The importance of stupidity in scientific research

Ramesh Dhakal
Graduate Student of Chemistry
Clemson University

Lets go through this article. I find it really really interesting. We must have to thanks to Dr. Martin A. Scwartz, Professor of University of Virginia for providing his long time experience via Journal of cell science. I can not hide this article to myself, so I disclose here.

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I recently saw an old friend for the first time in many years. We had been Ph.D. students at the same time, both studying science, although in different areas. She later dropped out of graduate school, went to Harvard Law School and is now a senior lawyer for a major environmental organization. At some point, the conversation turned to why she had left graduate school. To my utter astonishment, she said it was because it made her feel stupid. After a couple of years of feeling stupid every day, she was ready to do something else. I had thought of her as one of the brightest people I knew and her subsequent career supports that view. What she said bothered

me. I kept thinking about it; sometime the next day, it hit me. Science makes me feel stupid too. It’s just that I’ve gotten used to it. So used to it, in fact, that I actively seek out new opportunities to feel stupid. I wouldn’t know what to do without that feeling. I even think it’s supposed to be this way. Let me explain. For almost all of us, one of the reasons that we liked science in high school and college is that we were good at it. That can’t be the only reason – fascination with understanding the physical world and an emotional need to discover new things has to enter into it too. But high-school and college science means taking courses, and doing well in courses means getting the right answers on tests. If you know those answers, you do well and get to feel smart. A Ph.D., in which you have to do a research project, is a whole different thing. For me, it was a daunting task. How could I possibly frame the questions that would lead to significant discoveries; design and interpret an experiment so that the conclusions were absolutely convincing; foresee difficulties and see ways around them, or, failing that, solve them when they occurred? My Ph.D. project was somewhat interdisciplinary and, for a while, whenever I ran into a problem, I pestered the faculty in my department who were experts in the various disciplines that I needed. I remember the day when Henry Taube (who won the Nobel Prize two years later) told me he didn’t know how to solve the problem I was having in his area. I was a third-year graduate student and I figured that Taube knew about 1000 times more than I did (conservative estimate). If he didn’t have the answer, nobody did. That’s when it hit me: nobody did. That’s why it was a research problem. And being my research problem, it was up to me to solve. Once I faced that fact, I solved the problem in a couple of days. (It wasn’t really very hard; I just had to try a few things.) The crucial lesson was that the scope of things I didn’t know wasn’t merely vast; it was, for all practical purposes, infinite. That realization, instead of being discouraging, was liberating. If our ignorance is infinite, the only possible course of action is to muddle through as best we can.

I’d like to suggest that our Ph.D. programs often do students a disservice in two ways. First, I don’t think students are made to understand how hard it is to do research. And how very, very hard it is to do important research. It’s a lot harder than taking even very demanding courses. What makes it difficult is that research is immersion in the unknown. We just don’t know what we’re doing. We can’t be sure whether we’re asking the right question or doing the right experiment until we get the answer or the result.

Admittedly, science is made harder by competition for grants and space in top journals. But apart from all of that, doing significant research is intrinsically hard and changing departmental, institutional or national policies will not succeed in lessening its intrinsic difficulty. Second, we don’t do a good enough job of teaching our students how to be productively stupid – that is, if we don’t feel stupid it means we’re not really trying. I’m not talking about ‘relative stupidity’, in which the other students in the class actually read the material, think about it and ace the exam, whereas you don’t. I’m also not talking about bright people who might be working in areas that don’t match their talents. Science involves confronting our ‘absolute stupidity’. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. Preliminary and thesis exams have the right idea when the faculty committee pushes until the student starts getting the answers wrong or gives up and says, ‘I don’t know’. The point of the exam isn’t to see if the student gets all the answers right. If they do, it’s the faculty who failed the exam. The point is to identify the student’s weaknesses, partly to see where they need to invest some effort and partly to see whether the student’s knowledge fails at a sufficiently high level that they are ready to take on a research project.

Productive stupidity means being ignorant by choice. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows

us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. No doubt, this can be difficult for students who are accustomed to getting the answers right. No doubt, reasonable levels of confidence and emotional resilience help, but I think scientific education might do more to ease what is a very big transition: from learning what other people once discovered to making your own discoveries. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.

Artificial Intelligence version 0.01

Nabin k Malakar

When we identify the objects we assign them with function. That means our degree of intelligence has to do with its usage. The more efficiently we can make the connections, more intelligent we are pronounced. This explains our dominance in the whole animal kingdom. We can make use of things and think of possible ways of connecting the subjects. We can think! The process of thinking leads us to ability to discover. The tool from Thinking to discovery is exploration. The process of exploration is random, however, we would like to have smart ways to explore.

We are not being able to make objects with artificial intelligence because of the fact that we want to make camera but not associate it with the way our eye works. When we "see" an apple, a lot of logical connections are opened. Camera can take the picture of an apple but it can not associate with its use. When we see a car, we can immediately think of its use. Similarly we can take a picture of an apple. But again, the fact that we have taken picture of an apple will never be reflected in its use by camera. For a robot with camera, apple is an object. Which is not much different than another apple. Just think about an apple, how will you explain it to a robot?

For a record player, one song is no different than another. Yes, you can add the analyzer in terms of currents and voltages and spikes those go up and down and some reference of time in it. But can it sense the melody and harmony of the song? What makes you feel its difference? What makes intelligence to create music?

Similarly, we can have a lot of words in the dictionary. How does dictionary make us understand a word with the help of another word? Without an example of its usage, another word is just as confusing as the first one. During the learning process we start with associating the meanings of words. Association can be with the exact meaning or the nearest neighborhood of the meaning of the subject under construction.

When a robot is given with few objects, it needs to learn their use so that it can learn about the objects. The question here is robot can use the object, but can it learn how to use it? A simple example: Can a robot equipped with first kind of lever go, explore and discover the second or third kind of lever? For this purpose, it needs to associate the functional value of given objects and think of ways to make its usage. The stated example in itself has a lot of complications.

So, I think the next step in Artificial Intelligence is to make connections. Connections that are useful, which can be associated with the functional value, not as objects.


The sustained growth of social network is another example of how connections work. If anything can be identified as having some functional value, its growth is inevitable.

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Thinking abilities
Exploration
Discovery
Usage
Discovery
Thinking abilities
cycle et al.


Inspired by the reading of Rota, Feynmann's interview on joys of discovering things and the movie Gods must be crazy and other A.I. related articles.

[Mr. Malakar is a PhD student of Physics. He regularly writes on his own blog Time]