for upsetting the present basis of industry, and of reducing the economic value of the routine factory employee to a point at which he is not worth hiring at any price. If we combine our machine-potentials of a factory with the valuation of human beings on which our present factory system is based, we are in for an industrial revolution of unmitigated cruelty.

We must be willing to deal in facts rather than in fashionable ideologies if we wish to get through this period unharmed. Not even the brightest picture of an age in which man is the master, and in which we all have an excess of mechanical services will make up for the pains of transition, if we are not both humane and intelligent.

Finally the machines will do what we ask them to do and not what we ought to ask them to do. In the discussion of the relation between man and powerful agencies controlled by man, the gnomic wisdom of the folk tales has a value far beyond the books of our sociologists.

There is general agreement among the sages of the peoples of the past ages, that if we are granted power commensurate with our will, we are more likely to use it wrongly than to use it rightly, more likely to use it stupidly than to use it intelligently. [W. W. Jacobs’s] terrible story of the “Monkey’s Paw” is a modern example of this — the father wishes for money and gets it as a compensation for the death of his son in a factory accident, then wishes for the return of his son. The son comes back as a ghost, and the father wishes him gone. This is the outcome of his three wishes.

Moreover, if we move in the direction of making machines which learn and whose behavior is modified by experience, we must face the fact that every degree of independence we give the machine is a degree of possible defiance of our wishes. The genie in the bottle will not willingly go back in the bottle, nor have we any reason to expect them to be well disposed to us.

In short, it is only a humanity which is capable of awe, which will also be capable of controlling the new potentials which we are opening for ourselves. We can be humble and live a good life with the aid of the machines, or we can be arrogant and die.

Discounting the obvious

The main events in the history of science have involved new ideas overthrowing conventional wisdom. The notion that the earth was the center of the universe was upended by Copernicus. Species were thought to be permanent and fixed until Darwin. Physics was thought to be completely understood at the end of the nineteenth century and then came relativity theory and quantum mechanics to mess everything up. Godel overthrew the notion that mathematics was infallible. This story has been repeated so many times that people now seem to instinctively look for the counterintuitive answer to every problem. There are countless books on thinking outside of the box.  However, I think that the supplanting of “linear” thinking with “nonlinear” thinking is not always a good idea and sometimes it can have dire consequences.

A salient example is the current idea that fiscal austerity will lead to greater economic growth. GDP is defined as the sum of  consumption, investment, government spending and exports minus imports. If consumption or investment were to decline in an economic contraction, as in the Great Recession, then the simple linear idea would be that GDP and growth can be bolstered by increased government spending. This was the standard government response immediately after the financial crisis of 2008. However, starting in about 2010 when the recovery wasn’t deemed fast enough instead of considering the simple idea that the stimulus wasn’t big enough, the idea that policy makers, especially in Europe, adopted was that government spending was crowding out private spending so that a decrease in government spending would lead to a net increase in GDP and growth. This is very nonlinear thinking because it requires a decrease in GDP to induce an increase in GDP. Thus far this idea is not working and austerity has led to lower GDP growth in all countries that have tried it.  This idea was reinforced by a famous, now infamous, paper by Reinhart and Rogoff, which claimed that when government debt reaches 90% of GDP, growth is severely curtailed. This result has been taken as undisputed truth by governments and the press even though there were many economists who questioned it.  However, it turns out that the paper has major errors (including an Excel coding error). See here for a summary.  This is case where the nonlinear idea (as well as conflating correlation with causation) is probably wrong and has inflicted immense hardship on a large number of people.

 

Hepatitis C and the folly of prizes

The scientific world was set slightly aflutter when Michael Houghton turned down the prestigious Gairdner Award for the the discovery of Hepatitis C. Harvey Alter and Daniel Bradley were the two other recipients. Houghton, who had previously received the Lasker Award with Alter, felt he could not accept one more award because two colleagues Qui-Lim Choo and George Kuo did not receive either of these awards, even though their contributions were equally important.

Hepatitis, which literally means inflammation of the liver, was characterized by Hippocrates and known to be infectious since the 8th century. The disease had been postulated to be viral at the beginning of the 20th century and by the 1960′s two viruses termed Hepatitis A and Hepatitis B had been established. However, there still seemed to be another unidentified infectious agent which was termed Non-A Non-B Hepatitis NANBH.

