A water world ruled by octopus-like creatures. A planet divided by light and dark where the sun never rises. These are not descriptions of foreign worlds in science fiction novels, these are some of the'exoplanets' most likely to be harboring aliens right now. Dozens of these exoplanets - planets that orbit stars outside our solar system - which have been classified as'potentially habitable' or'Earth-like' have been documented in tantalizing detail in a new book. Humanity is in a'new golden era of exploration,' according to Dr. Lisa Kaltenegger, whose new book explores what science now knows about how distant worlds in our galaxy could support life.

'The Big Weekend Show' analyzes the possibilities of artificial intelligence when it comes to influencing voters. NEW YORK, NEW YORK - MAY 02: Katy Perry attends The 2022 Met Gala Celebrating "In America: An Anthology of Fashion" at The Metropolitan Museum of Art on May 02, 2022 in New York City. IT'S SUPERNATURAL: A picture of Perry at the bottom of the Met steps circulated online, leading fans to believe the "Wide Awake" singer was attending the event. In the picture, Perry is wearing an off-white ball gown adorned with roses and moss. GROWING WITH AI: Over 3,000 micro business owners were surveyed by Venture Forward, GoDaddy's international research initiative, in February 2024 about leveraging generative artificial intelligence to compete with large brands and level the playing field across a multitude of industries.

The United States has taken further action to limit China's technological advancement, revoking licenses that allowed Intel and Qualcomm to buy and sell chips to Huawei Technologies, the Financial Times reports. The decision will impact chips Huawei uses for computers and mobile phones and is effective immediately. Huawei has been on US trade restrictions lists since 2019 but has recently made progress that worries the US government, such as last month's AI-enabled laptop. "We continuously assess how our controls can best protect our national security and foreign policy interests, taking into consideration a constantly changing threat environment and technological landscape. As part of this process, as we have done in the past, we sometimes revoke export licenses," a spokesperson for the Department of Commerce stated.

There are plenty of people like Sun who want to use AI to preserve, animate, and interact with lost loved ones as they mourn and try to heal. The market is particularly strong in China, where at least half a dozen companies are now offering such technologies and thousands of people have already paid for them. But some question whether interacting with AI replicas of the dead is truly a healthy way to process grief, and it's not entirely clear what the legal and ethical implications of this technology may be. Still, if only 1% of Chinese people can accept AI cloning of the dead, that's still a huge market. To read more about China's flourishing market for deepfakes that clone the dead, check out the latest edition of China Report, our weekly newsletter covering tech in China.

President Biden will travel to Wisconsin on Wednesday to announce the creation of an artificial intelligence data center, highlighting one of his administration's biggest economic accomplishments in a crucial battleground state -- and pointing up a significant failure by his immediate predecessor and 2024 challenger. At a technical college in Racine, Mr. Biden will announce that Microsoft will invest 3.3 billion to build the center, which the tech giant estimates will create 2,300 union construction jobs and 2,000 permanent jobs, according to the White House. The project is part of Mr. Biden's "Investing in America" agenda, which has focused on bringing billions of private-sector dollars into manufacturing and industries such as clean energy and artificial intelligence. In his fourth trip to Wisconsin this year, Mr. Biden will continue an aggressive campaign to paint a contrast between him and former President Donald J. Trump, the presumptive Republican nominee, who is in the fourth week of his criminal trial in connection with payments to a pornographic film star. While in Wisconsin, Mr. Biden will also attend a campaign event, where he will speak to Black voters about the stakes in the election.

Saudi Arabia would divest from China if the U.S. asked it to do so, the head of country's new investment fund for semiconductor and artificial intelligence technology said. "So far, the requests have been to keep manufacturing and supply chains completely separate, but if the partnerships with China would become a problem for the U.S., we will divest," said Amit Midha, the chief executive officer of Alat, an investment firm backed by 100 billion in capital from the Public Investment Fund. U.S. officials have told their Saudi Arabian counterparts that they need to choose between Chinese and American technology as they aim to build out the Saudi Arabian semiconductor industry, Bloomberg has reported, as part of ongoing talks on a range of national security issues.

Numerous applications in biology, statistics, science, and engineering require generating samples from high-dimensional probability distributions. In recent years, the Hamiltonian Monte Carlo (HMC) method has emerged as a state-of-the-art Markov chain Monte Carlo technique, exploiting the shape of such high-dimensional target distributions to efficiently generate samples. Despite its impressive empirical success and increasing popularity, its wide-scale adoption remains limited due to the high computational cost of gradient calculation. Moreover, applying this method is impossible when the gradient of the posterior cannot be computed (for example, with black-box simulators). To overcome these challenges, we propose a novel two-stage Hamiltonian Monte Carlo algorithm with a surrogate model. In this multi-fidelity algorithm, the acceptance probability is computed in the first stage via a standard HMC proposal using an inexpensive differentiable surrogate model, and if the proposal is accepted, the posterior is evaluated in the second stage using the high-fidelity (HF) numerical solver. Splitting the standard HMC algorithm into these two stages allows for approximating the gradient of the posterior efficiently, while producing accurate posterior samples by using HF numerical solvers in the second stage. We demonstrate the effectiveness of this algorithm for a range of problems, including linear and nonlinear Bayesian inverse problems with in-silico data and experimental data. The proposed algorithm is shown to seamlessly integrate with various low-fidelity and HF models, priors, and datasets. Remarkably, our proposed method outperforms the traditional HMC algorithm in both computational and statistical efficiency by several orders of magnitude, all while retaining or improving the accuracy in computed posterior statistics.

Leveraging neural control variates for enhanced precision in lattice field theory Paulo F. Bedaque 1, and Hyunwoo Oh 1, 1 Department of Physics and Maryland Center for Fundamental Physics, University of Maryland, College Park, MD 20742 USA (Dated: May 10, 2024) Results obtained with stochastic methods have an inherent uncertainty due to the finite number of samples that can be achieved in practice. In lattice QCD this problem is particularly salient in some observables like, for instance, observables involving one or more baryons and it is the main problem preventing the calculation of nuclear forces from first principles. The method of control variables has been used extensively in statistics and it amounts to computing the expectation value of the difference between the observable of interest and another observable whose average is known to be zero but is correlated with the observable of interest. Recently, control variates methods emerged as a promising solution in the context of lattice field theories. In our current study, instead of relying on an educated guess to determine the control variate, we utilize a neural network to parametrize this function. Using 1+1 dimensional scalar field theory as a testbed, we demonstrate that this neural network approach yields substantial improvements.

Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam email. By applying descriptive analytic, the specific differences between AI-generated emails and manually crafted scam emails are profiled, and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that is made open to the public, and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing email is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI.

A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and representative patient datasets with appropriate annotations may be burdensome due to high acquisition cost, safety limitations, patient privacy restrictions or low disease prevalence rates. In silico data offers a number of potential advantages to patient data, such as diminished patient harm, reduced cost, simplified data acquisition, scalability, improved quality assurance testing, and a mitigation approach to data imbalances. We summarize key research trends and practical uses for synthetically generated data for radiological applications of AI. Specifically, we discuss different types of techniques for generating synthetic examples, their main application areas, and related quality control assessment issues. We also discuss current approaches for evaluating synthetic imaging data. Overall, synthetic data holds great promise in addressing current data availability gaps, but additional work is needed before its full potential is realized.