AI is popping up in countless business functions across every industry. But what does AI actually mean? And more importantly, how does it work? 

Artificial Intelligence (AI) itself is a general concept that describes machines and computers that can complete tasks that require “intelligence”. An AI model can analyse vast amounts of data, identify patterns and make informed decisions in almost no time, meaning a specific task can be completed at scale and speed. 

AI allows computers to do things that would usually require human intelligence, such as understanding language, recognising objects in images, playing games or even driving cars. The process by which machines learn from data is called Machine Learning. The more data the models intake and the more they “train”, the better their accuracy becomes over time. Currently, all forms of AI are trained this way. 

We've listed some AI terminology or common AI buzzwords and try to decode them into plain English. We also dive into the history of AI and unpack its recent acceleration. 

What is Artificial Narrow Intelligence? 

Artificial Narrow Intelligence (ANI) is the only form of AI on the market now. ANI can solve a single problem and perform one task to satisfaction, such as suggesting a related product for a shopper or creating an AI generated image. 

What is Artificial General Intelligence? 

Artificial General Intelligence (AGI) is a form of Artificial Intelligence that mimics human intelligence and how a human reasons. This is still only a theoretical concept to date.
 

What is Artificial Super Intelligence? 

Artificial Super Intelligence (ASI) is also only conceptual in nature. It involves highly complex and logical Artificial Intelligence capable of reasoning beyond human capabilities with the ability to build emotional relationships.

What is Generative AI? 

Generative AI is Artificial Intelligence capable of generating text, images, videos, or other data using generative models, often in response to prompts. 

Generative AI is a type of Artificial Intelligence that can create new content, such as images, music or text, based on patterns it’s learned from existing data. For example, a Generative AI model trained on a database of images of cats could generate new, realistic-looking pictures of cats even though it’s never seen those specific images before. Generative AI works by learning the underlying patterns and features of the data it’s trained on and then uses that knowledge to generate new content that fits those patterns.

Paul Solomon, Head of Data Science at Xelix, compares Generative AI to the human brain:

"Neural networks are a recent development in Machine Learning. Inspired by human brains, you can teach ‘neurons’ to perform incredibly complex tasks. Neural networks are essentially limitless in scope. Their adaptability means they can take in any kind of data and perform any process, so long as we provide examples. 

Their ability to scale far exceeds a human brain. These new approaches mean we can create a more and more sophisticated AI system. At Xelix, we’re building AI that can read and understand emails and documents, pull relevant information from databases and then summarise its findings for a user. And this is just the start.” 

What is Machine Learning? 

Machine Learning is a branch of Artificial Intelligence (AI) that uses data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. 

What is a Neural Network? 

A Neural Network is a Machine Learning model that makes decisions in a manner similar to the human brain by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions. 

What is a Large Language Model (LLM)? 

A Large Language Model (LLM) is an AI model trained using Machine Learning techniques to take existing written human language and generate new, convincing text in response to a prompt. It can automate a range of language-based tasks without the need to create task-specific machine learning models, which require significantly more time and effort.

What is a multi-step LLM? 

If you chain multiple prompts together in a Large Language Model (LLM), you will get a multi-step LLM. 

The rise of AI

The “birth” of AI is dated back to the 1950s when interest in it came to a head. Alan Turing published his work “Computer Machinery and Intelligence”, which eventually became The Turing Test: a test experts used to measure computer intelligence. 

During this period, the term “Artificial Intelligence” was coined and came into popular use. Since then, AI technology hasn’t stopped evolving and has developed especially quickly over the last seven years.

Artificial Intelligence has existed for decades in various forms, but its recent acceleration can be attributed to a few factors: 

Powerful machines

The remarkable growth of processing speed and memory capacity has powered AI. Modern computers can easily process large volumes of data and handle complex tasks at speed. Older computers simply couldn’t do this.

Abundant data 

AI feeds on data. Today, every business holds vast amounts such as operational metrics, customer data or supplier information. These can come in the form of statistics, information, images, videos and anything else that AI models can use. AI trains on and learns from this data to deliver huge value.

Open-source ecosystem

While a cutting-edge AI application may be private, many algorithms are available for public use. This open-source culture has fuelled collective learning, the gen ai boom and further AI capabilities. Developers have created powerful AI tools by using valuable insights from previous AI systems.

Digital efficiency drive

There’s a worldwide movement for businesses to invest in digitisation. They want to streamline processes, improve productivity and enhance customer service. AI solutions hold the key to this digital transformation.

Keep learning about AI

If you're keen to learn more about Artificial Intelligence, Generative AI and AI application, feel free to get in touch with our team. 

You can learn more about how Gen AI can drive your P2P transformation in our recent webinar about The Gen AI sweet spot for P2P teams or read more about the different Gen AI use cases for P2P in our resources.