Radar must adapt to changing environments, and we propose changepoint detection as a method to do so. In the world of increasingly congested radio frequencies, radars must adapt to avoid interference. Many radar systems employ the prediction action cycle to proactively determine transmission mode while spectrum sharing. This method constructs and implements a model of the environment to predict unused frequencies, and then transmits in this predicted availability. For these selection strategies, performance is directly reliant on the quality of the underlying environmental models. In order to keep up with a changing environment, these models can employ changepoint detection. Changepoint detection is the identification of sudden changes, or changepoints, in the distribution from which data is drawn. This information allows the models to discard "garbage" data from a previous distribution, which has no relation to the current state of the environment. In this work, bayesian online changepoint detection (BOCD) is applied to the sense and predict algorithm to increase the accuracy of its models and improve its performance. In the context of spectrum sharing, these changepoints represent interferers leaving and entering the spectral environment. The addition of changepoint detection allows for dynamic and robust spectrum sharing even as interference patterns change dramatically. BOCD is especially advantageous because it enables online changepoint detection, allowing models to be updated continuously as data are collected. This strategy can also be applied to many other predictive algorithms that create models in a changing environment.

4.3 Deep Learning Segment Accounted for the Largest Market Share in 2021. 4.4 North America is the Fastest-Growing Regional Market for AI in Drug …

The past month has seen a frenzy of articles, interviews, and other types of media coverage about Blake Lemoine, a Google engineer who told The Washington Post that LaMDA, a large language model created for conversations with users, is "sentient." After reading a dozen different takes on the topic, I have to say that the media has become (a bit) disillusioned with the hype surrounding current AI technology. A lot of the articles discussed why deep neural networks are not "sentient" or "conscious." This is an improvement in comparison to a few years ago, when news outlets were creating sensational stories about AI systems inventing their own language, taking over every job, and accelerating toward artificial general intelligence. But the fact that we're discussing sentience and consciousness again underlines an important point: We are at a point where our AI systems--namely large language models--are becoming increasingly convincing while still suffering from fundamental flaws that have been pointed out by scientists on different occasions.

Threat actors have several ways to fool or exploit artificial intelligence and machine learning systems and models, but you can defend against …

As Ben Buchanan and Andrew Imbrie note in their recent book, "AI's [artificial intelligence's] new capabilities are both marvels and distractions." The marvel versus distraction dichotomy is an interesting one: due to the two possible natures of AI, the question of whether advances in AI will favor autocracies or democracies has come to the forefront of the tech and global power debate. On the one hand, AI has the potential to tackle some of the world's most challenging social problems, such as issues related to healthcare, the environment, and crisis response, leading some to believe that democracies will wield AI to create a future for human good. On the other hand, some fear AI-enabled surveillance, information campaigns, and cyber operations will empower existing tyrants and produce new ones, leading to a future where autocracies thrive and democracies struggle. By examining how advances in AI capabilities in the near future could benefit autocracies and democracies, as well as how these advances could benefit both, I believe that AI is likely to favor autocracies in the near term, but under one necessary condition: that democracies are negligent in their response to autocracies' destructive use of AI.

To many they are art's next big thing--digital images of jellyfish pulsing and blurring in a dark pink sea, or dozens of butterflies fusing together into a single organism. The Argentine artist Sofia Crespo, who created the works with the help of artificial intelligence, is part of the "generative art" movement, where humans create rules for computers which then use algorithms to generate new forms, ideas and patterns. The field has begun to attract huge interest among art collectors--and even bigger price tags at auction. US artist and programmer Robbie Barrat--a prodigy still only 22 years old--sold a work called "Nude Portrait#7Frame#64" at Sotheby's in March for £630,000 ($821,000). That came almost four years after French collective Obvious sold a work at Christie's titled "Edmond de Belamy"--largely based on Barrat's code--for $432,500.

Linear regression is both a statistical and a machine learning algorithm.

K-pop has been unstoppable, affecting everything from movies to music and TV shows, which have combined to form a staggeringly massive entertainment industry juggernaut, with few if any equals. Now let's talk about K-pop deepfakes. Netflix, for example, recognizes this and is currently pouring millions of dollars into funding new original Korean dramas and movies, like the newly released emotional masterpiece Move to Heaven, about a "trauma cleaner" and his uncle who pack up the belongings of people who've died and help their families to move on. All told, Netflix reportedly plans to spend half a billion dollars in 2021 on South Korean content, which also coincides with a moment that finds South Korean pop groups (like Blackpink and BTS) being among the biggest in the world. Those two groups, in particular, have millions of global fans and incomprehensibly massive audiences across social media -- which might explain why the four members of Blackpink, in particular, got their own documentary treatment on Netflix, via their movie Light Up The Sky.

MIT researchers previously demonstrated a robotic arm that combines visual information and radio frequency (RF) signals to find hidden objects that were tagged with RFID tags (which reflect signals sent by an antenna). Building off that work, they have now developed a new system that can efficiently retrieve any object buried in a pile. As long as some items in the pile have RFID tags, the target item does not need to be tagged for the system to recover it. The algorithms behind the system, known as FuseBot, reason about the probable location and orientation of objects under the pile. Then FuseBot finds the most efficient way to remove obstructing objects and extract the target item.

VAST Data, the data platform company for the AI-powered world, announced that Ingenuity Studios selected VAST to help deliver next-level realism to a host of long- and short-form works, including feature films, television series, music videos, commercials, and more. Ingenuity Studios tapped VAST Data's Universal Storage architecture to leapfrog the status quo by deploying a simple data storage platform that allows artists and animators to easily collaborate on projects with ultra-fast performance and low-latency, without the classic flash storage tax. AI and ML News: Why SMBs Shouldn't Be Afraid of Artificial Intelligence (AI) Rendering workflows are changing rapidly for animators and visual effects (VFX) artists, with new machine learning and deep learning technologies being used to automate and enhance much of the creative process. To train and deploy these new deep learning models, studios need to provide high-speed access to rich content for high-throughput computation. All-flash infrastructure is not just a critical advantage for metadata-intensive render farms, but also an essential component of demanding deep learning pipelines.