Artificial Intelligence (AI) is the number one tech trend with many industries researching and developing products that utilise the technology. Google and Microsoft have released their artificial intelligence assistance however many do not understand how artificial intelligence works. Here is a simple guide to how artificial intelligence works.

How AI Works

Learning From Examples

Just like a child learns from pictures and examples, AI learns from examples. The AI system is fed lots of data such as pictures, text, or sounds to learn from. 

Neural Networks

The core of AI consists of neural networks that are composed of interconnected nodes, or “neurons” that work together to process and interpret information. Each neuron receives input from other neurons, performs a calculation, and then passes the result to the next layer of the neurons in the work.  This process continues until the final layer produces an output. When AI learns something new it strengthens the connection between neurons.

Deep Learning

A subset of AI is deep learning that utilises multiple layers to interpret and analyse complex data. Deep neural networks can identify complex patterns and relationships within the data enabling them to make highly accurate predictions and decisions.

Teaching AI

Layers of Learning

There are multiple layers of neural networks, these layers act as a series of increasingly detailed filters. For example, the first layer might recognise the outline of the shape, the next layer identifies complex shapes, and further layers identify details.  

Making Decisions

Once the AI has learned from the data it has gathered, it can start making decisions or predictions. The more times the AI exposes itself to similar data, AI can make decisions quicker based on previous results.

Improvement Over Time

AI improves over time through trial and error. AI uses rewards in a virtual environment to learn from its actions refining its decision-making process.

Applications

AI can be applied in various fields from recognising speech to diagnosing diseases by analysing medical images or driving autonomous vehicles.

Two Types of Learning

Supervised Learning

In a supervised learning approach, the AI is provided labelled data such as a dataset of images or data that have been tagged with their corresponding object names. The AI uses the labelled data to learn characteristics and features that distinguish one object from another. By analysing many examples, the AI can develop a model that accurately classifies new unseen data.

Unsupervised Learning

With unsupervised learning, it challenges the AI to identify patterns and relationships within the data with no pre-given labels or answers. The AI must explore the data on its own and discover hidden structures or groupings. This method is useful for tasks such as anomaly detection, customer segmentation, and data compression.

Summary

AI technology is advancing at a rapid pace providing opportunities that can revolutionise industries, solve complex patterns, and improve efficiency. There are concerns that AI systems can displace jobs by being able to perform tasks previously done by humans. Another issue is privacy and security as AI algorithms can process and analyse large amounts of personal. Recently the emergence of AI has seen an influx of deep fakes from images to international news. One deep concern that has been associated with AI is its ability to become self-aware. Of course, the fear is further deepened by TV series and movies such as The Terminator. There are many ethical and societal questions about AI however, the tool is created by humans and the choices we make in its development and deployment will shape its impact on our world. We must ensure that the AI technology developed is used in ways that benefit humanity and align with our values and ethics.