Michael Hougton, George Kuo and Qui-Lim Choo were all working at the Chiron corporation in the early 1980′s.   Houghton started a project to discover the cause of NANBH in 1982 with Choo joining a short time later. They made significant process in generating mouse monoclonal antibodies with some specificity to NANBH infected materials from chimpanzee samples received from Daniel Bradley at the CDC. They used the antibodies to screen cDNA libraries from infected materials but they had not isolated an agent. George Kuo had his own lab at Chiron working on other projects but would interact with Houghton and Choo. Kuo suggested that they try blind cDNA immunoscreening on serum derived from actual NANBH patients. This approach was felt to be too risky but Kuo made a quantitative assessment that showed it was viable. After two years of intensive and heroic screening by the three of them, they identified one clone that was clearly derived from the NANBH genome and not from human or chimp DNA. This was definitive proof that NANBH was a virus, which is now called Hepatitis C. Kuo then developed a prototype of a clinical Hepatitis C antibody detection kit and used it to screen a panel of NANBH blood provided by Harvey Alter of the NIH. Kuo’s test was a resounding success and the blood test that came out of that work has probably saved 300 million or more people from Hepititis C infection.

The question then is who deserves the prizes. Is it Bradley and Alter, who did careful and diligent work obtaining samples or is it Houghton, Choo, and Kuo, who did the heroic experiments that isolated the virus? For completely unknown reasons, the Lasker was awarded to just Houghton and Alter, which primed the pump for more prizes to these two. Now that the Lasker and Gairdner prizes have been cleared, that leaves just the Nobel Prize. The scientific community could get it right this time and award it to Kuo, Choo, and Houghton.

 

Addendum added 2013-5-2:  I should add that many labs from around the world were also trying to isolate the infective agent of NANBH and all failed to identify the correct samples from Alter’s panel.  It is not clear how long it would have been and how many more people would have been infected if Kuo, Choo, and Houghton had not succeeded when they did.

Is irrationality necessary?

Much has been made lately of the anti-science stance of a large segment of the US population. (See for example Chris Mooney’s book). The acceptance of anthropomorphic climate change or the theory of evolution is starkly divided by political inclinations. However, as I have argued in the past, seemingly irrational behavior can actually make sense from an evolutionary perspective. As I have posted on before, one of the best ways to find an optimal solution to a problem is to search randomly, the Markov Chain Monte Carlo method being the quintessential example. Randomness is useful for searching in places you wouldn’t normally go and in overcoming unwanted correlations, which I recently attributed to most of our current problems (see here). Thus, we may have been evolutionarily selected to have diverse viewpoints and degrees of rational thinking. Given some situation, there is only one rationally optimal response and in the case of incomplete information, which is almost always true, it could be wrong. Thus, when a group of individuals is presented with a challenge, it may be more optimal for the group if multiple strategies, including irrational ones, are tried rather than putting all the eggs into one rational basket. I truly doubt that Australia could have been discovered 60 thousand years ago without some irrationally risky decisions. Even within science, people pursue ideas based on tenuous hunches all the time. Many great discoveries were made because people ignored conventional rational wisdom and did something irrational. Many have failed as a result as well. However, society as a whole is arguably better since generally success goes global while failure stays local.

It is not even necessary to have great differences in cognitive abilities to produce a wide range in rationality. One only needs to have a reward system that is stimulated by a wide range of signals.  So while some children are strongly rewarded by finding self-consistent explanations to questions others are rewarded by acting rashly. Small initial differences would then amplify over time as the children seek environments that maximize their rewards. Sam Wang and Sandra Aamodt covered this in their book, Welcome to Your Brain. Thus you would end up with a society with a wide variety of rationality.

 

 

Von Neumann’s response

Here’s Von Neumann’s response to straying from pure mathematics:

“[M]athematical ideas originate in empirics, although the genealogy is sometimes long and obscure. But, once they are so conceived, the subject begins to live a peculiar life of its own and is better compared to a creative one, governed by almost entirely aesthetic considerations, than to anything else, and, in particular, to an empirical science. There is, however, a further point which, I believe, needs stressing. As a mathematical discipline travels far from its empirical source, or still more, if it is a second and third generation only indirectly inspired by ideas coming from ‘reality’, it is beset with very grave dangers. It becomes more and more purely aestheticising, more and more purely l’art pour l’art. This need not be bad, if the field is surrounded by correlated subjects, which still have closer empirical connections, or if the discipline is under the influence of men with an exceptionally well-developed taste. But there is a grave danger that the subject will develop along the line of least resistance, that the stream, so far from its source, will separate into a multitude of insignificant branches, and that the discipline will become a disorganised mass of details and complexities. In other words, at a great distance from its empirical source, or after much ‘abstract’ inbreeding, a mathematical subject is in danger of degeneration.”

Thanks to James Lee for pointing this out.

