AI & GhatGPT

AI: Core technologies explained

You will need to understand some of the AI technology concepts to grasp  this subject.

1. The outer layer is Artificial Intelligence: Machines that attempt to do things that

normally require human intelligence like learning, problem-solving, and processing language.

An applied example of this is Deep blue, the first computer to win a chess game against a reigning champion.

2. Machine Learning (ML) is a type of AI that allows computers to learn how to do

things without being specifically programmed to do them. It's like teaching a computer to learn from examples, just like how you learn from examples. For example, if you want to teach a computer to recognize a cat, you can show it a bunch of pictures of cats and tell it that those are cats. Then, the computer can use what it learned from those examples to recognize cats in new pictures that it hasn't seen before.

An applied example of this is email spam detection. The email is either categorized as spam or not spam based on the text in the email.

Artificial Neural networks are a type of machine learning. A neural network is made up of many parts they call neurons, like the human brain. Each neuron takes in some information, processes it, and then sends out a signal to other neurons. In terms of the previous analogy, neural networks don't need to be shown the pictures of cats by someone else and told they are cats, they can look at many images of cats, dogs, and other animals and start to learn the differences between them. It is like teaching yourself something, rather than being taught by someone else.

Deep Learning is a subsection of Artificial Neural Networks in which there is a greater number of connections between "neurons". These require huge datasets.

3. Large Language Models (LLMs) are a type of artificial intelligence that excels at

generating human-like text and performing various natural language processing tasks. They are trained on massive datasets and use deep learning techniques, particularly neural networks, to understand and generate language. LLMs use deep learning to produce text.

LLMs can be used for a wide range of applications, including translation, summarization, code generation, and chatbot development (ChatGPT, Microsoft Copilot, and Google Gemini).