AI and Machine learning: what’s the difference and why does it matter?

If Facebook’s recent chatbot story demonstrates anything, it’s that a lack of understanding really does breed fear.

When the company shut down its artificially intelligent (AI) bots after they created their own language, the immediate reaction was anxiety that smart technology was about to replace humanity. Yet once it was reported that the bots were always designed to experiment with linguistics, they suddenly seemed less menacing.

It’s this kind of ambiguity-fuelled concern that drives the confusion around both AI and another concept that has become synonymous with it; machine learning. Often used interchangeably, it’s hard to tell these terms apart, let alone how they will impact our world. Consequently, they still remain a source of trepidation for many.

To dispel the apprehension surrounding these technologies and realise their potential, one thing is crucial: a complete understanding of what they are, how they differ, and what they mean for the future of business and — in particular — digital advertising.

AI: destined to outsmart us all?

Used to describe a range of tools and applications, AI is a term most consumers, marketers, and brands are familiar with. The broad nature of the concept is also relatively well known; AI is understood to describe the process of machines conducting tasks and thinking without instruction. Or in other words, smart tech that operates independently.

But the different types of AI are less widely recognised.

For instance, AI was first used to define computers that could simulate basic actions, such as working out equations. Now, we are in the era of narrow AI (also called applied AI) where tech can emulate human capability in one area - like Google’s AlphaGo. Pioneers are working towards reaching the next stage — general AI — in which tools will match human intelligence and perform multiple functions as we do. And once this is achieved, super-intelligent AI that supersedes human knowledge in every aspect will be next.

Machine learning: the foundation of AI

While machine learning is often seen as an alternative name for AI, and vice versa, it’s actually a related but unique concept. The main difference is that AI encompasses the general idea of machines turning data into autonomous action, whereas machine learning specifically refers to tech that can use information as a basis for perfecting and acquiring skills. 

In contrast to traditional hand-coded systems that follow programming, machine learning uses advanced algorithms to analyse data, recognise patterns, and decide how it should be applied: teaching itself to master various tasks. For instance, given sufficient examples of faces, voice recordings, and objects, it can become highly proficient in facial, speech, and object recognition — at least to human level, and usually at a greater scale. 

This makes it a crucial subfield of AI. Moreover, it also has strains of its own. Deep learning for example, which leverages neural networks that mimic human decision-making, is an offshoot of machine learning.

Can AI and machine learning improve digital advertising?

With advertisers aware of a number of industry challenges that are often considered to go hand-in-hand with programmatic, it’s important to understand how AI and machine learning can add value to digital advertising and enhance ad quality. 

But focusing on AI’s role alone sidelines the vital part humans play in making smart tech work. Machines, after all, must be correctly implemented and maintained to function well. As a result, marketers and publishers need to select tools and vendors carefully, and consistently monitor the input of intelligent machines as well as the output. 

Let’s look at three key areas where the use of AI and machine learning technology can improve digital advertising:  

1. Data-driven targeting

One of the biggest advantages AI-powered tools offer is an unrivalled ability to instantly collate, assess, and deploy multiple data streams. In digital advertising, this capacity can be used to significantly improve efficiency and impact. For instance, sophisticated algorithms can help cut through big data to establish which ad types audiences respond to and adapt delivery accordingly, saving wastage and boosting engagement. Furthermore, insight into consumer behaviour can determine what individuals like and when they are most receptive, enabling marketers to retarget ads at the ideal moment without being disruptive.  

2. Increased contextual relevance

There was a time when achieving contextual and personal impact at scale was a tall order, but thanks to machine learning advances, such as dynamic creative optimisation (DCO), new possibilities are opening up. With DCO, marketers can instantaneously tailor messages for large audiences to meet an array of variables, from geographic location to the weather, as well as selecting creative to match each consumer’s position on the path to purchase. So, every message can form part of a data-driven story with an inspiring, personal touch. 

3. Optimising campaign performance

Last but not least, it’s the performance enhancement prospects of machine learning. For example, data-driven practices and machine learning can be leveraged to trace consumer interactions over time and identify patterns in their activity. Using the insight this produces, marketers can then predict what individuals will want in the future and how, when, and where, messages should be served to increase engagement, and sales. 

AI and machine learning have the potential to improve not overtake the industry, giving marketers the means to deliver better experiences for consumers, and greater results. Yet understanding is crucial to make this vision a reality. With a clear view of what AI and machine learning are, and why careful human guidance still matters to make them a success, the industry can utilise the power of machines to secure a brighter collaborative future.