Everyone wants to stay fit and healthy, and AI can help you to do that even better. While IBM Watson supports personalised apps like the Nutrino pregnancy tracker, it can also help doctors in hospitals draw more accurate conclusions. "Doctors have tens of thousands of brain scans from screening people for cancer," explains Auer-Welsbach. "If IBM Watson went through these it could perhaps recognise signs that no one has been able to know before – whether the brain looks slightly different in people who go on to develop Alzheimer's, for example." Read more: Would you trust artificial intelligence with your pregnancy? Auer-Welsbach says that its food production is also shifting to adopt new AI.

Artificial intelligence (AI) is the convenient future. It is one of the most promising and transformative opportunities of our time. We are closer to the near future where virtual assistants, bots, and software agents will act more and more like people. Some the biggest advances in AI are being developed inside tech giants such as Google (Deep Mind) and IBM (Watson). But there are still a lot of great opportunities for young startups to explore.

Don't worry: IBM's Watson didn't whip up a bunch of needlessly complicated granola recipes for a cookbook that you must make (for science!). No, we're talking about its partnership with Kellogg's subsidiary Bear Naked, which is the first consumer brand to sell Chef Watson-inspired food. The partnership made it possible for Bear Naked to launch a website where granola enthusiasts can make custom blends. After you select a base -- cacao cashew butter, chocolate or honey -- Watson looks through thousands of possible flavors to find ingredients it can suggest. It's a very simple process, and we wish Watson can customize each can of granola even further.

He had just flown into Augusta, Georgia for the Masters. Bad weather caused a pileup of arrivals. The airport ran out of open gates. His flight was stuck on the tarmac. If air traffic controllers had access to Watson, they might have been able to forecast the flight congestion.

We propose a general framework for inconsistency-tolerant query answering within existential rule setting. This framework unifies the main semantics proposed by the state of art and introduces new ones based on cardinality and majority principles. It relies on two key notions: modifiers and inference strategies. An inconsistency-tolerant semantics is seen as a composite modifier plus an inference strategy. We compare the obtained semantics from a productivity point of view.

Previous work has proposed consistent query answering as a way to resolve inconsistencies in ontologies. In these approaches to consistent query answering, however, only inconsistencies due to errors in the underlying database are considered. In this paper, we additionally assume that ontological axioms may be erroneous, and that some database atoms and ontological axioms may not be removed to resolve inconsistencies. This problem is especially well suited in debugging mappings between distributed ontologies. We define two different semantics, one where ontological axioms as a whole are ignored to resolve an inconsistency, and one where only some of their instances are ignored. We then give a precise picture of the complexity of consistent query answering under these two semantics when ontological axioms are encoded as different classes of existential rules. In the course of this, we also close two open complexity problems in standard consistent query answering under existential rules.

A referring expression in linguistics is any noun phrase identifying an object in a way that will be useful to interlocutors. In the context of a query over a first order knowledge base K, constant symbols occurring in K are the artifacts usually used as referring expressions in certain answers to the query. In this paper, we begin to explore how this can be usefully extended by allowing a class of more general formulas, called Singular Referring Expressions, to replace constants in this role. In particular, we lay a foundation for admitting Singular Referring Expressions in certain answer computation for queries over K. An integral part of this foundation are characterization theorems for identification properties of Singular Referring Expressions for queries annotated with a domain specific language for referring concept types. Finally, we apply this framework in the context of tractable description logic dialects, showing how identification properties can be determined at compile-time for conjunctive queries, and how off-the-shelf conjunctive query evaluation for these dialects can be used in query evaluations, preserving, in all cases, underlying tractability.

Classical inconsistency-tolerant query answering relies on selecting maximal components of an ABox/database which are consistent with the ontology. However, some rules in ontologies might be unreliable if they are extracted from ontology learning or written by unskillful knowledge engineers. In this paper we present a framework of handling inconsistent existential rules under stable model semantics, which is defined by a notion called rule repairs to select maximal components of the existential rules. Surprisingly, for R-acyclic existential rules with R-stratified or guarded existential rules with stratified negations, both the data complexity and combined complexity of query answering under the rule repair semantics remain the same as that under the conventional query answering semantics. This leads us to propose several approaches to handle the rule repair semantics by calling answer set programming solvers. An experimental evaluation shows that these approaches have good scalability of query answering under rule repairs on realistic cases.

We developed a course in which students train an instance of Watson and develop an application that interacts with the trained instance. Additionally, students learn technical in-formation about the Jeopardy! version of Watson and they discuss a future infused with cognitive assistants. In this poster, we justify this course, characterize major assessment items and provide advice on choosing a domain.

As the amount of knowledge bases (KBs) grows rapidly, the problem of question answering (QA) over multiple KBs has drawn more attention. The most significant distinction between multiple KB-QA and single KB-QA is that the former must consider the alignments between KBs. The pipeline strategy first constructs the alignments independently, and then uses the obtained alignments to construct queries. However, alignment construction is not a trivial task, and the introduced noises would be passed on to query construction. By contrast, we notice that alignment construction and query construction are interactive steps, and jointly considering them would be beneficial. To this end, we present a novel joint model based on integer linear programming (ILP), uniting these two procedures into a uniform framework. The experimental results demonstrate that the proposed approach outperforms state-of-the-art systems, and is able to improve the performance of both alignment construction and query construction.