The title of Brian Eno's new album ForeverAndEverNoMore sounds fairly doom and gloom. When you realize the name is inspired by a book on the fall of the Soviet Union it sounds even more so. Ultimately, though, its tone and message could be a hopeful one: Things can change--and change quickly. Eno is probably best known as an endlessly inventive ambient music pioneer and prolific producer/collaborator who has worked with the likes of David Byrne, David Bowie, and Grace Jones. But more recently, his eyes have been on the planet.

Artificial intelligence has predicted what the Kardashian-Jenner family would look like if they had aged naturally. The famous family, who are known for their love of cosmetic enhancements, appear very different in a digitally altered video that recently went viral on TikTok. The clip, created by popular Australian streamers Vandahood Live, estimates what Kim Kardashian, Kylie Jenner, Khloe Kardashian, Kris Jenner and Kourtney Kardashian would look like today without cosmetic intervention. A viral TikTok video has revealed using artificial intelligence (AI) what the Kardashian-Jenner family would look like if they had aged naturally. In the video, footage from last year's Keeping Up with the Kardashians finale special is played alongside doctored versions of the same clip.

Skillcircle has won the 2022 Global Artificial Intelligence Summit & Awards. Skillcircle was recognized as the winner in the ‘Excellence and Innovation in AI Education. They integrated Education with Capstone projects to give exposure to the students and Data professionals. Skillcircle, the global leader in providing technology education, has won the 2022 Global Artificial Intelligence Summit & Awards. Skillcircle was recognized as the winner in the ‘Excellence and Innovation in AI Education. All the awards were announced at the 3rd edition of the GAISA summit held recently. They integrated Education...

We present a novel technique for hierarchical super resolution (SR) with neural networks (NNs), which upscales volumetric data represented with an octree data structure to a high-resolution uniform grid with minimal seam artifacts on octree node boundaries. Our method uses existing state-of-the-art SR models and adds flexibility to upscale input data with varying levels of detail across the domain, instead of only uniform grid data that are supported in previous approaches. The key is to use a hierarchy of SR NNs, each trained to perform 2x SR between two levels of detail, with a hierarchical SR algorithm that minimizes seam artifacts by starting from the coarsest level of detail and working up. We show that our hierarchical approach outperforms baseline interpolation and hierarchical upscaling methods, and demonstrate the usefulness of our proposed approach across three use cases including data reduction using hierarchical downsampling+SR instead of uniform downsampling+SR, computation savings for hierarchical finite-time Lyapunov exponent field calculation, and super-resolving low-resolution simulation results for a high-resolution approximation visualization.

In 2008, the journalist Jonah Lehrer paid a visit to a lab in Lausanne, Switzerland, to profile Henry Markram, a world-renowned neuroscientist. Markram, a South African, had trained at a series of élite institutions in Israel, the United States, and Germany; in the nineties, he published foundational papers on neural connections and synaptic activity. Markram's work in the laboratory, which involved piercing neural membranes with what Lehrer described as an "invisibly sharp glass pipette," was known for its painstaking precision. Lehrer's visit, however, had been occasioned not by Markram's incremental contributions to the field--it's not easy to sell a colorful profile on the basis of such publications as "The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability"--but by Markram's pivot, in the early two-thousands, to brain simulation. Neuroscience, Markram declaimed to Lehrer, had reached an impasse. Researchers had generated an enormous wealth of fine-grained data, but the marginal returns had begun to diminish.

Yejin Choi, a University of Washington computer science professor and senior research manager at Seattle's Allen Institute for Artificial Intelligence (AI2), won a $800,000 "genius grant" given annually by the John D. and Catherine T. MacArthur Foundation. Choi, one of 25 MacArthur Fellows for 2022 revealed Wednesday, is an expert in natural language processing. Her work aims to improve the ability of computers and artificial intelligence systems to perform commonsense reasoning and understand implied meaning in human language. "This is such a great honor because there have been only two other researchers in the natural language processing field who have received this award," Choi told UW News. Choi spoke to GeekWire earlier this year about the debate over a robot's ability to have human-like feelings.

At RE•WORK, we are strong advocates for supporting women working towards advancing technology, so ahead of the upcoming Toronto AI Summit, on November 9-10, we set out to highlight inspirational women who are working at the forefront of AI developments, and who deserve recognition for their achievements. While we set out to create a list of just 20 – we couldn't narrow it down, as there are so many inspiring and prominent females in this space! Hear from many of them at our Toronto AI Summit, and more at our Women in AI Reception, both being held in Toronto next month. Help us to continue highlighting leading women in AI by nominating your influential woman for our next edition. RE•WORK holds Women in AI events, podcasts, and blogs. Get in touch if you'd like to collaborate or support our initiatives! Doina Precup is a researcher living in Montreal, Canada.

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin executes a neural-network-driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multi-arm bandits-based learning problem is proposed for reducing the gap between the expected explained score and the current obtained score of the proposed neuro-symbolic XAI twin. To address the challenges of extensible, modular, and stateless management functions in ZSM, the proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) an implicit learner that acts as an unconscious learner in physical space, and 2) an explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation.

Mark Minevich is a highly regarded and trusted Digital Cognitive AI Strategist, Artificial Intelligence expert, Global Social Innovation and Technology Executive, UN Advisor, Leading Author and Columnist, Private Investor/Venture Capitalist, and the principal founder and President of Going Global Ventures. He is an award-winning technology executive and has published two books and over 40 articles on AI, Industry 4.0, IoT. Mark is newly appointed Chairman of the Executive committee of AI for Good Foundation. Mark is a Chief Digital Strategist at the International Research Centre for AI, under the auspices of UNESCO. Mark is Sr. Advisor to Boston Consulting Group Boston Consulting Group. Currently, he serves as the strategic advisor and Global ambassador to the CEO and Chairman of New York based Amelia/ IPsoft Inc. Mark collaborates and advises large global enterprises both in the US and Japan (Hitachi). Mark is Advisory Partner to Canadian Growth Investments and Business Advisor to Infosec Global.   What is the definition of leadership for you? As a leader, should one create more followers or leaders? I believe that a leader should create more leaders, not followers. A leader is someone who inspires and motivates others to achieve their goals. A leader also has […]

What do you think about driverless cars? Would you ride in one? Do you think they are the way of the future? In "Stuck on the Streets of San Francisco in a Driverless Car," the Times technology reporter Cade Metz went for a ride in the back seat of an experimental autonomous vehicle and wrote about his experience: It was about 9 p.m. on a cool Tuesday evening in San Francisco this month when I hailed a car outside a restaurant a few blocks from Golden Gate Park. A few minutes later, as I waited at a stoplight, a white Mercedes pulled up next to me.