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  • Flower, dog, anxious, senior, car, Item, president, worried, avacado, Zendaya,

  • licorice, Nerdfighter, toothbrush, zany, expedient,

  • This isn't really a vlogbrothers video.

  • It's just a random string of words.

  • There aren't any coherent sentences.

  • It looks like John Green bot could use some help speaking a bit more like human John Green

  • - sounds like an excellent task for Natural Language Processing.

  • INTRO

  • Hey, I'm Jabril and welcome to Crash Course AI!

  • Today, we're going to tackle another hands-on lab.

  • Our goal today is to get John-Green-bot to produce language that sounds like human John

  • Greenand have some fun while doing it.

  • We'll be writing all of our code using a language called Python in a tool called Google

  • Colaboratory, and as you watch this video, you can follow along with the code in your

  • browser from the link we put in the description.

  • In these Colaboratory files, there's some regular text explaining what we're trying

  • to do, and pieces of code that you can run by pushing the play button.

  • Now, these pieces of code build on each other, so keep in mind that we have to run them in

  • order from top to bottom, otherwise we might get an error.

  • To actually run the code and experiment with changing it you'll have to either click

  • open in playgroundat the top of the page or open the File menu and clickSave

  • a Copy to Drive”.

  • And just an fyi: you'll need a Google account for this.

  • Now, we're going to build an AI model that plays a clever game of fill-in-the-blank.

  • We'll be able to give John-Green-bot any word prompt likegood morning,” and he'll

  • be able to finish the sentence.

  • Like any AI, John-Green-bot won't really understand anything, but AI generally does

  • a really good job of finding and copying patterns.

  • When we teach any AI system to understand and produce language, we're really asking

  • it to find and copy patterns in some behavior.

  • So to build a natural language processing AI, we need to do four things:

  • First, gather and clean the data.

  • Second, set up the model.

  • Third, train the model.

  • And fourth, make predictions.

  • So let's start with the first step: gather and clean the data.

  • In this case, the data are lots of examples of human John Green talking, and thankfully,

  • he's talked a lot online.,

  • We need some way to process his speech.

  • And how can we do that?

  • Subtitles.

  • And conveniently there's a whole database of subtitle files on the nerdfighteria wiki

  • that I pulled from.

  • I went ahead and collected a bunch and put them into one big file that's hosted on

  • crash course ai's GitHub..

  • This first bit of code in 1.1 loads it.

  • So if you wanted to try to make your AI sound like someone else, like Michael from Vsauce,

  • or me, this is where you'd load all that text instead.

  • Data gathering is often the hardest and slowest part of any machine learning project, but

  • in this instance its pretty straightforward.

  • Regardless, we still aren't done yet, now we need to clean and prep our data for our

  • model.

  • This is called preprocessing.

  • Remember, a computer can only process data as numbers, so we need to split our sentences

  • into words, and then convert our words into numbers.

  • When we're building a natural language processing program the termwordmay not capture

  • everything we need to know.

  • How many instances there are of a word can also be useful.

  • So instead, we'll use the terms lexical type and lexical token.

  • Now a lexical type is a word, and a lexical token is a specific instance of a word, including

  • any repeats.

  • So, for example, in the sentence:

  • The goal of machine learning is to make a learning machine.

  • We have eleven lexical tokens but only nine lexical types, becauselearningand

  • machineboth occur twice.

  • In natural language processing, tokenization is the process of splitting a sentence into

  • a list of lexical tokens.

  • In English, we put spaces between words, so let's start by slicing up the sentence at

  • the spaces.

  • Good morning Hank, it's Tuesday.”

  • would turn into a list like this.

  • And we would have five tokens.

  • However there are a few problems.

  • Something tells me we don't really want a lexical type for Hank-comma and Tuesday-period,

  • so let's add some extra rules for punctuation.

  • Thankfully, there are prewritten libraries for this.

  • Using one of those, the list would look something like this.

  • In this case we would have eight tokens instead of five, and tokenization even helped split

  • up our contractionit's” intoitandapostrophe-s.”

  • Looking back at our code, before tokenization, we had over 30,000 lexical types.

  • This code also splits our data into a training dataset and a validation dataset.

  • We want to make sure the model learns from the training data, but we can test it on new

  • data it's never seen before.

  • That's what the validation dataset is for.

  • We can count up our lexical types and lexical tokens with this bit of code in box 1.3.

  • And it looks like we actually have about 23,000 unique lexical types.

  • But remember how many instances of a word can also be useful.

  • This code block here at step 1.4 allows us to separate how many lexical types occur more

  • than once twice and so on.

  • It looks like we've got a lot of rare words -- almost 10,000 words occur only once!

  • Having rare words is really tricky for AI systems, because they're trying to find

  • and copy patterns, so they need lots of examples of how to use each word.

  • Oh Human John Green.

  • Your master of prose.

  • Let's see what weird words you use.

  • Pisgah?

