Field of Science

Stephen Hawking's advice for twenty-first century grads: Embrace complexity

Charles Joseph Minard's famous graph showing the decreasing size of Napoleon's Grande Armée as it marches to Moscow; a classic in data visualization (Image: Wikipedia Commons)
As the economy continues to chart its own tortuous, uncertain course, there seems to have been a fair amount of much-needed discussion on the kinds of skills new grads should possess. These skills of course have to be driven by market demand. As chemist George Whitesides asks for instance, what's the point of getting a degree in organic synthesis in the United States if most organic synthesis jobs are in China?

Upcoming grads should indeed focus on what sells. But from a bigger standpoint, especially in the sciences, new skill sets are also inevitably driven by the course that science is taking at that point. The correlation is not perfect (since market forces still often trump science) but a few examples make this science-driven demand clear. For instance if you were growing up in the immediate post-WW2 era, getting a degree in physics would have helped. Because of its prestige and glut of government funding, physics was in the middle of one of its most exciting periods. New particles were literally streaming out of woodwork, giant particle accelerators were humming and federal and industrial labs were enthusiastically hiring. If you were graduating in the last twenty years or so, getting a degree in biology would have been useful because the golden age of biology was just entering its most productive years. Similarly, organic chemists enjoyed a remarkably fertile period in the pharmaceutical industry from the 50s through the 80s because new drugs were flowing out of drug companies at a rapid pace and scientists like R. B. Woodward were taking the discipline to new heights.

Demand for new grads is clearly driven by the market, but it also depends on the prevalence of certain scientific disciplines at specific time points. This in turn dictates the skills you should have; a physics-heavy market would need skills in mathematics and electronics for instance, a biology-heavy market would mop up people who can run Western blots and PCR. Based on this trend, what kind of skills and knowledge would best serve graduates in the twenty-first century?

To me the answer partly comes from an unlikely source: Stephen Hawking. A few years ago, Hawking was asked what he thought of the common opinion that the twentieth century was that of biology and the twenty-first century would be that of physics. Hawking replied that in his opinion the twenty-first century would be the "century of complexity". That remark probably holds more useful advice for contemporary students than they realize since it points to at least two skills which are going to be essential for new college grads in the age of complexity: statistics and data visualization.

Let's start with the need for statistics. Many of the most important fields of twenty-first century research including neuroscience, synthetic and systems biology, materials science and energy are inherently composed of multilevel phenomena that proliferate across different levels of complexity. While the reductionist zeitgeist of the twentieth century yielded great dividends, we are now seeing a movement away from strict reductionism toward emergent phenomena. While the word "emergence" is often thrown around as a fashionable place-card, the fact is that complex, emergent phenomena do need a different kind of skill set.

The hallmark of complexity is a glut of data. These days you often hear talk of the analysis of 'Big Data' as an independent field and you hear about the advent of 'data scientists'. Big Data now has started making routine appearances in the pharmaceutical and biotech industry, whether in the form of extensive multidimensional structure-activity relationship (SAR) datasets or as bushels of genomic sequence information. It's also important in any number of diverse fields ranging from voter behavior to homeland security. Statistical analysis is undoubtedly going to be key to analyzing this data. In my own field of molecular modeling, statistical analysis is now considered routine in the analysis of virtual screening hits although it's not as widely used as it should.

Statistics was of course always a useful science but now it's going to be paramount; positions explicitly looking for 'data scientists' for instance specifically ask for a mix of programming skills and statistics. Sadly many formal college requirements still don't include statistics and most scientists, if they do it at all, learn statistics on the job. For thriving in the new age of complexity this scenario has to change. Statistics must now become a mandatory part of science majors. A modest step in this direction is the publication of user-friendly, popular books on statistics like Charles Wheelan's "Naked Statistics" or Nate Silver's "The Signal and the Noise" which have been quickly devoured by science-savvy readers. Some of these are good enough to be prescribed in college courses for statistics non-majors.

