Machine learning is trending across diverse industries and its benefits have impressed one and all. A deeper insight into the concept of ‘machine learning’ along with its industrial applications have been discussed here.
Machine learning is defined as an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. What does this mean? Let’s break it down to get a better understanding. Machine learning basically means that the machines are learning, understanding and even reasoning from the data that is fed into the system.
Based on this data, the machine is able to carry out tasks and does not need to be explicitly programmed. In machine learning, the programmes have been created in such a manner that the system learns and even improves when exposed to new data. Amazon’s Alexa is an excellent example of machine learning. Alexa uses machine learning to interact with people, playback music, play audios, provide weather updates, and so on.
Machine learning and AI
Machine learning is often confused with AI however; there is a thin differentiation between them. Machine learning is a part of AI. In artificial intelligence, the machines are trained to emulate like humans. For instance, the popular Sophia robot makes use of artificial intelligence to imitate human like gestures and speech whereas, machine learning as mentioned earlier is the ability of the machine to automatically learn and improve from experience based on the data fed into it.
Types of machine learning
There are three main forms of machine learning – supervised learning, unsupervised learning and reinforcement learning.
Supervised learning: In supervised learning, the learning is imparted via a dataset which acts as a guide. The data guide is responsible for training the model or machine so that the correct responses or predictions can be obtained once it is fed with new data. Another way to understand this is that the data which is fed into the system is labelled with specific characteristics and features. This labelled data is used to train the model.
Unsupervised learning: In unsupervised learning, the data does not have labels but rather the machine identifies the pattern of the data and then clusters them to derive an output. In other words, the model learns through observation and finds structures in the data. Once the model is given a dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it.
Reinforcement learning: Reinforcement learning is all about giving feedback to the system so that the system is able to correct the errors and deliver correct responses. If the response or output is incorrect then the machine or system is given feedback until it learns to give the right output.
Industrial applications of machine learning
Autonomous vehicle is one segment that operates on the machine learning concept. Tesla’s CEO Elon Musk states that his company’s driverless vehicle is constantly learning and improving owing to machine learning algorithms. The revolutionary company feeds massive data into a computer’s data model so that the car’s computer learns from the data and is also able to predict and make better decisions on the roads. The data which is fed into the system is collected from different sources such as data from GPS, maps, customers driving cars as well as research cars. Recent news reports have also mentioned that Tesla may roll out full self-driving cars by 2019. Just like Musk, other players in the self-driving car space are exploring machine learning too.
The Swedish multinational manufacturing company Volvo is also making use of machine learning to enhance its productivity. Wondering how? Well a machine learning model can also be programmed to carry out predictive maintenance of systems. Hence, Volvo makes use of the data to predict if a particular part or machine needs servicing or has to be replaced. It also uses the data to enhance customer experience. In addition to this, Netflix improves its streaming quality with the assistance of machine learning. Through this model, the American media-services provider can statistically analyse and accordingly undertake data-driven decisions.
Machine learning is already creating waves and with the speedy development of automation or Industry 4.0 across the globe, the manufacturing industry is set to become one of the major ‘transformational’ industries in the future.