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“Generous and affectionate and thoroughly imbued with an enchanting grace.”
— Marcel Proust, from The Complete Short Stories; “Violante or High Society,”
There are no great secrets. To become world class, you have to do the work. The highest pay-off comes from doing exactly those things you find most difficult. Progress is paid for in the pains you take to overcome your weaknesses.
Do not suffer aimlessly. Know where to put your efforts. Do not waste energy on things that do not help your progress.
Recuperate strategically. Mind-numbing scrolling for hours is neither particularly enjoyable nor replenishing. Read a good book. Go for a walk. Visit a museum or the theatre. Have a talk with a good friend. Cook a good meal.
Work with your circumstances. Do not fight against something you cannot change. Make your environment work for you.
Do not waste time on planning or thinking about how to work. Just work. Reading books about productivity and learning might bring some benefit if you apply it, but you need to actually spend more time applying it than reading about it.
“Go on and dare all of it. All the way. In your own way.”
—Anaïs Nin, from The Diary of Anaïs Nin: Volume 6 (1955-1966)
Ikenaga Yasunari
His website:
http://ikenaga-yasunari.com/index.htm
Galton Board demonstrating a normal distribution
Newton-Raphson Method − A Root Finding Algorithm
The red curves are F(x,y)=0, and the blue ones are G(x,y)=0.
The algorithm’s convergences is guaranteed locally (not globally), so determination of initial value is really important.
You can try any functions in Desmos from the link below! https://www.desmos.com/calculator/wrz40wvbhz
When algorithms surprise us
Machine learning algorithms are not like other computer programs. In the usual sort of programming, a human programmer tells the computer exactly what to do. In machine learning, the human programmer merely gives the algorithm the problem to be solved, and through trial-and-error the algorithm has to figure out how to solve it.
This often works really well - machine learning algorithms are widely used for facial recognition, language translation, financial modeling, image recognition, and ad delivery. If you’ve been online today, you’ve probably interacted with a machine learning algorithm.
But it doesn’t always work well. Sometimes the programmer will think the algorithm is doing really well, only to look closer and discover it’s solved an entirely different problem from the one the programmer intended. For example, I looked earlier at an image recognition algorithm that was supposed to recognize sheep but learned to recognize grass instead, and kept labeling empty green fields as containing sheep.
When machine learning algorithms solve problems in unexpected ways, programmers find them, okay yes, annoying sometimes, but often purely delightful.
So delightful, in fact, that in 2018 a group of researchers wrote a fascinating paper that collected dozens of anecdotes that “elicited surprise and wonder from the researchers studying them”. The paper is well worth reading, as are the original references, but here are several of my favorite examples.
Bending the rules to win
First, there’s a long tradition of using simulated creatures to study how different forms of locomotion might have evolved, or to come up with new ways for robots to walk.
Why walk when you can flop? In one example, a simulated robot was supposed to evolve to travel as quickly as possible. But rather than evolve legs, it simply assembled itself into a tall tower, then fell over. Some of these robots even learned to turn their falling motion into a somersault, adding extra distance.
[Image: Robot is simply a tower that falls over.]
Why jump when you can can-can? Another set of simulated robots were supposed to evolve into a form that could jump. But the programmer had originally defined jumping height as the height of the tallest block so - once again - the robots evolved to be very tall. The programmer tried to solve this by defining jumping height as the height of the block that was originally the *lowest*. In response, the robot developed a long skinny leg that it could kick high into the air in a sort of robot can-can.
[Image: Tall robot flinging a leg into the air instead of jumping]
Hacking the Matrix for superpowers
Potential energy is not the only energy source these simulated robots learned to exploit. It turns out that, like in real life, if an energy source is available, something will evolve to use it.
Floating-point rounding errors as an energy source: In one simulation, robots learned that small rounding errors in the math that calculated forces meant that they got a tiny bit of extra energy with motion. They learned to twitch rapidly, generating lots of free energy that they could harness. The programmer noticed the problem when the robots started swimming extraordinarily fast.
Harvesting energy from crashing into the floor: Another simulation had some problems with its collision detection math that robots learned to use. If they managed to glitch themselves into the floor (they first learned to manipulate time to make this possible), the collision detection would realize they weren’t supposed to be in the floor and would shoot them upward. The robots learned to vibrate rapidly against the floor, colliding repeatedly with it to generate extra energy.
[Image: robot moving by vibrating into the floor]
Clap to fly: In another simulation, jumping bots learned to harness a different collision-detection bug that would propel them high into the air every time they crashed two of their own body parts together. Commercial flight would look a lot different if this worked in real life.
Discovering secret moves: Computer game-playing algorithms are really good at discovering the kind of Matrix glitches that humans usually learn to exploit for speed-running. An algorithm playing the old Atari game Q*bert discovered a previously-unknown bug where it could perform a very specific series of moves at the end of one level and instead of moving to the next level, all the platforms would begin blinking rapidly and the player would start accumulating huge numbers of points.
A Doom-playing algorithm also figured out a special combination of movements that would stop enemies from firing fireballs - but it only works in the algorithm’s hallucinated dream-version of Doom. Delightfully, you can play the dream-version here
[Image: Q*bert player is accumulating a suspicious number of points, considering that it’s not doing much of anything]
Shooting the moon: In one of the more chilling examples, there was an algorithm that was supposed to figure out how to apply a minimum force to a plane landing on an aircraft carrier. Instead, it discovered that if it applied a *huge* force, it would overflow the program’s memory and would register instead as a very *small* force. The pilot would die but, hey, perfect score.
Destructive problem-solving
Something as apparently benign as a list-sorting algorithm could also solve problems in rather innocently sinister ways.
