Artificial intelligence may seem similar to a calculator but the relationship of humans with the former is not as serene as with the latter. We trust the results of computers, even if we don’t know exactly how it can arrive at the result of a complex operation in a short time, while the relationship with artificial intelligence (AI) generates discomfort in people. Why? The reason is that machines never stop learning and the more they perform new and unexpected tasks, hitherto entrusted to human intelligence, the more users distrust them, because they do not like to find their own prerogatives embodied in machines. This is what creates discomfort.Artificial intelligence may seem similar to a calculator but the relationship of humans with the former is not as serene as with the latter. We trust the results of computers, even if we don’t know exactly how it can arrive at the result of a complex operation in a short time, while the relationship with artificial intelligence (AI) generates discomfort in people. Why? The reason is that machines never stop learning and the more they perform new and unexpected tasks, hitherto entrusted to human intelligence, the more users distrust them, because they do not like to find their own prerogatives embodied in machines. This is what creates discomfort.Machine learning & AI[#item_full_content]
Smart digital image sensors that can perform visual perception capabilities, such as scene recognition, are the result of recent research at KAUST.Smart digital image sensors that can perform visual perception capabilities, such as scene recognition, are the result of recent research at KAUST.[#item_full_content]
Researchers have developed a new artificial intelligence (AI) framework that is better than previous technologies at analyzing and categorizing dialogue between individuals, with the goal of improving team training technologies. The framework will enable training technologies to better understand how well individuals are coordinating with one another and working as part of a team.Researchers have developed a new artificial intelligence (AI) framework that is better than previous technologies at analyzing and categorizing dialogue between individuals, with the goal of improving team training technologies. The framework will enable training technologies to better understand how well individuals are coordinating with one another and working as part of a team.[#item_full_content]
Researchers have developed a new artificial intelligence (AI) framework that is better than previous technologies at analyzing and categorizing dialogue between individuals, with the goal of improving team training technologies. The framework will enable training technologies to better understand how well individuals are coordinating with one another and working as part of a team.Researchers have developed a new artificial intelligence (AI) framework that is better than previous technologies at analyzing and categorizing dialogue between individuals, with the goal of improving team training technologies. The framework will enable training technologies to better understand how well individuals are coordinating with one another and working as part of a team.Computer Sciences[#item_full_content]
Researchers at NYU Tandon School of Engineering have fabricated a microprocessing chip using plain English “conversations” with an AI model, a first-of-its-kind achievement that could lead to faster chip development and allow individuals without specialized technical skills to design chips.Researchers at NYU Tandon School of Engineering have fabricated a microprocessing chip using plain English “conversations” with an AI model, a first-of-its-kind achievement that could lead to faster chip development and allow individuals without specialized technical skills to design chips.Hardware[#item_full_content]
People hear a lot about blockchain technology in relation to cryptocurrencies like bitcoin, which rely on blockchain systems to keep records of financial transactions between people and businesses. But a crash in public trust in cryptocurrencies like TerraUSD—and therefore a massive drop in their market value—doesn’t mean their underlying technology is also worthless.People hear a lot about blockchain technology in relation to cryptocurrencies like bitcoin, which rely on blockchain systems to keep records of financial transactions between people and businesses. But a crash in public trust in cryptocurrencies like TerraUSD—and therefore a massive drop in their market value—doesn’t mean their underlying technology is also worthless.[#item_full_content]
Just making a small tweak to algorithms typically used to enhance images could dramatically boost computer vision recognition capabilities in applications ranging from self-driving cars to cybernetic avatars, RIKEN researchers have shown.Just making a small tweak to algorithms typically used to enhance images could dramatically boost computer vision recognition capabilities in applications ranging from self-driving cars to cybernetic avatars, RIKEN researchers have shown.[#item_full_content]
Most of us buy goods on the internet without reading the terms and conditions. We take it as a given that the clauses in these standardized agreements are non-negotiable, and hope that they are in our best interests.Most of us buy goods on the internet without reading the terms and conditions. We take it as a given that the clauses in these standardized agreements are non-negotiable, and hope that they are in our best interests.Consumer & Gadgets[#item_full_content]
Researchers from MIT, the MIT-IBM Watson AI Lab, IBM Research, and elsewhere have developed a new technique for analyzing unlabeled audio and visual data that could improve the performance of machine-learning models used in applications like speech recognition and object detection. The work, for the first time, combines two architectures of self-supervised learning, contrastive learning and masked data modeling, in an effort to scale machine-learning tasks like event classification in single- and multimodal data without the need for annotation, thereby replicating how humans understand and perceive our world.Researchers from MIT, the MIT-IBM Watson AI Lab, IBM Research, and elsewhere have developed a new technique for analyzing unlabeled audio and visual data that could improve the performance of machine-learning models used in applications like speech recognition and object detection. The work, for the first time, combines two architectures of self-supervised learning, contrastive learning and masked data modeling, in an effort to scale machine-learning tasks like event classification in single- and multimodal data without the need for annotation, thereby replicating how humans understand and perceive our world.[#item_full_content]
Researchers from MIT, the MIT-IBM Watson AI Lab, IBM Research, and elsewhere have developed a new technique for analyzing unlabeled audio and visual data that could improve the performance of machine-learning models used in applications like speech recognition and object detection. The work, for the first time, combines two architectures of self-supervised learning, contrastive learning and masked data modeling, in an effort to scale machine-learning tasks like event classification in single- and multimodal data without the need for annotation, thereby replicating how humans understand and perceive our world.Researchers from MIT, the MIT-IBM Watson AI Lab, IBM Research, and elsewhere have developed a new technique for analyzing unlabeled audio and visual data that could improve the performance of machine-learning models used in applications like speech recognition and object detection. The work, for the first time, combines two architectures of self-supervised learning, contrastive learning and masked data modeling, in an effort to scale machine-learning tasks like event classification in single- and multimodal data without the need for annotation, thereby replicating how humans understand and perceive our world.Computer Sciences[#item_full_content]
