Its latest is a call center as a service, Amazon Connect, charged for per call and per minute. This offers integrations with Amazon's speech recognition and understanding services, allowing businesses to create more sophisticated interactive voice-response (IVR) systems.
When tomorrow comes
Those services are all in production, but there are plenty of others waiting in the wings.
Microsoft, for example, is already inviting businesses to test "preview" versions of several other services. These include the Emotion API image analysis tool that can identify the emotion expressed by faces in photos, assigning relative probabilities to anger, contempt, disgust, fear, happiness, sadness and surprise. (You can send it a selfie to try it out.) Coming enhancements to the company's speech tools will allow businesses to tune the engine to specific regions or environments (Custom Speech Service), and even to recognize the speaker.
A new tool called QnA Maker extracts frequently asked questions from a corpus of text, and serves them up as answers for a chat bot. The results so far are somewhat obtuse, but that could be a problem with the source text rather than QnA Maker, which in all likelihood has not yet read a billion FAQs to learn its art.
At Google's Cloud Next '17 conference in San Francisco in March the company unveiled a private beta test of its Cloud Video Intelligence API, which will allow beta testers to find relevant video clips by searching for nouns or verbs describing the content. Google hopes to stimulate further demand for its services with a new machine learning startup competition it is running with venture capital firms Data Collective and Emergence Capital, and with the opening of its Machine Learning Advanced Solution Lab in Mountain View, California, where customers can work with Google experts to apply machine learning to their own problems.
Two months later, at Google I/O, the company showed the TensorFlow Lite platform for mobile phones, and a beefier processor for running machine learning workloads, the Cloud TPU (Tensor Processing Unit). It also published details of some of the machine learning APIs it had been using internally.
The big companies don't have a monopoly on research into AI, but competition for qualified personnel is fierce. Facebook, which has its own internal AI research division, organizes internal training events to raise awareness of machine learning among its staff.
Some of the biggest companies engaged in AI research are showing a willingness to publish their results and to release much of their code under open-source licenses. Even the notoriously secretive Apple published its first research paper in the field late last year.
But they're not giving away the crown jewels. Those machine learning toolkits and cloud services are all very well, but it's clear that an untrained neural network is about as useful to the typical enterprise as a 16-year-old high-school drop-out.
Experience counts, just as it does in recruiting, and companies like Google, Facebook, Amazon, and even Apple and Microsoft, are gathering those billions of little examples that Kurzweil spoke of. Every search result clicked on or shopping recommendation accepted, each photo tagged or sports score asked for is added to the collection.
Of course, a billion examples may not always be necessary: Computers can learn to do some things nearly as well as a human with a lot less data, and for many tasks today, nearly may be good enough, especially if the computer is able to refer situations it can't deal with to a human supervisor.
Right by your side
That's what many of the organizations building AI-powered chat bots are counting on, in any case. They have far fewer than one billion data points to go on, but they're still hoping that services like Microsoft's QnA Maker will help them serve customers in new ways.
One such is Arthritis Research UK, a charitable organization that funds medical studies of joint inflammation and provides advice to sufferers. It is using IBM's Watson Conversation API to build a virtual assistant that will answer questions about joint pain and suggest appropriate exercises to alleviate symptoms.
The organization's goals are twofold: To reduce the load on its existing telephone support staff, and to create a new conversational channel through which it might deliver other services in the future.
The assistant has already learned 1,000 answers to common questions about 50 musculoskeletal conditions.
"We will be extending its capabilities to include information about medical and surgical treatments as well as diet in due course," said Shree Rajani, communications campaign manager at Arthritis Research UK.
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 CEO 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.
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