Around midnight on June 28, Calvin Hu was driving with his girlfriend near San Francisco's Golden Gate Park when he pulled up at an intersection behind two white and orange autonomous Chevrolet Bolts operated by Cruise, a subsidiary of General Motors. Another was stopped to his right in the adjacent lane. The light turned green but the cars, which operate in the city without drivers, didn't move. When Hu prepared to reverse to make space to go around the frozen vehicles, he says, he noticed that several more Cruise vehicles had stopped in the lanes behind him. Hu, another driver, and a paratransit bus were trapped in a robotaxi sandwich.

ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning. The algorithms in this package first build large tree ensembles that prioritize basis functions with few features and then select a feature-sparse subset of these basis functions using a weighted lasso optimization criterion. The package includes visualizations to analyze the features selected by the ensemble and their impact on predictions. Hence ControlBurn offers the accuracy and flexibility of tree-ensemble models and the interpretability of sparse generalized additive models. ControlBurn is scalable and flexible: for example, it can use warm-start continuation to compute the regularization path (prediction error for any number of selected features) for a dataset with tens of thousands of samples and hundreds of features in seconds. For larger datasets, the runtime scales linearly in the number of samples and features (up to a log factor), and the package support acceleration using sketching. Moreover, the ControlBurn framework accommodates feature costs, feature groupings, and $\ell_0$-based regularizers. The package is user-friendly and open-source: its documentation and source code appear on https://pypi.org/project/ControlBurn/ and https://github.com/udellgroup/controlburn/.

Douglas Hofstadter, a cognitive scientist, recently wrote in the Economist that he believes that GPT-3 is "cluelessly clueless." By this he means that GPT-3 has no idea about what it is saying. To illustrate, he and a colleague asked it a few questions. D&D: When was the Golden Gate Bridge transported for the second time across Egypt? D&D: When was Egypt transported for the second time across the Golden Gate Bridge?

PHNOM PENH – Defense Minister Nobuo Kishi said Wednesday during talks with his ASEAN counterparts that maintaining a rules-based international order in the Indo-Pacific region is important, apparently with China's growing maritime assertiveness in mind. In pushing for Japan's vision of a "free and open" Indo-Pacific, Kishi called for a regional code of conduct in the South China Sea to be "effective, substantial and consistent with international law," his ministry said in a press release. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page.

Increasingly frequent wildfires significantly affect solar energy production as the atmospheric aerosols generated by wildfires diminish the incoming solar radiation to the earth. Atmospheric aerosols are measured by Aerosol Optical Depth (AOD), and AOD data streams can be retrieved and monitored by geostationary satellites. However, multi-source remote-sensing data streams often present heterogeneous characteristics, including different data missing rates, measurement errors, systematic biases, and so on. To accurately estimate and predict the underlying AOD propagation process, there exist practical needs and theoretical interests to propose a physics-informed statistical approach for modeling wildfire AOD propagation by simultaneously utilizing, or fusing, multi-source heterogeneous satellite remote-sensing data streams. Leveraging a spectral approach, the proposed approach integrates multi-source satellite data streams with a fundamental advection-diffusion equation that governs the AOD propagation process. A bias correction process is included in the statistical model to account for the bias of the physics model and the truncation error of the Fourier series. The proposed approach is applied to California wildfires AOD data streams obtained from the National Oceanic and Atmospheric Administration. Comprehensive numerical examples are provided to demonstrate the predictive capabilities and model interpretability of the proposed approach. Computer code has been made available on GitHub.

The Chinese navy, under instruction from President Xi Jinping, has undergone a modernisation and expansion programme that is nothing short of spectacular. Friday's launch of its third and most advanced aircraft carrier, the Fujian, for sea trials underscores just how far it has come, and how fast. The first two carriers, the Liaoning and Shandong, were ex-Soviet designs; the Liaoning initially bought for scrap from Ukraine and refitted. While antiquated, they have been used to train new generations of naval officers and pilots in the complex science and art of aircraft carrier operations. This new design of aircraft carrier is a quantum leap in capabilities from these older models and will greatly enhance China's combat power.

There's nothing more romantic than a hunt for hidden treasure--and when those riches are located in the watery depths of the ocean, it can seem even more exciting. Shipwrecks spark the imagination, prompting dreams of untold riches and swashbuckling adventure. More vessels lie at the bottom of the sea than you might think; the National Oceanic and Atmospheric Administration's database lists over 10,000 known wrecks off of United States shores alone--and that's not a complete list. According to United Nations cultural agency UNESCO, there are at least 3 million such wrecks worldwide, some thousands of years old. And then there's the booty some of those ships carried. Though there's an argument to be made that the treasure aboard now-sunken vessels is priceless, some experts estimate as much as $60 billion in precious metals lies on the ocean floor.

Blake Lemoine poses for a portrait in Golden Gate Park in San Francisco. Blake Lemoine poses for a portrait in Golden Gate Park in San Francisco. Can artificial intelligence come alive? That question is at the center of a debate raging in Silicon Valley after a Google computer scientist claimed over the weekend that the company's AI appears to have consciousness. Inside Google, engineer Blake Lemoine was tasked with a tricky job: Figure out if the company's artificial intelligence showed prejudice in how it interacted with humans.

IT IS ONE of the oldest tropes in science fiction. On June 11th the Washington Post reported that an engineer at Google, Blake Lemoine, had been suspended from his job for arguing that the firm's "LaMDA" artificial-intelligence (AI) model may have become sentient. The newspaper quotes Mr Lemoine as saying: "If I didn't know exactly what it was, which is this computer program we built recently, I'd think it was a seven-year-old, eight-year-old kid that happens to know physics." And if not, might another machine do so one day? Arguing about intelligence is tricky because, despite decades of research, no one really understands how the main example--biological brains built by natural selection--work in detail.

Tech companies are constantly hyping the capabilities of their ever-improving artificial intelligence. But Google was quick to shut down claims that one of its programs had advanced so much that it had become sentient. According to an eye-opening tale in the Washington Post on Saturday, one Google engineer said that after hundreds of interactions with a cutting edge, unreleased AI system called LaMDA, he believed the program had achieved a level of consciousness. In interviews and public statements, many in the AI community pushed back at the engineer's claims, while some pointed out that his tale highlights how the technology can lead people to assign human attributes to it. But the belief that Google's AI could be sentient arguably highlights both our fears and expectations for what this technology can do.