What is AI and why is it topical?

Published on 03 June 2024

Whilst there is no universal definition of what constitutes artificial intelligence, at its core, AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. This encompasses the ability to reason, learn from experience, understand complex concepts, interact with their environment and look to solve problems.

In its AI White Paper published in March 2023, the UK government avoided a rigid definition of AI on the basis that it may quickly become outdated and restrictive, given the pace of developments in the technology.  Instead, the government sought to define AI by reference to the two key characteristics of AI that give rise to the need for a bespoke regulatory response, namely adaptivity and autonomy.  The ‘adaptivity’ of AI can make it difficult to explain the intent or logic of the system’s outcomes.  AI systems operate by inferring patterns and connections in data, and quite often the logic and decision-making in AI systems cannot always be understood, or meaningfully explained in a way that can be understood, by humans.  Furthermore, AI systems may develop the ability to perform new forms of inference not anticipated by the developers of the system.  Some AI systems can make decisions without the express intent or control of a human.  This ‘autonomy’ gives rise to many concerns, one of which is that it becomes difficult to assign responsibility for outcomes caused by AI systems.  

By contrast, the EU AI Act defines an AI system as "a machine-based system that … infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can affect physical or virtual environments". 

Technically, AI is often an umbrella term used to describe a range of technologies, from simple rule-based algorithms to complex neural networks mimicking the human brain. A significant turning point in AI has been the development of sophisticated Large Language Models (LLMs).  These models have revolutionised natural language processing, demonstrating capabilities in generating human-like text, translating languages and even coding.  AI's ability to interpret, understand and classify visual data has also seen remarkable growth, as has AI-driven automation.  These are only a number of examples of AI systems which are in use, and each reflects a part of the diverse landscape of current AI capabilities.  

We have seen already how far AI technology has come to date.  In fact, it has already become deeply integrated into various aspects of modern life. Moving forward, how far will (and should) it go?


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