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AI vs. Machine Learning vs. Deep Learning 7wData

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Since before the dawn of the computer age, scientists have been captivated by the idea of creating machines that could behave like humans. But only in the last decade has technology enabled some forms of artificial intelligence (AI) to become a reality. Interest in putting AI to work has skyrocketed, with burgeoning array of AI use cases. Many surveys have found upwards of 90 percent of enterprises are either already using AI in their operations today or plan to in the near future. Eager to capitalize on this trend, software vendors – both established AI companies and AI startups – have rushed to bring AI capabilities to market.


12 Artificial Intelligence Terms You Need to Know - InformationWeek

@machinelearnbot

Suddenly, artificial intelligence (AI) is everywhere. For decades, the dream of creating machines that can think and learn like humans seemed like it would be perpetually out of reach, but now artificial intelligence is embedded in the phones we carry everywhere, the websites we use every day and, in some cases, even in the appliances we use around our homes. The market researchers at IDC have predicted that companies will spend $12.5 billion on cognitive and AI systems in 2017, 59.3% more than they spent last year. And by 2020, total AI revenues could top $46 billion. In many cases, AI has crept into our lives and our work without us realizing it.


Explainer: What Is Machine Learning?

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Machine learning (ML) has become a hot topic in the last few years, but what you may not realize is that the concept of machine learning has been around for decades. The design of machine-learning systems used to this day is based on the human brain model described by Donald Hebb in 1949 in his book "The Organization of Behavior." Hebb noted that when cells in the brain fire in a repeated pattern, synaptic knobs are formed or enlarge if they already exist. The same principle is applied to nodes in a digital neural network. Nodes develop relationships that grow stronger if they are activated simultaneously and weaken if they fire separately.


What is machine learning? Everything you need to know ZDNet

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Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people. The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started. From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence -- helping software make sense of the messy and unpredictable real world. But what exactly is machine learning and what is making the current boom in machine learning possible? At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.


How Intelligent Machines Learn to Make Sense of the World

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Home Depot uses it to show which bathtubs in its huge inventory will fit someone's oddly shaped bathroom. Apple uses it to present customers with relevant apps from the app store. Intuit uses it to display the right help page when a user is filling out a particular tax form. And organizations are turning to it in droves to differentiate and innovate their offerings. In a recent interview, Gartner Fellow and Vice President Tom Austin noted that about half of large enterprises are experimenting with "smart computing" projects.