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You can’t have escaped the headlines, media reports, television coverage and all the talk on the rise in the applications of artificial intelligence, data science and machine learning. You might be surprised to know that machine learning has been around for decades. However, the field has only recently began to get increasing media attention as a result of the huge amounts of data available today, and increase in computational powers available.

Artificial intelligence (AI) is concerned with the development of computers and softwares that are capable of intelligent behavior. Machine learning is a subset of AI, and its major concern is the construction of algorithms that can learn from data. Machine learning is in two forms: Unsupervised learning and Supervised learning. Supervised learning is preferred when the training data is labelled and categories are known. However, unsupervised learning is used when there are no categories — just data — and it is necessary to figure out patterns. A lot of industries such as advertising, health, entertainment, retail, computing, manufacturing and real estate are being completely reshaped as machine learning is getting applied to them.

Advertising agencies have, for as long as products have been manufactured and services have been developed, been trying to get people to buy their clients’ products. With the advent of computers, the Internet and digital advertising, advertising has gotten more targeted and sophisticated. Decades ago, it was nearly impossible for advertising agencies to get data on people brought into contact with their campaigns. Nowadays, these agencies collect a whale lot of data about customers, which are then analyzed. Machine learning algorithms help to sniff out patterns in the data and this makes it easier to understand and predict customer behaviour. This ultimately helps to increase conversion rates of advertising campaigns.

In medicine and healthcare, a number of startups are looking at the advantages of using machine learning with big data to provide healthcare professionals with better-informed data in order to enable them make better decisions. An example is the IBM Watson computer which is now used by doctors to access millions of pages of medical research and thousands of pages of information on medical evidence.

Also, identifying patients at risk could get a lot of help from machine learning. A lot of life-threatening diseases, in their early stages, show symptoms that are very similar to non-life-threatening ones. Identifying truly risky situations then starts to get difficult, but less so when a lot of data on previously infected people are analyzed and patterns are detected. This makes it easy to know those who might be infected with the life-threatening ones and get them urgent medical attention. This would also make it easy to predict future occurrences of such diseases in other patients.

The automobile industry is also going to benefit a lot from machine learning. There has been a lot of talk about self-driving cars recently, and the major idea behind it is teaching cars how to drive themselves. These cars are fitted with computers, sensors, software and network connectivity with which they collect a lot of data about their environment and then send the data to the cloud for processing and storage. A machine learning algorithm called Neural Network which works by mirroring how the human brain works, is applied in a lot of these self-driving cars.

These cars are driven by human drivers at first and they are able to recognize patterns in the way the humans drive by monitoring his driving decisions. The fitted sensors collect lots of data about the type of lane, cars or objects around, and traffic signs. These data are passed to learning algorithms which analyze them and generate driving decisions. In the future, there would be central storage locations for the huge size of traffic data that is collected and this would make it easier for new cars to drive themselves without a human driving them first.

The race is on for machine learning to be used in Banking and Finance analytics. Detection of fraudulent transactions, loans analysis and stock trading are areas that could very well benefit from machine learning. Fraudulent financial transactions share a lot of patterns that makes them easy to detect. However, filtering through millions of transactions while searching for fraudulent ones is no simple task for a human. A computer that has been trained to recognize fraudulent transactions can very easily detect them, even at their earlier stages. These transactions can be flagged and humans review them afterwards. This can go a long way towards reducing fraudulent financial transactions. Also, stock trading uses a lot of machine learning. There are lots of platforms that aim to help users make better stock trades. These platforms do a lot of analysis and computations in order to make their recommendations. They analyze tons of data about the historical opening and closing prices of stocks and they are able to make really accurate stock predictions.

Machine learning and artificial intelligence can be put to use — and are being put to use — in a lot of industries. A lot of applications are currently being discovered as well, and the amount of data available today is just colossal. Although it’s easy enough to analyze data, protecting the privacy of user data is another story. Obviously, some users are more concerned about how their data is used, especially in the case of it being sold to third-party companies. The increased volume of data available is new, but the privacy debate will be the deciding factor about how the algorithms will ultimately be used.


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