Difference Between Machine Learning & Artificial Intelligence

The world is constantly evolving and so is marketing, we cannot deny that. We are constantly being introduced to new and advanced ways of utilising technology to solve modern problems. The constant drop in human attention spans has led to our need to constantly improve how we communicate and how information is transferred from our generation to the next. With this in mind we can also agree with the Greek philosopher, Plato, that necessity is the mother of invention. In essence, his words demonstrate that we’re inspired and naturally inclined to invent new solutions that fulfil a need.

The need to better refine how we utilise technology to better our lives has culminated in the introduction of Artificial Intelligence and Machine Learning. For us to explore this subject, we need to first ask the most relevant questions. What are they, how do they work, and how can we leverage their importance to better utilise technology to maximise output in our various areas of expertise.

These two are very closely related and connected in certain aspects. However, let us define them for more clarity.

Artificial intelligence is the proficiency of a computer system or rather software to think intelligently as a human mind. It is accomplished by studying the patterns of the human brain and by analysing the cognitive process. Let’s look at Nike as a practical example of artificial intelligence. In 2023 Nike launched a new campaign allowing customers to design their own sneakers in-store, this did not only drive more sales but also collects data that would help with creating algorithms that can inspire future designs and deliver personalised marketing messages to the customers.

Now, what is the connection between AI and Machine Learning? A computer uses AI to think like humans while performing tasks on its own, while Machine learning is how a computer system develops its intelligence.

Machine learning is an application of Artificial Intelligence, it is the process of using mathematical models of data to help a computer learn without direct instruction. This enables the computer system to continue learning and improving on its own based on experience. Now looking at the very same Nike example made above, Machine learning would play a role whereby the very same software has collected data and is now able to create Nike sneakers.

Nike’s benefit from the entire experience was giving a top-tier consumer experience while increasing their sales and collecting data for future marketing communication.

You must be thinking about how will you benefit from both Mechanic Learning and Artificial Intelligence, especially be you are not selling sneakers. You have more sources of data input, AI and Machine learning enable brands to discover new valuable insights in a wider range of structured and unstructured data sources.

You as a brand or organisation are able to make better and faster decisions, having machine learning you improve your data integrity and use AI to reduce human error. That combination on its own helps you make better decisions based on data.