Machine Learning: Where It’s Been and Where It’s Going

Artificial intelligence (AI) is a budding field. Nowadays, many businesses are trying to figure out how to use it to their advantage. Indeed, AI can help reduce operational costs, improve efficiency, generate revenue, and enhance customer experiences. In an article for the Register, Danny Bradbury says that there’s a chasm between many current AI deployments and a mature approach with sensible business benefits, and companies need to know how to get from here to there.

Broaden the Future of Business

Because of new computing technologies, machine learning today is more useful and popular than ever. Machine learning is composed of algorithms that tell computers to perform tasks that human beings do naturally on a daily basis. For example, if we want to teach a computer to make recommendations based on the weather, we can write a rule that says bringing an umbrella if it rains or wearing a hat if it’s sunny. In other words, machine learning has evolved to have the pattern-matching ability that human brains perform. Its algorithms teach computers to recognize features of an object—it will show an orange if it’s told an orange. The computer then uses that information to classify characteristics of an orange, building up new data that it gets exposed to So how does it really benefit the world?

Machine learning can be applied to any mental task that a typical person can do with less than a second of thought, such as photo matching for check deposit approvals or language translation. Key factors that have led to these interesting and useful applications include data availability. The amount of digital data being generated thanks to smart devices and the Internet of Things is increasing every day. Bradbury says this:

Nick Patience, founder and research vice president of software at analyst firm 451 Research, highlights two broad kinds of application for machine learning. “It’s either a known use case and already done by people, or it’s something that people didn’t know was possible,” he says. Tasks may not have been possible because humans simply couldn’t get hold of the data to start with, or because they had the data but couldn’t process it.

”Something like IoT just would not be possible without machine learning because there is just too much data coming from all sorts of devices,” he explains.

Machine learning has also been adopted very often for healthcare technology, such as spotting a tumor or diagnosing symptoms. It can predict outcomes and improve treatment for patients, and is used by both Patience and McKinsey. In addition, machine learning provides a real-world use case in the oil and gas sector. IBM Canada’s vice president of Cognitive Solutions says that her company has been using Watson, its cognitive AI system, to help the company with engineering training by answering relevant questions regarding engineering studies, environmental reports, risk analyses, and developmental concepts.

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