LinkedIn is proliferated with articles on Machine Learning and Artificial Intelligence. It doesn't matter where you turn the words AI and ML appear. But what is machine learning?
Machine learning (ML) is the process of employing data-driven mathematical models to support an automated learning system. Machine learning employs algorithms to find patterns in data, and then uses those patterns to build a predictive data model. Machine learning produces more accurate outcomes with more data and expertise, much like people do with more practise. Machine learning consists of getting computers to learn from experiences-past data.
In plain English, we can automatically recognise if an image contains an apple, machine learning can do this task if we have set it up for the task of recognising fruit. This type of machine learning is image recognition.
Consider your supermarket mailer, how accurate are the online predictions on what you have missed in your cart or items you may be interested in purchasing? If you regularly use your supermarket loyalty card your past history along with those customers just like you are available to the supermarket data scientists to make predictions based on. These are then used to personalise your advertising and offers.
Internet search engines, spam-filtering email software, websites that offer personalised recommendations, banking software that spots suspicious transactions, and many phone apps like speech recognition all employ machine learning.
The ability to think, which is uniquely human, is one skill that machine learning has not yet mastered. The range of algorithms that are now accessible is limited in scope and primarily focused on certain use-cases. They are unable to question why a specific strategy is working the way it is or "introspect" their own results.
One critical and very human part of all of this is bias. We need to ensure we do not train our models on data which is biased. As we become more and more dependent on machine learning telling us what to buy, where to live, what news to read we fall into a trap of echo chambers.
This is where the danger lives, this is where we need to add the human back in. Question what you see, expand your knowledge and educate others of the echo chambers that are being built around us.