Did you hear that Donald Trump got indicted this week? The first-ever indictment of a former US president had been looming for weeks. And now that it's happened, the move by a Manhattan grand jury is deepening fissures in America's already-fraught political divide. But while Trump headlines flood your feeds, there were plenty of other big stories this week, none of which have anything to do with any of that. In Germany, police are cracking down on people who post adult content to websites and platforms that lack age-verification checks, like Twitter.

Abstract: In practice, encoding invariances into models helps sample complexity. In this work, we tighten and generalize theoretical results on how invariances improve sample complexity. In particular, we provide minimax optimal rates for kernel ridge regression on any manifold, with a target function that is invariant to an arbitrary group action on the manifold. Our results hold for (almost) any group action, even groups of positive dimension. For a finite group, the gain increases the "effective" number of samples by the group size.

Computer vision and intelligent video solutions now have the potential to revolutionize industries, improve operations, and enhance the quality of life for citizens. And with recent advancements in artificial intelligence and machine learning, it's easier and more accessible for businesses to unleash these solutions and compete in today's fast-paced, data-driven world. But developing and deploying computer vision and intelligent video applications at the edge requires strategic partnerships within the ecosystem. Intel systems integrators and IoT solution aggregators, for example, provide critical expertise and technologies necessary for success. You can see this for yourself at the International Security Conference & Exposition, ISC West, taking place March 28 to 31 at the Venetian Expo in Las Vegas.

As the calendar turns to 2023, Intuit's leading technologists share the trends they'll be watching closely. Ranging from the generative AI phenomenon, the rise in data protection regulations, and implications of Web3 for the fintech landscape, to how "thinking like criminals" will pay off for companies looking to stave off bad actors, Intuit leaders weigh in on what the future holds. Opportunities to catalyze innovation abound for tech companies. Generative AI is rapidly becoming more powerful and more prominent, popularized by chatbots and apps such as ChatGPT and Lensa, but it still needs to develop and mature before it can safely be used in industries where the accuracy of statements are critical, such as finance or medicine. Within the next several years, generative AI will likely play a pivotal role in helping create personalized conversational systems to provide financial or medical advice and guidance directly to customers.

Hurricane Ian made its way across Florida in late September 2022, causing tens of billions in estimated insurance losses due to wind and flood damage. Now, half a year later after the disaster, homeowners are still picking up the pieces and rebuilding with the payouts that have been slowly coming out from insurance policies. However, many have had the unexpected shock to learn that flooding was not a part of their homeowners insurance. Here we explain natural disasters, AI and insurance risk assessment. This event and many like it are stark reminders to both individuals and businesses that checking in with their insurance company to review insurance policies is something that needs to happen regularly, not because something may have gone unnoticed, but because things change.

Unanimous AI's chief scientist and CEO Louis Rosenberg and Center for AI and Digital Policy founder Marc Rotenberg explain why they signed an open letter with Elon Musk calling for a pause of artificial intelligence developments. Tesla and SpaceX CEO Elon Musk has been waging a battle for the last several months over what he called "woke" artificial intelligence, a fight that appears to have factored into his call for a six-month pause in the development of next generation AI systems. Musk was one of several signatories to a letter this week that warned of advanced AI technology that could pose "profound risks to society and humanity." The letter said one of those risks is that AI might be used to "flood our information channels with propaganda and untruth." The letter was signed by several notable technology experts, and it's not clear who might have pushed for the inclusion of that specific phrase.

The incredible abilities of ChatGPT - the chatbot powered by artificial intelligence (AI) - have opened the world's eyes to how far the technology behind it has come. Since it was released to the public in November, ChatGPT has been used to pass exams, deliver a sermon, write software and give relationship advice. Many, like Bill Gates and Google CEO Sundar Pichai, hail AI technology as the'most important' innovation of our time, saying it could solve climate change, cure cancer and enhance productivity. But its advancement has also struck fear in the hearts of others, with Elon Musk, Apple co-founder Steve Wozniak and the late Stephen Hawking believing it poses a'profound risk to society and humanity'. MailOnline takes a look at some of the biggest successes and terrible failures that have stemmed from powerful AI tools in recent months.

Consciousness And Light Are Explored. The Inter Mind Bridges The Gap Between The Physical Mind And The Conscious Mind.

Machine learning is no longer hype but at the core of everyday products. Ever faster hardware makes it possible to train ever larger machine learning models -- in shorter times, too. With around 100 papers submitted per day on machine learning or related domains to arXiv, chances are high that at least one-third of them have leveraged the hardware's capabilities to do hyperparameter searches to optimize their used model. And that's straightforward, is it not? At least, that's what frequently happens with TensorFlow.

This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs. The notebooks are intended to show how to initialize, train and explain an AutoML model in a few lines of code. The notebooks also cover many of the advanced features available in the AutoMLx package. Pre-executed copies of each of the demo notebooks are available as html files, which can be viewed without installing anything. The demo notebooks in this repository serve as supplementary documentation for the AutoMLx package.