The Edge question this year is "How has the Internet changed the way you think?". The result is eminently readable – my favorite so far is George Dyson’s answer, which is quoted here in its entirety:

GEORGE DYSON
Science Historian; Author, Darwin Among the Machines

KAYAKS vs CANOES

In the North Pacific ocean, there were two approaches to boatbuilding. The Aleuts (and their kayak-building relatives) lived on barren, treeless islands and built their vessels by piecing together skeletal frameworks from fragments of beach-combed wood. The Tlingit (and their dugout canoe-building relatives) built their vessels by selecting entire trees out of the rainforest and removing wood until there was nothing left but a canoe.

The Aleut and the Tlingit achieved similar results — maximum boat / minimum material — by opposite means. The flood of information unleashed by the Internet has produced a similar cultural split. We used to be kayak builders, collecting all available fragments of information to assemble the framework that kept us afloat. Now, we have to learn to become dugout-canoe builders, discarding unnecessary information to reveal the shape of knowledge hidden within.

I was a hardened kayak builder, trained to collect every available stick. I resent having to learn the new skills. But those who don’t will be left paddling logs, not canoes.

Short and sweet, in other words. Now, where did I leave that informational adze, what P. J. O’Rourke referred to as the "brief-but-insightful-summary" button?

How will the Internet change how we think?
Espen
Fri, 06 Aug 2010 13:47:02 GMT

Anyone with an interest in the continuing role and development of Smalltalk has had lots to chew on over the past few days.

As part of a series of investigations into the most widely-used programming languages, Computerworld Australia has published a conversation with Alan Kay about his role in the development of the “foundation of much of modern programming today: Smalltalk-80″, Object-Oriented Programming, and modern software development.

The Weekly Squeak: Smalltalk: the past, the present, and the future?
Michael Davies
Thu, 15 Jul 2010 10:00:45 GMT

Here’s a sample of Alan’s thinking :

What are the hurdles to those leaps in personal computing technology and concepts? Are companies attempting to redefine existing concepts or are they simply innovating too slowly?

It’s largely about the enormous difference between “News” and “New” to human minds. Marketing people really want “News” (= a little difference to perk up attention, but on something completely understandable and incremental). This allows News to be told in a minute or two, yet is interesting to humans. “New” means “invisible” “not immediately comprehensible”, etc.

So “New” is often rejected outright, or is accepted only by denaturing it into “News”. For example, the big deal about computers is their programmability, and the big deal about that is “meta”.

For the public, the News made out of the first is to simply simulate old media they are already familiar with and make it a little more convenient on some dimensions and often making it less convenient in ones they don’t care about (such as the poorer readability of text on a screen, especially for good readers).

For most computer people, the News that has been made out of New eliminates most meta from the way they go about designing and programming.

One way to look at this is that we are genetically much better set up to cope than to learn. So familiar-plus-pain is acceptable to most people.

[ComputerWorld Australia]

Alan can occasionally be a bit cryptic, but that’s because he assumes that you will do your share of the thinking when you listen to what he has to say.

Tech Support Cheat Sheet
Mon, 24 Aug 2009 04:00:00 GMT

There’s a striking amount of wisdom and good advice packed into this flowchart. It’s not about the body of knowledge stored away in your head. It’s about a robust strategy for generating and testing ideas that are likely to be productive.

What puzzles me is why individuals choose not to employ such a simple strategy.

The first is a clip by the late Nobel prize winning physicist Richard Feynman. In it he talks about his drive to figure out how nature works and the need to comfortable with not knowing. He believes in the process that has been given the fancy name of "the scientific method" despite its underlying simplicity. Make a guess, work out the consequences of your guess, run an experiment to compare your guess to reality, accept what reality tells you, and revise your guess for the next iteration. It’s very powerful, once you learn how to say "I don’t know, let’s find out."

The second clip comes from TED and shows Princeton molecular biologist, Bonnie Bassler describing her quest to understand how bacteria communicate. It’s a riveting look at how one person’s simple curiosity works in practice. Who knows, maybe she’ll get to go to Sweden some day.

