Microsoft is reportedly developing a version of its year-old digital assistant, named Cortana, for iOS and Android. The move seems to indicate Microsoft is trying new ways to get its products onto the two dominant mobile operating systems across the US. For mobile users, the question will be: how does Cortana stack up next to Siri? The Cortana project came out of a branch of Microsoft developing artificial intelligence, dubbed "Einstein." Cortana, which is named after a central character in the massively popular Microsoft-owned video game series Halo, has been installed on Microsoft phones for the past year.

Apple personal virtual assistant, Siri, might soon be competing for your time with Cortana, its counterpart from Microsoft. Cortana will be coming to iOS and Android at some point after Windows 10 rolls out with an updated version of Microsoft's virtual assistant software, Reuters reported Friday, citing people who claim to have knowledge of the software giant's plans. It would be a standalone app, available in the Google Play marketplace and Apple App Store, and work just as it already does on Windows Phone, according to the report. Microsoft also is working toward a more advanced version of Cortana, drawing from a research project called Einstein. "This kind of technology, which can read and understand email, will play a central role in the next rollout of Cortana, which we are working on now for the fall time frame," Eric Horvitz, Microsoft Research managing director, told Reuters in an interview. The company has already incorporated Cortana into its Windows 10 operating system, which will be coming to PCs in the latter part of this year.

Essentially, a connection graph is merely a data structure for a set of clauses indicating possible system. To use the graph, one has to introduce operations on the graph.

Specifically, it is concerned with control strategies governing the formation and refinement of partial hypotheses about the identity of an utterance that can guarantee the discovery of the best possible interpretation. We assume a system that contains the following components: a) A Lexical Retrieval component that can find the k best matching words in any region of an utterance subject to certain constraints and can be recalled to continue enumerating word matches in decreasing order of goodness (where possible constraints include anchoring the left or right end of the word to particular points in the utterance or to particular adjacent word matches).

The search is "small" in the sense that the size of

In AI we talk about expert systems.

The strategy has been implemented in a simple QLISP program.

This is the second of two papers about MOLGEN.

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Instead they have commonly assigned structure to a domain using statistical techniques such as Bayes' Theorem