Mihai Budiu’s Blog

The higher (i.e., university) education in the United States is really good. In fact, it is so good that it is a significant “export” of the United States. Here is some data from an Oct 2006 Congressional Report:

In FY2005, the Department of State issued 565,790 [student] visas, making up 10.5% of all nonimmigrant visas issued.

And also:

Data from the National Science Foundation (NSF) shows that in 2004, foreign students on nonimmigrant visas accounted for 28.4% of all the doctorates in the sciences and 57.2% of all the doctorates in engineering.

Personally, I came to the US for graduate studies: in 1996 at Cornell, and I moved in 1997 at Carnegie Mellon for a Ph.D in computer science. I can testify that I found the system very good indeed.

One important thing I have learned here is the respect for hard facts, for supporting your statements with data, and for quantifying your assertions with numbers. (This is one reason that I have provided you with the quotations above.) Many European systems are built around the respect of lofty “traditional ideas,” or even worse, “personalities,” incarnated in the untouchable professor, who is always right.

In the US everything is open for debate, and in universities the scientific argument, based on facts, is a powerful weapon. This banishes rigidity, keeping the flow of ideas open.

However great, we should not believe that the system is perfect. In this text I want to point out a weakness of the US higher education system. I won’t be the first to do it: for a much more entertaining account, see this talk by Sir Ken Robinson.

When you talk about a system being “perfect,” you have to first define a measure by which you quantify its quality. While I can’t provide an objective metric, what I have in mind is the degree by which the system produces people which are prepared for the challenges offered by the “real world,” that awaits them at graduation, and, in particular, for a job.

I have to agree with Sir Ken Robinson: the entire education is optimized for producing professors. The higher you go in the educational hierarchy, the more likely you will be to come out a professor.

One can find many explanations for this state of affairs. Let me try to propose one: a positive feedback loop self-reinforcing across generations. The more you stay in school, the more your role models are professors. These are the people you see every day. I know it very well: I have been in school for almost 30 years (that’s a really long time!), and I became convinced I have to be a professor myself. Why? I had never really seen any other profession in front of my eyes. In graduate school there was an implicit, not-very-clearly-stated assumption that the successful graduates go to become professors, and the failed ones go to industry or some other “shady” places like that.

This is how the feedback loop starts. You see only professors around you, and your students also become professors, and they have only seen professors all their lives. A second problem is the information intake. Professors are really smart people. They generate a lot of ideas. They like to write and talk a lot about these ideas. That’s why they write papers and organize conferences where they meet other people like themselves. What they don’t like to acknowledge is that there are lots of other sources of ideas which are not universities. Speaking in particular about computing, where I know the situation better, there are a lot of very good ideas generated in industry, both in mature companies and start-ups, in open-source, by independent consultants, or even just by random hackers. Well, academics very seldom acknowledge such ideas — they don’t know how to cite something which is not a formal paper. For this reason the feedback loop is somewhat closed. Not completely closed, some “traitors” do occasionally slip in at least new problems to work on from the “real world.”

This is where the title of this post comes from: I came to believe that in general academics do love themselves more than the real world.

Professors defend their status quo with two technical weapons:

• The academic freedom: “don’t you interfere with my teaching and my work.”
• The fundamental principles: “everything changes too quickly, so I am not going to teach the latest technological fad, I am teaching the principles.”

Both of these are great things, but can be used to just reinforce the feedback cycle I described above. And the problem is, not everybody should be a professor.

The meaning given to the word [computer-related] “research” is not the same in universities (i.e. academia) and in industrial research labs. (Research has yet other meanings, that I won’t touch, for example, “market research.”) The fact that “research” has two meanings is quite subtle, because these two meanings do overlap substantially.

The duality of meanings is most apparent when you see how people from the two environments regard the same piece of work. While I saw people from academia regard a paper as a masterpiece, at the same time people in industry declared it useless. And each party had perfectly valid arguments to their side. How could that be possible?

To reconcile these seemingly contradictory points of view, one has to understand that people in these two camps play two different games, and thus optimize for two different criteria. In academia the reward is tenure, and (perhaps surprisingly) the respect of peers, and the main measure of success seems to be the number of publications (and various awards). In industrial research labs the main reward is having your work translated into a product, and the measures of success are more varied, but include mainly technology transfer (hard to quantify) and patents. What blurs this picture is the fact that there are quite a few people who cross the lines: academics who create companies (i.e., start-ups) or consult for industry, people in labs who publish papers. But in the end, there are two different games that are played here, with the same name, “research”, but with different rewards, and different rules.

There is this famous quote:

Life is a game. Money is how we keep score.
— Ted Turner

I believe that this is pushing the “game” framework a little too far, as I will explain in a minute. But the “game” framework has become really useful for me. Let me give you one more example.

Let’s take software developers. I have met some absolutely brilliant software developers, who can craft some amazing pieces of software, and who have proven that they have a very deep understanding of computers, perhaps much deeper than many people who have stayed much more time in school and gotten a Ph.D. exactly to study the behavior of the machine.

I can imagine how a guy with a Ph.D. can snicker about these developers being just “code monkeys,” who can’t put a paper together, and who don’t know the “related work.” I can also imagine how the coder can snicker about the Ph.D. guy having never produced a piece of code which doesn’t break any time the wind blows. So, who is better?

This is the wrong question. You have to understand that the developer and the graduate student are playing different games. One of them is optimizing for producing software, and the other is optimizing for producing papers. Both of these are really hard and intellectually deep activities. Both of them, when done well, can be very useful for society, and society can pay top dollar for them. You can’t ask one of them to play the other’s game. It would be like asking Tiger Woods to write equations and Einsten to play golf. These are just different games.

That’s why I think that Ted Turner’s quote is not always appropriate: not all people want to play together at the same table.

But I have drifted from my original subject.

Once you understand that academia and industry are different, you can adjust your career accordingly. This has important consequences for the interview style – which should be different in academia and industry, and for managing your career, and even for networking with people from these environments. But I hope to write about some of this stuff in another post.