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Machine learning is moving beyond the hype

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Machine learning has been around for decades, but for much of that time, businesses were only deploying a few models and those required tedious, painstaking work done by PhDs and machine learning experts. Over the past couple of years, machine learning has grown significantly thanks to the advent of widely available, standardized, cloud-based machine learning platforms. Today, companies across every industry are deploying millions of machine learning models across multiple lines of business. Tax and financial software giant Intuit started with a machine learning model to help customers maximize tax deductions; today, machine learning touches nearly every part of their business. In the last year alone, Intuit has increased the number of models deployed across their platform by over 50 percent.


Your guide to artificial intelligence in April 2018, by nathan.ai

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Grab your beverage of choice and enjoy the read! Do hit reply if you're up for a brainstorming session on use cases, new research or ways to future proof your SaaS or enterprise product by implementing ML where it makes sense. On the current "AI revolution": In a lovely piece, Prof. Michael Jordan of Berkeley explores many of the central tenets driving the excitement around AI today. He makes the case for a new engineering discipline, defines the differences between human-imitative AI (i.e. "The current focus on doing AI research via the gathering of data, the deployment of "deep learning" infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills -- with little in the way of emerging explanatory principles -- tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems that perform natural language processing, the need to infer and represent causality, the need to develop computationally-tractable representations of uncertainty and the need to develop systems that formulate and pursue long-term goals. These are classical goals in human-imitative AI, but in the current hubbub over the "AI revolution," it is easy to forget that they are not yet solved."


Why Even More M&A Activity Will Chase Machine Learning Startups in 2017

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Startups focused on artificial intelligence and machine learning will be top acquisition targets in 2017 as chip manufacturers, software firms, and the automobile industry increasingly seek to add those features to their products. After decades of research, the push to make smarter computing devices with AI and programs that can learn on their own via machine learning techniques has reached the point of market readiness, analysts at 451 Research wrote in a report this week about possible acquisition activity in these fields. "The artificial intelligence winter spanning 30 years or more is finally over," they wrote. AI and machine learning have been deployed for a wide array of uses, covering everything from analyzing image files and other big data to piloting drones, cars, and robots. And there has already been plenty of related M&A dealing this year, such as Intel's (intc) $400 million purchase of Nervana and ARM Holdings' $350 million deal for Apical.


Why Even More M&A Activity Will Chase Machine Learning Startups in 2017

#artificialintelligence

Startups focused on artificial intelligence and machine learning will be top acquisition targets in 2017 as chip manufacturers, software firms, and the automobile industry increasingly seek to add those features to their products. After decades of research, the push to make smarter computing devices with AI and programs that can learn on their own via machine learning techniques has reached the point of market readiness, analysts at 451 Research wrote in a report this week about possible acquisition activity in these fields. "The artificial intelligence winter spanning 30 years or more is finally over," they wrote. AI and machine learning have been deployed for a wide array of uses, covering everything from analyzing image files and other big data to piloting drones, cars, and robots. And there has already been plenty of related M&A dealing this year, such as Intel's intc $400 million purchase of Nervana and ARM Holdings' $350 million deal for Apical.