Several years ago, MIT anthropologist Héctor Beltrán ’07 attended an event in Mexico billed as the first all-women’s hackathon in Latin America. But the programmers were not the only women there. When the time came for the hackathon pitches, a large number of family members arrived to watch.Several years ago, MIT anthropologist Héctor Beltrán ’07 attended an event in Mexico billed as the first all-women’s hackathon in Latin America. But the programmers were not the only women there. When the time came for the hackathon pitches, a large number of family members arrived to watch.[#item_full_content]
AI is everywhere—driving cars, diagnosing illnesses, making credit decisions, ranking job candidates, identifying faces, assessing parolees. These headlines alone should be enough to convince you that AI is far from ethical. Nonetheless, terms like “ethical AI” prevail alongside equally problematic terms like “trustworthy AI.”AI is everywhere—driving cars, diagnosing illnesses, making credit decisions, ranking job candidates, identifying faces, assessing parolees. These headlines alone should be enough to convince you that AI is far from ethical. Nonetheless, terms like “ethical AI” prevail alongside equally problematic terms like “trustworthy AI.”Machine learning & AI[#item_full_content]
A new biopic on the life of Edith Piaf will use artificial intelligence to allow the French star to narrate her own story, Warner Music and her estate said on Tuesday.A new biopic on the life of Edith Piaf will use artificial intelligence to allow the French star to narrate her own story, Warner Music and her estate said on Tuesday.Machine learning & AI[#item_full_content]
Unsupervised domain adaptation has garnered a great amount of attention and research in past decades. Among all the deep-based methods, the autoencoder-based approach has achieved sound performance for its fast convergence speed and a no-label requirement. The existing methods of autoencoders just serially connect the features generated by different autoencoders, which poses challenges for discriminative representation learning and which fails to find the real cross-domain features.Unsupervised domain adaptation has garnered a great amount of attention and research in past decades. Among all the deep-based methods, the autoencoder-based approach has achieved sound performance for its fast convergence speed and a no-label requirement. The existing methods of autoencoders just serially connect the features generated by different autoencoders, which poses challenges for discriminative representation learning and which fails to find the real cross-domain features.[#item_full_content]
Unsupervised domain adaptation has garnered a great amount of attention and research in past decades. Among all the deep-based methods, the autoencoder-based approach has achieved sound performance for its fast convergence speed and a no-label requirement. The existing methods of autoencoders just serially connect the features generated by different autoencoders, which poses challenges for discriminative representation learning and which fails to find the real cross-domain features.Unsupervised domain adaptation has garnered a great amount of attention and research in past decades. Among all the deep-based methods, the autoencoder-based approach has achieved sound performance for its fast convergence speed and a no-label requirement. The existing methods of autoencoders just serially connect the features generated by different autoencoders, which poses challenges for discriminative representation learning and which fails to find the real cross-domain features.Computer Sciences[#item_full_content]
Researchers at Carnegie Mellon University and Google DeepMind recently developed RoboTool, a system that can broaden the capabilities of robots, allowing them to use tools in more creative ways. This system, introduced in a paper published on the arXiv preprint server, could soon bring a new wave of innovation and creativity to the field of robotics.Researchers at Carnegie Mellon University and Google DeepMind recently developed RoboTool, a system that can broaden the capabilities of robots, allowing them to use tools in more creative ways. This system, introduced in a paper published on the arXiv preprint server, could soon bring a new wave of innovation and creativity to the field of robotics.[#item_full_content]
Researchers at Carnegie Mellon University and Google DeepMind recently developed RoboTool, a system that can broaden the capabilities of robots, allowing them to use tools in more creative ways. This system, introduced in a paper published on the arXiv preprint server, could soon bring a new wave of innovation and creativity to the field of robotics.Researchers at Carnegie Mellon University and Google DeepMind recently developed RoboTool, a system that can broaden the capabilities of robots, allowing them to use tools in more creative ways. This system, introduced in a paper published on the arXiv preprint server, could soon bring a new wave of innovation and creativity to the field of robotics.Robotics[#item_full_content]
Distributed cloud storage is a hot topic for security researchers around the globe pursuing secure data storage, and a team in China is now merging quantum physics with mature cryptography and storage techniques to achieve a cost-effective cloud storage solution.Distributed cloud storage is a hot topic for security researchers around the globe pursuing secure data storage, and a team in China is now merging quantum physics with mature cryptography and storage techniques to achieve a cost-effective cloud storage solution.[#item_full_content]
Princeton researchers have developed a flexible, lightweight and energy efficient soft robot that moves without the use of any legs or rotary parts. Instead, the device uses actuators that convert electrical energy into vibrations that allow it to wiggle from point to point using only a single watt.Princeton researchers have developed a flexible, lightweight and energy efficient soft robot that moves without the use of any legs or rotary parts. Instead, the device uses actuators that convert electrical energy into vibrations that allow it to wiggle from point to point using only a single watt.[#item_full_content]
An experimental computing system physically modeled after the biological brain has “learned” to identify handwritten numbers with an overall accuracy of 93.4%. The key innovation in the experiment was a new training algorithm that gave the system continuous information about its success at the task in real time while it learned. The study was published in Nature Communications.An experimental computing system physically modeled after the biological brain has “learned” to identify handwritten numbers with an overall accuracy of 93.4%. The key innovation in the experiment was a new training algorithm that gave the system continuous information about its success at the task in real time while it learned. The study was published in Nature Communications.Hi Tech & Innovation[#item_full_content]
