Despite widespread optimism and some early success, the applications of AI in management remain limited to a relatively small subset of -- mostly routine -- decisions. The key to understanding the promise and challenges for AI in management is to focus on the problem of delegation of decisions by managers to AI1,2,3,4. Consider the problem of delegation in mergers and acquisitions by companies, where decisions may imply legal and ethical concerns that are best deliberated by managers5,6. Here, AI will provide input to human decision-making while the manager will remain in the'driver seat'. Moreover, both humans and AI have relative strengths, where humans excel in intuition, empathy, broad judgment and complex reasoning.

Digital Twins are increasing in usage but are often used in multiple contexts and in a simplified manner. Most references to the Digital Twin actually refer to a Digital shadow i.e. maintaining a digital copy of a physical object that is updated periodically. I am interested in the idea of Digital Twin because my teaching at the #universityofoxford applies more to AI in engineering (as opposed to say financial services). Also, Digital Twins relate to the idea of Physics based modelling in Engineering. A wind tunnel is an example of Physics based model.

Zillow Group (NASDAQ:Z) (NASDAQ:ZG) operates the most popular real estate websites and mobile apps in the world, with its internet properties receiving 2.8 billion visits during the most recent quarter. And while many investors are likely familiar with the company, it's easy to overlook the technology that powers its digital platform. In this Backstage Pass video, which aired Sept. 27, 2021, Motley Fool contributor Toby Bordelon explains how Zillow uses artificial intelligence to create a better experience for home buyers and sellers. If you have been searching for a house or even selling a house, you probably used Zillow or a competitor. What are they doing in AI? It's very interesting.

Artificial Intelligence (AI) has been much talked about over the last few years. Several interpretations of the potential of AI and its outcomes have been shared by technologists and futurologists. With the focus on the customer, the possibilities range from predicting trends to recommending actions to prescribing solutions. The potential for change due to AI applications is energised by several factors. The first is the concept of AI itself which is not a new phenomenon.

Artificial Intelligence (AI) has been much talked about over the last few years. Several interpretations of the potential of AI and its outcomes have been shared by technologists and futurologists. With the focus on the customer, the possibilities range from predicting trends to recommending actions to prescribing solutions. The potential for change due to AI applications is energised by several factors. The first is the concept of AI itself which is not a new phenomenon.

Artificial intelligence is reshaping how we live, learn, and work, and this past fall, MIT undergraduates got to explore and build on some of the tools and coming out of research labs at MIT. Through the Undergraduate Research Opportunities Program (UROP), students worked with researchers at the MIT Quest for Intelligence and elsewhere on projects to improve AI literacy and K-12 education, understand face recognition and how the brain forms new memories, and speed up tedious tasks like cataloging new library material. Six projects are featured below. Nicole Thumma met her first robot when she was 5, at a museum. "It was incredible that I could have a conversation, even a simple conversation, with this machine," she says.

In the age of Alexa, YouTube recommendations, and Spotify playlists, artificial intelligence has become a way of life, improving marketing and advertising, e-commerce, and more. But what are the ethical implications of technology that collects and learns personal information? How should society navigate these issues and shape the future? A new curriculum designed for middle school students aims to help them understand just that at an early age, as they grow up surrounded by the technology. The open-source educational material, designed by an MIT team and piloted at this year's Massachusetts STEM Week this past fall, teaches students how AI systems are designed, how they can be used to influence the public -- and also how to use them to be successful in jobs of the future.

Streamlining multiple targeting sensors to destroy long-range targets, arming forward- positioned robots to penetrate enemy defenses and receiving organized weather-specific terrain mapping from nearby drones - are all emerging combat dynamics increasingly made possible by AI-enabled weapons and technologies. New applications of AI are consolidating data from otherwise disparate sensor systems, analyzing seemingly limitless amounts of targeting data in seconds and instantly sifting through hours of drone video to massively improve attack options and shorten "sensor-to-shooter" time. "We are developing an AI stack regarding how we pull together the sensors, computing layer and analytics to manage the data," Col. Doug Matty, Army AI Task Force Deputy Director, told Warrior in an interview. New algorithms, AI-enabled computer processing and high-speed networking are all specific elements of work now underway with the Army's AI Task Force, an emerging Army effort to collaborate with industry and academia, find technology breakthroughs and develop new applications for AI, Matty explained. The Task Force is now working on prototyping systems for integration onto UH-60 Black Hawk helicopters, Long-Range Precision Fires systems and the Army's emerging fleet of Next-Gen Combat Vehicles, he said.

Artificial intelligence – The IOT has evolved in the last several years. IOT has a fascinating evolution. It started way back when sensors famous referred to as "things" where used in the industries as disparate things. Moreover, then IOT as we know today came into existence, the Internet of things. Connecting sensors created this to the Internet.

Kustomer, the SaaS platform that is reimagining enterprise customer service, today introduced KustomerIQ, embedding Artificial Intelligence and Machine Learning across the Kustomer platform to enhance the customer service experience of companies competing in today's on-demand world. KustomerIQ uniquely integrates Machine Learning models and other advanced AI capabilities with the Kustomer platform's powerful data, workflow, and rules engines to enable companies to provide smarter, automated customer experiences that are more personalized, efficient, and effortless. The Kustomer platform stands out among customer service solutions for the comprehensiveness of available customer data and its business process automation that is driven by branchable, multi-step workflows and custom business logic. "In today's crowded market, excellent customer service is often the differentiator that builds loyalty and trust between one brand to another," said Brad Birnbaum, Co-Founder and CEO of Kustomer. "With KustomerIQ and the inclusion of Artificial Intelligence and Machine Learning into our omnichannel platform, Kustomer will now go even further in helping brands automate their business processes, while making it easier for their agents to take action on customer information, ultimately developing a stronger and more profitable customer relationship."