The time has arrived when every marketer, every business owner should buckle up themselves to face the shift from Text Based Search to Voice Search in the digital world.

IBM's Watson has done everything from winning at Jeopardy to cooking exotic meals, but it appears to have accomplished its greatest feat yet: saving a life. University of Tokyo doctors report that the artificial intelligence diagnosed a 60-year-old woman's rare form of leukemia that had been incorrectly identified months earlier. The analytical machine took just 10 minutes to compare the patient's genetic changes with a database of 20 million cancer research papers, delivering an accurate diagnosis and leading to proper treatment that had proven elusive. Watson has also identified another rare form of leukemia in another patient, the university says.

What do you see when you think about artificial intelligence? With a new competition, XPRIZE and IBM Watson challenge us to think slightly more pragmatically (or at least less apocalyptically dystopian) about the implications of artificial intelligence research on the future of humanity. To date, more than 1,000 people have registered to form teams with plans to tackle such issues as health, climate, transportation, space travel, robots, city planning, surgery, education and even civil rights. Because the competition is an open challenge, teams are expected to come up with criteria on which they will ultimately be judged in 2020. Some teams may be backed by corporations or angels, while others may go it alone as a team.

An anonymous reader writes: Jill Watson is an artificial intelligence bot, it is also Ashok Goel's teaching assistant. Ashok Goel, a computer science professor at Georgia Tech, hired Jill Watson to answer questions online for his students so that his teaching staff wasn't so overworked. On average, Goel and his staff receive more than 10,000 questions from students online each semester. So he decided to use IBM Watson, an artificial intelligence system designed to answer questions. After training and tweaking it for months, he was able to spit out good enough answers.

The 2011 triumph of IBM's Watson supercomputer in US game show, Jeopardy, was the moment it became a real-world commercial venture within the enterprise services giant. The question-answering system, named in honour of IBM's first CEO, Thomas J Watson, defeated two former winners of the show, Brad Rutter and Ken Jennings, to clinch a 1 million prize, using onboard (rather than cloud-based) data. IBM began offering Watson as a cloud service in 2015, and since then the company has found itself at the centre of a range of new, speculative ventures. As we will explore, some of these blur the lines between classical computing, AI, and machine learning, and may point towards a networked future for humanoid robots. Duncan Anderson, IBM's European CTO of the Watson Program, picks up the story: We started to think about how we could make the Watson technology more consumable and less resource intensive.

Now an evangelist for the cognitive cause, no one is better placed to tell us what lies in store for AI as part of The Drum's AI issue, guest edited using IBM Watson technology. Firstly, AI is an incredibly vibrant field. We're discovering ways of evolving the technology and applying it to solve profound social and business problems – problems where previous generations of computing systems were not able to provide much benefit. It has a tremendous ability to amplify our own cognitive strengths – it contributes to my ability to make better decisions, to see the world through a lens I would have otherwise been blind to. There are tremendous opportunities and we are only at the threshold of what is possible.

Datalog+/- is a family of ontology languages that combine good computational properties with high expressive power. Datalog+/- languages are provably able to capture the most relevant Semantic Web languages. In this paper we consider the class of weakly-sticky (WS) Datalog+/- programs, which allow for certain useful forms of joins in rule bodies as well as extending the well-known class of weakly-acyclic TGDs. So far, only non-deterministic algorithms were known for answering queries on WS Datalog+/- programs. We present novel deterministic query answering algorithms under WS Datalog+/-. In particular, we propose: (1) a bottom-up grounding algorithm based on a query-driven chase, and (2) a hybrid approach based on transforming a WS program into a so-called sticky one, for which query rewriting techniques are known. We discuss how our algorithms can be optimized and effectively applied for query answering in real-world scenarios.

Official IBM Watson Analytics Twitter account managed by @HollyNielsen, @CatNickery & @ForsythMAlexand.

Pokémon Go has opened up the consumer world to augmented reality and some agencies have said this could be a good sign for future advertising opportunities. A developer has now added artificial intelligence to the mix, using IBM's Watson to find Pokémon for users. The Pokémon Go Smart Stop was created by San Francisco developer Michael Hsu and just won the Best Use of Watson challenge at the AT&T Shape Tech Expo Hackathon. "Ever since Pokémon Go came out last week, I've been playing it nonstop," Hsu said at the event. "The Hackathon just happened to be this weekend and I looked at the available sponsor APIs and saw the new Watson Visual Recognition API.

IBM Watson Health has formed a medical imaging collaborative with more than 15 leading healthcare organizations. The goal: To take on some of the most deadly diseases. The collaborative, which includes health systems, academic medical centers, ambulatory radiology providers and imaging technology companies, aims to help doctors address breast, lung, and other cancers; diabetes; eye health; brain disease; and heart disease and related conditions, such as stroke. Watson will mine insights from what IBM calls previously invisible unstructured imaging data and combine it with a broad variety of data from other sources, such as data from electronic health records, radiology and pathology reports, lab results, doctors' progress notes, medical journals, clinical care guidelines and published outcomes studies. As the work of the collaborative evolves, Watson's rationale and insights will evolve, informed by the latest combined thinking of the participating organizations.