Hundreds of Lithuanians contributed to a fundraiser to buy an advanced military drone for Ukraine in its war against Russia in a show of solidarity with a fellow country formerly under Moscow's rule. The target of 5 million euros ($5.4m) was raised in just three and a half days in Lithuania – a country of 2.8 million people – largely in small amounts to fund the purchase of a Byraktar TB2 unmanned aerial vehicle from Turkey. Laisves TV, a Lithuanian internet broadcaster, launched the fund-raising drive. "Before this war started, none of us thought that we would be buying guns. Something must be done for the world to get better," said Agne Belickaite, 32, who sent 100 euros as soon as the effort began last week.

This paper addresses the issue of the real-time tracking quality of moving objects in large-scale video surveillance systems. During the tracking process, the system assigns an identifier or label to each tracked object to distinguish it from other objects. In such a mission, it is essential to keep this identifier for the same objects, whatever the area, the time of their appearance, or the detecting camera. This is to conserve as much information about the tracking object as possible, decrease the number of ID switching (ID-Sw), and increase the quality of object tracking. To accomplish object labeling, a massive amount of data collected by the cameras must be searched to retrieve the most similar (nearest neighbor) object identifier. Although this task is simple, it becomes very complex in large-scale video surveillance networks, where the data becomes very large. In this case, the label retrieval time increases significantly with this increase, which negatively affects the performance of the real-time tracking system. To avoid such problems, we propose a new solution to automatically label multiple objects for efficient real-time tracking using the indexing mechanism. This mechanism organizes the metadata of the objects extracted during the detection and tracking phase in an Adaptive BCCF-tree. The main advantage of this structure is: its ability to index massive metadata generated by multi-cameras, its logarithmic search complexity, which implicitly reduces the search response time, and its quality of research results, which ensure coherent labeling of the tracked objects. The system load is distributed through a new Internet of Video Things infrastructure-based architecture to improve data processing and real-time object tracking performance. The experimental evaluation was conducted on a publicly available dataset generated by multi-camera containing different crowd activities.

Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep Learning models over scarce datasets bearing the risk of overfitting. Additionally, previous studies have revealed the fact that deep networks are not reliable for classification since their decisions may originate from irrelevant areas on the CXRs. Therefore, in this study, we propose Operational Segmentation Network (OSegNet) that performs detection by segmenting COVID-19 pneumonia for a reliable diagnosis. To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121,378 CXRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community. Consequently, OSegNet has achieved a detection performance with the highest accuracy of 99.65% among the state-of-the-art deep models with 98.09% precision.

If business wants to use AI at scale, adhering to the technical guidelines for responsible AI development isn't enough. It must obtain society's explicit approval to deploy the technology. Six years ago, in March 2016, Microsoft Corporation launched an experimental AI-based chatbot, TayTweets, whose Twitter handle was @TayandYou. Tay, an acronym for "thinking about you," mimicked a 19-year-old American girl online, so the digital giant could showcase the speed at which AI can learn when it interacts with human beings. Living up to its description as "AI with zero chill," Tay started off replying cheekily to Twitter users and turning photographs into memes. Some topics were off limits, though; Microsoft had trained Tay not to comment on societal issues such as Black Lives Matter. Soon enough, a group of Twitter users targeted Tay with a barrage of tweets about controversial issues such as the Holocaust and Gamergate. They goaded the chatbot into replying with racist and sexually charged responses, exploiting its repeat-after-me capability. Realizing that Tay was reacting like IBM's Watson, which started using profanity after perusing the online Urban Dictionary, Microsoft was quick to delete the first inflammatory tweets. Less than 16 hours and more than 100,000 tweets later, the digital giant shut down Tay.

Arm is a chip architecture company that licenses its designs to others, and its customers have shipped more than 100 billion chips in the past five years. The new architecture has processing that balances economics, design freedom, and accessibility advantages of general-purpose computing devices with specialized processors that handle tasks like digital signal processing and machine learning. At the current rate, 100% of the world's shared data will soon be processed on Arm; either at the endpoint, in the data networks or the cloud, Segars said. Back in 2011, Arm launched its 64-bit processing architecture, enabling Arm devices to make the leap from low-power mobile devices to high-end supercomputers. To address the greatest technology challenge today -- securing the world's data -- the Armv9 roadmap introduces the Arm Confidential Compute Architecture (CCA).

MBZUAI has announced plans for a strategic collaboration with IBM (NYSE: IBM). Senior leaders from both organizations signed a Memorandum of Understanding aimed at advancing fundamental AI research, as well as accelerating the types of scientific breakthroughs that could unlock the potential of AI to help solve some of humanity's greatest challenges. Professor Eric Xing, President of MBZUAI, delivered short remarks, as did Jonathan Adashek, IBM's Senior Vice President and Chief Communications Officer, and Saad Toma, General Manager, IBM Middle East, and Africa. The agreement was then signed by Sultan Al Hajji, Vice President for Public Affairs and Alumni Relations at MBZUAI and Wael Abdoush, General Manager IBM Gulf and Levant. "The creation of a center of excellence between MBZUAI and IBM is a natural next step in the evolution of the UAE's groundbreaking AI university. The partnership will strengthen MBZUA's capacity to make practical contributions to the country's sustainable economic development. Working with IBM, MBZUAI will aim to deliver tangible results in wide-ranging sectors, including healthcare, biotech, digital and financial services. Importantly, this collaboration will help also develop the highly skilled talent we need to lead the Fourth Industrial Revolution," HE Dr. Sultan bin Ahmed Al Jaber, UAE Minister of State and Chairman of the Board of Trustees of MBZUAI said.

Analysis - Two years ago this weekend, GPT-3 was introduced to the world and although you may not have heard of it there's a good chance you've read its work. It is likely that you have already read work composed by AI model, GPT-3. Or you may have used a website that runs GPT-3 code, or even conversed with it through a chatbot or a character in a game. GPT-3 is an AI model - a type of artificial intelligence - and its applications have quietly trickled into our everyday lives over the past couple of years. In recent months, that trickle has picked up force: more and more applications are using AI like GPT-3, and these AI programmes are producing greater amounts of data, from words, to images, to code.

Two years ago this weekend, GPT-3 was introduced to the world. You may not have heard of GPT-3, but there's a good chance you've read its work, used a website that runs its code, or even conversed with it through a chatbot or a character in a game. GPT-3 is an AI model -- a type of artificial intelligence -- and its applications have quietly trickled into our everyday lives over the past couple of years. In recent months, that trickle has picked up force: more and more applications are using AI like GPT-3, and these AI programs are producing greater amounts of data, from words, to images, to code. A lot of the time, this happens in the background; we don't see what the AI has done, or we can't tell if it's any good.

On July 3rd, Tom Cruise will be sixty years old. The fact that he does not look it, at all, even in IMAX closeups so tight you can study the grain of his tooth enamel, adds a note of cognitive dissonance to "Top Gun: Maverick," the long-aborning sequel in which he's called back to mentor a squad of younger stick-jockeys who address him as Pops and Old-Timer until he wins their respect in the air. Even for a physical performer like Cruise, sixty is no longer an expiration date. Mick Jagger blew by that milestone in 2003, as did Sylvester Stallone in 2006, and, thanks presumably to healthy habits and/or medical technology dreamt of only by science fiction, they're both still out there, doing a version of the kind of thing they've always done. But the level of performance expected of a Rolling Stone or an Expendable is one thing, and the work that Tom Cruise appears to demand of himself is something else entirely.

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.