If you have been following the technology news lately, you may be aware of the pace at which companies like IBM are quickly announcing innovations in Machine Learning. For example, their new services on the Watson platform that incorporates predictive analytics, data mining, and pattern recognition are by themselves new paradigms in the business world. Machine Learning, unlike artificial intelligence, is more about the ability of machines to learn when exposed to more data.
Examples of machine learning in use
In very simple terms, what machine learning does is detect patterns and predict outcomes by using different mechanisms. These mechanisms are based on mathematical models and statistics. For example, the way search technology works. It extracts information, gathers them together, and classifies before displaying it to the end user. Behind all machine learning, there are algorithms that help the machine process and fetch information. In doing this, they could look for patterns, for example, the way Amazon predicts user preferences. Machines are trained to recognize patterns and cluster information. That is why it is also referred to as cognitive automation, it is trained to recognize traits in data.
Supervised and unsupervised learning mechanisms
In one stream of machine programming, the machines are given instructions on what actions to take and the sequence of those actions. They may also be told about expected outcomes and the machine is expected to derive the function that would achieve the result. In such instances, where the input, as well as the output, is provided to the machine, the programming world refers to it as supervised learning. The machines are actively supervised to train them on inputs and then validate the output before being put to execution. Since it is very difficult for users to predict or reasonably guess all outcomes for complex systems, machine learning emerged as an alternative that would let IT systems respond rapidly to changes. All of this, of course, is largely dependent on the machine being provided an initial set of sample data that is best suited for the purpose, determining the appropriate learning algorithms and then verifying the output. Only when the output is verified, can the machine be said to have learned the process and allowed to analyze data sets. This sort of machine learning is commonly used on structured data to solve classification and regression problems.
On the other hand, machine learning is said to be unsupervised when though there is a set of input data, there is no outcome provided to the machine. In such instances, the machine is expected to learn by looking at the data. What it does is looks at data sets and studies their structure and distribution and then identify inherent groupings. For example, it can, by studying data sets, look for common phrases. This sort of analysis usually happens on unstructured data.
Though mostly used in the context of IBM’s Watson system, cognitive automation systems are increasingly being deployed across businesses in multiple forms. Such systems essentially use the machine learning algorithms to be able to acquire knowledge from data sets they are provided with. As they keep looking for patterns in the information and process the data, machines are increasingly able to anticipate probable new problems and derive appropriate models based on what they have learned. They are then deployed in many applications that include artificial intelligence (AI) applications, neural networks, robotics, and virtual reality systems.
Cognitive computing is increasingly being recognized as the next stage in the evolution of computing capabilities. It is all about making computing systems more user-friendly and that can interact with the end user based on understanding from signals, what a user wants to do and contextualize the output to deliver personalized outcomes. Be it your car, or your refrigerator at home, applications like Siri, that understand context is becoming the norm of the day.