2017 saw an explosion of machine learning in production use, with even deep learning and artificial intelligence (AI) being leveraged for practical applications.
“Basic analytics are out; machine learning (and beyond) are in,” says Kenneth Sanford, U.S. lead analytics architect for collaborative data science platform Dataiku, as he looks back on 2017.
Sanford says practical applications of machine learning, deep learning, and AI are “everywhere and out in the open these days,” pointing to the “super billboards” in London’s Piccadilly Circus that leverage hidden cameras gathering data on foot and road traffic (including the make and model of passing cars) to deliver targeted advertisements.
Enterprises will operationalize AI
AI is already here, whether we recognize it or not.
“Many organizations are using AI already, but they may not refer to it as ‘AI,’” says Scott Gnau, CTO of Hortonworks. “For example, any organization using a chatbot feature to engage with customers is using artificial intelligence.”
But many of the deployments leveraging AI technologies and tools have been small-scale. Expect organizations to ramp up in a big way in 2018.
“Enterprises have spent the past few years educating themselves on various AI frameworks and tools,” says Nima Negahban, CTO and co-founder of Kinetica, a specialist in GPU-accelerated databases for high-performance analytics. “But as AI goes mainstream, it will move beyond small-scale experiments to being automated and operationalized. As enterprises move forward with operationalizing AI, they will look for products and tools to automate, manage, and streamline the entire machine learning and deep learning life cycle.”
Negahban predicts 2018 will see an increase in investments in AI life cycle management, and technologies that house the data and supervise the process will mature.
AI reality will lag the hype once again
Ramon Chen, chief product officer of master data management specialist Reltio, is less sanguine. Chen says there have been repeated predictions for several years that tout potential breakthroughs in the use of AI and machine learning, but the reality is that most enterprises have yet to see quantifiable benefits from their investments in these areas.
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