Along with statistics, the other important skill for students of complexity is going to be data visualization and formal college courses should also reflect this increasingly important skill set. Complex systems often yield data that's spread over different levels of hierarchy and even different fields. It's quite a challenge to visualize this data well. One resource that's often recommended for data visualization is Edward Tufte's pioneering series of books. Tufte shows us how to present complex data often convoluted by the constrains of Excel spreadsheets. Pioneering developments in human-computer interaction and graphics will nonetheless ease visual access to complicated datasets. Sound data visualization is important not just to simply understand a multilayered system or problem but also to communicate that understanding to non-specialists. The age of complexity will inherently involve researchers from different disciplines working together. And while we are at it it's also important to stress - especially to college grads - the value of being able to harmoniously co-exist with other professionals.

Hawking's century of complexity will call upon all the tools of twentieth century problem solving along with a few more. Statistics and data visualization are going to be at the forefront of the data-driven revolution in complex systems. It's time that college requirements reflected these important paradigms.

First published on the Scientific American Blog Network.

Some thoughts on the events around Boston

We were enjoying a quiet evening of music and reading on Thursday when my wife alerted me to a message she got from the MIT emergency system that there had been a shooting somewhere on the campus. A while later we came to know that a police officer had been shot and killed right in front of my wife's department. After making sure that folks we knew from MIT were safe, we stayed awake for about two more hours reading the news updates. By the time we went to sleep we had found out that there was a connection between the shooting of the MIT police officer - a promising young man who later tragically died - and the Boston marathon bombing.

When we woke up the next day the situation was a little surreal: "Has Boston turned into Baghdad?", a friend tweeted. The police had pursued the two bombing suspects into the neighboring suburb of Watertown where there had been a terrific firefight. One suspect died (died, as it turned out, because his brother ran him over) and his brother escaped. By the time we woke up in Cambridge, Watertown was already in lockdown and police were getting ready for house to house searches within a 20 block perimeter.

Then we heard that Boston and a few of its suburbs - roughly an area comprising a million inhabitants - were in lockdown and all residents had been asked to stay at home. I thought then and I still think that this was an overreaction. Watertown, where the suspect was thought to be hiding? Sure. But Boston, Cambridge, Belmont, Newton and four others? A little over the top in my opinion. I understand that many people stayed home out of deference to authorities' wishes to be able to do their job unfettered. It's also ok to ask residents to be vigilant and to venture out at their own risk, but we do this anyway. Every time we are out we run the risk of being in a traffic accident. I suspect that this risk in a random suburb which is not Watertown is probably higher than  a 19 year-old fanatic suffering blood loss coming out of the blue with guns blazing and shooting at you. Now I understand that the police did not force people to stay indoors but they were also quite emphatic about this; I watched a woman who stepped out in the middle of the day to walk her dog being emphatically told to stay inside by two officers out on patrol.

The huge police presence in Watertown also seemed like an overreaction to me. By one account there were 9000 local, state, and federal authorities looking for this kid. Armored vehicles patrolled the streets, and I am not sure what additional purpose they would have served. Sure, the authorities were erring on the side of safety and they were clearly anxious to apprehend the suspect as soon as possible, but I think it's constitutionally healthy to be skeptical when your whole neighborhood resembles a war zone and armed officers wielding every kind of weapon perform intrusive house searches.

For me the ultimate irony may be that this guy was located - not by one of the 9000 officers and military personnel - but by an ordinary citizen. In a boat in an area that was not part of the 20 block perimeter. After the lockdown order had been rescinded.

What happened there? I know that hindsight is always twenty-twenty but here's something that bothers me: From what I read it seems that the spectacular shootout occurred at the intersection of Laurel St and Dexter Ave in Watertown. The suspect was found hiding in the boat at 67 Franklin St. If you look at these locations on Google Maps they are less than a mile apart. For all the meticulous house-to-house searches and lockdowns, why did the perimeter not include a location that was less than a mile from where the shootout took place? And most importantly, how could the police miss the boat, a large, roomy object that's ideal for a human being to hide? Can you say that your operation was really successful when an ordinary citizen locates a suspect only after you are done with house-to-house searches? So on one hand there seemed to be an overreaction and on the other, the meticulous operation seems to have been unsuccessful in its primary purpose.