  • What even is a lilliputian?

  • Some of these are pretty tricky and are going to be too hard for John-Green-bot's AI to

  • learn with just this dataset

  • But others seem doable if we take advantage of morphology.

  • Morphology is the way a word gets shape-shifted to match a tense, like you'd add anED

  • to make something past tense, or when you shorten or combine words to make them totes-amazeballs.

  • Dear viewers, I did not write that in the script.

  • In English, we can remove a lot of extra word endings, like ED, ING, or LY, through a process

  • called stemming.

  • And so, with a few simple rules, we can clean up our data even more.

  • I'm also going to simplify the data by replacing numbers with the hashtag or pound signs. Whatever you want to call it.

  • This should take care of a lot of rare words.

  • Now we have 3,000 fewer lexical types and only about 8,000 words only occur once.

  • We really need multiple examples of each word for our AI to learn patterns reliably, so

  • we'll simplify even more by replacing each of those 8,000 or so rare lexical tokens with

  • the word 'unk' or unknown.

  • Basically, we don't want John-Green-bot to get embarrassed if he sees a word he doesn't

  • know.

  • So by hiding some words, we can teach John-Green-bot how to keep writing when he bumps into a one-time

  • made-up words like zombicorns.

  • And just to satisfy my curiosity

  • Yeah, John-Green-bot doesn't need words likewhippersnappersorzombification”.

  • John what's up with the fixation with zombies?

  • Anyway, we'll be fine without them.

  • Now that we finally have our data all cleaned and put together, we're done with preprocessing

  • and can move on to Step 2: setting up the model for John-Green-bot.

  • There are a couple key things that we need to do.

  • First, we need to convert the sentences into lists of numbers.

  • We want one word for every lexical type, so we'll build a dictionary that assigns every

  • word in our vocabulary a number.

  • Second, unlike us, the model can read a bunch of words at the same time, and we want to

  • take advantage of that to help John-Green-bot learn quickly.

  • So we're going to split our data into pieces called batches.

  • Here, we're telling the model to read 20 sequences (which have 35 words each) at the

  • same time!

  • Alright!

  • Now, it's time to finally build our AI.

  • We're going to program John-Green-bot with a simple language model that takes in a few

  • words and tries to complete the rest of the sentence.

  • So we'll need two key parts, an embedding matrix and a recurrent neural network or RNN.

  • Just like we discussed in the Natural Language Processing video last week, this is anEncoder-Decoder

  • framework.

  • So let's take it apart.

  • An embedding matrix is a big list of vectors, which is basically a big table of numbers,

  • where each row corresponds to a different word.

  • These vector-rows capture how related two words are.

  • So if two words are used in similar ways, then the numbers in their vectors should be

  • similar.

  • But to start, we don't know anything about the words, so we just assign every word a

  • vector with random numbers.

  • Remember we replaced all the words with numbers in our training data, so now when the system

  • reads in a number, it just looks up that row in the table and uses the corresponding vector

  • as an input.

  • Part 1 is done: Words become indices, which become vectors, and our embedding matrix is

  • ready to use.

  • Now, we need a model that can use those vectors intelligently.

  • This is where the RNN comes in.

  • We talked about the structure of a recurrent neural network in our last video too, but

  • it's basically a model that slowly builds a hidden representation by incorporating one

  • new word at a time.

  • Depending on the task, the RNN will combine new knowledge in different ways.

  • With John-Green-bot, we're training our RNN with sequences of words from Vlogbrothers

  • scripts.

  • Ultimately, our AI is trying to build a good summary to make sure a sentence has some overall

  • meaning, and it's keeping track of the last word to produce a sentence that sounds like

  • English.

  • The RNN's output after reading the final word so far in a sentence is what we'll

  • use to predict the next word.

  • And this is what we'll use to train John-Green-bot's AI after we build it. All of this is wrapped up in code block 2.3

  • So Part 2 is done. We've got our embedding matrix and our RNN.

  • Now, we're ready for Step 3: train our model.

  • Remember when we split the data into pieces called batches?

  • And remember earlier in Crash Course AI when we used backpropagation to train neural networks?

  • Well we can put those pieces together, iterate over our dataset, and run backpropagation

  • on each example to train the model's weights.

  • So in step 3.1 we're defining how to train our model and in step 3.2 we're defining

  • how to evaluate our model and in step 3.3 we're actually creating our model.

  • Which means training and evaluating it.

  • Over the span of one epoch of training this model, the network will loop over every batch

  • of data -- reading it in, building representations, predicting the next word, and then updating

  • its guesses.

  • This will train over 10 epochs, which might take a couple minutes.

  • We're printing two numbers with each epoch, which are the model's training and validation

  • perplexities.

  • As the model learns, it realizes there are fewer and fewer good choices for the next

  • word.

  • The perplexity is a measure of how well the model has narrowed down the choices.

