Eight humanoid robots will be the star attractions when the United Nations hosts its first summit since the start of the pandemic on the benefits of artificial intelligence, it said Wednesday. The AI for Good Global Summit, first held in 2017, will return to Geneva on July 6 and 7 after a three-year Covid-imposed break, the UN's International Telecommunication Union (ITU) said. The event will aim to showcase how artificial intelligence and other new technologies can help reach the UN's so-called sustainable development goals (SDGs), including on fighting climate change and boosting humanitarian action. "It's in our collective interest that we can shape AI faster than it is shaping us," the ITU's new chief Doreen Bogdan-Martin said in a statement. "This summit, as the UN's primary platform for AI, will bring to the table leading voices representing a diversity of interests to ensure that AI can be a powerful catalyst for progress in our race to rescue the SDGs," she added.

The Justice Department has been urged by representatives of a U.S. national-security panel to consider economic-espionage charges against leaders of TuSimple Holdings Inc., an American self-driving-truck company with ties to China, according to people familiar with the matter. The recommendation for criminal charges, made late last year, stemmed from concerns that two founders and the current chief executive of the San Diego-based company were improperly transferring technology to a Chinese startup, the people said. The concerns were based on material gathered as part of a national-security review of TuSimple launched earlier last year.

Spain's regional elections are still nearly four months away, but Irene Larraz and her team at Newtral are already braced for impact. Each morning, half of Larraz's team at the Madrid-based media company sets a schedule of political speeches and debates, preparing to fact-check politicians' statements. The other half, which debunks disinformation, scans the web for viral falsehoods and works to infiltrate groups spreading lies. Once the May elections are out of the way, a national election has to be called before the end of the year, which will likely prompt a rush of online falsehoods. "It's going to be quite hard," Larraz says.

OpenAI CEO Sam Altman believes AI can help usher in "unbelievable abundance," but he says he wants to ensure that such abundance is shared. Toward that end, Altman has embraced a theory of 19th century political economist Henry George, who in his own lifetime worried about wealth amassing in the hands of the few following the Industrial Revolution. George posited that greater equality could be enjoyed if the economic value of land belonged equally to all members of society. Altman similarly believes that in a world where jobs may create less economic value, a land tax could make up for income tax and guarantee that all individuals' assets rise as land -- a fixed asset -- grows in value. He's putting his money where his mouth is, too, leading a seed round in a six-month-old startup that represents a step in that same direction.

We are all aware of the Internet's explosive expansion as a primary source of information and a platform for opinion expression. It has now become essential to gather and analyze the ever-expanding data that follows. While in the past, manual analysis of data has been possible and even served us well, the same cannot be said true for this digital era. Let us say a large chunk of data has to be manually analyzed. Can you do the math involving time and resources associated with it?

Cybersecurity protects internet-connected systems, including hardware, software, and data, from attack, damage, or unauthorized access. The importance of cybersecurity has grown in recent years as more and more of our daily activities and important information are stored and transmitted online. There are many different types of cybersecurity threats, including hacking, malware, phishing, and ransomware. Hacking refers to unauthorized access to a computer system or network. Malware is software specifically designed to harm or exploit a computer or network.

Brian Comiskey, director of thematic programs at the CTA, hosted a panel walking through why innovation at the long term is important, with a focus on cybersecurities, Cloud Ai and robotics. The panel included Robert Blumofe from Akamai, Veronica Lancaster from the CTA and Efram Slen from Nasdaq. "I would characterize them as being in a reality phase," said Blumofe. These are colliding with reality, whereas AI and Cloud is delivering on their promise." According to the panelists, the realities of rising costs are going to transform the future.

A strange formation that resembles a bear's face was captured on the surface of the Red Planet by NASA's Mars Reconnaissance Orbiter last month. Two perfectly placed craters make up the eyes, a hill with a "V-shaped collapse structure" makes up the nose, and a circular fracture pattern forms the head, according to the University of Arizona's Lunar and Planetary Laboratory, which controls the orbiter's camera. "The circular fracture pattern might be due to the settling of a deposit over a buried impact crater," the lab explained. "Maybe the nose is a volcanic or mud vent and the deposit could be lava or mud flows?" The University of Arizona released this photo of a formation on the surface of Mars that resembles a bear's face.

Producing thousands of simulations of the dark matter distribution in the Universe with increasing precision is a challenging but critical task to facilitate the exploitation of current and forthcoming cosmological surveys. Many inexpensive substitutes to full $N$-body simulations have been proposed, even though they often fail to reproduce the statistics of the smaller, non-linear scales. Among these alternatives, a common approximation is represented by the lognormal distribution, which comes with its own limitations as well, while being extremely fast to compute even for high-resolution density fields. In this work, we train a generative deep learning model, mainly made of convolutional layers, to transform projected lognormal dark matter density fields to more realistic dark matter maps, as obtained from full $N$-body simulations. We detail the procedure that we follow to generate highly correlated pairs of lognormal and simulated maps, which we use as our training data, exploiting the information of the Fourier phases. We demonstrate the performance of our model comparing various statistical tests with different field resolutions, redshifts and cosmological parameters, proving its robustness and explaining its current limitations. When evaluated on 100 test maps, the augmented lognormal random fields reproduce the power spectrum up to wavenumbers of $1 \ h \ \rm{Mpc}^{-1}$, and the bispectrum within 10%, and always within the error bars, of the fiducial target simulations. Finally, we describe how we plan to integrate our proposed model with existing tools to yield more accurate spherical random fields for weak lensing analysis.

Neural networks that solve regression problems map input data to output data, whereas neural operators map functions to functions. This recent paradigm shift in perspective, starting with the original paper on the deep operator network or DeepONet [1, 2], provides a new modeling capability that is very useful in engineering - that is, the ability to replace very complex and computational resource-taxing multiphysics systems with neural operators that can provide functional outputs in real-time. Specifically, unlike other physics-informed neural networks (PINNs) [3] that require optimization during training and testing, a DeepONet does not require any optimization during inference, hence it can be used in realtime forecasting, including design, autonomy, control, etc. An architectural diagram of a DeepONet with the commonly used nomenclature for its components is shown in Figure 1. DeepONets can take a multi-fidelity or multi-modal input [4, 5, 6, 7, 8] in the branch network and can use an independent network as the trunk, a network that represents the output space, e.g. in space-time coordinates or in parametric space in a continuous fashion. In some sense, DeepONets can be used as surrogates in a similar fashion as reduced order models (ROMs) [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]. However, unlike ROMs, they are over-parametrized which leads to both generalizability and robustness to noise that is not possible with ROMs, see the recent work of [20].