An Algorithm for Discovery: a reflection

As I recently began working in the Cellular Reprogramming Lab, I have become acquainted with a short, one-page article called "An Algorithm for Discovery". It was published by two neurologists (David Paydafar and William Schwartz) in Science (292, 2001), and offer all scientists five key principles that summarize the pseudo-algorithm's structure [1].

1. Slow down to explore. The authors advocate setting aside time during the day to critically think about your problems. They also warn against the tendency to seek quick closure to projects and problems. This is not a call to procrastinate -- rather, an advocacy of following up on anomalies in the data.

2. Read, but not too much. Again, this is not a call to keep the reading queue light. Instead, it is a call to ignore (in an educated manner) other people's skepticism about a problem or approach. Just because something has not been done before does not mean it cannot be done. Even previous failures might yield new discoveries in a different context.

* a comment on the Science website (with which I happen to agree) suggests that in effect an uncluttered (e.g. ultra-focused) mind has the potential for inattentional blindness. Coincidentally, this book came out around the same time:

Mack, A. and Rock, I.   Inattentional Blindness. Bradford Books (2000).

3. Pursue quality for its own sake. This point has to do with the refinement of methods and experimental design. Good experimental design can make or break a line of investigation.

4. Look at the raw data. Doing exploratory data analysis is an unappreciated but critical aspect of discovery. Exploration of the data can guide and improve future data analysis.

* this is something some people like to summarize as "outlier detection", but that does not do the process justice. The process should be much less punitive than that.

5. Cultivate smart friends. This should be self-explanatory. However, it's not simply a matter of knowing smart people, but gaining the trust of someone who can complement your style of thinking.

NOTES:

[1] A related article is the following letter by David Klahr: Directions to "Eureka! Science, 292, 2009-2010 (2001).

Innovation Class/Book

Here is the outline for my innovation course/book, which teaches people all the skills they need to become an inventor (it's intentionally tongue-in-cheek).

I. Beyond the internet: how to escape the grind of software development and reinvent the wheel.

Why does it seem like all of our innovators go into software development? Perhaps it's where innovation is most encouraged. But, there's a hell of a lot more to life than Facebook and vidiot games.

II. Keep a notetaking device handy vs. it can't be done any other way.

Taking notes to record fragments of inspiration are indispensible for an inventor. Finally, a "use" for Twitter (providing your insights are under 140 characters).

III. Sleep is good (as a series of short bursts)! a.k.a. How to take a catnap and still work hard.

Edison was famous for his catnaps. I like to take short to moderate naps during the late afternoon as well. It's like two days in one -- you work during the business day, and then you have the evening free (as you are refreshed and cogent). Sleep is horribly underrated in our society.

IV. What to do when they want your head.

Every now and again, people will gang up on you for not approaching a problem in the same manner they do. Probably for the same reasons ideological coercion and ethnic cleansing are so common.

V. What to do when only a part of what you've worked on works.

This happens all the time. Keep it modular and think laterally.

Notice that "how to file a patent" has yet to be covered. That's because we're still working on defining what ever it is we're inventing.

Biology and Physics: how one hand washes the other

Today at Soft Active Materials there were a lot of interesting talks, and after each session (morning and afternoon) there was a discussion panel.

For the afternoon panel, the following question was posed: what does Physics have to offer Biology, and vice versa? The question did not elicit much discussion, perhaps because it is so philosophical. So that is the question of the day -- what can Physics and Biology contribute to each other? I am not talking about in areas such as biophysics. I am referring to, say, the marriage of genomics and how cells/organisms respond to pressure gradients.

One concern was the nature of funding agencies. Another comment suggested that physics can provide a mimimal set of criterion and parameterization for understanding biological systems. My opinion is that physics can contribute two things of value: a set of tools to tackle hard and potentially intractable problems, and a way to look at old problems anew. This second point is of no small consequence -- paradigm shifts occur this way, and is extraordinarily valuable to the advancement of technological applications.

So, what do other people think? Please let me know in the comments section. Otherwise, good first day, and look forward to tommorrow.

Soft Active Materials Conference I

This week (5-18 through 5-21) I will be attending "Soft Active Materials: from granular rods to flocks, cells, and tissues" in Syracuse, NY.

Here is the program for more information. Here are a set of lecture notes for the session. I participated with a brief slideshow called "Emergent Natural Selection and the Evolution of Novel Biological Surfaces". Enjoy.

Review of "Intelligent Movement Machine"

Review of: Graziano, M.S.A. (2009). Intelligent Movement Machine: an ethological perspective on the primate motor system. Oxford University Press, Oxford, UK
by Bradly Alicea

Introduction

The field of human, animal, and robotic movement, portions of which have gone by many names in the literature, is a vast and moderately synthesized area of study. Part of the problem is that only a few people bridge the multiple areas of specialty that are essential for a truly comprehensive understanding of movement. Michael Graziano is one of these key people. Graziano works on the electrophysiology of primate motor cortex, but also has an interest in how this translates into animal behavior. This explains the subtitle of the book: an ethological perspective. Graziano's synthesis is actually two-fold: one is to organize the literature on movement in a systematic manner, and the other is to unite observations of animal behavior with what we know about the underlying neural mechanisms.

Structure of the book
Since the field of movement as defined in the last section is only moderately synthesized, Graziano devotes a large section of the book towards bringing evidence to bear from the literature. The focus is on electrophysiology in Primates, so those hoping to understand movement phenomena outside of simple limb movements and feeding behaviors might come away disappointed. However, he does spend some time discussing personal space and movement behavior, which draws from his work on defensive postures. Despite his somewhat diverse choice of topics, he certainly does not make a unified field theory-type proposition.

While he may spend a few too many chapters on reviewing the current state-of-the-art, it sets up the last few chapters of the book. In this portion of the book, Graziano explains how he spent some time at the Bronx zoo to better understand what he was observing in electrophysiological experiments. Anyone who was spent time at a zoo realizes that non-human Primates exhibit many complex and sophisticated physical behaviors, even in captivity. In this sense, Graziano's approach is a sorely needed addition to the literature.

Heart of the neuroethological approach
Ethology is different from approaches to human behavior in that the former focuses on fixed action patterns. These action patterns can range from preening to arm waving to head shaking. Typically, such behaviors are relatively easily quantifiable in that they are discrete and repetitive. In this way, fixed action patterns are also thought to have a relatively simple neural basis. Graziano uses this framework to explain how behaviors observed in nature are produced at the single-cell level. This is one way of resolving a frustration from earlier in his career that involved the constricted behavioral repertoire observed during traditional electrophysiological recordings.

One criticism of this approach, of course, is how complex movement observed in naturalistic settings map to electrophysiological results collected in highly-artificial environments. This returns us to Graziano's early-career angst and how these two approaches can be reconciled in the first place. On the one hand, he has certainly succeeded at thought-provocation. This work adds a critical missing piece to the story behind movement, and how it relates to both species differences and symbolic behavior. However, we must keep in mind that movement involving many different components is not simply additive, and that what might explain an isolated movement might not be the whole story when naturalistic movements are taken into account.

Conclusions

One statement that particularly resonated with me was a statement to the effect that some electrophysiologists think Graziano's approach is crazy. I think this statement sums up one of the biggest unsung problems in science today: although "interdisciplinary research" is a popular buzzword, most scientists have no idea how to appreciate the approaches of their colleagues in other fields. This has been an ongoing frustration in my own life, and I am not entirely convinced that such skills can be taught. Aside from sociological griping, the scientific basis of the book is most definitely sound. I find it a reasonable and innovative approach to a problem that has in some ways been studied to death, yet in other ways is still vaguely understood.