Introduction to AI

An introduction to modern AI, its origins, current uses and issues for the future. This course provides a comprehensive introduction to the field of AI, including its history, applications, and future prospects. Participants will learn about applications of AI and big data in different industries, including statistical analysis for medicine and generative processes for creative design. By the end of the course participants will have a solid understanding of the fundamental concepts of AI and be well-equipped to pursue further study in this exciting field.

Unit 1

This first unit of this course will help you understand machine learning and the history of modern AI (Artificial Intelligence). The idea of Artificial Intelligence has been around for decades, and the work that has been done over the last 70 years helps us to understand and use today’s technology. This knowledge also helps us to understand the future of AI so that we can incorporate the technology into our lives in a way that makes sense for us, and so that we can use or create new AI that is safe and relevant in our communities.

Intro - How does Artificial Intelligence work?

AI works by training computer systems to learn patterns and make decisions similar to humans. AI programs teach computers to recognize patterns in data, learn from examples, and then use that learning to solve problems or make predictions.

AI programs are different from other programs because the outcomes are not explicitly coded into the program. Instead the program is written to find patterns without being explicitly programmed for each step. In this way AI programs can learn from experience.

TERMINOLOGY

Machine Learning: An AI technique where machines learn from data to improve their performance on tasks without being explicitly programmed.

Neural Networks: These are computing systems inspired by the human brain. They are used in AI programs that have deep learning coded into the programs so that they recognize patterns and make decisions.

Algorithm: In AI, an algorithm is a set of rules or instructions that a machine follows to perform a task or solve a problem. For example, algorithms are used in machine learning to train models and make predictions.

Dataset: A dataset refers to an organised collection of data, often stored in a file or a database. It contains information organised in ways to allow for easy access, analysis, and use in machine learning.

Labelled data: Data that is given to an AI program that contains information about what the data contains. This helps the program to sort and analyse it, but requires a lot of manual work to sort and label the data.

Unlabelled data: Data that is given to an AI program without any information about what it contains.

Data Preprocessing: These are the steps taken to clean, transform, and prepare data for analysis or training in AI systems. This process ensures that the data is in a suitable format for the machine to learn effectively.

Ethical AI: This term refers to the responsible and fair development, deployment, and use of AI technologies. It involves considerations around privacy, bias, transparency, and the societal impact of AI systems. Understanding ethical implications is crucial when working with AI.

EXAMPLE

To see how an AI program can use huge amounts of data, you can have a go at using a fun website developed by Google - Quick Draw https://quickdraw.withgoogle.com

This website asks you to draw your representation of a word, and the system will work out what you’re drawing. The way that you draw is compared to millions of other drawings to work out what the object is. Find a partner and see if you can successfully draw images that the AI can recognise faster than them.

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