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  • Hi I'm Jake.

  • I have a question for you.

  • How human are you.com?

  • Well, if you're not sure you can find out because this website will quiz you on how

  • robotic you really are.

  • I didn't score very high, but we can find out why because the site gives some more info

  • on how computers learn how to mimic human behavior.

  • For example, most programs designed to act like us will deliberately include errors of

  • spelling or grammar to seem more person-like.

  • Some programs, like the predictive text feature on smartphones, use data gathered from users

  • and thousands of sentences toguesswhat you might type next.

  • This type of idiomatic sentence construction is also at the heart of r/subredditsimulator,

  • a site on reddit that will create headlines that sound real-ish.

  • All of the comments on the articles are created by bots as well, which leads to some pretty

  • interesting conversations.

  • This is used on an even greater scale by Botnik, a website that has collected text from various

  • sources, genres and even particular people or shows, and allows you to assist a computer

  • in writing a quote or even a whole episode of a show like Scrubs, Seinfeld and more.

  • You can also generate music festival lineups and robo tweets.

  • It's a really cool example of machine learning, but allow me to give you some more examples

  • with even more DONGS, things you can do online now guys.

  • All these systems are based on the probability system called Markov Chains.

  • Markov Chains describe when a random probability is linked to the outcome of the previous probability.

  • This website gives a really good visualization for how they work.

  • Each chain is made up of multiplestates,” which have a certain chance of either

  • moving on to a new state, or looping back onto itself, starting the process over.

  • For example, let's say each iteration of the chain is the probability of a rainy versus

  • a sunny day

  • A sunny day is more likely to be followed by another sunny day and rain follows rain,

  • so the probability of the sunnystatelooping back on itself is higher than moving

  • to the rainystate.”

  • This exact chain is used to test the strength of dams and structural simulations.

  • It's also a nifty tool forrandomname generation, which it's not, because

  • if it were totally random it would sound like this: fprqodpmq.

  • So how do name generators work?

  • Marvok chains have the answer.

  • In the english language, the chance of Q being followed by U is pretty much 100%, but the

  • chance of U being followed by I isn't any more likely than it being by A or any other

  • vowel.

  • So you end up with words that are technically pronounceable.

  • Depending on the way system wastrained,” it may even create readable sentences of real

  • words, even if combined they're total nonsense.

  • A great example of this are found in the album Bot Prownies by DADABOTS.

  • The titles were created using a Markov chain, and so it's basically random noise, but

  • reads like actual song titles, even if, again, they're just a little wrong.

  • Which leads us to another type of machine learning, Neural Networks and Procedural Generation.

  • The thing is, this album was not created by musicians, or even humans, but was instead

  • created by a deep-learning algorithm.

  • This algorithm was fed hours of black metal and math rock albums, and was programed to

  • try to guess what would happen next as it was listening.

  • If it got it right it would strengthen the connection to that particular waveform, and

  • would repeat those guesses hundreds of times until it started to sound more and more like

  • a real piece of music.

  • It's a little wonky, but still sounds like it could have been played by actual humans,

  • even if they sound like the Shaggs.

  • To quote CJ Carr, one of the programmers of the algorithm, “Early in its training, the

  • kinds of sounds it produces are very noisy and grotesque and textural, but as it improves

  • its training, you start hearing elements of the original music it was trained on come

  • through more and more.”

  • Here's the original music it was trained on

  • and here's the computer's approximation of it.

  • Pretty cool huh?

  • Well not as cool as a 3 year old Vsauce1 video.

  • A while back Michael mentioned a computer program that learned to play old video games

  • distressingly well, going so far as to pause a game of tetris right at the last minute

  • so it would never lose.

  • That's pretty neat, but how about a computer program that makes video games.

  • Games By Angelina does just that.

  • Although it's still in its early stages, Angelina is being fed data on hundreds of

  • games and topics and uses imagery and its connotations to create it's own settings

  • and gameplay.

  • Although it sometimes doesn't work very well, it occasionally has moments of simple

  • genius, like when given the prompt to make a horror game, it placed blood red walls and

  • creepy statues all around the environment. Good job Angelina!

  • But using seemingly random generation for video games is nothing new.

  • I'm sure you've experienced it before.

  • The entireroguelikegenre of videogames, in which levels are randomly designed and

  • never repeat, is based on procedural systems similar to Markov chains and machine learning.

  • Although the first game to use this structure was the perfectly namedRogue,” the first commercially

  • successful version was called Beneath Apple Manor, which you can play right now!

  • This game and others like it start with a “seedthat informs the general pattern

  • that the dungeon will follow, and then starts with a single tile.

  • Each adjacent tile is added according to a chain of probability that increases the chances

  • of various blocks while always allowing the player to get to the end of the level.

  • When your done fighting slime monsters and looting crypts head over to Brilliant.org/doonline/

  • to sign up for free to learn more about Markov Chains and machine learning.

  • Brilliant was nice enough to sponsor this episode and their mission aligns really well with Vsauce's.

  • The first 36 people that follow the link will get 20% off the annual premium subscription.

  • So I would highly recommend checking it out

  • In the lesson for Markov Chains I have to figure out where a tourist will be at the

  • end of a three day trip in Tailand, using probabilities from actual tourist data.

  • We did it. Yay!

  • Links to all the DONGs can be found in the description below

  • And if you want more DONGs there's a playlist right here, filled with a gaggle of DONGs.

  • All for your enjoyment.

  • right over there

  • I find it interesting that we're talking about computers and machine learning and all these kind of things

  • I'm actually talking to a computer right now

  • I mean I'm talking to you, but through a camera which is then going to be through a computer

  • And that's you there.

  • So we have two computers in between us

  • so I wonder do either of us exist at all? Or are we just machines?

  • I don't know! Find out in the next DONG episode!

  • Have a wonderful life and as always thanks for watching ok bye.

Hi I'm Jake.

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マルコフ鎖 (Markov Chains)

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    林宜悉 に公開 2021 年 01 月 14 日
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