Watson, the cognitive computer that can be an expert in any subject, is moving to the cloud, and will soon be accessible via smartphone app

Go on, ask me anything (Image: Joachim Ladefoged/VII)

If you could quiz Watson, IBM’s all-knowing supercomputer, from an app on your phone, what would you ask it?

That is the question facing app developers now that IBM has shrunk its cognitive computer from the bedroom-sized monster that won the TV quiz show Jeopardy! in 2011 to the size of just three stacked pizza boxes. Mini Watsons can now easily be installed in data centres worldwide and made available as a cloud service to cellphone users.

Until now it has been unclear what type of apps would make best use of Watson’s capabilities. It is no ordinary computer, answering complex questions using data mining and machine learning. On 28 April, IBM unveiled the 25 best app ideas in response to the challenge it issued at February’s Mobile World Congress in Barcelona, Spain. Three of the ideas will be developed, making them among the first apps powered by Watson-in-the-cloud. IBM has set aside $100 million for Watson apps and has already invested in Fluid of San Francisco. Fluid is writing an app for outdoor clothing firm The North Face to advise hikers on the right gear for the conditions, based on product data, user reviews and expert websites.


Watson’s ability to learn is what sets it apart from conventional supercomputers that only carry out superfast number crunching. Instead, Watson’s role is to learn all it can about different subjects using data gleaned from a wide range of sources, including databases, encyclopedias, news stories, reviews, dictionaries, peer-reviewed research papers and textbooks. Watson’s wealth of knowledge means that it will be able to answer questions beyond the scope of mere web searches.

For example, one of the shortlisted apps could be great news for any worried new parents, if it makes the final three. Developer Biovideo of San Antonio, Texas, wants to train Watson on neonatal and infant medical data from sources such as medical journals, the American Academy of Pediatrics and the UK National Health Service. “So a mother with a sick child at 4 am will be able to use Watson to ask what is wrong with her baby and get a 100 per cent accurate response using data from these trusted sources,” the firm claims in its proposal.

Crucially, Watson’s source data can be “unstructured” and so does not need converting and organising into narrow database-field categories. When a question is posed in natural language – currently English though more languages are planned – Watson’s linguistic processor examines it in 120 different ways to work out what is being asked.

Reasoning algorithms begin to find hypothetical answers in the data, scoring them with ever greater confidence levels and ultimately returning its best possible answer. Watson has already put that capability to good use at a number of cancer hospitals in the US (see “Dr Watson will see you now”).

But it is changes to Watson’s hardware that make it ready to hit our smartphones. To play Jeopardy!, in which contestants are given an answer and have to work out the question, Watson was trained on 200 million pages of data. That machine comprised 10 server racks containing 2880 processor cores and 15 terabytes of RAM. Now, says Rob High, chief technology officer of the IBM Watson Group in New York, the latest version of Watson performs even better than the original with just 32 processor cores and 256 gigabytes of RAM.

This is made possible because Watson’s processors run in parallel, while its operating system runs concurrent software routines, known as threads, within each of the parallel-processing cores, which lets it learn much faster and more efficiently than before.

The new mini Watson, being no bigger than three pizza boxes, can easily be slotted into racks in a cloud data centre. The idea is that every developer will give Watson a mountain of data from their chosen area for the computer to learn from. Once trained, the apps let users ask Watson questions from their phone, says High. Watson can adapt to demand too. “We can expand the Watson resource elastically depending on the number of people asking it questions,” he says. This is done by making more processors and memory available at the data centre if an app proves surprisingly popular.

One app that might do well is an ultimate guide to New York City. Proposed by Ontodia, a developer there, it aims to vastly improve on the disconnected and unimpressive hits Siri and Google offer up. Training Watson on municipal, state, federal, tourist and commercial databases will provide answers to complex, natural language searches like “what is the average income of this apartment block, correlated with property taxes and construction activity over the last ten years?”. The firm’s aim is to give New Yorkers a detailed profile of any neighbourhood.

Azoft of Novosibirsk, Russia, meanwhile, wants to provide phone users with an animated, intelligent avatar they can ask for advice – with Sigmund Freud and Albert Einstein cited as examples. Watson, Azoft says, would be trained with all Freud’s and Einstein’s books, articles, speeches, letters and interviews and would answer questions just as they might – perhaps with accurate speech synthesis, too.

A major theme in IBM’s 25 shortlisted app ideas is to ask Watson for product recommendations. One of the most compelling ideas aims to clear up the conflicting information people encounter when doing research before buying a new car. US developer Activepackets would train Watson on car recall data, service bulletins, carmakers’ data, user ratings and reviews to give people more confidence in their purchase.

Boosting confidence in the news media is the aim of Rumble of Tel Aviv, Israel, which wants a Watson trained on the archives of 100 newspapers and encyclopedias like Wikipedia to challenge bald statements made in news stories. The idea is that as you read a story on a smartphone or a tablet, icons flash up that suggest Watson disagrees with a statement in the story – and tapping it lets you check out its accuracy. On the finance side, 9W Search based in Austin, Texas, wants Watson to mine and learn from the views expressed by quoted companies in unofficial presentations to partners, customers and investors to create a more accurate picture of their prospects for potential investors.

These and other Watson app ideas will be considered by an IBM jury over the next month and on 30 May it will choose three for full development and smartphone access via the cloud. But Watson might not have the cognitive stage to itself for long.

“Watson is a very clever piece of kit and it’s great that the technology is being made available to developers in the cloud,” says Peter Bentley, a computer scientist and app developer at University College London. “But in the long term I think this is a temporary solution. Within five years this kind of cognitive technology will be mainstream and will even run on our everyday devices. Watch Siri and her siblings become ever more clever over the next few years,” he says.

Dr Watson will see you now Its Jeopardy! win seems so long ago. IBM’s Watson supercomputer is now helping to tailor treatments for cancer patients at a number of US hospitals. At the Memorial Sloan Kettering Cancer Center in New York City, Watson is being trained on the latest lung and breast cancer findings from peer-reviewed journals, as well as the medical records and outcomes of patients. This lets doctors customise treatments based on a patient’s age and medical history – in some cases down to genome level – and their specific variant of cancer. For instance, you can customise the chemotherapy, the frequency it is applied and the dosage, says Barry Mason, healthcare vice-president at the IBM Watson Group, also in New York. If Watson seems to have made a mistake in its recommendation doctors can delve into Watson’s decision-making process – called its “inference chain” – and examine how it came up with its hypothesis and see where it went wrong. If they find an error they can correct it – so Watson will know better next time. Meanwhile, WellPoint, a health insurer in Indianapolis, Indiana, is using Watson’s natural language analysis of a patient’s condition to quickly decide whether their condition is covered by insurance so that treatment can go ahead.