  • Okay, it looks like the model is done training and has a perplexity of about 45 on train

  • and 72 on validation, but it started with perplexities in the hundreds!

  • We can interpret perplexity as the average number of guesses the model makes before it

  • predicts the right answer.

  • After seeing the data once, the model needed over 300 guesses for the next word, but now

  • it's narrowed it down to fewer than 50.

  • That's a pretty good improvement, even though it's far from perfect.

  • Time to see what the model can write, but to do that, we need one final ingredient.

  • So far in Crash Course AI, we've talked a lot about the one best label or the one

  • best prediction an AI can make, but this doesn't always make sense to solve certain problems.

  • If you wrote stories by always having characters do the next obvious thing, they'd be pretty

  • boring.

  • So Step 4 is inference, the part of AI where the machine gets to make some choices, but

  • we can still help it a little bit.

  • Let's think about what the final layer of the RNN is actually doing.

  • We talk about it like it's outputting a single label or prediction, but actually the

  • network is producing a bunch of scores or probabilities.

  • The most likely word has the highest probability, the next most likely word has the second highest

  • probability, and so on.

  • Because we get probabilities at every step, instead of taking the best one each time to

  • produce 1 sentence, we could sample 3 words and start 3 new sentences.

  • Each of those 3 sentences could then start 3 more new sentencesand then we have a

  • branching diagram of possibilities.

  • Inference is so important because what the model can produce and what we want aren't

  • necessarily the same thing.

  • What we want is a really good sentence, but the model can only tell us the score for one

  • word at a time.

  • Let's look at this branching diagram.

  • Whenever we choose a word, we create a new branch, and keep track of its score or probability.

  • If we multiply each score through to the end of the branch, we see that the top branch,

  • made the best scoring choice, but a worse sentence overall.

  • So we're going to implement a basic sampler in our program.

  • This will take a bunch of random paths, so we can sort the results by the probability

  • of the full sentences, and we can see which sentences are best overall.

  • Also, when asking John-Green-bot to generate all these sentences, we need to give him a

  • word to start.

  • I'm going to tryGoodfor now, but you can try other things by changing the code

  • in 4.1.

  • Remember the preprocessing we did on our data?

  • That's why these sentences look a little off, with hashtags for numbers, and the space

  • before word endings that we introduced when stemming.

  • And look at the sentence you get from taking the highest probability word each time.

  • Good morning Hank, it's Tuesday.

  • I'm going to be like, I'm going to be like, I'm going to be like, I'm going to

  • see it isn't as interesting as the ones where we

  • mixed it up a bit and took different branches.

  • To be honest thoughnone of these are great Vlogbrothers scripts.

  • That's because of two important things:

  • First, there's our data.

  • Remember, we didn't have many examples of how to use each word.

  • In fact, we had to cut out a lot ofrare wordsduring training because they only

  • showed up once, so we couldn't teach John-Green-bot to recognize any patterns related to them.

  • Lots of state-of-the-art models address this by downloading data from Wikipedia, large

  • collections of books, or even Reddit when they train their models.

  • We'll include some links in the description if you want to play with some fancier models.

  • But the second, bigger issue is that AI models are missing the understanding we have as humans.

  • Even if John Green Bot split up words perfectly and predicted sentences that sound like English,

  • it's still John-Green-bot using tools like tokenization, an embedding matrix, and a simple

  • language model to predict the next word.

  • When human John Green writes, he uses his understanding of the world, like in Vlogbrothers

  • videos, he considers Hank's perspective or whoever's watching.

  • He's not just trying to predict which next word has the highest probability.

  • Building models that interact with people, and the world, is why natural language processing

  • is so exciting, but it's also why it'll take a lot more work to get John-Green-bot

  • to generate language as well as human John Green does.

  • We've left a bunch of notes in the code for you to play if you want to make your own

  • AI.

  • You can train for longer, change the sentence prompt, or, if you're feeling adventurous,

  • replace the text data to speak in someone else's voice.

  • If you end up using this to make something cool let us know in the comments.

  • Thanks for watching, see you next week.

  • PBS Digital Studios wants to hear from you.

  • We do a survey every year that asks what you're into, your favorite pbs shows, and things you

  • would like to see more from PBS Digital Studios. You even get to vote on potential new shows.

  • All of this helps us make more stuff that you want to see.

  • The survey takes about 10 minutes and you might win a sweet t-shirt. Link is in the description. Thanks.

  • Crash Course AI is produced in association with PBS Digital Studios!

  • If you want to help keep all Crash Course free for everybody, forever, you can join

  • our community on Patreon.

  • And if you want to learn more about NLP check out this video from Crash Course Computer Science.

Flower, dog, anxious, senior, car, Item, president, worried, avacado, Zendaya,

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讓人工智能聽起來像個優步(LAB)。速成班人工智能#8 (Make an AI sound like a YouTuber (LAB): Crash Course AI #8)

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    林宜悉 發佈於 2021 年 01 月 14 日
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