Rating: 7.5 / 10
You don't have to specialize to be the best. Great read.
Early sampling (trying multiple different things) and then late specialization seems to be the way to go if you want to become great.
Deliberate practice and experience will yield success depending on the field that you are in. It works in chess, golf and firefighting, but not so much for predictors (financial etc) for example.
People are good at chess because they can recognize patters. The same goes for elite athletes. They recognize patters. When this is tested outside of their sport context, their superhuman reactions disappear.
Narrow fields like chess are very easy for AI to conquer. However, humans are still king in open fields (driving is one where AI is gaining ground, but still has challenges). Health care (curing cancer) is a field where AI seems to be very much behind.
"Scientists and members of the general public are about equally likely to have artistic hobbies, but scientists inducted into the highest national academies are much more likely to have avocations outside of their vocation. And those who have won the Nobel Prize are more likely still."
Students need to learn how to think in interdisciplinary ways. Like Arnold Toynbee said, "No tool is omnicompetent". Often people go into a career unrelated to their field of study, yet they've only become competent with the tools of a single discipline.
The modern world demands people who have conceptual reasoning skills that can connect new ideas and work across contexts.
It seems to be the same case in music. You have to go through a sampling period of a few instruments, and then eventually specialize. The students at a specific school who had done this was recognized by the school as exceptional, whereas those who simply specialized in one instrument early and had a lot of practice seemed to fall into the 'average' category.
Frustration is not a sign that you are not learning, but ease is.
Learn to interleave - for example when doing math, you'd want to mix up the types of exercises that you do. Shaq was asked to practice free throws not from the single free throw line, but by varying length so that he'd get better motor skills. Vary the difficulty.
Figure out the type of problem that you are facing instead of just slamming a procedure on it. Deep learning is important.
Learning deeply means learning slowly.
Kepler made his theory of Planetary Motions by using analogies. Taking concepts and situations from other domains and applying it to what became astrophysics.
This analogical thinking is something that really contributes to the human-superiority over other animals. Something that reminds me of this is 'eksemplaritet' from PBL in one of my courses at college.
Adapt an outside view (Daniel Kahneman & Amos Tversky) more often.
The more details you know about something the more you favor it to (happen/succeed). The more details you have to consider, the more extreme your judgment will be.
It's actually better to use analogies from outside the field than from inside the field.
Don't worry about switching careers or the likes in the middle of one. People say that 'quitters never win and winners never quit', but that is actually horrible advice. People who are unhappy often become happier by switching paths.
When you are young, you should try the higher-risk options before anything else. The potential reward is huge, and you're young, so if you fail; try something else.
Seth Godin found that winners actually quit often and fast, and do not feel bad about it. He didn't just mean to quit when things got hard. But knowing when to quit is a huge advantage to have. It's about differentiating between the desire to quit for a lack of perseverance or because you've found that there are better matches for you accessible - in which case you'd quit your current task and pursue one of those.
Van Gogh had an incredible work ethic. He, however, placed in the 40th percentile of Grit scores because he did not stick to individual tasks - he switched goals often. But look where he ended up. You shouldn't feel bad about picking a better option when it is presented.
"No one in their right mind would argue that passion and perseverance are unimportant, or that a bad day is a cue to quit. But the idea that a change of interest, or a recalibration of focus, is an imperfection and competitive disadvantage leads to a simple, one-size-fits-all Tiger story: pick and stick, as soon as possible. Responding to lived experience with a change of direction, like Van Gogh did habitually, like West Point graduates have been doing since the dawn of the knowledge economy, is less tidy but no less important. It involves a particular behavior that improves your chances of finding the best match, but that at first blush sounds like a terrible life strategy: short-term planning."
Paul Graham: "I propose instead that you don't commit to anything in the future, but just look at the options available now, and choose those that will give you the most promising range of options afterward".
Test-and-learn. Experiment. Try things to see if you like them; flirt with your possible selves.
Sometimes highly-specialized experts can be stuck on a problem that would be solved by someone with outside-knowledge very quickly, simply because the outsider can think of solutions that they cannot.
"Knowledge is a double-edged sword. It allows you to do some things, but it also makes you blind to other things that you could do."
Interesting chapter starting off by detailing the rise of Nintendo.
Basically ends up making the point that having range makes you a better inventor. At least from my point of view reading it.
"There is a particular kind of thinker, one who becomes more entrenched in their single big idea about how the world works even in the face of contrary facts, whose predictions become worse, not better, as they amass information for their mental representation of the world. They are on television and in the news every day, making worse and worse predictions while claiming victory, and they have been rigorously studied."
Danish proverb: "It is difficult to make predictions, especially about the future".
The 'experts' predictions were always either right, and they had completely understood the world, or they were just an inch off. A near miss. Had one more detail happened 'right' they would have had it nailed (obv. not the case).
Foxes and hedgehogs. Foxes represent people who are generalists (know a little about a lot), and the reverse is the case for the hedgehogs (knows a lot about a little). Hedgehogs are actually worse at predicting the future in their own field the better they became at their specialization.
Foxes are pretty good at predicting the future. In fact, gather them as a team, and they become greater than the sum of their parts.
The best forecasters view their ideas as hypotheses in need of testing.
To be fair; Einstein was a hedgehog. But because of this, he spent the last 30 years of his life chasing a singular theory to explain the randomness of the universe because "god does not play dice with the universe".
Sometimes you have to drop your familiar tools and methods to arrive at the right answer. In the case of the NASA engineers that failed the Challenger launch, they depended so much upon their quantitative methods that they failed to accept some qualitative evidence right in front of them.
The dependency on your familiar tools come from overlearning. Performing a specific action so many times that you forget that the tools you use to do it are situation-specific. No tool fits all cases.
Feynman: "When you don't have any data, you have to use reason" - when investigating the Challenger failure.