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 This is Grant Gussman

This is Grant Gussman. He watched an old video of mine about how we think that there are two systems of thought. System two is the conscious slow effortful system. And system one is subconscious. Fast and automatic.

To explore how these systems work in his head, Grant decided to memorize a hundred digits of pi. – Three eight four four six… – Then he just kept going.

He has now memorized 23,000 digits of pi in preparation to challenge the north American record – .95493038196. That’s 200. (Derek laughs) – That’s amazing. I have wanted to make a video about experts for a long time.

This is Magnus Carlsen, the five-time world Chess champion. He’s being shown chessboards and asked to identify the game in which they occurred. – This looks an awful lot like Tal V Botvinnik. (playful music) – Whoops. – Okay.

This is the 24th game from Sevilla. (chuckling) – Now I’m going to play through an opening.

And stop me when you recognize the game. And if you can tell me who was playing black in this one. Okay.

(playful music) I’m sure you’ve seen this opening before. – Okay. It’s gonna be Anand. (laughs) – Against? – Zapata.

– How can he do this? It seems like a superhuman ability. Well decades ago, scientists wanted to know what makes experts like chess masters special. Do they have incredibly high IQs, much better spatial reasoning than average, and bigger short-term memory spans? Well, it turns out that as a group, chess masters are not exceptional on any of these measures.

But one experiment showed how their performance was vastly superior to amateurs. In 1973, William Chase and Herbert Simon recruited three chess players, a master, an A player, who’s an advanced amateur, and a beginner. A chess board was set up with around 25 pieces positioned as they might be during a game. And each player was allowed to look at the board for five seconds. Then they were asked to replicate the setup from memory on a second board in front of them.

The players could take as many five-second peeks as they needed to get their board to match. From just the first look, the master could recall the positions of 16 pieces. The A player could recall eight, and the beginner only four. The master only needed half the number of peeks as the A player to get their board perfect. But then the researchers arranged the board with pieces in random positions that would never arise in a real game.

And now, the chess master performed no better than the beginner. After the first look, all players regardless of rank could remember the location of only three pieces. The data are clear. Chess experts don’t have a better memory in general, but they have better memory specifically for chess positions that could occur in a real game. The implication is what makes the chess master special, is that they have seen lots and lots of chess games.

And over that time, their brains have learned patterns. So rather than seeing individual pieces at individual positions, they see a smaller number of recognizable configurations. This is called ‘chunking’. What we have stored in long-term memory allows us to recognize complex stimuli as just one thing. For example, you recognize this as pi rather than a string of six unrelated numbers or meaningless squiggles for that matter.

– There’s a wonderful sequence I like a lot which is three zero one seven three. Which to me, means Stephen Curry number 30, won 73 games, which is the record back in 2016. So three oh one seven three. – At its core, expertise is about recognition. Magnus Carlsen recognizes chess positions the same way we recognize faces.

And recognition leads directly to intuition. If you see an angry face, you have a pretty good idea of what’s gonna come next. Chess masters recognize board positions and instinctively know the best move. – Most of the time, I know what to do. I don’t have to figure it out.

– Developing the long-term memory of an expert takes a long time. 10,000 hours is the rule of thumb popularized by Malcolm Gladwell, but 10,000 hours of practice by itself is not sufficient. Four additional criteria must be met. And in areas where these criteria aren’t met, it’s impossible to become an expert. So the first one is many repeated attempts with feedback.

Tennis players hit hundreds of forehands in practice. Chess players play thousands of games before they’re grand masters and physicists solve thousands of physics problems.

Each one gets feedback. The tennis player sees whether each shot clears the net and is in or out. The chess player either wins or loses the game.

And the physicist gets the problem right or wrong. But some professionals don’t get repeated experience with the same sorts of problems. Political scientist, Philip Tetlock picked 284 people who make their living commenting or offering advice on political and economic trends. This included journalists, foreign policy specialists, economists, and intelligence analysts. Over two decades, he peppered them with questions like Would George Bush be re-elected?

Would apartheid in South Africa end peacefully? Would Quebec secede from Canada? And would the .com bubble burst? In each case, the pundits rated the probability of several possible outcomes.

And by the end of the study, Tetlock had quantified 82,361 predictions. So, how did they do? Pretty terribly. These experts, most of whom had post-graduate degrees, performed worse than if they had just assigned equal probabilities to all the outcomes. In other words, people who spend their time and earned their living studying a particular topic, produce poorer predictions than random chance.

