Playform AI launched the limited-time exhibition this week with work from four artists, who include established names in the fine art word, new media specialists and a pseudonymous Instagram creator. The pieces range in price from about $60 to $1,500 and include wall prints, jigsaw puzzles, framed video screens and masks. The shop also features an augmented reality tool that allows prospective buyers to project artworks into their home before purchase. The startup is one of a handful of companies to venture into the AI art exhibition space as new advances in machine learning research have spawned a small but growing community of creatives, artists and technologists attempting to harness the power of AI in art. The key piece of a technology involved is a form of neural network called a generative adversarial network (GAN), which brands have already experimented with using for everything from deepfaked commercials to product design.

WASHINGTON — Artificial intelligence is all the rage within the military right now, with the services working to integrate machine learning algorithms …

This is some Kodachrome level color voodoo – color grading and shot matching powered by machine-learning. And it comes from a collaboration with some friends of ours from the artist and live visual side, so it's doubly worth mentioning. What if the current techniques called AI turned out to be really important to creative artists – just not for the reason the general public expected? That's sure what Colourlab Ai looks like. It harnesses the powers of massive data crunching of pixels, the thing "AI" in the current generation was designed to do, and then applies it to making your video look amazing.

Artificial intelligence technologies are now so advanced that even facets of technology like sentiment analysis, cutting edge in 2010, have been …

The work will hone in on military-centered machine learning application development, research into trustworthy AI tech--and could help boost DoD's …

Citi Ventures is shifting from artificial intelligence to artificial enlightenment.

… which announced the most recent advance, hailed the achievement as another step toward the merger of the human brain and artificial intelligence, …

Apple's new, cheaper HomePod is a tough smart speaker to nail down. On the one hand, the HomePod Mini boasts impressive audio quality for its size. The HomePod Mini also has a Thread radio that lets it act as a smart home hub, but for now, there are only a few Thread-enabled smart devices available to control. And while Apple's new Intercom feature makes for an easy way to broadcast messages to household members, it doesn't allow for two-way calling. Now, if you're a dedicated Apple user and you've been waiting for a more affordable Siri-powered smart speaker than the $300 HomePod, the $99 HomePod Mini is your best--and only--bet.

We propose a new regularization method to alleviate over-fitting in deep neural networks. The key idea is utilizing randomly transformed training samples to regularize a set of sub-networks, which are originated by sampling the width of the original network, in the training process. As such, the proposed method introduces self-guided disturbances to the raw gradients of the network and therefore is termed as Gradient Augmentation (GradAug). We demonstrate that GradAug can help the network learn well-generalized and more diverse representations. Moreover, it is easy to implement and can be applied to various structures and applications. GradAug improves ResNet-50 to 78.79% on ImageNet classification, which is a new state-of-the-art accuracy.

They are capable of seemingly sophisticated results, but they can also be fooled in ways that range from relatively harmless -- misidentifying one animal as another -- to potentially deadly if the network guiding a self-driving car misinterprets a stop sign as one indicating it is safe to proceed. A philosopher with the University of Houston suggests in a paper published in Nature Machine Intelligence that common assumptions about the cause behind these supposed malfunctions may be mistaken, information that is crucial for evaluating the reliability of these networks. As machine learning and other forms of artificial intelligence become more embedded in society, used in everything from automated teller machines to cybersecurity systems, Cameron Buckner, associate professor of philosophy at UH, said it is critical to understand the source of apparent failures caused by what researchers call "adversarial examples," when a deep neural network system misjudges images or other data when confronted with information outside the training inputs used to build the network. They're rare and are called "adversarial" because they are often created or discovered by another machine learning network -- a sort of brinksmanship in the machine learning world between more sophisticated methods to create adversarial examples and more sophisticated methods to detect and avoid them. "Some of these adversarial events could instead be artifacts, and we need to better know what they are in order to know how reliable these networks are," Buckner said.