Artificial Intelligence in simple terms

As technology rapidly advances, so does our ability to create machines that can think and learn like humans. This relatively new field of study is called artificial intelligence, or AI for short.

There are many different applications for AI, from making cars safer to helping doctors diagnose diseases. But the most common use of AI is in computer programs that can learn and think for themselves.

One of the most famous examples of AI is IBM's Deep Blue computer, which beat world chess champion Garry Kasparov in 1997. Deep Blue was able to evaluate 200 million positions per second and make strategic decisions accordingly.

More recently, Google's AlphaGo AI program made history by defeating a professional Go player for the first time ever. Go is an ancient Chinese game that is considered to be much more complex than chess, making AlphaGo's victory all the more impressive.

AI technology is constantly evolving and becoming more sophisticated. As AI gets better at imitating human intelligence, it will become increasingly important in our lives.

The difference between AI and Machine Learning

There is a big distinction between AI and Machine Learning. AI is the broader term that refers to machines that can think and learn like humans. Machine Learning is a subset of AI that is concerned with teaching computers how to learn from data.

Machine learning algorithms are able to improve as they are exposed to more data. This is in contrast to traditional computer programs, which are only as good as the programmer. With machine learning, computers can get better at tasks on their own by learning from experience.

There are two main types of machine learning: supervised and unsupervised. Supervised learning is where the computer is given a set of training data, and it is then able to learn and generalize from that data. Unsupervised learning is where the computer is given data but not told what to do with it. It will have to figure out for itself what patterns exist in the data.

Applications for Machine Learning

There are many different applications for Machine Learning. Some of the most popular ones include facial recognition, spam filtering, and recommenders systems.

Facial recognition

Facial recognition is a technology that can be used for security purposes, such as unlocking your phone or getting into a building. It works by training a Machine Learning algorithm on a dataset of facial images. The algorithm then learns to recognize faces by looking for certain patterns.

Spam filtering

Spam filtering is another common application of Machine Learning. When you set up a new email account, you usually have to mark a few emails as spam before the system gets good at filtering them out. This is because the Machine Learning algorithm needs to be trained on what spam looks like. Once it has been trained, it can then filter out spam emails with a high degree of accuracy.

Recommender systems

Recommender systems are used to suggest items to users based on their past behavior. For example, if you buy a lot of books about history, a recommender system might suggest that you buy a book about the American Revolution. This is because the system has learned that people who buy books about history are also interested in books about the American Revolution.

Recommender systems are used by many different companies, including Amazon, Netflix, and YouTube. They are an important part of modern life, and they would not be possible without Machine Learning.

Neural networks

A neural network is a computer system that is designed to work like the human brain. It is composed of a large number of interconnected processing nodes, or neurons, that work together to perform complex tasks. Neural networks are used in a variety of applications, including pattern recognition, data classification, and prediction.

Neural networks are similar to other machine learning algorithms, but they are composed of a large number of interconnected processing nodes, or neurons, that work together to perform complex tasks. Neural networks are used in a variety of applications, including pattern recognition, data classification, and prediction.

Written by Steve Thompson, published 2022-02-24