Generative AI - Introduction to Large Language Models (LLM)

  Note: The below is from my learning from https://www.cloudskillsboost.google/course_templates/536 (Introduction to Generative AI).

Large Language Models (LLMs) are a subset of Deep Learning.

They can be pre-trained and then fine tuned for specific purposes.

What do we mean by pre-trained and fine tuned?

Assume in our everyday life we train dogs basic commands such as sit/stand/walk etc. This is basic training. But if need to train a dog to be a police dog, we need more fine training apart from the basic ones. This is the difference between pre-trained and fine tuned.

Similar idea applies to LLMs.

LLMs are trained to solve common language problems like document summarization, text classification, text generation etc.

They can then be tailored to solve specific problems in the field of finance, retail etc.

Benefits of using LLMs:

• Single model can be used for various purposes
• Fine tuning a LLM requires minimum field data
• Performance grows continuously as more data and parameters are added.

Note: the above image has been taken from the internet.

If we think of an example of getting answers for questions, the question answering model is able to search for an answer from a large document. Depending on the model used, the answer will either be extracted from a document or a new answer will be generated.

e.g. see the answer from Google's AI chatbot BARD which can be access by going to bard.google.com

In the above example, we gave the prompt (question) to get the desired answer.

Prompts involve instructions and context passed to the language model to achieve the desired result.

There are three main LLMs (and each need prompting in a different way).

• Generic (or Raw) Language Model
• Predict the next word
• Instruction Tuned
• Predict a response
• Dialog Tuned
• Have a dialog by predicting the next response

Generative AI (Introduction)

 Note: The below is from my learning from https://www.cloudskillsboost.google/course_templates/536 (Introduction to Generative AI).

What is Generative AI?

Generative AI is a type of artificial Intelligence technology that can produce various types of content like text, images, speech, audio etc.

What is AI?

AI has to do with the theory and methods to build machines that think and act like humans.

Machine learning (ML), which is a subfield of AI, is a program or system that trains a model from input data. Trained model can make useful predictions. ML gives the computers the ability to learn without programming. 

Two of the most common classes of machine learning models are unsupervised and supervised (Labeled data) ML models.

What problem can a supervised ML model solve?

Supervised ML model implies the model has labelled data. If we have historical labelled data of bill amount and how much different people tipped based on order type, the model learns from past examples to predict future values.

If you want to look at tenure and income and then group or cluster employees to see whether someone is on the fast track, would be a case for Unsupervised ML model. Unsupervised problems are all about discovery, about looking at the raw data and seeing if it naturally falls into groups.

What is Deep Learning?

Deep learning is a type of machine learning that uses artificial neural networks, allowing them to process more complex patterns than machine learning. Deep learning models typically have many layers of neurons, which allows them to learn more complex patterns than traditional machine learning models.

Gen AI is a subset of deep learning. Large language models are also a subset of deep learning.

Deep learning models can be divided into two types, generative and discriminative.

A discriminative model is a type of model that is used to classify or predict labels for data points. Discriminative models are typically trained on a data set of labeled data points. Once trained, it can be used to predict the label for new data points.

A generative model generates new data instances based on a learned probability distribution of existing data. Thus generative models generate new content.

A discriminative model, given input dog classifies it as a dog and not a cat.

The generative model learns and predicts the conditional probability that this is a dog and can then generate a picture of a dog.

So generative models can generate new data instances while discriminative models discriminate between different kinds of data instances. 

In generative AI, we as users can generate our own content, whether it be text, images, audio, video etc, for example models like PaLM or Pathways Language Model (PaLM API lets you test and experiment with Google's large language models and gen AI tools) ingest very, very large data from the multiple sources across the internet and build foundation language models we can use simply by asking a question using a prompt.

Given the above, lets now formally define Generative AI.

• Generative AI is a type of artificial intelligence that creates new content based on what it has learned from existing content.
• The process of learning from existing content is called training and results in the creation of a statistical model when given a prompt.
• AI uses the model to predict what an expected response might be and this generates new content.

Generative AI Studio helps developers create and deploy Generative AI models by providing a variety of tools and resources that make it easy to get started.

Generative AI App Builder lets you create gen AI apps without having to write any code. It has a drag and drop interface.