I played chess in my high school team, but then switched to Go at university. I felt like my mind opened up to more universal concepts of cause-and-effect and harmony with Go, as opposed to chess which is so narrow and constrained in comparison.<p>With Go it felt like something "switched on" in my thinking that allowed me to better evaluate life situations and to act with a better appreciation of possible consequences.<p>When AlphaGo showed superiority, I was dismayed that the "last frontier" of human intellect has been surpassed. Chess being easily defeated long before was one of the reasons I began to disregard it and rather consider Go.<p>After AlphaGo I began to appreciate that computer superiority doesn't mean an end to human play, as it is like an art. Maybe playing a game of chess or Go is like painting a picture, one with a distinct humanness to it. There is something lifeless and mechanical in GAN-produced pictures, which makes one yearn for a human touch. Although newer generatives like DALL-E are starting to encroach upon this too.<p>Also, a game is like a vigorous exchange of ideas with a social aspect to be enjoyed.<p>Anyway, I have to admit that I still have reservations against Chess, as I see it maybe like drawing with crayons on the playground. Each to their own, but I wonder when they will take up a paintbrush and canvas and upgrade to Go.
This article agrees somewhat with my pet model of the relationship between intelligence and achievement. That is, (assuming you pass a certain threshold; you aren’t going to be a particle physicist with an 85 IQ) natural talent is only the dominating predictor of performance twice: when you don’t have that much practice at something and when you’re bumping up against the current limits of what can be done (e.g. when you’re late in your chess career or have seen all the gains from practice you can).<p>Obviously intelligence matters when you’re starting out in a field: if you can learn things more quickly and more completely than the next person, you’ll have a head start that can’t be beat with raw practice. It’s similarly self-evident that it’s necessary when you’re close to the “end” of your field: it’s /hard/ to push boundaries and do things no one has before. Mark Kac’s quote about “magical geniuses” comes to mind.<p>I think people spend a lot of time in the early stages of a field (I graduated with a math major and I’m still just starting out in math!) and I feel like this tends to bias people towards saying that intelligence is a dominating factor for performance over a lifetime. However, I think this neglects the fact that 99% of a given field lies somewhere between the absolute beginning and the absolute cutting edge.<p>This is something I’ve noticed: nearly all the high achievers I know are reasonably intelligent people who are crazy passionate. I knew people in my friend group at college who went on to get {Masters,PhDs} at {Stanford,MIT} and out of all 4 of them only one was exceptionally smart IMO (over 1/1000 rarity).<p>This also leads me to the conclusion that most people you see who seem crazy smart are probably a fairly normal level of smart with effort added to taste. The argument for this isn’t ideological - it’s statistical. There are simply so many more above-average people than there are exceptional people that most of the really high (but not world class) performers come from the former group.
There is <a href="https://listudy.org" rel="nofollow">https://listudy.org</a> which offers spaced-repetition based chess training. I really wish the project received more contribution though.<p><a href="https://github.com/ArneVogel/listudy/" rel="nofollow">https://github.com/ArneVogel/listudy/</a>
>The not very surprising conclusion is that both intelligence and practice are important factors in chess skill development. On their own, however, they can only explain certain aspects of development. For example, practice has its strongest effect in the beginning of expertise development, whereas intelligence’s strongest effect is at the peak and in the later stages. Together, they explain the changes across the whole life span much better than they do on their own.
I'm confused about the status of this study and the ELO graphs. The post gives some detail about the methods for measuring intelligence, but stops short of describing the sample group and calls both IQ 100 and IQ 120 groups "hypothetical". Are those graphs real data, or just descriptive of the authors' current hypothesis?