Transformers Explained in 60 Seconds! #AI #DeepLearning #NeuralNetworks #Transformers #chatgpt

Key Takeaways

You've definitely heard of ChatGPT, but do you know what it actually stands for? GPT, or generative pre-trained transformer. Generative just means the model is generating new data. Pre-trained means the AI was trained on a large data set before using it. But, what does the transformer piece mean? No, not those types of transformer. A transformer is actually a deep learning architecture used throughout the AI world. From personal chatbots to even car sensors. Unlike older RNN models that process data sequentially, one after another, transformers analyze entire sequences in parallel using multi-headed attention and positional encoding. Transformers actually embed tokens into vectors. Let's take a look at this example. LeBron James is the goat. Specifically, at the word goat. Technically, tokens usually aren't specific words, but for convenience, we'll consider a word as a token for our example. Essentially, the attention block is getting a vector representation of the word. In that vector, it first embeds where the word actually is in the sentence, positional encoding. Then, it processes the contextual relationships that that word has with the rest of the words in that sentence, and it alters the vector accordingly. This is what's known as self-attention. For example, after using attention on this one word, maybe now the transformer knows that the word goat correlates with LeBron James. Now, we just looked at one word. Imagine this happening in parallel with every single other word in that sentence. That's how the multi-headed attention block actually works. Now, combining this novel attention block with other layers, such as feedforward neural networks, softmax and ReLU functions, normalization techniques, and after stacking multiple of these layers together, we get an extremely advanced model that can learn hierarchical representations of language, vision, or any modality of data. It's insane to see how the transformer can be applied to essentially any aspect of AI. Amazing, right? Follow for more.