Name: 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)
Uploaded: 2021-01-14T10:29:22.000Z
Duration: 16 min
Description: 【看影片學英語】數萬部 YouTube 影片，搭配英漢字典即點即查，輕鬆掌握單字發音與用法，長久累積看電影不必再看字幕。

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

licorice, Nerdfighter, toothbrush, zany, expedient,

程度副詞

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.

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

Green… and 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 playground” at the top of the page or open the File menu and click “Save

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 like “good morning,” and he'll

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:

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,

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

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

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

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 term “word” may not capture

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

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

We have eleven lexical tokens but only nine lexical types, because “learning” and

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

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

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 contraction “it's” into “it” and “apostrophe-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

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

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.

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

But others seem doable if we take advantage of morphology.