Check the comments section of many social media and digital news platforms, and you’re likely to find a cesspool of insults, threats and even harassment. In fact, a Pew Research Center survey found that 41% of American adults have personally experienced online harassment, and 1 in 5 adults say they’ve been harassed online for their political views.Check the comments section of many social media and digital news platforms, and you’re likely to find a cesspool of insults, threats and even harassment. In fact, a Pew Research Center survey found that 41% of American adults have personally experienced online harassment, and 1 in 5 adults say they’ve been harassed online for their political views.[#item_full_content]
Check the comments section of many social media and digital news platforms, and you’re likely to find a cesspool of insults, threats and even harassment. In fact, a Pew Research Center survey found that 41% of American adults have personally experienced online harassment, and 1 in 5 adults say they’ve been harassed online for their political views.Check the comments section of many social media and digital news platforms, and you’re likely to find a cesspool of insults, threats and even harassment. In fact, a Pew Research Center survey found that 41% of American adults have personally experienced online harassment, and 1 in 5 adults say they’ve been harassed online for their political views.Computer Sciences[#item_full_content]
Artificial intelligence is already in widespread use, yet it is still difficult to understand how an AI system reaches its decisions. Scientists at the Fraunhofer Heinrich-Hertz-Institut (HHI) and the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin have collaborated for many years to make AI explainable. Now the scientists led by Prof. Thomas Wiegand (Fraunhofer HHI, BIFOLD), Prof. Wojciech Samek (Fraunhofer HHI, BIFOLD) and Dr. Sebastian Lapuschkin (Fraunhofer HHI) have achieved another milestone.Artificial intelligence is already in widespread use, yet it is still difficult to understand how an AI system reaches its decisions. Scientists at the Fraunhofer Heinrich-Hertz-Institut (HHI) and the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin have collaborated for many years to make AI explainable. Now the scientists led by Prof. Thomas Wiegand (Fraunhofer HHI, BIFOLD), Prof. Wojciech Samek (Fraunhofer HHI, BIFOLD) and Dr. Sebastian Lapuschkin (Fraunhofer HHI) have achieved another milestone.Machine learning & AI[#item_full_content]
Imagine grasping a heavy object, like a pipe wrench, with one hand. You would likely grab the wrench using your entire fingers, not just your fingertips. Sensory receptors in your skin, which run along the entire length of each finger, would send information to your brain about the tool you are grasping.Imagine grasping a heavy object, like a pipe wrench, with one hand. You would likely grab the wrench using your entire fingers, not just your fingertips. Sensory receptors in your skin, which run along the entire length of each finger, would send information to your brain about the tool you are grasping.[#item_full_content]
Softbank CEO Masayoshi Son on Wednesday said he believes artificial intelligence will surpass human intelligence within a decade, urging Japanese companies to adopt it or be left behind.Softbank CEO Masayoshi Son on Wednesday said he believes artificial intelligence will surpass human intelligence within a decade, urging Japanese companies to adopt it or be left behind.Machine learning & AI[#item_full_content]
When Deirdre Loughridge first began teaching classes on music technology in 2012, there was a lot of talk about how computers were “dehumanizing” music. The general thought among her students then was that computers could not make music.When Deirdre Loughridge first began teaching classes on music technology in 2012, there was a lot of talk about how computers were “dehumanizing” music. The general thought among her students then was that computers could not make music.Machine learning & AI[#item_full_content]
Imagine you’re in an airplane with two pilots, one human and one computer. Both have their “hands” on the controllers, but they’re always looking out for different things. If they’re both paying attention to the same thing, the human gets to steer. But if the human gets distracted or misses something, the computer quickly takes over.Imagine you’re in an airplane with two pilots, one human and one computer. Both have their “hands” on the controllers, but they’re always looking out for different things. If they’re both paying attention to the same thing, the human gets to steer. But if the human gets distracted or misses something, the computer quickly takes over.Automotive[#item_full_content]
In contemporary deep learning-based methods for segmenting microscopic images, there’s a heavy reliance on extensive training data that requires detailed annotations. This process is both expensive and labor-intensive. An alternative approach involves using simpler annotations, such as marking the center points of objects. While not as detailed, these point annotations still provide valuable information for image analysis.In contemporary deep learning-based methods for segmenting microscopic images, there’s a heavy reliance on extensive training data that requires detailed annotations. This process is both expensive and labor-intensive. An alternative approach involves using simpler annotations, such as marking the center points of objects. While not as detailed, these point annotations still provide valuable information for image analysis.[#item_full_content]
In contemporary deep learning-based methods for segmenting microscopic images, there’s a heavy reliance on extensive training data that requires detailed annotations. This process is both expensive and labor-intensive. An alternative approach involves using simpler annotations, such as marking the center points of objects. While not as detailed, these point annotations still provide valuable information for image analysis.In contemporary deep learning-based methods for segmenting microscopic images, there’s a heavy reliance on extensive training data that requires detailed annotations. This process is both expensive and labor-intensive. An alternative approach involves using simpler annotations, such as marking the center points of objects. While not as detailed, these point annotations still provide valuable information for image analysis.Computer Sciences[#item_full_content]
What would we do without compression?What would we do without compression?[#item_full_content]