Here is what Asimov had to say about believing in data…

"Don’t you believe in flying saucers, they ask me? Don’t you believe in telepathy? – in ancient astronauts? – in the Bermuda triangle? – in life after death?

No, I reply. No, no, no, no, and again no.

One person recently, goaded into desperation by the litany of unrelieved negation, burst out "Don’t you believe in anything?"

"Yes," I said. "I believe in evidence. I believe in observation, measurement, and reasoning, confirmed by independent observers. I’ll believe anything, no matter how wild and ridiculous, if there is evidence for it. The wilder and more ridiculous something is, however, the firmer and more solid the evidence will have to be."

Isaac Asimov, The Roving Mind (1997), 43

Asimov on evidence

The trouble is how easily our desire to believe can overwhelm the evidence.

1000 Times
Fri, 20 Mar 2009 04:00:00 GMT

We live in complicated times. We’re all trying to make sense of what is going on. That sense making isn’t made any easier by lazy writing and thinking. Actually, I don’t think this is a matter of deliberate efforts to mislead so much as it represents a continuing laziness when it comes to dealing with numbers, particularly big ones.

Two good places to start if you want to improve your own ability to make sense out of the numbers getting thrown around are Guesstimation: Solving the World’s Problems on the Back of a Cocktail Napkin and Filters Against Folly : How to Survive Despite Economists, Ecologists, and the Merely Eloquent.

Elizabeth Gilbert on nurturing creativity | Video on TED.com

John Medina is a molecular biologist bent on sharing how what we know about the brain can help us be more effective in the world at large. His central argument is that there are simple, but very important, lessons to be drawn from what science has learned in recent years about how the brain operates. Many of these lessons run counter to the practices and conventions that hold sway in our schools and organizations.

You can be pretty sure that I�m going to like any book that concludes:

The greatest Brain Rule of all is something I cannot prove or characterize, but I believe in it with all my heart. As my son was trying to tell me, it is the importance of curiosity.

For his sake and ours, I wish classrooms and businesses were designed with the brain in mind. If we started over, curiosity would be the most vital part of both demolition crew and reconstruction crew.

If you harbor doubts about evolutionary biology as the force that shaped our brains over millennia, leave this book on the shelf. Much of Medina�s narrative is driven by showing how our origins as hunter-gatherers shaped our brains. Here are the twelve rules Medina uses to organize his story:

1. Exercise boosts brain power
2. The human brain evolved, too
3. Every brain is wired differently
4. We don�t pay attention to boring things
5. Repeat to remember
6. Remember to repeat
7. Sleep well, think well
8. Stressed brains don�t learn the same way
9. Stimulate more of the senses
10. Vision trumps all other senses
11. Male and female brains are different
12. We are powerful and natural explorers

For each rule, Medina takes us through what we know based on today�s research. He�s a good storyteller and distills the research findings nicely. He also ends each section with concrete suggestions and ideas on ways to put the findings into practice.

Let�s take Rule 4, for example, �we don�t pay attention to boring things.� Formal and informal studies suggest that we have about a 10-minute attention span before our mind wanders off in search of something new and exciting. Ignoring this rule leads to boring college lecture hours and death by PowerPoint across the corporate universe. Factoring the rule into how you design your lectures or presentations, however, will enhance your prowess and reputation.

Of course, there�s a website to support the book, www.brainrules.net and Medina has a blog, Brain Rules. Both are worth a visit. Garr Reynolds, who wrote the excellent Presentation Zen, has a review and a nicely done presentation summarizing the book that are also well worth checking out.

Stroke of insight: Jill Bolte Taylor on TED.com

Neuroanatomist Jill Bolte Taylor had an opportunity few brain scientists would wish for: One morning, she realized she was having a massive stroke. As it happened — as she felt her brain functions slip away one by one, speech, movement, understanding — she studied and remembered every moment. This is a powerful story of recovery and awareness — of how our brains define us and connect us to the world and to one another. (Recorded February 2008 in Monterey, California. Duration: 18:44.)

