Fox News anchor Bret Baier has the latest on the Murdoch Children's Research Institute's partnership with the Gladstone Institutes for the "Decoding Broken Hearts" initiative on "Special Report." It's becoming more common for images to be made with AI tools. As the artificial intelligence generation gets more advanced, it's getting trickier to tell the difference between AI-made and human-made images. However, there are still signs to look out for. Here are some key indicators that an image was created by AI.

There's now an entire subcategory of small, pocketable camera drones built just for capturing selfies, and ZeroZero Robotics' HoverAir X1 ProMax might be the most assured, impressive model in it. It's effortlessly portable, making even the likes of the DJI Mini 4 Pro seem bulky. Weighing just 6.79 ounces with a clever folding design and all-over cage to keep the propellers out of harm's way, the X1 ProMax can be tossed into a coat pocket or backpack without fear of it getting damaged. It's the kind of thing you can casually bring along on any day trip or vacation alongside your sunglasses and water bottle, just on the off chance it comes in useful. Pull it out, unfold it, hit the power button, select your preferred selfie video style with the left and right buttons, place the drone on your outstretched palm facing you, tap the power button again, and it'll take off, capture your selected shot, and return to land on your palm, ready to be put away again.

In September 2021, Jim Lanzone took over a company whose name once embodied the go-go spirit of the internet but had, over the years, become a joke: Yahoo. He accepted the CEO post from the new private-equity owner Apollo Global Management, which had bought the property from Verizon, the most recent and possibly most clueless caretaker (high bar alert) in a long series of management shifts. Visiting him at the company's offices in New York City, I ask him why he took the job. "I love turnarounds," he says. This is an essay from the latest edition of Steven Levy's Plaintext newsletter.

If there's ever an Olympics for robots, we might have found the American entry for breakdancing. In a recent video, Boston Dynamics shows off what Atlas has been up to lately, and it's probably one of the most impressive things I've seen from the company. The video starts by showing Atlas walking and then running. The walking was a little stiff, but the running was perfectly human-looking. Next, the robot "crawled," in the company's words, but it looked more like a series of mountain climber exercise movements.

We analyze continuous-time mirror descent applied to sparse phase retrieval, which is the problem of recovering sparse signals from a set of magnitude-only measurements. We apply mirror descent to the unconstrained empirical risk minimization problem (batch setting), using the square loss and square measurements.

The Configurable Markov Decision Process framework includes two entities: a Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance. This presupposes that the two actors have identical reward functions. What if the configurator does not have the same intentions as the agent? This paper introduces the Non-Cooperative Configurable Markov Decision Process, a framework that allows modeling two (possibly different) reward functions for the configurator and the agent. Then, we consider an online learning problem, where the configurator has to find the best among a finite set of possible configurations. We propose two learning algorithms to minimize the configurator's expected regret, which exploit the problem's structure, depending on the agent's feedback. While a naïve application of the UCB algorithm yields a regret that grows indefinitely over time, we show that our approach suffers only bounded regret.