Even in the areas, they knew best, experts were not significantly better than non-specialists. The problem is, that most of the events they have to predict are one-offs. They haven’t had the experience of going through these events or very similar ones many times before. Even presidential elections only happen infrequently, and each one is in a slightly different environment. So we should be wary of experts who don’t have repeated experience with feedback.

(upbeat music) The next requirement is a valid environment. One that contains regularities that make it at least somewhat predictable. A gambler betting at the roulette wheel, for example, may have thousands of repeated experiences with the same event. And for each one, they get clear feedback in the form of whether they win or lose, but you would rightfully not consider them an expert because the environment is low validity. A roulette wheel is essentially random, so there are no regularities to be learned.

In 2006, legendary investor, Warren Buffet offered to bet a million dollars that he could pick an investment that would outperform Wall Street’s best hedge funds over 10 years.

Hedge funds are pools of money that are actively managed by some of the brightest and most experienced traders on Wall Street. They use advanced techniques like short selling, leverage, and derivatives in an attempt to provide outsized returns. And consequently, they charge significant fees. One person took Buffet up on the bet; Ted Seides of Protege Partners.

For his investment, he selected five hedge funds. Well actually, five funds of hedge funds. So in total, a collection of over 200 individual funds. Warren Buffet took a very different approach. He picked the most basic, boring investment imaginable; a passive index fund that just tracks the weighted value of the 500 biggest public companies in America, the S&P 500.

They started the bet on January 1st, 2008, and immediately things did not look good for Buffet. It was the start of the global financial crisis, and the market tanked. But the hedge funds could change their holdings and even profit from market falls. So they lost some value, but not as much as the market average. The hedge funds stayed ahead for the next three years, but by 2011, the S&P 500 had pulled even.

And from then on, it wasn’t even close. The market average surged leaving the hedge funds in the dust.

After 10 years, Buffet’s index fund gained 125.8% to the hedge funds’ 36%. Now the market performance was not unusual over this time.

At eight and a half per cent annual growth, it nearly matches the stock market’s long-run average. So why did so many investment professionals with years of industry experience, research at their fingertips, and big financial incentives to perform, fail to beat the market? Well because stocks are a low validity environment. Over the short term, stock price movements are almost entirely random. So the feedback, although clear and immediate doesn’t reflect anything about the quality of the decision-making.

It’s closer to a roulette wheel than to Chess. Over 10 years, around 80% of all actively managed investment funds fail to beat the market average. And if you look at longer periods, underperformance rises to 90%. And before you say, “Well that means 10% of managers have the actual skill, consider that just through random chance, some people would beat the market anyway.

Portfolios picked by cats or throwing darts have been shown to do just that.

And in addition to luck, there are nefarious practices from insider trading to pump and dump schemes. Now I don’t mean to say there are no expert investors. Warren Buffet himself is a clear example. But the vast majority of stock pickers and active investment managers, do not demonstrate expert performance because of the low validity of their environment. Brief side note, if we know that stock picking will usually yield worse results over the long term and that what active managers charge in fees is rarely compensated for in improved performance, then why is so much money invested in individual stocks, mutual funds, and hedge funds?

Well, let me answer that with a story. There was an experiment carried out with rats and humans, where there was a red button and a green button that can each light up. 80% of the time, the green button lights up. And 20% of the time the red button lights up, but randomly. So you can never be sure which button will light.

And the task for the subject, either rat or human, is to guess beforehand which button will light up by pressing it. For the rat, if they guess right, they get a bit of food. And if they guess wrong, a mild electric shock. The rat quickly learns to press only the green button and accept the 80% win percentage. Humans on the other hand, usually press the green button.

But once in a while, they try to predict when the red light will go on. And as a result, they guess right only 68% of the time. We have a hard time accepting average results. And we see patterns everywhere, including in randomness. So we try to beat the average by predicting the pattern.

But when there is no pattern, this is a terrible strategy. Even when there are patterns, you need timely feedback to learn them. And YouTube knows this, which is why within the first hour after posting a video, they tell you how its performance compares to your last 10 videos. There are even confetti fireworks when the video is number one.

I know it seems like a silly thing, but you have no idea how powerful a reward this is and how much YouTuber effort is spent chasing this supercharged dopamine hit.