Watch Jill Bolte Taylor’s talk on TED.com, where you can download it, rate it, comment on it and find other talks and performances.

Read more about Jill Bolte Taylor on TED.com.

NEW: Read the transcript >>

…this morning I talked about hierarchies of thought–the system. Now I want to talk about methods of finding one’s way through these hierarchies–logic.

Two kinds of logic are used, inductive and deductive. Inductive inferences start with observations of the machine and arrive at general conclusions. For example, if the cycle goes over a bump and the engine misfires, and then it goes over another bump and the engine misfires, and then it goes over another bump and the engine misfires, and then it goes over a long smooth stretch of road and there is no misfiring, then it goes over a fourth bump and the engine misfires again, you can logically conclude that the misfiring is caused by the bumps. That is induction: reasoning from particular experiences to general truths.

Deductive inferences do the reverse. They start with general knowledge and predict a specific observation. For example, if from reading the hierarchy of facts about the machine, the mechanic knows the horn of the cycle is powered exclusively from the battery, then he can logically infer that if the battery is dead, the horn will not work. That is deduction. Solution of problems too complicated for common sense to solve is achieved by long strings of mixed inductive and deductive inferences that weave back and forth between the observed machine and the mental hierarchy of the machine found in the manuals. The correct program for this interweaving is formalized as scientific method.

Actually I have never seen a cycle-type maintenance problem complex enough to really require full-scale formal scientific method. Repair problems are not that hard. When I think of formal scientific method, an image sometimes comes to mind of an enormous juggernaut. A huge bulldozer–slow, tedious, lumbering, laborious, but invincible. It takes twice as long, five times as long, maybe a dozen times as long as informal mechanic’s techniques, but you know in the end you are going to get it. There is no fault isolation problem in motorcycle maintenance that can stand up to it. When you’ve hit a really tough one, tried everything, racked your brains and nothing works, and you know that Nature this time has really decided to be difficult, you say, “Okay, Nature, that’s the end of the nice guy,” and you crank up the formal scientific method.

For this you keep a lab notebook. Everything gets written down, formally, so you know at all times where you are, where you’ve been, where you’re going and where you want to get. In scientific work and electronics technology this is necessary because otherwise the problems get so complicated you get lost in them and confused and forget what you know and what you don’t know and have to give up. In cycle maintenance, things are not that involved, but when confusion starts, it’s a good idea to hold it down by making everything formal and exact. Sometimes just the act of writing down the problem straightens out your head as to what they really are.

The logical statements written down into the notebook are broken down into six categories: (1) statement of the problem, (2) hypothesis as to the cause of the problem, (3) experiments designed to test each hypothesis, (4) predicted results of the experiments, (5) observed results of the experiments and (6) conclusions from the results of the experiments. This is no different from the formal arrangement of many college and high school lab notebooks. But the purpose here is no longer just busywork. The purpose is precise guidance of thought that will fail if they are not accurate.

The real purpose of the scientific method is to make sure Nature hasn’t misled you into thinking you know something you don’t actually know. There is not a mechanic or scientist or technician alive who hasn’t suffered from that one so much that he’s not instinctively on guard. That’s the main reason why so much scientific and mechanical information sounds so dull and so cautious. If you get careless or go romanticizing scientific information, giving it a flourish here and there, Nature will soon make a complete fool out of you. It does it often enough anyway even when you don’t give it opportunities. One must be extremely careful and rigidly logical when dealing with Nature: one logical slip and an entire scientific edifice comes tumbling down. One false deduction about the machine and you can get hung up indefinitely.

