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 to “guess”
what 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 multiple “states,”
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 sunny “state”
looping back on itself is higher than moving to the rainy “state.” This exact chain is used to test the strength
of dams and structural simulations. It’s also a nifty tool for “random”
name 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 was “trained,”
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 entire “roguelike” genre 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 named “Rogue,” 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
“seed” that 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.
