The AI fight is escalating: This is the IT giants' next move

Google, IBM, Microsoft and Amazon Web Services are all piling artificial intelligence capabilities onto their software stacks

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Initial development took around five months, including a first round of testing with about 300 trial users, but the assistant is still not ready to meet the public. A second round of user acceptance testing is under way and it should appear on the organization's website later this year, Rajani said.

One thing that didn't gain acceptance was the name: Initially known as "Ask Arthy," the service is now known as the Arthritis Virtual Assistant, according to the Arthritis Research UK privacy policy.

That policy highlights one hazard European businesses face in using U.S. cloud services for chat bots: Around 460 of the policy's 2890 words are devoted to the virtual assistant, even though it has not yet launched, with another 490 words of warning about it in the site's terms and conditions. Together, they warn users that everything they tell the assistant will be transferred to IBM's servers in the U.S., and that they should not therefore volunteer any personal information in the conversation -- a tricky balancing act when they may be asking about sensitive medical matters.

Privacy is likely to be even more of a concern for another sector that is rushing to adopt machine learning to power a new wave of customer service: banking.

A recent survey by Accenture found that, within the next three years, 78 percent of U.S. banks are counting on AI to ensure a more human-like experience when dealing with automated systems, and 76 percent of them expect to compete on their ability to make technology appear invisible to the customer.

It's not just the U.S.: Belgian bank BNP Paribas Fortis is also working on a chat bot to answer some of the questions its 400 call center staff currently must deal with. When customers prefer to deal with a human being, the chat bot could even help staff find the right answers more quickly, the director general of the bank's retail division, Michael Anseeuw, told a Belgian newspaper recently.

Close working relationships between humans and machines like that make it easier for the machines to improve their performance.

"You want to get the automation pieces working to support people, because then what you're doing is creating the infrastructure to learn more closely from people about more abstract decision making," said Tim Estes, founder and President of Digital Reasoning.

Its Synthesys product applies machine learning techniques to the analysis of business information, and can be used to identify potentially fraudulent transactions or to flag risky employee communications for regulatory compliance purposes.

In any case, Estes foresees a time in the near future when it will become uneconomical to make such "triage" decisions without the help of computers.

"A machine can be taught human evaluation patterns, and apply them, but it's only in a few cases that you can take the decision of the machine -- what's important to read or not -- and go all the way to taking the human out of the loop.

For the next two or three years, machine learning systems will be most effective when used to filter and prioritize decisions for humans, he said.

"I don't really believe that unassisted triage is going to be a cost-effective business model," he said.

Copyright © 2017 IDG Communications, Inc.

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