Von Neumann

Steve Hsu has a link to a fascinating documentary on John Von Neumann. It’s definitely worth watching.  Von Neumann is probably the last great polymath. Mathematician Paul Halmos laments that Von Neumann perhaps wasted his mathematical gifts by spreading himself too thin. He worries that Von Neumann will only be considered a minor figure in pure mathematics several hundred years hence. Edward Teller believes that Von Neumann simply enjoyed thinking above all else.

Open Science Framework

In an effort to make published science to be less wrong, psychologist Brian Nosek and collaborators have started what is called the Open Science Framework.  The idea is that all results from experiments can be openly documented for everyone to see.  This way, negative results that are locked away in the proverbial “file drawer”,  will be available.  In light of the fact that many high impact results turn out to be wrong (e.g see here and here), we definitely needed to do something and I think this is a good start.  You can hear Nosek talk about this on Econtalk here.

The rise and fall of Jonah Lehrer

Jonah Lehrer , staff writer for the New Yorker and a best selling science author, resigned in disgrace today.  He admitted to fabricating quotes from Bob Dylan in his most recent book:

New York Times: An article in Tablet magazine revealed that in his best-selling book, “Imagine: How Creativity Works,” Mr. Lehrer had fabricated quotes from Bob Dylan, one of the most closely studied musicians alive. Only last month, Mr. Lehrer had publicly apologized for taking some of his previous work from The Wall Street Journal, Wired and other publications and recycling it in blog posts for The New Yorker, acts of recycling that his editor called “a mistake.”

…Mr. Lehrer might have kept his job at The New Yorker if not for the Tablet article, by Michael C. Moynihan, a journalist who is something of an authority on Mr. Dylan.

Reading “Imagine,” Mr. Moynihan was stopped by a quote cited by Mr. Lehrer in the first chapter. “It’s a hard thing to describe,” Mr. Dylan said. “It’s just this sense that you got something to say.”

Lehrer was a regular on Radiolab and he seemed to always really know his science. I have linked to his articles in the past (see here). His publisher is withdrawing his book and giving refunds to anyone returning it. I haven’t read the book, but from the excerpts and his interviews on it, I think the science is probably accurate. I don’t really know what he was thinking but my guess was that he was just trying to spice up the book and imagined a quote that Dylan might say. The fabricated quote above is pretty innocuous. He probably didn’t think anyone would notice. Maybe he felt pressure to write a best seller. Maybe he was overconfident. In any case, he definitely shouldn’t have done it. It is unfortunate because he was a gifted writer and boon to neuroscience and science in general.

Nobel dilemma

Now that the Higgs boson has been discovered, the question is who gets the Nobel Prize.  This will be tricky because  the discovery was made by two detector teams with hundreds of scientists using the CERN LHC accelerator involving hundreds more and although Higgs gets the eponymous credit for the prediction, there were actually three papers published almost simultaneously on the topic, with five of the authors still alive.  In fact, we only call it the Higgs boson because of a citation error by Nobel Laureate Steven Weinberg (see here).  One could even argue further that the Higgs boson is really just a variant of the Goldstone boson, discovered by Yoichiro Nambu (Nobel Laureate) and Jeffrey Goldstone (not a Laureate). This is a perfect example of why as I argued before (see here) that discoveries are rarely made by three or fewer people.  Whatever they decide, there will be plenty of disappointed people.

The false dichotomy of carbs and obesity

The law of the excluded middle is one of the foundations of logic. It says that if a proposition is false then the opposite must be true. There is no room for a middle ground in classical logic. However, one must be extremely careful when applying the law to  biology where hypotheses are generally situational and rest on many assumptions. In order to apply the law of the excluded middle, one must have only two alternatives and this is seldom true in biology and in particular human metabolism. Gary Taubes argued quite successfully in his book Good Calories, Bad Calories that fat probably doesn’t cause heart disease and in some cases may even be beneficial. A major theme of that book was that scientists can become irrationally attached to hypotheses and willfully ignore any evidence to the contrary. He recently penned a New York Times opinion piece arguing that the medical establishment is equally misguided in asserting that salt is unhealthy. One of the hypotheses that Taubes dislikes the most is that “a calorie is a calorie”, which proposes what you eat is not as important as how much you eat when it comes to weight gain and obesity. Taubes thinks that carbs and especially sugar is what makes you fat (and causes heart disease). This is summarized in his Times opinion piece  today, which covers the recent JAMA result that I posted about recently (see here).

