March 13 (Reuters) - Ukraine's defense ministry on Saturday began using Clearview AI's facial recognition technology, the company's chief executive told Reuters, after the U.S. startup offered to uncover Russian assailants, combat misinformation and identify the dead. Ukraine is receiving free access to Clearview AI's powerful search engine for faces, letting authorities potentially vet people of interest at checkpoints, among other uses, added Lee Wolosky, an adviser to Clearview and former diplomat under U.S. presidents Barack Obama and Joe Biden. The plans started forming after Russia invaded Ukraine and Clearview Chief Executive Hoan Ton-That sent a letter to Kyiv offering assistance, according to a copy seen by Reuters. Clearview said it had not offered the technology to Russia, which calls its actions in Ukraine a "special operation." Ukraine's Ministry of Defense did not reply to requests for comment.

Ukraine's defense ministry on Saturday began using Clearview AI's facial recognition technology, the company's chief executive told Reuters, after the U.S. startup offered to uncover Russian assailants, combat misinformation and identify the dead. Ukraine is receiving free access to Clearview AI's powerful search engine for faces, letting authorities potentially vet people of interest at checkpoints, among other uses, added Lee Wolosky, an adviser to Clearview and former diplomat under U.S. Presidents Barack Obama and Joe Biden. The plans started forming after Russia invaded Ukraine and Clearview Chief Executive Hoan Ton-That sent a letter to Kyiv offering assistance, according to a copy seen by Reuters. Clearview said it had not offered the technology to Russia, which calls its actions in Ukraine a "special operation." Ukraine's Ministry of Defense did not reply to requests for comment.

State-of-the-art spectral energy distribution (SED) analyses use a Bayesian framework to infer the physical properties of galaxies from observed photometry or spectra. They require sampling from a high-dimensional space of SED model parameters and take $>10-100$ CPU hours per galaxy, which renders them practically infeasible for analyzing the $billions$ of galaxies that will be observed by upcoming galaxy surveys ($e.g.$ DESI, PFS, Rubin, Webb, and Roman). In this work, we present an alternative scalable approach to rigorous Bayesian inference using Amortized Neural Posterior Estimation (ANPE). ANPE is a simulation-based inference method that employs neural networks to estimate the posterior probability distribution over the full range of observations. Once trained, it requires no additional model evaluations to estimate the posterior. We present, and publicly release, ${\rm SED}{flow}$, an ANPE method to produce posteriors of the recent Hahn et al. (2022) SED model from optical photometry. ${\rm SED}{flow}$ takes ${\sim}1$ $second~per~galaxy$ to obtain the posterior distributions of 12 model parameters, all of which are in excellent agreement with traditional Markov Chain Monte Carlo sampling results. We also apply ${\rm SED}{flow}$ to 33,884 galaxies in the NASA-Sloan Atlas and publicly release their posteriors: see https://changhoonhahn.github.io/SEDflow.

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhibits multi-scale, chaotic or turbulent behavior. In this work we attribute this shortcoming to the inability of existing PINNs formulations to respect the spatio-temporal causal structure that is inherent to the evolution of physical systems. We argue that this is a fundamental limitation and a key source of error that can ultimately steer PINN models to converge towards erroneous solutions. We address this pathology by proposing a simple re-formulation of PINNs loss functions that can explicitly account for physical causality during model training. We demonstrate that this simple modification alone is enough to introduce significant accuracy improvements, as well as a practical quantitative mechanism for assessing the convergence of a PINNs model. We provide state-of-the-art numerical results across a series of benchmarks for which existing PINNs formulations fail, including the chaotic Lorenz system, the Kuramoto-Sivashinsky equation in the chaotic regime, and the Navier-Stokes equations in the turbulent regime. To the best of our knowledge, this is the first time that PINNs have been successful in simulating such systems, introducing new opportunities for their applicability to problems of industrial complexity.

