Bing's new AI search engine program uses ChatGPT language modeling to provide its users with detailed human-like responses to questions and other inquiries. Microsoft, which owns Bing, and Google are working to create the most interactive and accurate chatbot possible in order to drive more engagement from users. Read below to find out Bing's new AI program is similar to the popular ChatGPT model. Microsoft's new AI program for its Bing search engine was developed and powered by Open AI technology. In early February, Microsoft, a major investor in Open AI, announced that its Bing search engine and Edge web browser would utilize an artificial intelligence program that is more advanced than ChatGPT.

The developers of GPT-4 claimed that the program can exhibit "human-level performance" on certain exams. While the chart of various academic test scores seems impressive at first, the software failed both AP English exams with a 2 out of 5. It scored extremely well on tests where all that a computer program needed was the right information, which isn't very impressive to anyone who has used Google in the past few decades. Again, I don't think that we're suddenly going to be bleeding lawyers. As someone who has actually performed reasonably well on the LSAT, I can also tell you that taking the test and practicing law are very different things.

At least one out of every five people who get the coronavirus doesn't show symptoms and can unknowingly spread the virus to others. Those who don't feel sick and aren't notified of exposure can't know that they should get tested. But researchers at the Massachusetts Institute of Technology may have found a way to identify these silent coronavirus carriers without a test. A study published in September describes an artificial-intelligence model that can distinguish between the coughs of people with the coronavirus and those who are healthy. It can even tell from voluntary, forced coughs whether people were healthy or were asymptomatic carriers, based on sound variations too subtle for the human ear to discern.

A new technology using artificial intelligence detects depressive language in social media posts more accurately than current systems and uses less data to do it. The technology, which was presented during the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, is the first of its kind to show that, to more accurately detect depressive language, small, high-quality data sets can be applied to deep learning, a commonly used AI approach that is typically data intensive. Previous psycholinguistic research has shown that the words we use in interaction with others on a daily basis are a good indicator of our mental and emotional state. Past attempts to apply deep learning techniques to detect and monitor depression in social media posts have been shown to be tedious and expensive, explained Nawshad Farruque, a University of Alberta Ph.D. student in computing science who is leading the new study. He explained that a Twitter post saying that somebody is depressed because Netflix is down isn't really expressing depression, so someone would need to "explain" this to the algorithm.

"Deep learning is usually very data hungry," said Farruque. "You basically need to feed your machine a lot of examples of what you're trying to teach it.

A new technology using artificial intelligence detects depressive language in social media posts more accurately than current systems and uses less data to do it. The technology, which was presented during the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, is the first of its kind to show that, to more accurately detect depressive language, small, high-quality data sets can be applied to deep learning, a commonly used AI approach that is typically data intensive. Previous psycholinguistic research has shown that the words we use in interaction with others on a daily basis are a good indicator of our mental and emotional state. Past attempts to apply deep learning techniques to detect and monitor depression in social media posts have been shown to be tedious and expensive, explained Nawshad Farruque, a University of Alberta PhD student in computing science who is leading the new study. He explained that a Twitter post saying that somebody is depressed because Netflix is down isn't really expressing depression, so someone would need to "explain" this to the algorithm.

The nightmare scenario for computer security – artificial intelligence programs that can learn how to evade even the best defenses – may already have arrived. That warning from security researchers is driven home by a team from IBM Corp. who have used the artificial intelligence technique known as machine learning to build hacking programs that could slip past top-tier defensive measures. The group will unveil details of its experiment at the Black Hat security conference in Las Vegas on Wednesday. State-of-the-art defenses generally rely on examining what the attack software is doing, rather than the more commonplace technique of analyzing software code for danger signs. But the new genre of AI-driven programs can be trained to stay dormant until they reach a very specific target, making them exceptionally hard to stop.

A new software program that uses artificial intelligence can help rapidly detect and analyze gravitational waves -- ripples in the cosmic fabric of space-time -- from catastrophic events such as collisions between black holes, a new study finds. The new technique, called deep filtering, can help researchers see cataclysmic events that current software might not detect, such as titanic mergers in the hearts of galaxies, according to the authors of a new paper describing the work. Gravitational waves are ripples in the fabric of space and time. They are generated when any object with mass moves, and they travel at the speed of light, stretching and squeezing space-time along the way. Gravitational waves are extraordinarily difficult to detect, and the ones that scientists can detect are from exceptionally massive objects. Although the existence of gravitational waves was first predicted by Albert Einstein in 1916, it took over a century for scientists to successfully detect the first direct evidence of gravitational waves, using the Laser Interferometer Gravitational-Wave Observatory (LIGO) to spot the gravitational aftermath of two black holes smashing together.

Google seems to have taken another step forward with their progress in artificial intelligence as their new AI program can now navigate the London Underground system without repetitive feeding of data. Most AI programs can do the same but the difference with the new Google AI agent is that it can learn the ropes in just one try. In addition, the same program also has the capability to answer several questions regarding a family tree. Google DeepMind researchers developed the program without having to pre-program it to know what and how to learn. Once the map of the London Underground subway was given, it took care of the rest.

Drone safety, from privacy issues to crashing over unsuspecting pedestrians, has been a concern since, well, the dawn of the drone. But one startup is working to use artificial intelligence to help drone pilots pick the safest route. Flock, an artificial intelligence company formed out of Imperial College London, Oxford University and Cambridge University, is currently developing a risk analysis program for commercial drones, from aerial photographers to drone use on a larger scale, such as delivering Amazon packages. The program uses real-time weather information and the location of buildings. But what's perhaps even more impressive is that the system can also predict what areas will be full of people so it can choose a route around congested areas or a time when those areas will be less crowded.