Concern about generative artificial intelligence technologies seems to be growing almost as fast as the spread of the technologies themselves. These worries are driven by unease about the possible spread of disinformation at a scale never seen before, and fears of loss of employment, loss of control over creative works, and, more futuristically, AI becoming so powerful that it causes extinction of the human species.Concern about generative artificial intelligence technologies seems to be growing almost as fast as the spread of the technologies themselves. These worries are driven by unease about the possible spread of disinformation at a scale never seen before, and fears of loss of employment, loss of control over creative works, and, more futuristically, AI becoming so powerful that it causes extinction of the human species.[#item_full_content]

In today’s increasingly interconnected world, high-quality communication has become more vital than ever. Accurately estimating the dynamic status of communication channels is a key factor in achieving this. Recently, a joint research team designed a new algorithm that offers high-level estimation accuracy and privacy protection with low computational and communication costs. This research was published in Intelligent Computing.In today’s increasingly interconnected world, high-quality communication has become more vital than ever. Accurately estimating the dynamic status of communication channels is a key factor in achieving this. Recently, a joint research team designed a new algorithm that offers high-level estimation accuracy and privacy protection with low computational and communication costs. This research was published in Intelligent Computing.[#item_full_content]

When combating complex problems like illegal poaching and human trafficking, efficient yet broad geospatial search tools can provide critical assistance in finding and stopping the activity. A visual active search (VAS) framework for geospatial exploration developed by researchers in the McKelvey School of Engineering at Washington University in St. Louis uses a novel visual reasoning model and aerial imagery to learn how to search for objects more effectively.When combating complex problems like illegal poaching and human trafficking, efficient yet broad geospatial search tools can provide critical assistance in finding and stopping the activity. A visual active search (VAS) framework for geospatial exploration developed by researchers in the McKelvey School of Engineering at Washington University in St. Louis uses a novel visual reasoning model and aerial imagery to learn how to search for objects more effectively.[#item_full_content]

Recent numerical studies investigating neural networks have found that solutions typically found by modern machine learning algorithms lie in complex extended regions of the loss landscape. In these regions, zero-energy paths between pairs of distant solutions can be established.Recent numerical studies investigating neural networks have found that solutions typically found by modern machine learning algorithms lie in complex extended regions of the loss landscape. In these regions, zero-energy paths between pairs of distant solutions can be established.[#item_full_content]

The image spoke for itself. University at Buffalo computer scientist and deepfake expert Siwei Lyu created a photo collage out of the hundreds of faces that his detection algorithms had incorrectly classified as fake—and the new composition clearly had a predominantly darker skin tone.The image spoke for itself. University at Buffalo computer scientist and deepfake expert Siwei Lyu created a photo collage out of the hundreds of faces that his detection algorithms had incorrectly classified as fake—and the new composition clearly had a predominantly darker skin tone.[#item_full_content]

Researchers from RUDN University have proposed a new scheme for the Internet of Things network. It uses flying drones that process data instead of cloud data centers, which speeds up the network. The results were published in Drones.Researchers from RUDN University have proposed a new scheme for the Internet of Things network. It uses flying drones that process data instead of cloud data centers, which speeds up the network. The results were published in Drones.[#item_full_content]

When artificial intelligence models pore over hundreds of gigabytes of training data to learn the nuances of language, they also imbibe the biases woven into the texts.When artificial intelligence models pore over hundreds of gigabytes of training data to learn the nuances of language, they also imbibe the biases woven into the texts.[#item_full_content]

Neural network learning techniques stem from the dynamics of the brain. However, these two scenarios, brain learning and deep learning, are intrinsically different. One of the most prominent differences is the number of layers each one possesses.Neural network learning techniques stem from the dynamics of the brain. However, these two scenarios, brain learning and deep learning, are intrinsically different. One of the most prominent differences is the number of layers each one possesses.[#item_full_content]

AI language models are booming. The current frontrunner is ChatGPT, which can do everything from taking a bar exam to creating an HR policy to writing a movie script.AI language models are booming. The current frontrunner is ChatGPT, which can do everything from taking a bar exam to creating an HR policy to writing a movie script.[#item_full_content]

ChatGPT and other solutions built on machine learning are surging. But even the most successful algorithms have limitations. Researchers from University of Copenhagen have proven mathematically that apart from simple problems it is not possible to create algorithms for AI that will always be stable. The study, posted to the arXiv preprint server, may lead to guidelines on how to better test algorithms and reminds us that machines do not have human intelligence after all.ChatGPT and other solutions built on machine learning are surging. But even the most successful algorithms have limitations. Researchers from University of Copenhagen have proven mathematically that apart from simple problems it is not possible to create algorithms for AI that will always be stable. The study, posted to the arXiv preprint server, may lead to guidelines on how to better test algorithms and reminds us that machines do not have human intelligence after all.[#item_full_content]