In Part One of formal scientific method , which is the statement of the problem, the main skill is in stating no more than you positively know. It is much better to enter a statement “Solve Problem: Why doesn’t cycle work?” which sounds dumb, but is correct, than it is to enter a statement “Solve Problem: what is wrong with the electric system?” when you don’t absolutely know the trouble is in the electric system. What you should state is “Solve Problem: What is wrong with the cycle?” and then state as the first entry in Part Two: “Hypothesis Number One: The trouble is in the electrical system.” You think of as many hypothesis as you can, then you design experiments to test them to see which are true and which are false.

This careful approach to the beginning questions keeps you from taking a wrong turn which might cause you weeks of extra work or can even hang you up completely. Scientific questions often have a surface appearance of dumbness for this reason. They are asked in order to prevent dumb mistakes later on.

Part Three, that part of formal scientific method called experimentation, is sometimes thought of by romantics as all of science itself because that’s the only part with much visual surface. They see lots of tubes and bizarre equipment and people running around making discoveries. They do not see the experiment as part of a larger intellectual process and so they often confuse experiments with demonstrations, which look the same. A man conducting a gee-whiz science show with fifty thousand dollars’ worth of Frankenstein equipment is not doing anything scientific if he knows beforehand what the results of his effort are going to be. A motorcycle mechanic, on the other hand, who honks the horn to see if the battery works is informally conducting a true scientific experiment. He is testing a hypothesis by putting the question to Nature. The T.V. scientist who mutters sadly “the experiment is a failure; we have failed to achieve what we had hoped for,” is suffering mainly from a bad script writer. An experiment is never a failure solely because it fails to achieve predicted results. An experiment is a failure only when it also fails adequately to test the hypothesis in question, when the data it produces don’t prove anything one way or another.

Skill at this point consists of using experiments that test only the hypothesis in question, nothing less, nothing more. If the horn honks, and the mechanic concludes the whole electrical system is working, he is in deep trouble. He has reached an illogical conclusion. The honking horn only tells him that the battery and horn are working. To design an experiment properly he has to think very rigidly in terms of what directly causes what. This you know from the hierarchy. The horn doesn’t make the cycle go. Neither does the battery, except in a very indirect way. The point at which the electrical system directly causes the engine to fire is at the spark plugs, and if you don’t test here, at the output of the electrical system, you will never really know whether the failure is electrical or not.

To test properly the mechanic removes the plug and lays it against the engine so the base around the plug is electrically grounded, kicks the starter lever and watches the spark-plug gap for a blue spark. If there isn’t any he can conclude one of two things: (A) there is an electrical failure or, (B) his experiment is sloppy. If he is experienced, he will try it a few more times, checking connections, trying every way he can think of to get that plug to fire. Then, if he can’t get it to fire, he finally concludes that A is correct, there is an electrical failure, and the experiment is over. He has proved that his hypothesis is correct.

In the final category, Conclusions, skill comes in stating no more than the experiment has proved. It hasn’t proved that when he fixes the electrical system the motorcycle will start. there may be other things wrong. But he does know that the motorcycle isn’t going to run until the electrical system is working and he sets up the next formal question: “Solve Problem: what is wrong with the electrical system?”

He then sets up hypotheses for these and tests them. By asking the right questions and choosing the right tests and drawing the right conclusions the mechanic works his way down the echelons of the motorcycle hierarchy until he has found the exact specific cause or causes of the engine failure, and then he changes them so that they no longer cause the failure.

An untrained observer will see only physical labor and often get the idea that physical labor is mainly what the mechanic does. Actually the physical labor is the smallest and easiest part of what the mechanic does. By far the greatest part of his work is careful observation and precise thinking. That is why mechanics sometimes seem taciturn and withdrawn when performing tests. They don’t like it when you talk to them because they are concentrating on mental images, hierarchies, and not really looking at you or the physical motorcycle at all. They are using the experiment as part of a program to expand their hierarchy of knowledge of the faulty motorcycle and compare it to the correct hierarchy in their mind. They are looking at underlying form.

(Chapter 9. pages 107-111 from Pirsig, R. M. (1999). Zen and the art of motorcycle maintenance. 25th Anniversary Edition. Morrow.)