For IBM, the answer is Watson. But what is the question?

Vast volumes of material

Applications that help businesses make sense of vast volumes of written material could benefit from using Watson, says Surya Mukherjee, senior analyst with research firm Ovum.

For example, consultancy Deloitte is working with IBM’s Watson team to offer a service that absorbs greater volumes of legal information than would be humanly possible, helping businesses save on regulatory compliance, says Mukherjee.

“Some businesses might have 20,000 pages of regulations to sift through every month to keep on top of compliance,” he says. “To understand what is relevant to them, it takes an army of lawyers. The Watson application can parse the documents, and because it knows what to look for, flag up the relevant parts.”

Crucially, Watson learns from its errors, he adds. “There are false positives and false negatives, but with heuristic algorithms and human feedback, the software learns from its mistakes over time.”

Businesses that invest in these types of application could save time and money on employing experts to analyse large volumes of text or other unstructured data – but Watson does not come cheap, says Mukherjee.

“It is not commodity technology, so it will not be commodity priced,” he says. “There will be cheques to sign.”

While users will be able to select the APIs for their applications from the cloud on a pay-as-you-go basis, they will also need to spend money to ‘train’ Watson in a particular ontology and employ human expertise to check that the applications’ output makes sense.

“You have to ask: do you have the talent to use Watson for your purpose?” says Mukherjee. “Those people are expensive, not a commodity.”

IBM is heavily promoting Watson with the term “cognitive computing”, in an attempt to move its core business beyond the technologies that it pioneered, but which have become commoditised and less profitable.

“Cognitive computing, cloud and big data are the areas where IBM is investing billions, and cognitive might just be the priority,” says Mukherjee. “In technology markets such as databases, analytics and business applications, IBM has lots of competition.

“You could say that what IBM offers, Oracle and SAP also offer. But the last frontier is cognitive, and that is IBM’s story. Tactically, it is betting the farm on Watson.”  

IBM has not released pricing for Watson per se, because it will depend on the particular combination of APIs and add-on services that customers consume. It has also not announced how much it is investing in its Watson venture or discussed the computing capacity it has created to support Watson worldwide.

However, it does make strong claims about Watson’s abilities in cognitive computing.

Phil Westcott, European ecosystem leader at IBM Watson Group, says: “Watson is based on systems that learn at scale, reason with purpose and interact with humans naturally. It understands the world in the way that humans do: through senses, learning and experience.”

Elsewhere in IBM’s promotion of Watson, the company claims: “Watson and its cognitive capabilities mirror some of the key cognitive elements of human expertise: systems that reason about problems like a human does.”

Watson lacks common sense

But John Carroll, professor of computational linguistics at the University of Sussex, says that despite Watson’s impressive performance in natural language processing and question answering in Jeopardy! and elsewhere, he is sceptical about the claim that it can reason or understand the world the way humans do.

“It is different and complementary,” he says. “Humans don’t have the ability to read millions of documents an hour, so it goes beyond human ability. But, at the same time, it does not have common sense. It does not have the ability to reason inductively or understand how humans act, move and behave in the real world.  

“It can do something that a human can do in same way IBM’s Deep Blue can play chess and Google AlphGo can play Go, but it is still not the answer to replicating human intelligence. It can only do the types of things it was set up to do: to get information from text and from databases and integrate them. It cannot act like a human in the real world and does not have any notion of what the real world is.”

Watson bases its responses on the expert human texts it processes, but is not able to reason much beyond this evidence, says Carroll.

“Ontologies are inconsistent and incomplete, and once you make two or three inferences, it is quite possible to go badly wrong,” he says. “The computer won’t know what is an inconsistency and what an incorrect inference is because it does not have any common sense. It can be led astray very easily. It is safer to work from documents that people have written or individual facts that people have input.”

Carroll says artificial intelligence is being applied to a range of business problems (see below) and some problems may be applicable to more specialist technologies other than Watson.

IBM has impressed businesses, academics and analysts with Watson’s performance in answering natural language questions based on vast amounts text and other unstructured data. Experts agree it has many applications that could benefit businesses and other organisations, but whether its capacity for human-like reasoning stands up to IBM’s claims remains an open question.