To understand the difference between immediate and delayed feedback, psychologist Daniel Kahneman contrasts the experiences of anesthesiologists and radiologists. Anesthesiologists work alongside the patient and get feedback straight away. Is the patient unconscious with stable vital signs? With this immediate feedback, it’s easier for them to learn the regularities of their environment. Radiologists, on the other hand, don’t get rapid feedback on their diagnoses if they get it at all.

This makes it much harder for them to improve. Radiologists typically correctly diagnose breast cancer from x-rays just 70% of the time. Delayed feedback also seems to be a problem for college admissions officers and recruitment specialists. After admitting someone to college, or hiring someone at a big company, you may never, or only much later find out how they did. This makes it harder to recognize the patterns in ideal candidates.

In one study, Richard Melton tried to predict the grades of freshmen at the end of their first year of college. A set of 14 counsellors interviewed each student for 45 minutes to an hour. They also had access to high school grades, several aptitude tests, and a four-page personal statement. For comparison, Melton created an algorithm that used as input, only a fraction of the information.

Just high school grades and one aptitude test.

Nevertheless, the formula was more accurate than 11 of the 14 counsellors. Melton’s study was reported alongside over a dozen similar results across a variety of other domains, from predicting who would violate parole to who’d succeed in pilot training. If you’ve ever been denied admission to an educational institution or turned down for a job, it feels like an expert has considered your potential and decided that you don’t have what it takes to succeed. I was rejected twice from film school and twice from a drama program. So it’s comforting to know that the gatekeepers at these institutions aren’t great predictors of future success.

So if you’re in a valid environment, and you get repeated experience with the same events, with clear, timely feedback from each attempt, will you become an expert in 10,000 hours or so? The answer unfortunately is no.

Because most of us want to be comfortable. For a lot of tasks in life, we can become competent in a fairly short period. Take driving a car, for example, initially, it’s pretty challenging.

It takes up all of system two. But after 50 hours or so it becomes automatic. System one takes over, and you can do it without much conscious thought.

After that, more time spent driving doesn’t improve performance. If you wanted to keep improving, you would have to try driving in challenging situations like new terrain, higher speeds, or in difficult weather.

Now I have played the guitar for 25 years, but I’m not an expert because I usually play the same songs. It’s easier and more fun. But to learn, you have to be practising at the edge of your ability, pushing beyond your comfort zone. You have to use a lot of concentration and methodically repeatedly attempt things you aren’t good at. – You can practice everything exactly as it is and exactly as it’s written, but at just such a speed that you have to think about and know exactly where you are and what your fingers are doing and what it feels like.

– This is known as deliberate practice. And in many areas professionals don’t engage in deliberate practice, so their performance doesn’t improve. Sometimes it declines. If you’re experiencing chest pain and you walk into a hospital, would you rather the doctor is a recent graduate or someone with 20 years of experience? Researchers have found that the diagnostic skills of medical students increase with their time in medical school, which makes sense.

The more cases you’ve seen with feedback, the better you are at spotting patterns. But this only works up to a point. When it comes to rare diseases of the heart or lungs, doctors with 20 years of experience were worse at diagnosing them than recent graduates.

And that’s because they haven’t thought about those rare diseases in a long time. So they’re less able to recognize the symptoms.

Only after a refresher course, could doctors accurately diagnose these diseases. And you can see the same effect in chess. The best predictor of skill level is not the number of games or tournaments played, but the number of hours dedicated to serious solitary study. Players spend thousands of hours alone learning chess theory, studying their games and those of others. And they play through compositions, which are puzzles designed to help you recognize tactical patterns.

In chess, as in other areas, it can be challenging to force yourself to practice deliberately. And this is why coaches and teachers are so valuable. They can recognize your weaknesses and assign tasks to address them. To become an expert, you have to practice for thousands of hours in the uncomfortable zone, attempting the things you can’t do quite yet. True expertise is amazing to watch.

To me, it looks like magic, but it isn’t. At its core, expertise is recognition. And recognition comes from the incredible amount of highly structured information stored in long-term memory. Building that memory requires four things: a valid environment, many repetitions, timely feedback, and thousands of hours of deliberate practice. When those criteria are met, human performance is astonishing.

And when it’s not, you get people we think of as experts who aren’t. (techno sound) If you want to become a STEM expert, you have to actively interact with problems. And that’s what you can do with Brilliant, the sponsor of this video.

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 This is Grant Gussman


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