Donald Trump's jaunt to the Middle East featured an entourage of billionaire tech bros, a fighter-jet escort, and business deals designed to reshape the global landscape of artificial intelligence. On the final stop of the tour in Abu Dhabi, the US President announced that unnamed US companies would partner with the United Arab Emirates to create the largest AI datacenter cluster outside of America. Trump said that the US companies will help G42, an Emirati company, build five gigawatts of AI computing capacity in the UAE. Sheikh Tahnoon bin Zayed Al Nahyan, who leads the UAE's Artificial Intelligence and Advanced Technology Council, and is in charge of a 1.5 trillion fortune aimed at building AI capabilities, said the move will strengthen the UAE's position "as a hub for cutting-edge research and sustainable development, delivering transformative benefits for humanity." A few days earlier, as Trump arrived in Riyadh, Saudi Arabia announced Humain, an AI investment firm owned by the kingdom's Public Investment Fund.

This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uniform combination solution that has proven to be difficult to improve upon in the past, as well as other algorithms for learning kernels based on convex combinations of base kernels in both classification and regression. Our algorithms are based on the notion of centered alignment which is used as a similarity measure between kernels or kernel matrices. We present a number of novel algorithmic, theoretical, and empirical results for learning kernels based on our notion of centered alignment. In particular, we describe efficient algorithms for learning a maximum alignment kernel by showing that the problem can be reduced to a simple QP and discuss a one-stage algorithm for learning both a kernel and a hypothesis based on that kernel using an alignment-based regularization. Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment. We also report the results of experiments with our centered alignment-based algorithms in both classification and regression.

Ryan and Randy met at a sex party in 2019 and started dating shortly after. By month four, they made the relationship official, eventually moved into a two-story house in Los Angeles together, and did all the things happy couples do: date nights, vacation with friends, support one another's ambitions. Then, in 2022, they decided to open the relationship. As Covid-19 restrictions loosened, "we were being exposed to other attractions and to other people who were seeking our attention," Ryan says. "We both knew we had attractions to other people. It was, let's talk about being open and see what that means for us. Because being open can mean different things to different people."

Content marketing is about to get a whole lot more interesting, folks. Because generative AI is about to disrupt the way we create, distribute and measure the success of our content. And let me tell you, the possibilities are endless. First things first, let's talk about the elephant in the room: Generative AI can create content that is indistinguishable from human-generated content. That's right, we're talking about machine-generated text, images and videos that are so good, they'll have you questioning the very nature of humanity.

I heard the MC call my name and felt my legs carry me toward the stage. It was time to enter the Punderdome. I'd never competed in a pun contest, much less in front of hundreds of people at an event considered the Roman Colosseum of punditry. My stage presence could be described as lacking. I had done basically no preparation.

The Monitor is a weekly column devoted to everything happening in the WIRED world of culture, from movies to memes, TV to Twitter. When it premiered six years ago, Westworld epitomized prestige sci-fi at its peak. An expensive HBO series with an old-school Michael Crichton pedigree, it featured a stellar cast and a mind-trippy premise: What if all the sentient robots, or "hosts," at a Western theme park decided they'd had enough of being kicked and dragged around? Subsequent seasons revealed the influence of artificial intelligence and reached far beyond the borders of the Westworld attraction, a global mess of money, corruption, and consciousness-tampering that was nightmare fuel for viewers watching at home while scrolling through Twitter. It was a hit--even if a modest one.

The Human Brain Project (HBP) has announced the start of its final phase as an EU-funded FET Flagship. The European Commission has signed a grant agreement to fund the HBP with 150 million Euros from now until 2023. Over the next three years, the project will narrow its focus to advance three core scientific areas – brain networks, their role in consciousness, and artificial neural nets – while expanding its innovative EBRAINS infrastructure. EBRAINS offers the most comprehensive atlas and database on the human brain, directly coupled with powerful computing and simulation tools, to research communities around neuroscience, medicine and technology. Currently transitioning into a sustainable infrastructure, EBRAINS will remain available to the scientific community, as a lasting contribution of the HBP to global scientific progress.

When a robot almost looks human--almost, but not quite--it often comes across as jarringly fake instead of familiar. Robots that are clearly artificial, like WALL-E or R2-D2, don't have this problem. But androids like this one that imperfectly mimic human mannerisms and facial expressions are weird enough to be haunting. This phenomenon is known as the uncanny valley. It's a major obstacle for designers who try to make their robots look like people--and it may be just as much of a hurdle for developers who are creating bots that talk like people, but that don't have a body at all.

Andrew Ng hands me a tiny device that wraps around my ear and connects to a smartphone via a small cable. It looks like a throwback--a smartphone earpiece without a Bluetooth connection. In a way, this tiny device allows the blind to see. Ng is the chief scientist at Chinese tech giant Baidu, and this is one of the company's latest prototypes. The device contains a tiny camera that captures whatever is in front of you--a person's face, a street sign, a package of food--and sends the images to an app on your smartphone.

Using machine learning, researchers from MIT have developed a system that produces sound effects that are so realistic they even fool human listeners. The new algorithm, developed by researchers from MIT's Computer Science and Artificial Intelligence Laboratory, can predict the precise acoustical qualities of a sound, and then simulate it in an extremely realistic way. When analyzing a silent video clip, such as an object being hit by a drumstick, the system can produce a sound for the hit that's realistic enough to fool human listeners. To make it work, PhD student Andrew Owens and his team applied a technique known as "deep learning" that enables computers to pick out important patterns buried in massive amounts of raw data completely autonomously. Over the course of several months, the researchers recorded about 1,000 videos of an estimated 46,000 sounds that represented an array of objects being hit, scraped, and prodded by a drumstick.