Here’s a blog post covering some of the most frequently asked questions we get on Machine Learning and Artificial Intelligence, or Cognitive Computing. We start off with “What is Machine Learning?” and finish off by addressing some of the fears and misconceptions about Artificial Intelligence.
So, what is machine learning? A simple search on Google for the answer will yield many definitions for it that leave most non-analytical people confused and entering more “What is…” statements into Google. So, I asked our Head of Marketing to try his hand at defining Machine Learning in the most simplistic way he can: explain Machine Learning to someone you’ve just met at a social gathering. Here’s his definition – a “Machine Learning for Beginners’ ” definition if you will.
What is Machine Learning?
“To begin to understand Machine Learning you need to first understand what an algorithim is. An algorithm is a series of “If / Then” statements that essentially equate to actions and outcomes or results. For example, if I stick my hand into a fire, then my hand will burn. Which is an “if / then” statement – an action and an outcome of that action. The more we repeat the action and the outcome is the same or similar, the more we are able to accurately predict the outcome if we do it again. We can then choose to do it again if the predicted outcome is positive, or change our action if the predicted outcome is negative. This is a basic way we as humans learn: we try different actions and remember the outcome. Now apply this method of learning to a computer, which creates the algorithms (“If / then” statements) from patterns observed in data and then analyses all of the data to predict the outcome of an action. This is Machine Learning.”
Read our blog posts on Machine Learning to learn about the various ways that companies across industries are adopting Machine Learning to improve profitability, loyalty, the customer experience, and marketing results to name a few.
How does Machine Learning relate to Artificial Intelligence?
To us, when we talk about Artificial Intelligence, we are referring to computational systems that behave like a human – i.e. they can understand the written and spoken language, they can converse and interact “like a human”, they can process complex concepts like sarcasm and humour. In developing this computational system, the computer needs to learn…a lot….and this is where Machine Learning comes in. The more we use Machine Learning to automate decision-making and actions taken the closer we get to Artificial Intelligence.
How does Machine Learning relate to Predictive Analytics?
Although most applications involve the requirement to predict something (like a response, a fraudulent application, an attrition account), there is a tranche of Machine Learning that does not predict anything specific. Customer segmentation is a good example, where one looks at the customer base and the requirement is to group the accounts into homogenous clusters without pointing to any specific target variable.
Anything that involves the prediction of something can be considered to be under the wider Predictive Analytics banner. There are two types of predictive Machine Learning: static (or batch) and dynamic (or incremental). The more stable and trusted approach would be to manually extract the relevant data, train the models with care, validate and then implement the models. With the computing systems and algorithms that are available today, full dynamic Machine Learning is possible which allows for the retraining process to be carried out automatically with little or no human intervention.
A good example of this is the iPhone’s Siri which learns the lifestyle patterns of the user and automatically adjusts its recommendations accordingly. For most Machine Learning projects within the financial services industry, we do not advocate this black box approach, but rather the development of stable and explainable predictive models. Dynamic Machine Learning algorithms can then be applied on top of this solid foundation in order to bring in more recent patterns and trends, further lifting the performance of the predictive analytics system.
Why has Machine Learning or Cognitive Computing become such a hot topic?
The reason for the increasing interest is due to the significant increase in data that is now available, which makes machine learning more relevant, accurate and more effective for more businesses than ever before. Machine Learning automates decisions by analysing large and diverse datasets at lightning speeds, predicting what would lead to a positive outcome and making or taking the recommended action.
The accuracy of Machine Learning depends on the volume and the reliability of the available data. With the data deluge happening now thanks to a combination of smartphones, the Internet of Things, the Internet, RFID technology and social media there is now an unprecedented amount of data being generated every second of the day for Machine Learning algorithms to analyse, search for and recognise patterns and trends in the data, and to then output a decision or recommended action.
What are the potential benefits of creating machines that can be programmed to learn?
