Person re-identification (ReID) is aimed at identifying the same person across videos captured from different cameras. In the view that networks extracting global features using ordinary network architectures are difficult to extract local features due to their weak attention mechanisms, researchers have proposed a lot of elaborately designed ReID networks, while greatly improving the accuracy, the model size and the feature extraction latency are also soaring. We argue that a relatively compact ordinary network extracting globally pooled features has the capability to extract discriminative local features and can achieve state-of-the-art precision if only the model's parameters are properly learnt. In order to reduce the difficulty in learning hard identity labels, we propose a novel knowledge distillation method: Factorized Distillation, which factorizes both feature maps and retrieval features of holistic ReID network to mimic representations of multiple partial ReID models, thus transferring the knowledge from partial ReID models to the holistic network. Experiments show that the performance of model trained with the proposed method can outperform state-of-the-art with relatively few network parameters.

In this paper, we propose a cognitive system that acquires knowledge on elderly daily activities to ensure their wellness in a smart home using a Knowledge-Information-Data (KID) model. The novel cognitive framework called high dimensional conceptual space is proposed and used as KID model. This KID model is built using geometrical framework of conceptual spaces and formal concept analysis (FCA) to overcome imprecise concept notation of conceptual space with the help of topology based FCA. By doing so, conceptual space can be represented using Hilbert space. This high dimensional conceptual space is quantum inspired in terms of its concept representation. The knowledge learnt by the KID model recognizes the daily activities of the elderly. Consequently, the model identifies the scenario on which the wellness of the elderly has to be ensured.

Sputnik / Evgenya Novozhenina The contribution of artificial intelligence (AI) to the global GDP will increase 16-fold in the next 12 years, according to the head of Russia's largest bank Sberbank Herman Gref. "Expansion of artificial Intelligence in the coming years is likely to only grow. According to forecasts of a number of companies, if today AI contributes $1 trillion to global GDP, then according to forecasts of consulting companies, this figure will increase 16-fold over the next 12 years, until 2030," he said. The number of specialists in demand in the area will also increase significantly, he added, explaining that in 10 years the need will reach ten million people. A recent study by McKinsey Global Institute suggested that AI could boost annual GDP growth by 1.2 percent for at least the next decade.

As artificial intelligence (AI) advancements -- including cutting-edge robotics and silicon-based image recognition technology -- have now pushed the once-fantastic idea of'killer robots' onto the global stage, modern autonomous war machines that fire live ammo could soon seek and destroy battlefield combatants, leading many to wonder if there is an'off' switch. Among other nations, China and the US are working to make advancements in artificial intelligence, machine image recognition and semi-autonomous robotics to be used in combination with sensors and targeting computers, according to a New York Post published Thursday. Britain and Israel are currently using missiles and drones with autonomous features; such weapons can attack enemy radar, vehicles or ships without human commands. Technology for weapon systems to autonomously identify and destroy targets has existed for several decades. In the 1980s and 90s, Harpoon and Tomahawk missiles, which could identify targets autonomously, were developed by US war planners.

Companies should focus on "Augmented Intelligence", digital product management, and in creating a digital twin of an organisation (DTO) for their next level of digital transformation and boost in growth, a top Gartner analyst has said. Augmented Intelligence is the step beyond Artificial Intelligence (AI), where you marry AI with human capability, Partha Iyengar, Vice President and Gartner Fellow, told IANS in a telephonic interaction. The concept refers to the implementation of AI not just as a replacement of human work through automation, but as a means to augment their abilities. "Augmented Intelligence could be applied across processes, across verticals and even across job functions," Iyengar said, adding that some organisations in India, including Indian Oil, have already started focusing on AI augmentation in a big way. Globally, Singapore is at the forefront of implementing AI augmentation, according to Iyengar.

Companies should focus on "Augmented Intelligence", digital product management, and in creating a digital twin of an organisation (DTO) for their next level of digital transformation and boost in growth, a top Gartner analyst has said. Augmented Intelligence is the step beyond Artificial Intelligence (AI), where you marry AI with human capability, Partha Iyengar, Vice President and Gartner Fellow, told IANS in a telephonic interaction. The concept refers to the implementation of AI not just as a replacement of human work through automation, but as a means to augment their abilities. "Augmented Intelligence could be applied across processes, across verticals and even across job functions," Iyengar said, adding that some organisations in India, including Indian Oil, have already started focusing on AI augmentation in a big way. Globally, Singapore is at the forefront of implementing AI augmentation, according to Iyengar.

Head of R&D of Google Cloud AI Jia Li has left her position with the company. Li informed Synced in a text message yesterday and the Google team confirmed her departure this morning. An Adjunct Professor at Stanford University's School of Medicine and a widely respected AI researcher, Li told Synced "I'm now pursuing the impact of AI for good in healthcare and working full-time at Stanford University's AIMI (Center for Artificial Intelligence in Medicine & Imaging). In healthcare, I am interested in how AI can improve the outcomes of individual patients as well as hospitals." Chinese media is reporting that Li will start her own AI company with the aim of bringing machine learning solutions to the healthcare industry; and that a number of leading global venture capitals are interested.

Even casual video game fans know Sid Meier's name. They've seen it countless times, printed in sturdy text across every box in the Civilization series for the past 27 years, the most recent one being 2016's Sid Meier's Civilization VI. It's come to the point where most gamers can't hear "Civilization" without immediately thinking, "Sid Meier," and vice versa. "People know who Sid Meier is because his name's on the front," indie developer Ben Wander said on the busy Tulsa Pop Culture XPO show floor. He was showing off his first game as independent developer The Wandering Ben, a noir murder mystery called A Cast of Distrust.

In this Oct. 31, 2018 photo, Huang Yongzhen, CEO of Watrix, demonstrates the use of his firm's gait recognition software at his company's offices in Beijing. Chinese authorities have begun deploying a new surveillance tool: "gait recognition" software that uses people's body shapes and how they walk to identify them, even when their faces are hidden from cameras.

When U.N. member states unanimously adopted the 2030 Agenda in 2015, the narrative around global development embraced a new paradigm of sustainability and inclusion--of planetary stewardship alongside economic progress, and inclusive distribution of income. This comprehensive agenda--merging social, economic and environmental dimensions of sustainability--is not supported by current modes of data collection and data analysis, so the report of the High-Level Panel on the post-2015 development agenda called for a "data revolution" to empower people through access to information.1 Today, a central development problem is that high-quality, timely, accessible data are absent in most poor countries, where development needs are greatest. In a world of unequal distributions of income and wealth across space, age and class, gender and ethnic pay gaps, and environmental risks, data that provide only national averages conceal more than they reveal. This paper argues that spatial disaggregation and timeliness could permit a process of evidence-based policy making that monitors outcomes and adjusts actions in a feedback loop that can accelerate development through learning. Big data and artificial intelligence are key elements in such a process. Emerging technologies could lead to the next quantum leap in (i) how data is collected; (ii) how data is analyzed; and (iii) how analysis is used for policymaking and the achievement of better results. Big data platforms expand the toolkit for acquiring real-time information at a granular level, while machine learning permits pattern recognition across multiple layers of input. Together, these advances could make data more accessible, scalable, and finely tuned. In turn, the availability of real-time information can shorten the feedback loop between results monitoring, learning, and policy formulation or investment, accelerating the speed and scale at which development actors can implement change.