LoginGet started

But what is a GPT? Visual intro to transformers | Chapter 5, Deep Learning

3Blue1Brown・2 minutes read

GPTs use transformers to generate new text by predicting the next word based on embeddings and context, with tools like Dolly and Midjourney utilizing this technology for image generation. Understanding word embeddings, softmax, dot product similarity, and matrix multiplication is crucial for comprehending the attention mechanism in modern AI advancements.

Insights

  • GPT, short for Generative Pretrained Transformer, is a key technology in AI that generates new text by learning from vast amounts of data and fine-tuning on specific tasks, with models like ChatGPT focusing on predicting text passages.
  • The transformation process in GPT involves breaking input into tokens, associating them with vectors, passing through attention and multi-layer perceptron blocks, and using the Softmax function to predict the next word accurately, showcasing the intricate mechanisms behind text generation and the importance of understanding word embeddings, dot product similarity measurement, and matrix multiplication for modern AI advancements.

Get key ideas from YouTube videos. It’s free

Recent questions

  • What is GPT?

    Bot generating new text.

  • What is the purpose of the original transformer by Google?

    Translate text between languages.

  • How does ChatGPT function?

    Predicts next word in text.

  • What is the significance of word embeddings in machine learning?

    Turn words into vectors for analysis.

  • How does the Softmax function impact text generation?

    Normalizes values into probability distribution.

Related videos

Summary

00:00

"Transformers: AI Text Generation and Prediction"

  • GPT stands for Generative Pretrained Transformer, which are bots that generate new text.
  • Pretrained refers to the model learning from a vast amount of data, with room for fine-tuning on specific tasks.
  • Transformer is a neural network core invention driving the AI boom.
  • Different models using transformers include audio-to-transcript and text-to-speech.
  • Tools like Dolly and Midjourney in 2022 are based on transformers, generating images from text.
  • The original transformer by Google in 2017 was for translating text between languages.
  • ChatGPT focuses on predicting what comes next in a text passage.
  • Predicting the next word and generating new text are linked through prediction models.
  • Data flows through a transformer involve breaking input into tokens, associating them with vectors, and passing through attention and multi-layer perceptron blocks.
  • The process repeats until the final vector produces a probability distribution over possible next tokens.

13:03

"Word Embeddings and Softmax in GPT-3"

  • The first matrix encountered in the text is the embedding matrix, with a column for each of the 50,000 words, determining the vectors each word transforms into.
  • The embedding process involves turning words into vectors, a common practice in machine learning, setting the foundation for subsequent steps.
  • Word embeddings are visualized as points in high-dimensional space, with GPT-3 having 12,288 dimensions for these vectors.
  • Model training results in embeddings where directions in space carry semantic meaning, illustrated by similarities between word vectors.
  • The dot product of vectors measures alignment, being positive for similar directions, zero for perpendicular, and negative for opposite directions.
  • The network aims to enrich individual word meanings by incorporating context, with a context size of 2048 vectors in GPT-3.
  • The network predicts the next word by mapping the last vector in the context to a list of 50,000 values using the Unembedding matrix.
  • The Softmax function normalizes values into a probability distribution, crucial for predicting the next word accurately.
  • Softmax turns a list of numbers into a valid distribution, ensuring values range between 0 and 1 and sum up to 1.
  • Adjusting the temperature in Softmax influences the distribution, with higher temperatures allowing for more diverse word choices in text generation.

25:58

Understanding Unembedding Matrix in Machine Learning

  • In machine learning, the unembedding matrix contains the logits for next word prediction, setting the groundwork for understanding the attention mechanism, which is crucial in modern AI advancements. To grasp this concept smoothly, a strong grasp of word embeddings, softmax, dot product similarity measurement, and matrix multiplication is essential, with a detailed explanation available in the upcoming chapter, currently in draft for Patreon supporters and soon to be released publicly.
Channel avatarChannel avatarChannel avatarChannel avatarChannel avatar

Try it yourself — It’s free.