Beyond the Jargon: AI explained

Artificial Intelligence (AI) is a buzzy term that has overtaken the world - and the advertising industry over the past several years. Whether at CES or Cannes, on LinkedIn or X, AI-related content is everywhere. Yet, amidst the chatter and complex terminology, it can be hard to know what exactly companies are referring to when they discuss "AI," which can cover a variety of machines and systems.

This guide is here to help you cut through the hype and delve into AI's core components. From its fundamental principles to its more intricate mechanisms, we're covering the need-to-know terms of AI.

• Artificial intelligence is the broad term for machines/systems designed to mimic human intelligence. Its key advantage lies in analysing vast datasets to identify patterns, allowing for informed decision-making, reasoning and problem-solving. 
• Data Science is a multidisciplinary field combining computer science with statistics and mathematics to understand, visualise and extract actionable insights from data, ultimately leading to more informed and accurate decisions.
• Deep learning is a specialised form of machine learning that typically leverages neural networks with many hidden layers, which lets the networks learn and represent far more complex relationships and patterns in data. The machine learns on its own, usually without full supervision. Much of modern AI, particularly generative AI and image and speech recognition, relies on deep learning.
• Generative AI extends beyond text to create images, videos and music, pushing the boundaries of creativity and content creation. 
• Large Language Models (LLM) specialise in understanding and generating human-like text. A well-known example is GPT-4. (ChatGPT is a chatbot application trained with the LLM GPT-4.) 
• Machine Learning (ML), a subset of AI, allows systems to learn from data over time, improving accuracy. It is typically used to detect, classify, cluster, recognise, recommend, predict, optimise and decide. Google’s search algorithms and Netflix’s recommendation engine are prime examples of ML in action - the more use they receive, the more precise their responses become for future queries. 
• Neural Networks are a specific type of machine learning algorithm that tries to mimic how the brain works by connecting many neurons, each trained to detect or process very specific things, together to model highly complex patterns within data. Examples of neural networks in action include visual recognition in self-driving cars to recognise signs or automatic subtitling of meeting recordings. 

As AI evolves, its applications will only grow more sophisticated and integral. In adtech, this technology will empower advertisers to craft more engaging campaigns, optimise strategies in real time and gain deeper insights into consumer behaviour. Understanding the fundamental concepts of AI is just the first step in harnessing the power of this developing technology.

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