What’s the difference between artificial intelligence and machine learning?

If you’ve been following this blog for a while, you’ll already have come across terms like “machine learning” and “artificial intelligence” – and even if you’re new here, you’ll likely have heard these expressions thrown around elsewhere. But what exactly are these things, and how can they help patients in the real world?

Although these two words are often used interchangeably, they’re not exactly the same thing. To get technical, artificial intelligence – also referred to as AI – is a branch of computer science that explores intelligence displayed by machines. It’s essentially a relatively broad concept that explores the possibility of machines being able to carry out tasks that, generally speaking, require a certain level of “intelligence”.

Machine learning, on the other hand, is a sub-genre falling under the all-encompassing umbrella of artificial intelligence. To simplify what is a complex process, machine learning ultimately centers itself on the principle that machines - once provided with the relevant access to data - should be able to figure everything out for themselves, taking “responsibility” for their own learning with minimal human intervention. The purpose and intention of AI is for machines with this capability to make predictions using data analytics. This idea can be credited to AI pioneer Arthur Samuel, who argued that it might be entirely possible to teach computers how to learn for themselves rather than teaching computers everything they need to know manually back in 1959.

Giving computers the tools and algorithms to automatically improve their own understanding and intelligence is the key difference between these two concepts. Machine learning uses experience to further develop itself, rather than the more manual approach adopted by AI.

AI itself is by no means a new concept, with references to robots and the idea of inanimate objects coming to life as intelligent beings in ancient Greek literature. Conversely, machine learning is a relatively new development which has only gained mainstream media attention over the course of the past few years. Essentially, machine learning is one of the ways through which we hope to achieve artificial intelligence – and is one of the most exciting and promising avenues through which we might be able to do this.

Of course, some organizations do take advantage of machine learning and AI technologies and use them for questionable purposes. That being said, a growing awareness of the moral responsibilities companies have - alongside societal obligations to their users or clients – is highlighting the importance of the ethical employment of such strategies, with more and more countries adopting formal legislation in order to ensure that these powerful concepts are used for the right reasons.

Both AI and machine learning open up a realm of possibilities when it comes to harnessing this power to change our world for the better. There are countless relatively mundane uses for these technologies that simply aim to relieve humans of repetitive, dull or time-consuming tasks – but there’s also an increasing number of innovative and solutions-oriented approaches stemming from these concepts that could offer answers to some of the most difficult problems of our time, especially in the healthcare sector. Challenges within this field can be some of the most high-risk - something that’s to be expected when dealing with human patients. By incorporating both AI and machine learning into not only day-to-day approaches to healthcare but also the clinical trial stage of the research and development process, we’re opening doors to countless potential new treatments for notoriously stubborn illnesses and conditions.

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