It may very well be true that a calorie is not a calorie but that still may not mean carbs are the cause of the US obesity epidemic. I’ve posted on this a few times before (e.g. see here and here) but I thought it was important enough to reiterate and simplify the points here. In short, the carbs are bad argument is that 1) carbs induce insulin and insulin sequesters fat, and 2) carbs are metabolically more efficient so you burn fewer calories when you eat them compared to fat and protein. Even if this is true (and it may not all be) that still doesn’t mean that calories are unimportant. I don’t care how metabolically efficient carbs may be, you would starve to death if you only ate one sugar cube each day. Conversely, no matter how many excess calories you may burn eating fat, you will become obese if you eat two pounds of butter each day. Hence, even if a calorie is not a calorie, calories still matter. It is then a matter of degree. If you manage to burn everything you eat then your body won’t change. This is true if you eat a high carb or a low carb diet. Now it could be true that you could have a different amount of body fat and weight for the same calorie diet depending on diet composition. So a plausible hypothesis for the cause of the obesity epidemic is that we switched from a high fat diet to a low fat diet and everyone became fatter as a result. This is something that I’m planning to test using the same data that we used to show how the increase in food production is sufficient to explain the obesity epidemic. Ultimately though, the brain is what decides how much we eat and one of the biggest things we don’t understand is how diet composition affects food intake. It could be that low carb diets do make you thinner but the reason is that we tend to eat less when we’re on them.

2012-7-2: changed fat to carb in last  sentence.

Understandability

In my  post on panpsychism, a commenter, Matt Sigl, made a valiant defense of the ideas of Koch and Tononi about consciousness. I claimed in my post that panpsychism, where some or all the constituents of a system possess some elementary form of consciousness, is no different from dualism, which says that mind and body are separate entities. Our discussion, which can be found in the comment thread, made me think more about what it means for a theory to be monistic and understandable.  I have now revised my claim to be that panpsychism is either dualist or superfluous. Tononi’s idea of integrated information may be completely correct but panpsychism would not add anything more to it. In my view, a  monistic theory is one where all the properties of a system can be explained by the fundamental governing rules. Most importantly there can only be a finite set of rules. A system with an infinite set of rules is not understandable since every situation has its own consequence. There would be no predictability; there would  be no science. There would only be history where we could write down each rule whenever we observed it.

Consider a system of identical particles that can move around in a three dimensional space and interact with each other in a pairwise fashion. Let the motion of these particles obey Newton’s laws, where their acceleration is determined by a force that is given by an interaction rule or potential. The proportionality constant between acceleration and force is the mass, which is assigned to each particle. The particles are then given an initial position and velocity. All of these rules can be specified in absolute precise terms mathematically. Space can be discrete so the particles can only occupy a finite or countably infinite number of points or continuous where the particles can occupy an uncountable infinite number of points.

Depending on how I define the interactions, select the masses, and specify the initial conditions, various things could happen.  For example,  I could have an attractive interaction, start all the particles with no velocity at the same point, and they would stay clumped together. This clumped state is a fixed point of the system. If I can move one of the particles slightly away from the point and it falls back to the clump then the fixed point is stable.  However, even a stable fixed point doesn’t mean all initial conditions will end up clumped. For example, if I have a square law attraction like gravity, then particles can orbit one another or scatter off of each other. For many initial conditions, the particles could just bounce around indefinitely and never settle into a fixed point. For more than two particles, the fate of all initial conditions is generally  impossible to predict. However, I claim that the configuration of the system at any given time is explainable or understandable because I could in principle simulate the system from a given specific initial condition and determine its trajectory for any amount of time. For a continuous system, where positions require an infinite amount of information to specify, an understandable system would be one where one could prove that there is always an initial condition that can be specified with a finite amount of information that remains close to any arbitrary initial condition.

If I make the dynamics sufficiently complex then there could be some form of basic chemistry and even biology. This need not be fully quantum mechanical;  Bohr-like atoms may be enough. If the system can form sufficiently complex molecules then evolution could take over and generate multi-cellular life forms. At some point, animals with brains could arise.  These animals could possess memory and enough computational capability to strategize and plan for the future.  There could be an entire ecosystem of plants and animals at multiple scales interacting in highly complex ways. All of this could be understandable in the sense that all of the observed dynamics could be simulated on a big enough computer if you knew the rules and the initial conditions. You may even be lucky enough that almost all initial conditions will lead to complex life.

