Machine learning facilitates cognitive systems to engage, reason and learn with us in a personalised and natural way. Think of stock trades, Netflix movie recommendations, Internet ads that show up based on our browsing habits — these are all examples of how machine learning is helping us explore the world in powerful and creative ways. Earlier, the turning point in the history of humanity was the industrial revolution which enabled industries to create more jobs by being more productive and thus raising the overall standard of living. Today, machine learning is another such revolution that the world is going to face. We are on the verge of automation and artificial intelligence being the key player and if things are done right, machine learning would help companies grow their businesses and develop insights instantly. Like for the Industrial Revolution, the key component of machine learning is collaboration- we would need a smarter workforce together working for a successful process giving just the right output. The workforce that's being talked here would have data engineers, IT architects, business users, data scientists, data mining experts, system administrators, executives, developers, etc.
We are well aware of the machine learning applications that are plying in our lives today. For a long time, the algorithms of machine learning have been around but what recently developed was the ability to automatically apply complex mathematical calculations to big data, faster and over and over again. One of the examples that we already are familiar with is the self-driving Google car, that was heavily hyped and is based on machine learning. It has all the features of a modern car combined like adaptive cruise control, parking and navigator assistants, speech recognition and lane assistant that makes it close to a completely independent operating vehicle. Also online recommendation offers like those from Netflix and Amazon, fraud detection and combining machine learning with linguistic rule creation to know what customers are saying about you on Twitter, Nanotronics, that automates optical microscopes for improved inspections, Rethink Robotics using it to improve their production speeds and train their robotic arms, increasing customer segmentation accuracy, predicting a customer’s lifetime value, optimizing a user’s in-app experience, detecting customer shopping patterns, assessing health risks, improving personalized care, and diagnosing diseases more accurately are all everyday illustrations of machine learning.
A good machine learning system is created by basic and advanced algorithms, scalability, data preparation capabilities, ensemble modelling and automation and iterative processes. Machine learning is recently a lot in news because of its advances in "deep learning" that includes its much popular AlphaGo’s defeat of Go grandmaster Lee Sedol and other new impressive products around machine translation and image recognition. Machine learning consumes large amounts of data, is more forgiving of changing data points or parameters and supports greater complexity and variability. The generated output with these processes can be applied seamlessly across multiple different platforms, like analytics systems, cloud computing, edge networks and embedded systems. A step change from an era where insights were mainly technology platform-driven to a cognitive era, that enables business-driven insights. Machine learning, IoT and AI are kind of linked with each other. IoT beautifully complements artificial intelligence when it comes to real-time computing. Mankind would soon get completely replaced with walking machines who would be far more intelligent than we are. The machines have already begun to ply in the businesses for various purposes and in the coming time, we would see a wave of mechanistic transformation transmute our daily lives too. These human dynamos will change our ways of looking towards life by building perception from data they receive and in methods that humans never could. This would mean the machines will actually outdo human force in almost everything resulting in process change, cost savings and bigger and bolder levels of automation. Image and voice recognition systems would recognise individuals across various channels and according to a survey, the fastest-growing companies will have more smart machines than the employees.