Have you ever wondered what it would be like to make an AI sound like a YouTuber? In the latest episode of Crash Course AI, Jabril takes on the challenge of getting John-Green-bot to produce language that sounds like human John Green.
Contents
Hands-on Lab: Teaching John-Green-bot to Talk like Human John Green
The goal of this lab is to have some fun while creating an AI model that can play a clever game of fill-in-the-blank. Using a language called Python and a tool called Google Colaboratory, we’ll be able to prompt John-Green-bot with a word, and it will complete the sentence. However, it’s important to note that John-Green-bot won’t truly understand the meaning of the words; instead, it will rely on finding and copying patterns.
Gathering and Cleaning the Data
To train John-Green-bot, we need lots of examples of human John Green talking. Thankfully, there is a whole database of subtitle files on the nerdfighteria wiki that we can use. By collecting and cleaning the data, we can prepare it for our model.
Preprocessing: Splitting Sentences into Words
To process the speech of human John Green, we use subtitles and preprocess the data. In natural language processing, tokenization is the process of splitting a sentence into a list of words. We also remove extra word endings and perform stemming to simplify the data.
Setting Up the Model
To teach John-Green-bot how to generate language, we need two key components: an embedding matrix and a recurrent neural network (RNN). The embedding matrix assigns each word in our vocabulary a number, and the RNN helps in building a hidden representation by incorporating one new word at a time.
Training the Model
By splitting the data into batches, we can train the model’s weights using backpropagation. The model learns from the training data and is tested on new data it has never seen before. Throughout the training process, the model’s perplexity decreases, indicating that it is narrowing down the choices and making more accurate predictions.
Generating Sentences and Inference
After training the model, it’s time to see what John-Green-bot can write. By sampling different paths and using the model’s probabilities, we can generate sentences. However, it’s important to note that the AI’s understanding is limited compared to human understanding. While the AI can predict words based on probabilities, it lacks the comprehensive understanding and perspective that humans possess.
FAQs
-
How accurate is John-Green-bot in generating sentences?
- While John-Green-bot has improved over the training process, it still has limitations. The generated sentences may not match human John Green’s style perfectly.
-
Can we use different data to make the AI sound like someone else?
- Absolutely! By replacing the training data with text from other individuals, you can make the AI sound like someone else, such as Michael from Vsauce.
Conclusion
Teaching an AI to sound like a YouTuber is an exciting experiment in the world of natural language processing. While John-Green-bot can mimic some patterns and produce sentences that sound similar to human John Green, there is still much work to be done to achieve the same level of understanding and creativity. We encourage you to explore the code and experiment with your own AI models. Have fun and let us know in the comments if you create something cool!
