Artificial intelligence evokes hope and anxiety. Will we master it, or will it control us? Kwame A. A Opoku and Tendai Joe think a lot about artificial intelligence -- where it's come from and what lies ahead -- from different perspectives. Opoku is a futurist, global business speaker and founder of the think tank, Idea Factory Africa. Joe is involved in software and mobile application development, and digital publishing.

The Artificial Intelligence in sextech Market research report offers key values on the state of the industries. The study involved research on landsca

Artificial Intelligence tools and applications have skillfully tried to manage the analysis, diagnosis, tracing, and development of the pandemic in ways unthinkable with manpower solely. The greatest dilemma with this pandemic was that no one knew what it was and how it would react during the beginning of the pandemic. To make matters worse, Covid 19 has been rapidly mutating since its start, and researchers around the world aren't still quite prepared to interact with such a delicate mutating variant that has claimed hundreds and thousands of lives and has essentially changed the course of history forever. This is where AI's prowess comes into play. With deep learning and the combination of researchers from all around the world, Artificial Intelligence has helped us combat the pandemic in unimaginable ways. The foremost task of AI was to collect as much data as possible about the Coronavirus.

It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program.

Many of the interviewees highlighted that the government and civil society organizationsneed to counter the profit-orientation

It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program.

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.