At this point, all the properties of the system can be completely specified by an outside observer. Understandable means that all of these properties can be shown to arise from a finite set of rules and initial conditions. Now, suppose that some of the animals are also conscious in the sense that they have a subjective experience. The  panpsychic hypothesis is that consciousness is a property of some or all the particles. However, proponents must then explain why even the biggest rock does not seem conscious or human consciousness disappears when we are in deep sleep. Tononi and Koch try to finesse this problem by saying that it is only if one has enough integrated information does one notice the effect of the accumulated consciousness. However, bringing in this secondary criterion obviates the panpsychic hypothesis because there is now a systematic way to identify consciousness that is completely consistent with an emergent theory of consciousness. This doesn’t dispel the mystery of  ”the hard problem” of consciousness of what exactly happens when the threshold is crossed to give subjective experience. However, the resolution is either that consciousness can be described by the finite set of rules of the constituent particles or there is a dualistic explanation where the brain “taps” into some other system that generates consciousness.  Panpsychism does not help in resolving this dilemma. Finally, it might be that the question of whether or not a system has sufficient integrated information to exhibit noticeable consciousness may be undecidable in which case there would be  no algorithm to test for consciousness. The best that one could do is to point to specific cases. If this were true then panpsychism does not solve any problem at all. We would never have a theory of consciousness. We would only have examples.

Transit of Venus

Don’t forget to catch the Transit of Venus tomorrow (June 5) if you can.  The next one won’t be until 2117.  NASA will be broadcasting it live from Mauna Kea, Hawaii here.  It will start around 6PM US east coast time and end about 7 hours later so only those in the Pacific will catch all of it.  Check your local science museum, planetarium or university astronomy department for information on where telescopes will be available to see it.   Venus will be a tiny dot moving slowly across the face of the sun.

Friends on Quirks and Quarks

Two of my old colleagues were interviewed on the CBC radio science show Quirks and Quarks recently.  This is the show I used to listen to in my youth in Canada. In March, astrophysicist Arif Babul, a classmate at the University of Toronto, talked about recent work he had done on abnormal clumping of dark matter in a collision site between clusters of galaxies. Here is the link.  Neuroscientist  Sebastian Seung, whom I’ve known since graduate school, talked about his recent book Connectome. Link here.  I was impressed by how well both were able to explain their work in clear and simple terms.  Their use of metaphors was particularly good.   I think these are two very good examples of how to talk about science to the general public.

Proof by simulation

The process of science and mathematics involves developing ideas and then proving them true.   However, what is meant by a proof depends on what one is doing.  In science, a proof is empirical.  One starts with a hypothesis and then tests it experimentally or observationally.  In pure math, a proof means that a given statement is consistent with a set of rules and axioms.  There is a huge difference between these two approaches.  Mathematics is completely internal.  It simply strives for self-consistency.  Science is external.  It tries to impose some structure on an outside world.  This is why mathematicians sometimes can’t relate to scientists and especially physicists and vice versa.

Theoretical physicists don’t need to always follow rules.  What they can do is to make things up as they go along.  To make a music analogy – physics is like jazz.  There is a set of guidelines but one is free to improvise.  If in the middle of a calculation one is stuck because they can’t solve a complicated equation, then they can assume something is small or big or slow or fast and replace the equation with a simpler one that can be solved.  One doesn’t need to know if any particular step is justified because all that matters is that in the end, the prediction must match the data.

Math is more like composing western classical music.  There are a strict set of rules that must be followed.  All the notes must fall within the diatonic scale framework.  The rhythm and meter  is tightly regulated.  There are a finite number of possible choices at each point in a musical piece just like a mathematical proof.  However,  there are a countably infinite number of possible musical pieces just as there are an infinite number of possible proofs. That doesn’t mean that rules can’t be broken, just that when they are broken a paradigm shift is required to maintain self-consistency in a new system.  Whole new fields of mathematics and genres of music arise when the rules are violated.

The invention of the computer introduced a third means of proof.  Prior to the computer,  when making an approximation, one could either take the mathematics approach and try to justify the approximation by putting bounds on the error terms analytically or take the physicist approach and compare the end result with actual data.  Now one can numerically solve the more complicated expression and compare it directly to the approximation. I would say that I have spent the bulk of my career doing just that. Although, I don’t think there is anything intrinsically wrong with proving my simulation, I do find it to be unsatisfying at times. Sometimes it is nice to know that something is true by proving it in the mathematical sense and other times it is gratifying to compare predictions directly with experiments. The most important thing is to always be aware of what mode of proof one is employing.  It is not always clear-cut.

Are Strads overrated?