Prediction with expert advice is a foundational problem in online learning. In instances with $T$ rounds and $n$ experts, the classical Multiplicative Weights Update method suffers at most $\sqrt{(T/2)\ln n}$ regret when $T$ is known beforehand. Moreover, this is asymptotically optimal when both $T$ and $n$ grow to infinity. However, when the number of experts $n$ is small/fixed, algorithms with better regret guarantees exist. Cover showed in 1967 a dynamic programming algorithm for the two-experts problem restricted to $\{0,1\}$ costs that suffers at most $\sqrt{T/2\pi} + O(1)$ regret with $O(T^2)$ pre-processing time. In this work, we propose an optimal algorithm for prediction with two experts' advice that works even for costs in $[0,1]$ and with $O(1)$ processing time per turn. Our algorithm builds up on recent work on the experts problem based on techniques and tools from stochastic calculus.

Artificial intelligence (AI) is often used as a buzzword when companies are trying to sell their product. They often have some form of AI, but it really isn't as much of a game-changer as it is hyped up to be. However, three businesses with real AI products making a difference in the industry are Nvidia ( NVDA -2.46%), CrowdStrike ( CRWD -0.25%), and C3.ai ( AI -9.82%). This trio of stocks is highly diversified and gives investors three different avenues to approach an investment in AI. Nvidia provides the hardware powering AI technology, CrowdStrike uses AI in cybersecurity, and C3.ai's tools help enterprises predict the future across a massive organization. When deployed correctly, artificial intelligence can make a huge difference in a product, and each of these businesses achieves that.

Metal-organic frameworks, a class of materials with porous molecular structures, have a variety of possible applications, such as capturing harmful gases and catalyzing chemical reactions. Made of metal atoms linked by organic molecules, they can be configured in hundreds of thousands of different ways. To help researchers sift through all of the possible metal-organic framework (MOF) structures and help identify the ones that would be most practical for a particular application, a team of MIT computational chemists has developed a model that can analyze the features of a MOF structure and predict if it will be stable enough to be useful. The researchers hope that these computational predictions will help cut the development time of new MOFs. "This will allow researchers to test the promise of specific materials before they go through the trouble of synthesizing them," says Heather Kulik, an associate professor of chemical engineering at MIT.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A military drone that apparently flew all the way from the Ukrainian war zone over three European NATO-member states before crashing in an urban zone of the Croatian capital was armed with an explosive device, Croatia's defense minister said Sunday. Police inspect the site of a drone crash in Zagreb, Croatia, Friday, March 11, 2022. A drone that apparently flew from the Ukrainian war zone crashed overnight on the outskirts of the Croatian capital, Zagreb, triggering a loud blast but causing no injuries, Croatian authorities said Friday.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Iran claimed responsibility Sunday for a missile barrage that struck near a sprawling U.S. consulate complex in the northern Iraqi city of Irbil, saying it was retaliation for an Israeli strike in Syria that killed two members of its Revolutionary Guard earlier this week. No injuries were reported in Sunday's attack on the city of Irbil, which marked a significant escalation between the U.S. and Iran. Hostility between the longtime foes has often played out in Iraq, whose government is allied with both countries.

NITI Aayog, the policy think tank of the Government of India, is looking at expanding its "Medicines from the Sky" project, which uses unmanned aerial systems for the delivery of vaccines in remote areas, to the North-Eastern parts of the country. It is also exploring use of emerging technologies including artificial intelligence (AI) in medical diagnostics. NITI Aayog, in collaboration with the Government of Telangana and the World Economic Forum (WEF), launched the'Medicines from the Sky' project on piloting the use of unmanned aerial systems for the delivery of vaccines in remote areas. These drone trials are focused on laying the groundwork for a drone delivery network that will improve access to vital healthcare supplies for remote and vulnerable communities. The scope includes deliveries of MMR (maternal mortality rate), flu and C-19 vaccines.