Good advice from David Allen.

You can, however, use a sequence of five events to optimize your focus and resources, whether you’re trying to get it together in your kitchen, your conversation, your contract, your company or your country.

• Capture everything that has your attention, in your work and your personal life, in writing. Maybe it’s your departmental budget, a meeting with the new boss, an overdue vacation, or just the need to buy new tires and a jar of mayonnaise. For the typical professional, it can take one to six hours to “empty the attic” of your head. It may seem daunting, but this exercise invariably leads to greater focus and control.

• Clarify what each item means to you. Decide what results you want and what actions — if any — are required. If you simply make a list and stop there, without putting the items in context, you’ll be stuck in the territory of compulsive list-making, which ultimately won’t relieve the pressure. What’s the next action when it comes to your budget? The next step in arranging your vacation? Applying this simple but rigorous model puts you in the driver’s seat; otherwise, your lists will hold your psyche hostage. And keep in mind that much progress can be made and stress relieved by applying the magic two-minute rule — that any action that can be finished in two minutes should be done in the moment.

• Organize reminders of your resulting to-do lists — for the e-mails you need to send, the phone calls you need to make, the meetings you need to arrange, the at-home tasks you need to complete. Park the inventory of all your projects in a convenient place.

• Regularly review and reflect on the whole inventory of your commitments and interests, and bring it up to date. As your needs change, what can move to the front burner, and what can go further back? Make these decisions while considering your overall principles, goals and accountabilities. Schedule a two-hour, weekly operational review, allowing space to clean up, catch up and do some reflective overseeing of the landscape, for all work and personal goals, commitments and activities.

• Finally, deploy your attention and resources appropriately.

Does our productivity really depend on this basic set of behaviors and thought processes? So it seems. Everyone is already half-trying to do all of this, all the time. But many people just haven’t identified the process, or applied it.

I have never seen anyone apply these practices, with some degree of commitment and application, and not find significant improvement in focus, control and results.

Hmmm… Must put this on a to-do list.

BTW: 43 Folders had a great series about Allen’s GTD (Getting Things Done) system.

Seen in a NYTimes piece by productivity guru David Allen.

I have found that most professionals take action based on whatever is the latest and loudest in their universe, as opposed to a making a conscious, intelligent choice springing from the model I’ve described. This day-to-day, minute-to-minute arena of “reaction versus pro-action” is where the scales tip to “productive” or “unproductive.”

ONE possible path to that feeling of control is to return to a make-it-or-move-it existence. Find work that requires little if any thinking, but merely reacts and responds to what presents itself. That’s a real option: I once met a senior vice president in a global pharmaceutical company who, after taking an early retirement package, became a duck at Disney World. In such a job, it was probably much easier to have a good day at work, and then leave it behind.

Went to an excellent lecture this morning by Chris Bishop of Microsoft Research. It was part of the wonderful Cambridge Science Festival (of which I’m a Patron) and his topic — “Great Ideas in Computer Science” — is very germane to something I’m writing at the moment.

It’s always intriguing to see what other people regard as key ideas. (I’ve had my own go at this recently in relation to the Internet). So I was agog to see Chris’s list.

They are:

1. Photolithography — the technology that powers Moore’s Law, enabling us to take for granted massive increases in computing power.

2. Algorithms.

3. Cryptography.

4. Machine learning.

5. Biology as computation.

As you’d expect from such an accomplished lecturer (he did the 2008 Royal Institution Christmas Lectures) it was a presentation beautifully tailored to its audience (keen children and their even-keener parents). He illustrated the idea of algorithms by getting five kids to act out a sorting algorithm on stage. For machine learning he had a very nice exposition of how ‘recommender’ engines (eg on Amazon) work. And he had some amazing animated videos of simulations of DNA replication in action.

But for me the best bits of the lecture were:

I came away brooding about whether the term Computer Science might not be a bit of a misnomer. We use it when trying to persuade the government (and the public) that computing is an academic subject rather than a mere skill (like learning to use Microsoft Excel) because the word ‘science’ involves difficulty, abstraction and law-like generalisations which are dependable, empirically-supported and enduring. But as I walked back to my car I remembered a conversation I had with the late, great Roger Needham in which he argued that what we call “computer science” actually involves an awful lot of “technology” as well as “science”.

And, in a way, Chris Bishop’s lecture implicitly acknowledged that. Photolithography, for example, is a technology (though one based on the physics of light). Same goes for machine learning. Cryptography is mostly applied mathematics. So we’re left with the question: what is Computer Science? The Oxford CS department says that it’s “about learning and understanding the mathematical, scientific and engineering principles underlying every kind of computing system, from mobile phones and the internet, via systems that interpret natural language, to the supercomputers that forecast tomorrow’s weather or simulate the effects of disease on the human heart.” To be a successful Computer Science student, it continues, “you will need a curiosity about how things work, and the ability to use mathematics to solve problems creatively”. Cambridge describes CS as “a fast-moving field that brings together many disciplines, including mathematics, programming, engineering, the natural sciences, psychology and linguistics”. Carnegie-Mellon says that “Computer science can organize information, build smaller, faster, more secure systems, create channels of communication and delve deep into complex data sets” and goes on to link it to something called “Computational Thinking” — defined as “how computer scientists determine what can be computed and how to compute it. By looking at the world through the lenses of algorithms and abstraction”.

CMU makes a big deal of this Computational Thinking idea. (The phrase comes from a much-cited editorial in Communications of the ACM in 2006 by Jeanette Wing, a professor at CMU).

Computational thinking is a way of solving problems, designing systems, and understanding human behavior that draws on concepts fundamental to computer science. To flourish in today’s world, computational thinking has to be a fundamental part of the way people think and understand the world.

Computational thinking means creating and making use of different levels of abstraction, to understand and solve problems more effectively.

Computational thinking means thinking algorithmically and with the ability to apply mathematical concepts such as induction to develop more efficient, fair, and secure solutions.

Computational thinking means understanding the consequences of scale, not only for reasons of efficiency but also for economic and social reasons.

Hmmm… I’m not sure how I’d explain that to Michael Gove.

Postscript: Thanks to Miranda Gomperts, who was also at the lecture and provided me with the link to the DNA animations.