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It was 2017 when American businessman Mark Cuban said that if you don’t understand artificial intelligence, deep learning and machine learning “you’ll be a dinosaur within three years.” Time will tell as to whether he is right, but if his theory has substance, some companies are well into the 12-month countdown of becoming extinct.
What is Machine Learning?
In its purest form, machine learning teaches computers to learn in the same way that humans do. It collects and interprets data from the world around us and makes decisions on what to do with that information. Machine learning is one of the first applications of artificial intelligence.
Just think about every time you start a search using Google. How can it find all the relevant matches to your terms? Considering there are 30 trillion unique web pages that search engines trawl to retrieve what you need, it is even more impressive. It’s impossible for a human to explore that many pages in a lifetime. This is the essence of machine learning, without intervention computers learn to use data to accomplish human tasks in a fraction of the time.
Machine Learning and Data
It is almost impossible to stress just how vital data is to machine learning; in fact, they are just about synonymous with each other. This is probably best summarised within the Data Science Hierarchy of Needs penned by Rogati, 2017
At the top of the hierarchy is the AI or Deep Learning algorithm. This might be the algorithm that recommenders which Netflix show to watch or Amazon Alexa responding to your voice command. However, at the very start of the journey is data collection and the quality of what feeds the algorithm.
As an example, marketing teams use machine learning applications to hyper-personalize communications. This is why we tend to get emails or notifications that are highly relevant and tailored to our needs. The machine has studied our data and knows exactly what we need and when we want it. Had the initial data been incorrect or “dirty” in any way, customers would receive communications that are not relevant. What if somebody had accidentally entered a customer location as the U.K. on an order form instead of the U.S. and all pricing is calculated pounds instead of dollars? The customer would soon unsubscribe to an email list because it doesn’t pertain to them.
A company can have the best algorithms in the industry, but without quality data, they are effectively useless and possibly detrimental. To counter these problems, companies deploying machine learning technology will usually start by designing a data quality or governance strategy which negates the risk. Adopting AI is a journey and must begin with getting the simple things right.
Machine Learning Framework
Hiring a team to design and deploy machine learning applications can be costly. While Data Scientists are usually specialists in statistical methods and incredibly adept with coding languages like Python and R; they often find it hard to present findings to Data Analysts or Insight Managers. However, the algorithms also need to be deployed onto platforms requiring a Data Engineer or Developer. There also needs to be duplicate roles to avoid single points of failure, and of course, everybody needs powerful processors that can analyze vast amounts of data. Suddenly, one Data Scientist has become a team of 8 people with expensive hardware and costs have escalated!
The role of machine learning has been growing exponentially in the last few years, and it looks set to continue with recent developments in cloud, edge and quantum computing which will only increase the potential processing power. Companies who fail to realize the capability of AI will fall behind the competition.
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