Current modeling approaches for hydrological modeling often rely on either physics-based or data-science methods, including Machine Learning (ML) algorithms. While physics-based models tend to rigid structure resulting in unrealistic parameter values in certain instances, ML algorithms establish the input-output relationship while ignoring the constraints imposed by well-known physical processes. While there is a notion that the physics model enables better process understanding and ML algorithms exhibit better predictive skills, scientific knowledge that does not add to predictive ability may be deceptive. Hence, there is a need for a hybrid modeling approach to couple ML algorithms and physics-based models in a synergistic manner. Here we develop a Physics Informed Machine Learning (PIML) model that combines the process understanding of conceptual hydrological model with predictive abilities of state-of-the-art ML models. We apply the proposed model to predict the monthly time series of the target (streamflow) and intermediate variables (actual evapotranspiration) in the Narmada river basin in India. Our results show the capability of the PIML model to outperform a purely conceptual model ($abcd$ model) and ML algorithms while ensuring the physical consistency in outputs validated through water balance analysis. The systematic approach for combining conceptual model structure with ML algorithms could be used to improve the predictive accuracy of crucial hydrological processes important for flood risk assessment.

This paper presents a novel patch-based adversarial attack pipeline that trains adversarial patches on 3D human meshes. We sample triangular faces on a reference human mesh, and create an adversarial texture atlas over those faces. The adversarial texture is transferred to human meshes in various poses, which are rendered onto a collection of real-world background images. Contrary to the traditional patch-based adversarial attacks, where prior work attempts to fool trained object detectors using appended adversarial patches, this new form of attack is mapped into the 3D object world and back-propagated to the texture atlas through differentiable rendering. As such, the adversarial patch is trained under deformation consistent with real-world materials. In addition, and unlike existing adversarial patches, our new 3D adversarial patch is shown to fool state-of-the-art deep object detectors robustly under varying views, potentially leading to an attacking scheme that is persistently strong in the physical world.

You're probably reading this on a browser built by Apple or Google. If you're on a smartphone, it's almost certain those two companies built the operating system. You probably arrived from a link posted on Apple News, Google News or a social media site like Facebook. And when this page loaded, it, like many others on the Internet, connected to one of Amazon's ubiquitous data centers. Amazon, Apple, Facebook and Google -- known as the Big 4 -- now dominate many facets of our lives. But they didn't get there alone. They acquired hundreds of companies over decades to propel them to become some of the most powerful tech behemoths in the world.

Artificial intelligence companies are developing audio transcription tools that can create searchable archives of calls and meetings, WIRED reported April 15. Artificial intelligence companies have greatly improved their automated audio transcription in recent years, and the technology is now able to produce transcripts with impressive accuracy, according to WIRED. One example is Stedi, a company that makes business-to-business software. It developed a tool called Rewatch that records meetings and uses voice-dictation AI to transcribe it, providing employees with a searchable record of everything said during the meeting. AI companies Otter.ai and Trint also offer voice-dictation to produce meeting transcripts, and Zoom has built-in wares that offer meeting notes.

On April 21, 2021, the European Commission unveiled its long-awaited proposal for a regulation laying down harmonized rules on artificial intelligence and amending certain union legislative acts. The proposal is the result of several years of preparatory work by the commission and its advisers, including the publication of a "White Paper on Artificial Intelligence." The proposal is a key piece in the commission's ambitious European Strategy for data. The regulation applies to (1) providers that place on the market or put into service AI systems, irrespective of whether those providers are established in the European Union or in a third country; (2) users of AI systems in the EU; and (3) providers and users of AI systems that are located in a third country where the output produced by the system is used in the EU. The term "AI system" is broadly defined as "software that is developed with one or more of the techniques and approaches listed in Annex I and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing environments they interact with." The commission takes a risk-based but overall cautious approach to AI and recognizes the potential of AI and the many benefits it presents, but at the same time is keenly aware of the dangers these new technologies present to the European values and fundamental rights and principles.

Artificial intelligence-based algorithms can influence people to prefer one political candidate – or a would-be partner – over another, according to researchers. "We are worried that everyone is using recommendation algorithms all the time, but there was no information on how effective those recommendation algorithms are," says Helena Matute at the University of Deusto in Spain. Her work with her colleague Ujué Agudo, also at the University of Deusto, was designed to investigate the issue. The researchers carried out a series of four experiments in which participants were told they were interacting with an algorithm that would judge their personality. The'algorithm' did not actually do this: it was a mock algorithm that responded in the same way regardless of the information participants gave it.

A bill that aims to essentially ban law enforcement and intelligence agencies from buying data from Clearview AI has drawn bipartisan support from 20 senators. If the Fourth Amendment is Not For Sale Act were to pass as is, agencies wouldn't be able to buy location data from third-party brokers without a warrant. The bill would prevent agencies from purchasing data on people in the US and Americans outside of the country if the information was procured from "a user's account or device, or via deception, hacking, violations of a contract, privacy policy, or terms of service," according to Senator Ron Wyden's office. If the bill becomes law, Clearview AI would no longer be able to sell much of the data it has obtained to US government agencies. To power its facial recognition technology, Clearview AI reportedly scraped billions of images from social media platforms without consent.

The European Union proposed rules that would restrict or ban some uses of artificial intelligence within its borders, including by tech giants based in the US and China. The rules are the most significant international effort to regulate AI to date, covering facial recognition, autonomous driving, and the algorithms that drive online advertising, automated hiring, and credit scoring. The proposed rules could help shape global norms and regulations around a promising but contentious technology. "There's a very important message globally, that certain applications of AI are not permissible in a society founded on democracy, rule of law, fundamental rights," says Leufer says the proposed rules are vague, but represent a significant step towards checking potentially harmful uses of the technology. The debate is likely to be watched closely abroad.

As governments around the world consider how to regulate AI, the European Union is planning first-of-its-kind legislation that would put strict limits on the technology. On Wednesday, the European Commission, the body's executive branch, detailed a regulatory approach that calls for a four-tier system that groups AI software into separate risk categories and applies an appropriate level of regulation to each. At the top would be systems that pose an "unacceptable" risk to people's rights and safety. The EU would outright ban these types of algorithms under the Commission's proposed legislation. An example of software that would fall under this category is any AI that would allow governments and companies to implement social scoring systems.

Many key technology areas that were already growing prior to the pandemic have gotten accelerated, producing an even greater need for solution providers to focus in on areas such as cybersecurity, cloud and AI, a panel of channel chiefs said during the Best of Breed Virtual event Wednesday. Without a doubt, the IT industry has become even more essential amid the impacts of COVID-19, said Ron Dupler, CEO of Kittery, Maine-based solution provider GreenPages, who served as moderator for the panel. "Essentially, we kept the world running to a large degree during this," said Dupler (pictured top left) during the session at the Best of Breed Virtual Spring 2021 event, which was hosted by CRN parent The Channel Company. Now, the opportunity is to meet the increased demand for digital transformation among customers going forward--using expertise in segments such as AI and automation, advanced security and a variety of cloud technologies, panelists said. An emphasis on speed to market and customer experience have gotten "amplified" in the environment shaped by the pandemic, said Ryan Walsh (pictured bottom right), chief product officer and channel chief at cloud distributor Pax8.