Whether we like it or not – and as software developers, we can’t help but be excited by innovation – AI is here and likely to stay. Not everyone is thrilled at the prospect. Some fret about existential threats and Skynet, the terrifying digital enemy in the Terminator franchise, others focus purely on what this new technology can do for them and their customers. It’s a big subject so we’ve broken this into two blogs. In this blog, we’ll look at AI and how it is currently deployed in the world of work and beyond. In next month’s offering, Chris engages with ChatGPT to get the cyber view! But first, a quick briefing.

What is AI and ChatGPT?

First things first. Artificial Intelligence (AI) isn’t new. The term was first coined in the 1950s. And it’s really not that scary – it’s the natural evolution of all the work mankind has been putting in to make technology do a lot of the boring stuff we don’t want to do, while we get on with the innovation that gave us AI in the first place.

Essentially, at its core, AI is simply about automation and manufacturing has been using that technology for years. Think about a robot arm being programmed to insert screws, drill holes, sand, weld, insert rivets, spray paint, screw caps onto things and you’ve understood the principle in a different context.

In the world of technology, it’s a digital rather than analogue thing. AI is what happens when computers simulate processes originally created by humans. Good examples include expert systems, natural language processing and machine learning.

Expert systems

An expert system simulates the judgement of a human expert or organisation with expertise and experience in a particular area. These computer programs use data collated from past events to predict current or future outcomes. It’s particularly important in medicine. More here.

Do you speak Natural Language Processing?

Natural Language Processing (NLP) is a machine learning technology (more on that soon) that enables computers to interpret, manipulate, and comprehend human language. It’s probably where technology and actual people cross over in the most obvious way.

This technology is great for software developers like Purple Crane, as it uses everyday language rather than artificial scripts like Java or C to communicate with receptive technology.

What does that mean? Simply that non-experts in Java (and that’s most people) talk to computers without coding everything first. It’s the foundation of so much interactive technology. If you talk to it, and it responds, then that’s NLP.

Rise of the Robots – Machine Learning (ML)

Machine Learning (ML) is a subset of artificial intelligence that automatically enables a machine or system to make informed decisions based on learning and improving from experience, instead of explicit programming.

ML uses algorithms to learn what to do by analysing data, rather than having to be instructed. ML algorithms get smarter the more they’re exposed to data.

Neural Networking

All of this is underpinned by neural networks, the process that mimics the human brain (hence the name) by which artificial intelligence teaches computers to process data and do all this. If you’re looking for a handy reference point, Skynet was underpinned by a neural network-based system.

It’s machine learning plus – the industry calls it deep learning – that connects neurons in a layered structure (again like our own grey matter). All this comes together in the technology we call ChatGPT.

Chat GPT (or Chat Generative Pre-Trained Transformer) was developed by an AI research company, Open AI. It is an AI chatbot technology that processes our natural language and generates a response.

So, it’s all good, right?

Maybe. Now you know what it is, if you’re so inclined you can open a ChatGPT account here. But maybe wait until the next blog, where we look at the arguments for and against using the different strands of AI.