The next chapter of early childhood education may be coming courtesy of Sesame Workshop and the letters I-B-M. Sesame Workshop, which has made the beloved children's education show "Sesame Street" for decades, and IBM's Watson -- of "Jeopardy!" The firms will work together for three years to develop products for the classroom and the home, which combine the artificial intelligence prowess of Watson with Sesame Workshop's deep knowledge of how to teach to the preschool set. The hope is that Watson, which can learn and adapt based on its user, will be able to adjust its teaching based on a child's skill level and learning style. Sesame Workshop has worked for years to provide a mix of learning styles in its flagship show, but is looking to do more.

IBM (IBM, Tech30) Watson is teaming up with Sesame Workshop to develop a new suite of preschool products that could range from consumer apps and toys, to educational tools for schools. Sesame Workshop is the nonprofit that creates "Sesame Street." The company says it entered into this partnership because it wants to provide personalized learning to as many kids around the world as possible. "There's not necessarily anything wrong with preschool education today," Harriet Green, IBM's GM for Watson IoT, Commerce and Education told CNNMoney. But not enough kids have access to the right level of education at the right time in their lives.

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.