I understand that there were a lot of police officers and other personnel who immersed themselves into this investigation. Many of them had not slept in 24 hours and they were clearly committed to finding this guy as soon as possible. These people clearly did an admirable job and we should applaud their dedication. But in my opinion there seem to be a few important clues that were missed, and discussing these clues is not only an important part of a healthy democracy where public officials are answerable to the public but also a part of any system of self-improvement and feedback where you learn from your mistakes. Most importantly though, when a 19 year-old nutjob brings a major American city to a standstill, makes it resemble a state with martial law and makes people stay put in their houses and away from their jobs in anxiety, if not fear, the terrorists have already won (as the cliche goes, in this case because it's true). As Ben Franklin memorably put it, if you sacrifice freedom for security you risk losing both. And the key here is to realize that this sacrifice may not even be forced upon you by the state; it can be entirely self-imposed.

At 5 PM I grew really restless and decided to go outside to get some milk (I need my morning coffee fix, terrorist scares be damned). Everything except for one convenience store was closed. Parking on Massachusetts Ave never looked better. The next day we went to the Esplanade along the Charles River. The cherry blossoms were in full bloom. Something about fear being the only thing we should truly fear came to my mind.

Moore's Law for batteries: No dice

The REVAi/G-Wiz i electric car charging at an on-street station in London (Image: Wikipedia Commons)
Ever since Gordon Moore came up with the ubiquitous law bearing his name, it has been applied to paradigms far beyond those which it was intended for. This is perhaps not surprising; the history of science and technology - and of religion - has consistently demonstrated that the followers of a prophet usually extend his principles into domains which the prophet never really approved of.

Transistor technology does neatly seem to follow the Moore's Law curve and a few other cutting-edge technologies like genome sequencing also seem to do this. Yet Moore's proselytizers have extended his law to pretty much everything. The law especially seems to break down when applied to biomedical research; for instance a review from last year pointed out how the pace of drug development almost seems to have been following a reverse law, titled "Eroom's Law" of declining productivity. Kurzweilian prognostications notwithstanding, research in neuroscience might follow the same trajectory, with a burst of rapid mapping of neuronal connectivity followed by a long, fallow period in which we struggle to duplicate these processes by artificial means.

The basic reasons why an emerging technology may not follow Moore's Law is either because we tend to underestimate the complexity of the system to which the technology is applied, or we underestimate the basic principles of physics and chemistry which would inherently constrain a Moore-type breakthrough in that field. In case of medical research both these constraints seem to rear their ugly, emergent heads, and this is the main problem I have with futurists like Ray Kurzweil who seem to imagine an entire universe governed by Moore's Law-type exponential progress in every field. Not all levels of complexity are created equal, and we just don't have enough evidence to know how general Moore's Law (which I think should simply be re-named "Moore's Observation") is in the world of practical problem-solving.

The argument about basic science limitations may especially apply to much-touted battery research whose proponents often seem to declare the next breakthrough in battery technology as being just around the corner. But a perspective from Fred Schlachter from the American Physical Society in the Proceedings of the National Academy of Sciences puts a brake on these optimistic predictions. His point is simple: any kind of Moore's Law for batteries may be limited by the fundamental chemistry inherent in a battery's workings. This is unlike transistors, where finer lithography techniques have essentially enabled a repetitive application of miniaturization over the years.
There is no Moore’s Law for batteries. The reason there is a Moore’s Law for computer processors is that electrons are small and they do not take up space on a chip. Chip performance is limited by the lithography technology used to fabricate the chips; as lithography improves ever smaller features can be made on processors. Batteries are not like this. Ions, which transfer charge in batteries are large, and they take up space, as do anodes, cathodes, and electrolytes. A D-cell battery stores more energy than an AA-cell. Potentials in a battery are dictated by the relevant chemical reactions, thus limiting eventual battery performance. Significant improvement in battery capacity can only be made by changing to a different chemistry.
And even this different chemistry is going to be governed by fundamental parameters like the sizes of ions and the rates of chemical reactions and current flow. Schlachter goes on to note the problems that lithium batteries have recently encountered, including fires. There is thus no guarantee that there will be a breakthrough in battery technology that's equivalent to that in computer technology over the last thirty years. And the article is right that while we are waiting for such breakthroughs, it's a really good idea to push forward with improving energy efficiency in cars, making their lighter, smaller and and more powerful. Energy efficiency would not ultimately solve pollution problems since the cars would still be fueled by gasoline, but it would certainly take us a long way while we are waiting for the next battery breakthrough engineered by Moore's Law. A law which may not really hold when it comes to next generation electric technology.

First published on the Scientific American Blog Network.