In classical music, there is a mystique surrounding Seventeenth Century violins made in Cremona, Italy and especially the Stradivarius.  These violins can cost millions of dollars and are supposed to be unmatched in sound quality by any violin made since.  People have speculated that it is the wood, the glue, the varnish or some mysterious unknown quantity that makes them so much better although nothing has ever been pinpointed.  Now, a study recently published in PNAS (see here) finds  that the superiority of the Stradivarius may be more myth than substance.  The study found that top-level violinists preferred modern violins to the classic Cremonese ones.  It was the first every study that was double blinded so that neither the violinist nor tester knew which violin was being played.  It is well-known in psychology that people’s preferences are strongly influenced by context.  An example, is that wines perform better in taste tests when they are believed to be more expensive.  The study has been criticized in that it was done in a hotel room and not on a concert stage.  I’m sure a followup is in the works.

Metaphysics as mathematics

One of the branches of western philosophy is metaphysics, which asks about the nature of being and the world.  It is the extension of what was once known as natural philosophy.  Modern science is empirical  natural philosophy.  Instead of trying to answer questions about how the world is the way it is by thinking about it, it makes hypotheses and tests them experimentally or observationally.  The late twentieth century was a time when physics, specifically string theory, drifted back towards metaphysics.  String theorists attempt to answer questions about our reality by constructing theories that are mostly grounded on mathematically aesthetic principles.   I have no real problem with string theory per se, except in its claim to be more “fundamental” than other branches of physics.  As I have argued before (e.g. here), there are fundamental concepts at all energy and length scales.

What I will argue here is that we have been misguided in trying to reunite metaphysics with science.  As I have argued before (e.g. here  and here), it is not even simple to define what is meant by “fundamental laws” or a “theory of everything”.  If our universe can be approximated arbitrarily accurately by a computable one (yes I know some of you disagree with this assertion), then what constitutes the underlying theory?  Is it the program that generates the universe?  Is it the most simple description (in which case it is not computable)?  Or is it something else?

While metaphysics as science is a dead-end for me, metaphysics as mathematics is ripe for very interesting insights. Instead of asking directly about “our” reality, we should be asking about hypothetical realities.  We should be doing philosophy of science and metaphysics on artificial worlds.  This would then be a controlled situation.  Instead of speculating about the underlying laws of our universe, we can simply specify a given set of properties in some hypothetical or simulated universe and probe the consequences.  We can do this at arbitrary levels as well –  universe,  multiverse, meta-multiverse and so forth.

I think ironically that doing such a thing would give more  insights into our universe than what we are doing now.  For example, if we started to investigate what types of simulated worlds would generate life, it may inform us more about how probable life exists in our universe ( as well as force us to come up with some quantitative definitions for life) then sending out space probes (e.g. see here).  It could also give us an idea of how variable life can be.  We seem to be stuck on looking for biochemical life.  Well maybe there are electromagnetic plasma life forms out there.  If all it took to generate complex life-like objects was a nonlinear rule that didn’t blow up, then the answer to why our universe seems so well-tuned for us would be that any old rule would have worked although it would give entirely different looking life forms.  Also, if we thought more about how we could generate or detect any type of consciousness in a simulation, that may help us better understand the consciousness we have.

A new model for publishing

Two months ago in a guest editorial for DSWeb (see here), I expressed some dismay that while we have had great inovation in many aspects of our work lives, the current (broken) publication model has remained relatively unchanged.  Now my colleagues at NIH – Dwight Kravitz and Chris Baker have published a stimulating and provocative article (see here) highlighting the many problems with the current situation, especially with the wasteful treadmill of trying to get something into a “high impact” journal, and propose a new model.  Although this will mostly have salience for people in fields that try to publish in journals like Nature and Science, I recommend that anyone who publishes should read the paper and form their own opinion.  Here is mathematician Kreso Josic’s take on the paper.

From my view as a physicist cum mathematician cum biologist, I’ve seen publishing from several perspectives.  The theoretical physics/applied math world seems to have a good system already in place where everyone posts their papers on the arXiv and then publish in an “obvious” physics or math journal like one of the Physical Review or SIAM ones.  These journals are fairly low cost for the authors, if you don’t want colour figures or physical preprints (but not cheap), and they have a nice system of transferring to sister journals if you are rejected automatically so the review process is efficient.  However, publishing in the biology world is more of a nightmare that is well documented by Dwight and Chris in their paper.  Here, getting into a high impact journal like Nature or Science can make or break your career and the chances of getting in are slim.  Authors spend a lot of their time and energy trying to get their work published and if you have little name recognition in a field it is extremely difficult just to get your paper reviewed by the more prestigious journals. Dwight and Chris have some excellent ideas of how to fix this system, which  I think have a lot of merit.  The one thing I would like to see is to make the cost for authors be as low as possible so that it doesn’t impede low funded labs.

