There is a lot of buzz/hype around artificial intelligence (AI) and machine learning (ML) – some of it for good reason. With DeepMind beating the world’s number one player at Go, and Netflix utilising machine learning to recommend shows to users, AI and ML has many industries excited (and worried), about its transformative implications for how we work.
It has various members of the C-Suite getting excited about the potential revenue streams and new business opportunities. Yet, confusion reigns – artificial intelligence and machine learning mean the same thing for a lot of people/too many people.
This is simply not the case. This lack of understanding is the first fundamental hurdle organisations are facing around artificial intelligence and machine learning. After all, how are you supposed to extract value if they are used interchangeably when they represent different things?
Organisations must know the difference
As Bernard Marr states: AI is the broader concept of machines being able to carry out tasks in a way we’d consider ‘smart’ (for example, recognising a face in an image or reasoning an answer based on a question). Machine learning is an AI application based around an idea that we should be able to give machines access to data and let them learn, predict and classify based on the input they are given. It’s similar, but it’s not the same – and it’s making some in the tech industry look foolish!
Buzzwords are a problem for the enterprise. They become so overused they become meaningless, leading to miscommunication and misunderstanding – and possibly to wasted investment. It leads to an unfounded belief that AI and ML can be used to solve any business case or problem. The over-hype that exists around AI and ML right now will mean that a great number of enterprises will increase their odds of becoming

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