Experts urge caution when evaluating marketing claims for AI tools

TTlogo 379x201 Experts urge caution when evaluating marketing claims for AI tools

Unbridled enthusiasm for all things artificial intelligence is so last year.

While 2016 was marked by huge amounts of hype around the growing prominence of AI tools in the enterprise, the new year is shaping up to be much more skeptical. That hard-nosed realism around all things AI was a big part of the Gartner Data & Analytics Summit in Grapevine, Texas.

“Just because we have AI doesn’t mean we get a better decision. It’s just a tool,” said Scott Zoldi, chief analytics officer at software vendor FICO, in a presentation at the conference. Zoldi leads development of analytics products at FICO that include credit scoring models and fraud detection systems, some incorporating AI and machine learning algorithms.

For Zoldi, advanced machine learning practices, including AI, have followed in the wake of big data in terms of hype. After spending the last five years or so accumulating huge data sets, businesses are now looking for ways to extract value. Machine learning is widely seen as a way of making sense out of and learning from large data volumes. This has in part led to all the hype around AI we’re seeing today, and software vendors are looking to capitalize on the excitement, Zoldi said.

“There’s lots of applications for analytics and AI,” he said. “There’s also lots of ways it can go wrong. It takes a lot of time to learn how to use AI responsibly and not be misled.”

The need for caution when evaluating AI tools was echoed by several Gartner analysts. Alexander Linden said there’s currently a “zoo” of AI technologies, many of which are being promoted by software companies using lofty claims. He pointed to IBM’s marketing claim that its cognitive platform Watson “can think like a human.”

AI that solves everything will remain a fantasy for a very long time.

Alexander Lindenanalyst, Gartner

That’s not the case. Linden said most of today’s AI tools are far from this kind of general intelligence. Instead they focus on relatively narrow tasks. For example, the AI algorithm developed last year by Google’s DeepMind, AlphaGo, which mastered the chess-like game Go, would be unable to compete on the gameshow Jeopardy!, while Watson, which beat human contestants on the show in 2011, would struggle to play Go competently. General, human-like intelligence is still a long way off.

“Marketing messages like [IBM’s] confuse people,” Linden said. “AI that solves everything will remain a fantasy for a very long time.”

In the meantime, analyst Tom Austin of Gartner recommended that enterprises interested in AI tools think more about point products that address specific needs. This includes things like chatbots and intelligent customer assistants. There have been significant advances in AI technologies in recent years, he said, even if those achievements don’t quite match the vendor hype. Right now, purpose-built AI applications tend to perform better than more general-purpose tools, he said.

Austin also recommended that enterprises think about the near term. There’s so much development going on around AI that what looks sleek and shiny today could be obsolete six months from now.

Additionally, most of the big players in the space, like Amazon, Google, IBM and Microsoft, are fighting it out to position their platforms as the de facto standard. It’s too early to say which will win or which will offer the strongest set of AI tools. So getting locked into a longer-term contact at this stage would be a mistake, Austin said.

“You should be focused on quick time to business value,” he said. “Have no patience. There [are] many grandiose schemes that suppliers can talk about, but you want to pick something that will work.”

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