The U.S. Department of Agriculture (USDA) reported an outbreak of highly pathogenic avian flu in an Indiana turkey flock on Wednesday, the nation’s first case in a commercial poultry operation since 2020.
European scientists say they have made a major breakthrough in their quest to develop practical nuclear fusion — the energy process that powers the stars.
The UK-based JET laboratory has smashed its own world record for the amount of energy it can extract by squeezing together two forms of hydrogen.
Continue reading “Major breakthrough on nuclear fusion energy — BBC News” »
Smart cities are supposed to represent the pinnacle of technological and human advancement. They certainly deliver on that promise from a technological standpoint. Smart cities employ connected IoT networks, AI, computer vision, NLP, blockchain and similar other technologies and applications to bolster urban computing, which is utilized to optimize a variety of functions in law enforcement, healthcare, traffic management, supply chain management and countless other areas. As human advancement is more ideological than physical, measuring it comes down to a single metric—the level of equity and inclusivity in smart cities. Essentially, these factors are down to how well smart city administrators can reduce digital exclusivity, eliminate algorithmic discrimination and increase citizen engagement. Addressing the issues related to data integrity and bias in AI can resolve a majority of inclusivity problems and meet the above-mentioned objectives. make smart cities more inclusive for people and communities from all strata of society, issues related to digital exclusion and bias in AI need to be addressed by public agencies in these regions.
Artificial Intelligence is the ability of machines to seemingly think and act as humans do. Humans absorb data through our various senses, process data using our cognitive abilities, and then act. Machines also, in their own narrow way, absorb whatever information is made available to them and take relevant actions when prompted. Those actions may take the form of a conversational bot or a recommender engine. Over time, our decision-making sophistication has increased. We began making decisions relying solely on our judgment. We progressed to summarizing large swaths of data and then applying our judgment to that summary. And at present, we entrust AI with taking decisions across data and recommending actions. In narrow problems, machines have a greater ability than humans to process volumes of data and accurately identify the trends within. Was AI wrong about Nadal? Not really. It said that Nadal had a 4% chance of winning; at that snapshot in time, and based on all past data of similar matches, perhaps that was a fair assessment of his chances against Medvedev. Most humans would also have predicted a Medvedev win even if they hoped for a different outcome. I am sure that as the fifth set played out, the odds of Nadal winning rose steadily in his favor. So, the earlier prediction should not be considered wildly inaccurate just because Nadal ultimately won.
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Can AI measure the heart of a champion?