The Scientific Worldview

An article that has been making the rounds on the twitter/blogosphere is The Science of Why We Don’t Believe Science by Chris Mooney in Mother Jones.  The article asks why it is that people cling to old beliefs even in the face of overwhelming data against them.  It argues that we basically use values to evaluate scientific facts.  Thus if the facts go against a value system that was built over a lifetime, we will find ways to rationalize away the facts.  This is particularly true for climate change and vaccines causing autism.  The scientific evidence is pretty strong that our climate is changing and vaccines don’t cause autism but adherents to these beliefs simply will not change their minds.

I mostly agree with the article but I would add that the idea that the scientific belief system is somehow more compelling than an alternative belief system may not be on as solid ground as scientists think.  The concept of rationality and the scientific method was a great invention that has improved the human condition dramatically.  However,  I think one of the things that people trained in science forget is how much we trust the scientific process and other scientists.  Often when I watch a science show like  NOVA on paleontology, I am simple amazed that archeologists can determine that a piece of bone that looks like some random rock to me, is a fragment of a finger bone of a primate that lived two million years ago.  However,  I trust them because they are scientists and I presume that they have received  the same rigorous training and constant scrutiny I have received.  I know that their conclusions are based on empirical evidence and a line of thought that I could follow if I took the time.  But if I grew up in a tradition where a community elder prescribed truths from a pulpit, why would I take the word of a scientist over someone I know and trust?  To someone not trained or exposed to science, it would just be the word of one person over another.

Thus, I think it would be prudent for scientists to realize that they possess a belief system that in many ways is no more self-evident than any other system.  Sure, our system has proven to be more useful over the years but ancient cultures managed to build massive architectural structures like the pyramids and invented agriculture without the help of modern science and engineering.   What science prizes is parsimony of explanation but at the risk of being called a post-modern relativist, this is mostly an aesthetic judgement.  The worldview that everything is the way it is because a creator insisted on it is as self-consistent as the scientific view.  The rational scientific worldview takes a lot of hard work and time to master.  Some (many?) people are just not willing to put in the effort it takes to learn it.   We may need to accept that a scientific worldview may not be palatable to everyone.  Understanding this truth may help us devise better strategies for conveying scientific ideas.

Approaches to theoretical biology

I have  recently  thought about how to classify what theorists actually do and I came up with three broad approaches: 1) Model analysis, 2) Constraint driven modeling and 3) Data driven modeling.   By model, I mean a set of equations (and inequalities) that are proposed to govern or mimic the behavior of some biological system.  Often, a given research project will involve more than one category.  Model analysis is trying to understand what the equations  do. For example, there could exist some set of differential equations and the goal is to figure out what the solutions of these equations are or look like. Constraint driven modeling is trying to explain a phenomenon starting from another set of phenomena.  For example, trying to explain the rhythms in EEG measurements by exploring networks of coupled spiking neurons.  Finally, data driven modeling, is looking directly for patterns in the data itself and not worry about where the data may have come from.  An example would be trying to find systematic differences in the DNA sequence between people with and without a certain disease.

I have spent most of my scientific career in Approach 1).  What I have done a lot in the past is to construct approximate solutions to dynamical systems and then compare them to numerical simulations.  Thus, I never  had to worry too much about data and statistics or even real phenomena itself.  In fact, even when I first moved into biology in the early nineties, I still did mostly the same thing. (The lone exception was my work on posture control, which did involve paying attention to data). Computational neuroscience is a mature enough field that one can focus exclusively on analyzing existing models.    I started moving more towards Approach 2) when I began studying localized persistent neural activity or bumps.  My first few papers on the subject were mostly analyzing models but there was a more exploratory nature to them than my previous work.  Instead of trying to explicitly compute a quantity, it was more about exploring what networks of neurons can do.  The work on binocular rivalry and visual competition were attempts to explain a cognitive phenomenon using the constraints imposed by the properties of neurons and synapses. However, I was still only trying to explain the data qualitatively.

That changed when I started my work on modeling the acute inflammatory response.  Now, I was just given data with very few biological constraints. I basically took what the immunologists told me and constructed the simplest model possible that could account for the data.  Given that my knowledge of statistics was minimal, I simply used the “eye test” as a basis of whether or not the model worked or not.   The model somehow fit the data and did bring insights to the phenomenon but it was not done in a  systematic way.  When I arrived at NIH, I was introduced to Bayesian inference and this really opened my eyes.  I realized that when one doesn’t have strong biological or physical constraints, Approach 2) is not that useful.  It is easy to cobble together a system of differential equations to explain any data.  This is how I ended up moving more towards Approach 3). Instead of just coming up with some set of ODEs that can explain the data, what we did was to explore classes of models that could explain a given set of data and use Bayesian model comparison to decide which was better.  This approach was used in the work on quantifying insulin’s effect on free fatty acid dynamics.  While that work involved some elements of Approach 2) in that we utilized some constraints, my work on protein sequences is almost all within Approach 3).  The work on obesity and body weight change involves all three Approaches. The conservation of energy and the vast separation of time scales put a lot of strong constraints on the dynamics so one can get surprisingly far using Approach 1) and 2).

