Microsoft on Tuesday reported strong sales in its latest quarter, showing that its corporate customers have been shaking off jitters about spending heavily in the uncertain economy. The results also showed early signs that the company's investments in generative artificial intelligence were beginning to bolster sales, most notably reversing what had been slowing growth of the company's important cloud computing product. The company had $56.5 billion in sales in the three months that ended in September, up 13 percent from a year earlier. Profit hit $22.3 billion, up 27 percent. The results beat analyst expectations and Microsoft's own estimates.

While flying cars have long been a vision of science fiction movies, many companies, including NASA, have started turning them into a reality. However, the US space agency have left one'destroyed beyond expectations' after crashing it into the ground on purpose. This test was completed to see how the electric vertical takeoff and landing vehicle (eVTOL) would respond to such an event. Simulating a'severe crash', NASA engineers dropped a mock eVTOL containing six crash test dummies from a height. NASA has completed a crash test of its electric vertical takeoff and landing vehicle to test its response to such an event.

Almost a quarter century ago, a book was written about how organizations would focus on share of customer as opposed to share of market, building a personalized collaboration driven by big data. With advanced analytics, banking may finally getting close to realizing this vision. In 1993, a then revolutionary book, "The One to One Future: Building Relationships One Customer at a Time" was published, proposing the idea that as technology makes it affordable to track individual customers, marketing shifts from finding customers for products to finding products for customers. According to the authors, Don Peppers and Martha Rogers, Ph.D., a company could use technology to gather information about, and to communicate directly with, individuals to form a commercial bond. The book became a bestseller, and was on every marketer's bookshelf … almost a quarter century ago.

Sensor dependency is an affliction that affects an alarming number of robots, and the problem is spreading. In some situations, sensor use is advisable, perhaps even unavoidable. However, there is an important difference between sensor use and sensor abuse. This article lists some of the telltale signs of sensor dependency and reveals the tricks of the trade used on unwitting roboticists by wily sensor pushers.

This advertisement might be posted by any manager delegatcd the responsibility for investigating the applications and market possibilities of expert systems for his/her company . To the rescue have come the authors whose books are reviewed in this article. Each author provides answers to some of the questions raised by those considering the use of expert systems on microcomputers: What are expert systems? Can they be implemented on a PC? Have any successful PC applications been created? Do I really need an expert system?

Mind, that is based on a new television series shown on BBC, but not yet in America. The book is a very well edited transcription of fifteen interviews with psychologists, anthropologists, and sociologists, including such n,otables as George Miller, Jerome Bruner, and Rom Harre. The contributors probably familiar to most AI researchers are Daniel Dennett and Jerome Fodor, as well as two contributors well-known for their writing on art and perception, Ernst Gombrich and Richard Gregory. The interviews are uniformly intelligent, original, and stimulating. As summaries of basic arguments about mental models, perception, and ethical questions of mental problems, you can't do better than this collection.

Department of Coqmter Scieme, Carnegie-Melloll Ulziversity, Pittsburgh, Penmylvania 15213 One, is that most of these people make essentially no distinction between computers, broadly defined, and artificial intelligence-probably for very good reason. As far as they're concerned, there is no difference; they're just worried about the impact of very capable, smart computers. Enthusiasm and exaggerated expectations were very much in evidence. The computer seems to be a mythic emblem for a bright, high-tech future that is going to make our lives so much easier. But it was interesting to hear the subjects that people were interested in.

If human-robot interaction is mainly shaped by users' strategies to deal with their unfamiliar artificial com munication partner, as it is suggested here, robot dialogue design should orient toward reducing users' uncertainty about the affordances of the robot and the joint task. Two experiments are presented that investigate the impact of verbal robot utterances on users' behavior; results show that users react sensitively to subtle linguistic cues that may guide them into appropriate understandings of the robot. Furthermore, the role of user expectations and robot appearance are discussed in the light of the model presented. In this article I argue that the peculiarities of human-robot dialogue are best understood as users' strategies to deal with what they understand the challenges of the situation to consist in (Fischer 2006a). That is, users interact with artificial communication partners on the basis of what they consider to be potentially problematic, what the task comprises, what the robot can understand, and so on, that is, what they consider its affordances to be (Gibson 1977).

In this article, we first survey the three major types of computer music systems based on AI techniques: (1) compositional, (2) improvisational, and (3) performance systems. Representative examples of each type are briefly described. Then, we look in more detail at the problem of endowing the resulting performances with the expressiveness that characterizes human-generated music. This is one of the most challenging aspects of computer music that has been addressed just recently. The main problem in modeling expressiveness is to grasp the performer's "touch," that is, the knowledge applied when performing a score.

Humans learn from their mistakes. When things go badly, we notice that something is amiss, figure out what went wrong and why, and attempt to repair the problem. Artificial systems depend on their human designers to program in responses to every eventuality and therefore typically don't even notice when things go wrong, following their programming over the proverbial, and in some cases literal, cliff. This article describes our past and current work on the metacognitive loop, a domain-general approach to giving artificial systems the ability to notice, assess, and repair problems. The goal is to make artificial systems more robust and less dependent on their human designers.