Machine Learning automates decisions in a continuous improvement cycle. And machines automate actions. The combination of both greatly reduces the need for human intervention for a process to be completed. When we remove the reliance on human intervention we tend to achieve faster response times, a reduction in cost and a reduction in human error or bias for any process – from the granting of a loan to the landing of a plane.
Some examples of how Machine Learning is being used are:
- in everyday life! Check out our blog post on The Apps on Your Mobile That Use Machine Learning
- to instantly identify fraudulent behaviour on an account as it happens and automatically sending an alert;
- flagging customers who are most likely to unsubscribe or leave a business for a competitor based on real-time analysis of customer behaviour and transactional data and then taking appropriate action (or not as the case may be);
- adjusting pricing on thousands of items at once based on availability and demand; and
- making real-time recommendations of products or services based on a person’s purchase behaviour as they shop or complete a purchase.
Decisions based on Machine Learning get better or more accurate with time, as they learn what decisions and actions lead to a negative or positive outcome. As the algorithm is fed results of its actions and consequent outcomes, it learns and adjusts to do more of X to get the desired result instead of Y. It’s getting closer and closer to Artificial Intelligence every day.
The world is currently generating data more rapidly than we are able to analyse it, is Cognitive Computing the answer?
Yes. Although the human mind is complex and powerful, we are unable to compete with computers when it comes to the speed at which they are able to analyse large sets of data and recognise patterns and trends. And they are just getting faster and smarter. So, although the human race has been able to achieve some incredible things and there doesn’t seem to be any problems we cannot solve, in some cases, we lack the luxury of time to solve them. Cognitive computing could help us find answers or solutions to these problems faster.
Read our blog post: Machine Learning is Here to Stay
How did you use Machine Learning to forecast the results of the 2015 Rugby World Cup matches with a high level of accuracy? How was this possible?
Sports fans make their own predictions on match results based on their own experience and observations, as well as gut feel and personal biases. With Machine Learning, we based our predictions primarily on data we took from 6,000 matches played by 99 teams since 1995. As we then progressed through the World Cup matches, we incrementally added the results from each match to our model, so the model could adjust its prediction based on what happened in the last few matches.
What was interesting was that the model didn’t look at who played the matches, but rather the characteristics of the teams that played the matches. So, there was no bias towards any team – it was just looking at the data or the characteristics of a specific in making its prediction. When we added that new data, the model adjusted and compared the characteristics of the teams whose data was added and the result of these teams’ matches and adjusted its expectations for teams with the same or similar characteristics.
We continually added new data and the model continually adjusted to this new data, increasing the accuracy of our prediction with every match played. Two interesting sets of data we included that added a bit of the human element to the prediction were the fantasy value of the players and the bookie odds. We didn’t look at fields, or influencers, such as referees and weather as this would have taken a lot more time and effort and would not necessarily have resulted in a large enough increase in accuracy of the prediction to justify the added effort. Understand, this was a side project our team of data scientists did in their spare time. Had this been a client project, the level of effort would of course been a lot higher and we may have run a number of “challengers” in order to explore the predictive power of alternate data elements.
What are the myths or misconceptions around Cognitive Computing and Artificial Intelligence?
I think the main one is that machines or robots running on artificial intelligence will one day take over the world and rule over humans, like the HAL computer in the movie 2001 or The Terminator. I think enough of us are aware of or share this same fear that we will always ensure that mechanisms are in place to ever prevent a machine from making a decision that could put someone’s life in danger. With that being said, however, the advent of self-driving cars highlights the conundrums that have to be solved: do the manufacturers programme a car to hit a pedestrian crossing the street if it means swerving to avoid the pedestrian could lead to severe injury or death of the passenger? These are the kinds of scenarios – the kinds of scenarios where there isn’t a clear right or wrong answer – that must be thought of and addressed when programming artificial intelligence.
Read our blog on Automation and Machine Learning: How Much is too Much?
Image credit: FreePik / @dooder
This post was originally published 12 September 2016.