AI Storm Brewing

AI is coming. Now what?

The answer isn’t clear, because after decades of research and development, AI is finally starting to become a force to reckon with. The proof is in the M&A activity underway right now. Big companies are willing to pay huge sums to get out in front of this shift.

Here is a list of just some of the AI acquisitions announced or completed over the past few years:

• Microsoft: Maluuba (natural language processing/reinforcement learning), Netbreeze (social media monitoring).
• Google: DeepMind Technologies (famous for beating the world Go champion), Moodstock (image recognition), Clever Sense (social recommendations) and Api.ai (natural language processing).
• Facebook: Face.com (facial recognition).
• Intel: Itseez (machine vision), Nervana Systems (machine learning) and Movidius (machine vision).
• Apple: Turi and Tuplejump (both machine learning).
• Twitter: Magic Pony, Wetlab and Madbits (all machine learning).
• Salesforce: MetaMind (natural language processing) and PredictionIO (machine learning)
• GE: Bit Stew (analytics) and Wise.io (machine learning).

The list goes on and on. AI has turned into an arms race among big companies, which are pouring billions of dollars into this field after a lull that lasted nearly a quarter of a century. The last big explosion in AI research was in the 1980s and early 1990s, when most companies concluded they did not have the technology resources—compute power, memory and throughput—to develop effective AI solutions.

IBM was the big holdout, quietly developing Watson as a for-lease compute platform and showcasing it on Jeopardy (it won) and at the University of North Carolina’s UNC Lineberger cancer treatment center, where Watson proved its mettle with a team of trained oncologists. Others are racing to catch up.

Put in perspective, there are several trends that are emerging. First, while AI is not going to take over the world like HAL in the movie classic “2001: A Space Odyssey,” it will be a disruptive force that can eliminate high-paying as well as low-paying jobs. The more specialized and higher-paid, the greater the ROI. And as eSilicon Chairman Seth Neiman points out in an interview with Semiconductor Engineering, this can happen with breathtaking speed.

Second, as companies begin understanding how AI can be used, it will become obvious there is no single AI machine or architecture. When the IoT term was first introduced (Kevin Ashton, co-founder of the Auto-ID Center believes he first coined the term in a 1999 presentation, although it was Cisco that really made the term a household name) it was considered a single entity. It is now viewed as a general term that encompasses many different approaches and vertical market segments, each with its own set of architectures that may or may not interact with other market segments. AI will follow the same evolutionary path, splintering into architectures that are tailored for multiple markets.

And third, while the tech industry is still trying to wrap its arms around what this will mean, it’s clear that AI is here to stay this time. The investments by both companies and governments in this field will keep this part of the market well-funded for years to come.

However, what’s not clear yet is how this round of technology will mesh with society. In the past, most technology that was developed was viewed as helpful for a broad range of people. Rather than replacing people, it freed them from mundane tasks to do more creative tasks or to specialize further. Unlike previous technology booms, AI has the potential to displace people at all levels—truck drivers, business consultants, lawyers, accountants, medical specialists with many years of schooling.

Rather than sitting back and waiting for standards, it’s imperative that tech groups at every level get out in front of this shift and help develop policies that will guide future development. In the tech industry there is always a level of hype surrounding architectural changes, but this is hardly business as usual. Done right, AI can be a big opportunity for years to come, driving continued advances in both semiconductor technology and software. Done wrong, it can have a devastating impact on jobs—and how people use and view technology for years to come.

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