e) Transformers

Transformers are a type of neural network that are particularly well-suited for natural language processing tasks such as text generation, translation, and question answering.

Why is it called a 'Transformer'? What has the transformer got to do with 'Attention'?

The "Transformer" deep learning architecture was introduced by Vaswani et al. in the paper "Attention is All You Need" in 2017. It is called a "Transformer" because it transforms the input data (usually sequences) into representations by focusing on the most relevant parts of the input for a given task, such as translation or summarization. The Transformer architecture is fundamentally built around the concept of "attention."

Attention mechanisms in deep learning help a model to selectively focus on the most relevant parts of the input data for a given task. In the case of the Transformer architecture, the attention mechanism allows the model to weigh the importance of different input elements (tokens or words) when generating output representations. This is particularly useful for tasks that involve sequences, such as natural language processing, where the relationships between words in a sentence are essential for understanding and generating meaningful output.

The key innovation in the Transformer architecture is the "self-attention" mechanism, which computes attention scores for all pairs of input tokens, enabling the model to capture long-range dependencies and complex relationships in the data more effectively than traditional recurrent neural networks (RNNs) or long short-term memory (LSTM) networks. The self-attention mechanism is applied in parallel across all input positions, leading to a more computationally efficient architecture compared to RNNs and LSTMs, which process sequences sequentially.

Transformers are being used as generative models, as in GPT and T5. In early 2023, Large Language Models, or LLMs, such as ChatGPT, have taken the world by storm. Whether it's writing poetry or helping plan your upcoming vacation, we are seeing a step change in the performance of AI and its potential to drive enterprise value.

And it's this generative capability of the model-- predicting and generating the next word or pixel or MicroPhenome --based off of previous words, pixel or MicroPhenome that it's seen beforehand, that is why that foundation models are actually a part of the field of AI called Generative AI because we're Generating something new in this case, the next word in a sentence, the next pixel etc. And even though these models are trained to perform, at its core, predicting the next word in the sentence or a pixel in an image, we actually can take these models, and if you introduce a small amount of labeled data to the equation, you can tune them to perform traditional NLP tasks-- things like classification, or named-entity recognition --things that you don't normally associate as being a generative-based model or capability. And this process is called tuning. Where you can tune your foundation model by introducing a small amount of data, you update the parameters of your model and now perform a very specific natural language task. If you don't have data, or have only very few data points, you can still take these foundation models and they actually work very well in low-labeled data domains. And in a process called prompting or prompt engineering, you can apply these models for some of those same exact tasks.

In summary, the Transformer is called so because it transforms input data into meaningful representations by selectively focusing on the most relevant parts of the input. The attention mechanism, particularly the self-attention mechanism, is a core component of the Transformer architecture, enabling it to capture complex relationships and dependencies in sequential data more effectively and efficiently than traditional sequence models like RNNs and LSTMs.

LaMDA Foundation model:

LaMDA is a transformer-based language model.

Here is a table that summarizes the key differences between diffusion models and transformer-based language models:

Feature	Diffusion Model	Transformer-based Language Model
Type of model	Generative model	Discriminative model
Input	Random noise	Text or code
Output	Image, text, or other creative content	Text, translation, or answer to a question
Strengths	Can create realistic and creative content	Can perform a wide variety of natural language processing tasks
Weaknesses	Can be slow and computationally expensive	Can be less creative than diffusion models