When I was younger, some my fellow graduate students would lament that they missed out on the glory days of the 1930′s when quantum mechanics was discovered.  It is true that when a field matures, it starts to move from Approach 3) to 2) and 1).  Theoretical physics is almost exclusively in 1) and 2). Even string theory is basically all in Approach 1) and 2).  They are trying to explain all the known forces using the constraints of quantum mechanics and general relativity.   The romantic periods of physics involved Approach 3).  There was Galileo, Kepler and Newton inventing classical mechanics. Lavoisier, Carnot, Thompson and so forth coming up with conservation laws and thermodynamics. Faraday and Maxwell defining electrodynamics. Einstein invented the “Thought experiment” version of Approach 3) to dream up Special and General Relativity.  The last true romantic period  in physics was the invention of quantum mechanics.  Progress since then has basically been in Approaches 1) and 2).  However,  Approach 3) is alive and well in biology and data mining. The theoretical glory days of these fields might be now.

Machine ideology

I’ve been mesmerized the past two days by this three-part BBC documentary  All Watched Over by Machines of Loving Grace.  Steve Hsu has a YouTube link for the third episode.  The other two can be found on YouTube.   It is a rather cynical and dystopian view of how elites use machine metaphors to suppress the masses.  The  writer and director Adam Curtis is a genius in evoking a surreal nightmare with his use of images and music.  This is nothing like a Ken Burns documentary.  It is closer to modern video art.

The first episode was about how the ideas of Ayn Rand influenced Alan Greenspan who convinced Bill Clinton to deregulate the markets, which caused the internet bubble, the Asian crisis and the recent great recession.  The machine angle is that computer models were supposed to keep the markets stable.  The theme of the second part was that the concept of the ecosystem, where nature uses feedback loops to attain an equilibrium, has been co-opted by those in power to argue that the world as it is (with then on top) is the natural balance and everyone should just stay in their place and maintain the status quo.  The machine aspect is that these ideas were supported by cybernetics and a largely forgotten field called systems theory, which is basically linear control theory applied to complex systems.   The third part was about how evolution theorist William Hamilton with help from George Price in trying to understand altruism, came up with the selfish gene idea, (promoted by Richard Dawkins), which reduced humans to machines, with a parallel story of how the acts of Western powers (both selfish and altruistic) caused genocide in Africa.  The undercurrent of all three episodes is that machine-inspired ideas have provided elites with a sense of hubris and a rationale to control societies for their own interests.  Even more insidious is that these ideas, which includes the concepts of the network and self-organized systems, have made the general populace believe that we are  creating a society without hierarchy that will naturally reach a stable balance but in reality this is false and thinking so just leaves you defenseless to the whims of the elites.

I think the irony of the show is that developments in science and mathematics actually spawned two distinctly opposite world views in the twentieth century.  One view, as espoused by the series, is  that science, technology and industry can solve our problems and create a better world.  The second view is that the enlightenment goal of unbounded knowledge and rationality is dashed by thermodynamics, quantum mechanics, Godel’s incompleteness theorems, the Halting problem, and deterministic chaos.  In this second world view – disorder increases, physics is probabilistic, there are mathematical truths that can’t be proven, there are problems that can’t be solved by computers, and there is extreme sensitivity to initial conditions. It is  ironic that while the course of modern history and political power has been largely driven by the first world view, much of modern scientific and intellectual thought has been shaped by the second.  For example, the show is rather critical of people like Jay Forrester, a systems theory pioneer, who believed  he could model the world.  However, his work and ideas have had little impact on physics and mathematics where it is dogma that dynamical systems with just three degrees of freedom can exhibit all sorts of behavior and bifurcations.  Right now the study of networks is the rage but the main message is that they are complex, hard to understand and certainly don’t always ensure stable equilibria.   Although Curtis may be correct that the first world view has been the source of some of our major problems, I don’t think we should abandon it completely and take a Hayek attitude that it is impossible to understand complex systems so we shouldn’t even try.  Rather, there can be a middle course where we recognize the power and the limitations of science and technology.