Cybercriminals are turning to ChatGPT to generate extremely convincing phishing emails, researchers have warned - so how can internet users spot the scams? Cybersecurity company Norton warned that criminals are turning to AI tools such as ChatGPT to create'lures' to rob victims. A report in New Scientist suggested that using ChatGPT to generate emails could cut costs for cybercrime gangs by up to 96 percent. ChatGPT also completely removes the language barrier for cybercriminal gangs around the world, warns Julia O'Toole, CEO of MyCena Security Solutions. O'Toole said there are still ways to spot scam emails generated by AI tools, but the technology makes it far more difficult to spot scam emails.

Dr. Andrew Toole is the Chief Economist at the U.S. Patent and Trademark Office (USPTO) and a Research Associate at the Centre for European Economic Research (ZEW). Dr. Toole joined the USPTO with experience in the private sector, academia, and government. While completing his PhD in economics at Michigan State University, Andrew Toole was a Senior Economist for Laurits R. Christensen Associates where he conducted studies on total factor productivity, cost and price analysis, and competitive strategy. In 1998, Dr. Toole went to Stanford University as a postdoctoral student before becoming a faculty member at Illinois State University and Rutgers University in New Jersey. As an academic researcher, Dr. Toole was asked to advise on science and technology policy issues for institutions such as the U.S. National Academies of Science, U.S. National Institutes of Health, and the U.S. Department of Agriculture (USDA).

Facial recognition technology has advanced swiftly in the last five years. As University of Texas at Dallas researchers try to determine how computers have gotten as good as people at the task, they are also shedding light on how the human brain sorts information. UT Dallas scientists have analyzed the performance of the latest echelon of facial recognition algorithms, revealing the surprising way these programs--which are based on machine learning--work. Their study, published online Nov. 12 in Nature Machine Intelligence, shows that these sophisticated computer programs--called deep convolutional neural networks (DCNNs)--figured out how to identify faces differently than the researchers expected. "For the last 30 years, people have presumed that computer-based visual systems get rid of all the image-specific information--angle, lighting, expression and so on," said Dr. Alice O'Toole, senior author of the study and the Aage and Margareta Møller Professor in the School of Behavioral and Brain Sciences.

Facial recognition technology has advanced swiftly in the last five years. As University of Texas at Dallas researchers try to determine how computers have gotten as good as people at the task, they are also shedding light on how the human brain sorts information. UT Dallas scientists have analyzed the performance of the latest echelon of facial recognition algorithms, revealing the surprising way these programs -- which are based on machine learning -- work. Their study, published online Nov. 12 in Nature Machine Intelligence, shows that these sophisticated computer programs -- called deep convolutional neural networks (DCNNs) -- figured out how to identify faces differently than the researchers expected. "For the last 30 years, people have presumed that computer-based visual systems get rid of all the image-specific information -- angle, lighting, expression and so on," said Dr. Alice O'Toole, senior author of the study and the Aage and Margareta Møller Professor in the School of Behavioral and Brain Sciences.

Self-driving cars could soon'see' dangers before they come into view. Researchers are working on laser technology that, when combined with an algorithm, can create images of objects that are hidden around a corner. The system could also be used to see through foliage from aerial vehicles or to give rescue teams the ability to find people blocked from view by walls and rubble. Researchers are working on extremely sensitive lasers that reflect off nearby objects meaning driverless cars of the future may know what is around a sharp bend before reaching it. 'There is this preconceived notion that you can't image objects that aren't already directly visible to the camera – and we have found ways to get around these types of limiting situations,' said Dr Matthew O'Toole, a coauthor of the research from Stanford University told the Guardian.

Whether it's a child running after a ball, a herd of cows or a broken-down car, unexpected obstacles can prove deadly to drivers. But scientists say the cars of the future might be able to anticipate such perils. A team of researchers have come up with a new laser-based system that efficiently produces images of objects that are hidden around a corner – a development they say could allow autonomous vehicles to see obstacles before they come into the line of sight. "There is this preconceived notion that you can't image objects that aren't already directly visible to the camera – and we have found ways to get around these types of limiting situations," said Dr Matthew O'Toole, a coauthor of the research from Stanford University. The approach builds on technologies such as Lidar, a tool used in archaeological mapping that involves sending laser pulses towards a surface and measuring the time it takes for light to be reflected.

When the MIT Big Data Challenge asked, "What can you learn from data about 2.3 million taxi rides?" graduate students in professor Marta González's research lab had some answers. Based on their experience writing machine-learning algorithms that find meaningful patterns in very large data sets, and on their skill applying those patterns to understand how people use transportation in urban areas, the students were able to predict the number of taxi pickups that had occurred in 700 time intervals at 36 locations in the Boston area. Their predictions were the best in the competition, earning them the number one spot and $4,000 in prize money. The scientific visualization of the data prepared by one team member garnered a second-place prize and an additional $1,000. The awards were announced mid-March.