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

Nov 30, 2016

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. "We're finally in the AI spring."

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. Ford (f) has been buying firms to bolster self-driving car efforts while Salesforce.com (crm) has been adding machine learning capabilities to its platform.

Some of the larger companies that may be active acquirers next year include chip makers Advanced Micro Devices (amd) and ARM (armh) as well as database and software giant Oracle (orcl), the analysts noted. All three companies declined to comment on future M&A activity.

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AMD and (to a lesser extent) ARM may become more active to catch up to Intel and Nvidia (nvda), which are leading the field in providing chips for machine learning. Oracle will want to keep pace with cloud software vendor Salesforce, which has been active this year but may want to add even more capabilities through further acquisitions, the analysts said. The company declined to comment.

Potential targets include Wave Computing, a startup making chips that are claimed to be faster at machine learning than graphic processing chips and more energy efficient than field programmable gate array chips, also popular for some machine learning tasks. Graphcore, which makes dedicated machine learning chips, and Mobileye, a leading car sensor maker, could also make some shopping lists.

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