Kendall Square, home to MIT, was never a cultural hub. It did once have a cafe that was also an art gallery, called Voltage, owned by Lucy Valena. It was spacious, had rotating art exhibits by local artists and a wall-to-wall bookcase full of random books. You could have long private conversations without feeling cramped. I wrote much of my PhD thesis there. Voltage wasn’t some hippie commune, obviously; it was a high-end espresso shop and it was expensive, like the rest of Kendall Square. It managed to still feel somewhat cozy and had a slow pace about it.
First, Marvin Minsky on mathematics and being slow:
MINSKY: I think when I was a child I didn’t have the feeling that I could solve problems that other people could solve. On the contrary, I found things were quite difficult. And when I tried to read mathematics it would take an hour a page and I’d get some of the ideas but not others. And usually it would be six months later that suddenly it would click. And so I think I thought of myself as sort of slow. On the other hand, I thought of everyone else as incredibly slow. But I didn’t think of myself as particularly creative.
Back in February, much significance was attributed to the fact that some biologists, including Nobel laureate Carol Greider, were posting their research articles directly on the web. Amy Harmon wrote about it for the New York Times and others looked for reasons why a culture of preprints—research published online before being submitted for peer-review—developed in physics, but not biology1,2.
Notably missing from this coverage, however, was a critical look at the preprint movement itself and the roots of the problem it aims to fix. Academic biomedical science today is plagued with hard issues. There’s the rich-get-richer phenomenon, where 50% of NIH grants go to a small number of already well-funded labs3. The competition for funding and a dearth of academic positions drive what Marc Kirschner called a “perverted view of impact”: an obsessive desire to measure and rank scientific progress, which is often reduced to number of publications in high-profile journals or perception of clinical relevance4. The system also suffers from rampant sexism, conscious and unconscious, that repels women away from science5,6,7. There is also the corrupting influence of money on science, including hawkish patenting practices that conflict with the university’s basic tenets8. Working within this system can be an exercise in dissonance: the incentives for success are at odds with promoting open, diverse and adventurous basic science.
Last week, Harvard’s program in Science, Technology and Society (STS) hosted a conference titled “The Molecularization of Identity: Science and Subjectivity in the 21st Century”. It was advertised like an ordinary STS conference, but it wasn’t. I wrote about it here:
Yarden Katz is a fellow in the Dept. of Systems Biology at Harvard Medical School.
In this talk at the NYC Lisp meetup, Gerry Sussman was asked why MIT stopped teaching the legendary 6.001 course, which was based on Sussman and Abelson’s classic text The Structure and Interpretation of Computer Programs (SICP). Sussman’s answer was that: (1) he and Hal Abelson got tired of teaching it (having done it since the 1980s). So in 1997, they walked into the department head’s office and said: “We quit. Figure out what to do.” And more importantly, (2) that they felt that the SICP curriculum no longer prepared engineers for what engineering is like today. Sussman said that in the 80s and 90s, engineers built complex systems by combining simple and well-understood parts. The goal of SICP was to provide the abstraction language for reasoning about such systems.
In Fact, Fiction and Forecast, Nelson Goodman revisits Hume’s problem of induction. The problem Hume wrote about in the 18th century is: can we reliably know anything about the future, given experience of the past? Or alternatively, can we produce generalizable knowledge from observations of particulars?
Goodman breezes through a couple of unworkable solutions (or attempts at dissolutions) of Hume’s problem. He then zones in on the core issue: just as deductive inferences are justified if they conform to the rules of logic, Goodman argues that “predictions are justified if they conform to valid canons of induction.” The problem of induction, then, is the problem of defining the valid rules of induction.
From an interview of Claude Shannon with Robert Price…
Price: And for a long time I was under the misapprehension that you had been a student of Wiener’s [Norbert Wiener] before the war but that was never the case. You were in the same department together, right, and you must have seen each other, but you were never a student?
Shannon: I was a student in one class. I took a course in Fourier analysis.
Price: I see, and where were you?
Shannon: I didn’t have him as a doctoral student.
Price: No, I see. I was under that misapprehension for a while. Fortunately, I corrected that. But then where did you get the idea that information could be modeled, I mean when did you get it? You got it from Wiener, I believe,
Marvin Minsky, MIT professor and artificial intelligence pioneer, passed away last Sunday (January 24, 2016). Minsky is widely celebrated for his diverse scientific contributions to artificial intelligence, computer science, mathematics and even microscopy. Less well known are Minsky’s radical views on education.
Some mathematicians think that learning mathematics is about “reaching the summit and then swiftly burying the path.” Minsky didn’t. He was interested in why mathematics is hard for children to learn and what can be done about it. He thought that the difficulty was partly “caused by starting with the practice and drill of a bunch of skills called Arithmetic”. As a result, “instead of promoting inventiveness, we focus on preventing mistakes.” Some children, frustrated by this negativity, dismiss mathematics as repetitive, boring and punitive.