I spend much time alone, writing and thinking. Twitter helps me stay connected. I love the platform.

On Friday, I wanted to find the intersection between the users followed by any two individuals. Indeed, suppose that you like both Joe and Jill, and they have similar interests. Maybe whoever they both read is also interesting? I could not find a tool to do it, so I built it with python-twitter.

Anybody with a working knowledge of Python can do it in less than 5 minutes. I used only twenty lines of code (in total!!!). The code proved immediately useful.

If you do not know Python or Ruby. Learn one or the other. Tonight. It is powerful stuff.

Update: A few days after I wrote this post, Twitter made my script obsolete by changing their authentification requirements.

• Research results are more important than the number of publications or citations.
  This is fine. Yet, we don’t have time to read your papers. So, just keep publishing a lot of papers each year. And get your influential friends to cite you. That’s how we’ll know whether you are good.
• Science and truth are more important than spin and marketing.
  Yes, but keep pretending you will solve world hunger. And align your research results with the current fashionable trends.
• You cannot tell where the next science breakthrough is going to come from.
  Maybe. Still, we want a plan of your research activities for the next five years.

Further reading: The hard truth about research grants and The secret behind radical innovation.

Source : Manifesto for Half-Arsed Agile Software Development via John D. Cook.

• Permanent researchers publish more when they are in smaller labs.
• Having many Ph.D. students fails to improve productivity.
• Funding has little effect on research productivity.

Reference: Carayol, N. and Matt, M., Individual and collective determinants of academic scientists’ productivity, Information Economics and Policy 18 (1), 2006.

Further reading (on this blog): To be smarter, ignore external rewards, Is collaboration correlated with productivity?, Big schools are no longer giving researchers an edge?

We do too much. We carry too many projects. This overproduction creates problems which we try to fix by working even more.

We value most what we create (see Made by hand and The upside of irrationality). To be happy, you want to focus on making interesting stuff. This takes time and dedication. Yet, as Graham’s essay The top idea in your mind stresses, we often fall into the trap of thinking mostly about money and personal disputes. These thoughts pull us away from our interests and prevent us from doing great work. As an example, I hear that Tiger Woods isn’t playing great golf. I bet he is either stuck into money problems or personal disputes, or both.

It is hard to be overworked by writing a book, by writing research articles or by playing golf. People are overworked dealing with email, context switching, money, and touchy relationships. This abundance of work makes people sad and boring. And this type of work tends to reproduce. The more you have, the more you will have.

Unemployment and pollution are visible results of our overproduction. Yet, there are many more negative side effects. In academia, we train more and more Ph.D.s every year. Yet, we have had too many Ph.D.s in the job market since the seventies. We write more and more research papers every year, and spend more and more time applying for research grants… but professors spend less and less time on curiosity-driven research.

It is cool to produce great work, but it is not cool to work 60 hours a week unless it is out of passion. And nobody is passionate about grant applications, marking papers or handling difficult people. Moreover, working long hours does not scale: you can’t increase your output continuously.

Our productivity will keep improving. I can write software faster and better than ever. I can research prior work with ease. I can ask fancy mathematical questions on the Web and get answers in minutes. Instead of investing back this productivity into more silly work, we need to get smarter:

• Focus on the essential: programming great software, writing a fun book, a set of inspiring lecture notes or an insightful article.
• Automate, reduce or delegate. Reduce is best: doing fewer things is cool!
• A focus on money or on personal disputes makes you stupid. Yet, that’s where success often takes you. Watch out!
• Airplanes pollute. Travel takes you away from your family. Cars pollute and make you fat. Do you need all that junk?

Deolalikar claims to have solved the famous P versus NP problem. Is the proof correct? Some influential researchers doubt it: Scott Aaronson is betting 200k$ of his own money against Deolalikar.

What I find most interesting is that Deolalikar did not submit the paper to a journal, as far as I know. He didn’t even post it on arxiv like Perelman. Yet, he is receiving much attention. His name is being tweeted several times a minute. Many of the most influential theoretical computer scientists are reacting to the paper. He is getting the best peer review possible. Most similar papers don’t get so much attention.

Why is this paper different?

• Everyone seems to agree that the paper is well written, it has nice (color!) figures and the reference section appears up-to-date and complete.  If your result is important, communicate it well.
• Deolalikar has published just a handful of papers in theoretical computer science, and none at the major conferences. But he has enough peer-reviewed research papers to be treated as a peer.
• While I doubt he was hired to work on complexity theory, Deolalikar is an industry researcher at HP. Being paid to do research might make you more credible.

Further reading: Deolalikar’s publication list on DBLP, A Proof That P Is Not Equal To NP? by Lipton and P ≠ NP by Baker.

Update: Porreca has the best write-up on reactions to this paper.

Update 2: The consensus after two weeks is that the proof wrong and unfixable.