Well, it’s not unsorted: For example, there was an algorithm that was supposed to sort a list of numbers. Instead, it learned to delete the list, so that it was no longer technically unsorted.
Solving the Kobayashi Maru test: Another algorithm was supposed to minimize the difference between its own answers and the correct answers. It found where the answers were stored and deleted them, so it would get a perfect score.
How to win at tic-tac-toe: In another beautiful example, in 1997 some programmers built algorithms that could play tic-tac-toe remotely against each other on an infinitely large board. One programmer, rather than designing their algorithm’s strategy, let it evolve its own approach. Surprisingly, the algorithm suddenly began winning all its games. It turned out that the algorithm’s strategy was to place its move very, very far away, so that when its opponent’s computer tried to simulate the new greatly-expanded board, the huge gameboard would cause it to run out of memory and crash, forfeiting the game.
In conclusion
When machine learning solves problems, it can come up with solutions that range from clever to downright uncanny.
Biological evolution works this way, too - as any biologist will tell you, living organisms find the strangest solutions to problems, and the strangest energy sources to exploit. Sometimes I think the surest sign that we’re not living in a computer simulation is that if we were, some microbe would have learned to exploit its flaws.
So as programmers we have to be very very careful that our algorithms are solving the problems that we meant for them to solve, not exploiting shortcuts. If there’s another, easier route toward solving a given problem, machine learning will likely find it.
Fortunately for us, “kill all humans” is really really hard. If “bake an unbelievably delicious cake” also solves the problem and is easier than “kill all humans”, then machine learning will go with cake.
Mailing list plug
If you enter your email, there will be cake!
Today’s fastest boats use hydrofoils to lift most of a boat’s hull out of the water. This greatly reduces the drag a boat experiences, but it can also make the boat difficult to handle. One style of hydrofoil boat, called a single-track hydrofoil, uses two hydrofoils in line with one another to support and steer the boat. The pilot can steer the lead hydrofoil into the direction of a fall to correct it. Stability-wise, this is the same way that you keep a bicycle upright. On a boat, the situation is a bit tougher to manage, and, like riding a bike, it takes practice. A group of students published a full mathematical model for the dynamics of this kind of boat, which allows designers to test a prototype’s stability early in the design process and enables student teams to use computer simulators to train their pilots to drive a boat before putting them out on the water, similar to the way that airplane pilots train. (Image credit: TU Delft Solar Boat Team, source; research credit: G. van Marrewijk et al., pdf; via TU Delft News; submitted by Marc A.)
A couple of you have given us great topic ideas for inforgraphics, but if there is anything else that comes to your mind just let us know. Also if you are interested in printing this as a poster for your class send us an email to [email protected] so we can send you a pdf (easy for print).
Hi, I’m your new meteorologist and a former software developer. Hey, when we say 12pm, does that mean the hour from 12pm to 1pm, or the hour centered on 12pm? Or is it a snapshot at 12:00 exactly? Because our 24-hour forecast has midnight at both ends, and I’m worried we have an off-by-one error.
Meteorologist [Explained]
I got the teaching award!
Only five grad students in the entire school get the award each year, and this year I’m one of them! Fully funded for next year and super excited!
My department posted an article about me here! This has been a fantastic week :D
The evolution of the Latin alphabet, courtesy of data artist Matt Baker. For a deeper dive, see the out-of-print treasure Shapes for Sounds, a comprehensive visual history of the alphabet.
via Swiss Miss
Hippopocranuse.
“Growing up, I always assumed I would go into space. But I knew full well that people expected me to behave a certain way. I bucked the system. I don’t want mothers sayin’ ‘put that mud down, stop doing the because you’re going to ruin your dress.’ You get dirty sometimes. Who cares? You cannot do some of these things and keep your hair all nice.”—Mae Jemison
Today’s TechMAKER reached for the stars and then some. Mae Jemison saw the gender and racial discrimination in space exploration, but that didn’t stop her from becoming the very first African-American woman in space.
You can see our full interview with Mae Jemison over on MAKERS.
Pay attention after a rainfall, and you may notice beads of water gathering in the corners of a spider’s web or along the leaves of a cypress tree (bottom right). Look closely and you’ll notice that the largest droplets don’t form along a straight fiber. Instead they nestle into the corners of a bent fiber (top image). Researchers recently characterized this corner mechanism and found that the angle at which the largest droplets form is about 36 degrees. This angle provides the optimal conditions for capillary action and surface tension to hold large drops in place. At smaller angles, a growing droplet’s weight pulls it down until the thin film holding the droplet near the top ruptures and the droplet falls. At larger angles, a heavy droplet will slowly detach from one side of its fiber and shift toward the other side until its weight is too great for the wetted length of fiber to hold. Then it detaches completely and falls. (Research and image credit: Z. Pan et al.; via T. Truscott)
Several years ago Fabian Oefner started spinning paint, and it’s been a perennial favorite online ever since. Here the Slow Mo Guys revisit their own paint-spinning antics by super-sizing their set-up. In some respects, it’s a little dissatisfying; as with their first time around, they don’t moderate the drill speed at all, so after the initial spin-up, the centrifugal acceleration is so strong that it just shreds the paint instead of showing off the interplay between the acceleration and surface tension’s efforts to keep the paint together.
In their largest experiment, though, the Slow Mo Guys get some interesting physics. Here there’s only a single slot for paint to exit, so the set-up doesn’t lose all its paint at once. The centrifugal acceleration flings the paint out in sheets that stretch into ligaments and then tear into droplets as they move further out. But there’s some more complicated phenomena, too. Notice the bubble-like shapes forming in the yellow paint on the lower right. These are known as bags, and they form because of the relative speed of the paint and the air it’s moving through. This is actually the same thing that happens to falling drops of rain! (Video and image credit: The Slow Mo Guys)