Training an AI model to predict equipment failures is an engineering achievement. But it's not until prediction meets action--the moment that model successfully flags a malfunctioning machine--that true business transformation occurs. One technical milestone lives in a proof-of-concept deck; the other meaningfully contributes to the bottom line. Craig Partridge, senior director worldwide of Digital Next Advisory at HPE, believes the true value of AI lies in inference". Inference is where AI earns its keep. It's the operational layer that puts all that training to use in real-world workflows.

Spoken Keyword Spotting (KWS) is the task of distinguishing between the presence and absence of a keyword in audio. The accuracy of a KWS model hinges on its ability to correctly classify examples close to the keyword and non-keyword boundary. These boundary examples are often scarce in training data, limiting model performance. In this paper, we propose a method to systematically generate adversarial examples close to the decision boundary by making insertion/deletion/substitution edits on the keyword's graphemes. We evaluate this technique on held-out data for a popular keyword and show that the technique improves AUC on a dataset of synthetic hard negatives by 61% while maintaining quality on positives and ambient negative audio data.

Our aim in this paper is to outline how the design space for the ontologization process is richer than current practice would suggest. We point out that engineering processes as well as products need to be designed - and identify some components of the design. We investigate the possibility of designing a range of radically new practices, providing examples of the new practices from our work over the last three decades with an outlier methodology, bCLEARer. We also suggest that setting an evolutionary context for ontologization helps one to better understand the nature of these new practices and provides the conceptual scaffolding that shapes fertile processes. Where this evolutionary perspective positions digitalization (the evolutionary emergence of computing technologies) as the latest step in a long evolutionary trail of information transitions. This reframes ontologization as a strategic tool for leveraging the emerging opportunities offered by digitalization.

The lectern stands before a vast, grassy amphitheater. Above, the sky is a pristine blue, the cloudless quintessence of a spring morning. Perhaps the most arresting feature in this panorama is the stage: a towering neoclassical structure with Doric columns that soar heavenward. Suspended from the columns are banners emblazoned with the letters QU, for Quaranteen University. The name is a wink from the worldbuilders--college and high school students, homebound for weeks now--about the surreal circumstances of their situation.

The first networked electronic mail message was sent by Ray Tomlinson of Bolt Beranek and Newman in 1971. This year, according to market research firm Radicati Group, 3.8 billion email users worldwide will send 281 billion messages every day. You may feel like a substantial number of them end up in your inbox. And yet, some observers say email is dying. It's so'last century', they say, compared to social media messaging, texting, and powerful new collaboration tools.

Editor: On "Learning Language" I was dismayed by the inclusion of William Katke's article ("Learning Language Using A Pattern Recognition Approach," Spring 1985). Usually you do an excellent job of representing "the current state of the art in Artificial Intelligence" (to quote your Editorial Policy), but I consider this article an exception. First of all, although the article claims to be on "Learning Language," what it presents is at best a knowledge-free approach to learning syntax. I saw no evidence that the induced syntax is useful for anything, and good reasons to believe that it is not, such as the unmnemonic category names and the intrinsic limitations of finite state grammars. Second, this kind of stuff has been done before, and it didn't work too well then either; for a useful overview of the field and pointers into the literature, see the article on "Grammatical Inference" in Volume 3 of The Handbook of The plete specifications and the verification of proposed impleideas and issues presented were firmly focused on a conven-mentations, we should concentrate more on incremental tional view of the design process-a view I can caricaturize development of specifications as a result of assessment of as the SPIV methodology: performance.

The Workshop on the Foundations of AI (WFAI) was held at the Holiday Inn, Las Cruces, New Mexico, on 6, 7, and 8 February 1986. Financial support for the workshop came from the National Science Foundation; the American Association for Artificial Intelligence (AAAI); and the Computing Research Laboratory (CRL) at New Mexico State University, which also hosted the meeting. My original vague idea for this workshop was backed enthusiastically by CRL right from the start, first by Roger Schvaneveldt as acting director and later by Yorick Wilks when he took over as director. Andrew Ortony played a leading role in both casting and production for this workshop; hc claims that he doesn't love telephoning people, just doesn't mind it. These three and the rest of the program committee, as well as a number of other people, reviewed the considerable number of submitted papers.

The mobile phone has long ceased being a simple two-way communication device: today's handheld is a mini personal computer, complete with multimedia players, maps, and Web browsers. Now researchers at Palo Alto Research Center (PARC) want to push the phone farther. They have developed software that turns a phone into a thoughtful personal assistant, one that helps people find fun things to do. The software, called Magitti, uses a combination of cues–including the time of day, a person's location, her past behaviors, and even her text messages–to infer her interests. It then shows a helpful list of suggestions, including concerts, movies, bookstores, and restaurants.

And even if verification to be accommodated within the SPIV paradigm. But until were possible it would not contribute very much to the such time as we find these learning algorithms (and I development of production software. Hence "verifiability don't think that many would argue that such algorithms must not be allowed to overshadow reliability. Scientists will be available in the foreseeable future) we must face should not confuse mathematical models with reality." the prospect of systems that will need to be modified, in AI is perhaps not so special, it is rather an extreme nontrivial ways, throughout their useful lives. Thus incremental and thus certain of its characteristics are more obvious development will be a constant feature of such than in conventional software applications. Thus the SPIV software and if it is not fully automatic then it will be part methodology may be inappropriate for an even larger class of the human maintenance of the system. I am, of course, of problems than those of AI. not suggesting that the products of say architectural design I have raised all these points not to try to deny the (i.e., buildings) will need a learning capability. Nevertheless, worth of Mostow's ideas and issues concerning the design a final fixed design, that remains "optimal" in a process, but to make the case that such endeavors should dynamically changing world, is a rare event.The similarity also be pursued within a fundamentally incremental and between AI system development and the design of more evolutionary framework for design. The potential of the concrete objects is still present, but it is, in some respects, RUDE paradigm is deserving of more attention than it is rather tenuous I admit.