Artificial intelligence (AI) solves real-world problems. Last year, we saw droves of regional businesses move to the cloud and, once there, realise that scalable, affordable smart technologies were within reach. Proofs of concept quickly followed, as did several success stories. And then, as AI grew in popularity, a concept that had largely been the subject of conversation among tech experts began to go mainstream. Hundreds of billions of dollars in commercial AI revenue is expected to flow to the Middle East by 2030, and contribute heavily to double-digit GDP growth, with the United Arab Emirates (UAE) reaping the most benefits, followed by Saudi Arabia.

Paul Lipman has worked in cybersecurity for 10 years. Quantum computing is based on quantum mechanics, which governs how nature works at the smallest scales. The smallest classical computing element is a bit, which can be either 0 or 1. The quantum equivalent is a qubit, which can also be 0 or 1 or in what's called a superposition -- any combination of 0 and 1. Performing a calculation on two classical bits (which can be 00, 01, 10 and 11) requires four calculations. A quantum computer can perform calculations on all four states simultaneously.

For a long time, Humanoid Robots have been gaining popularity in India. Even though India is still catching up to other countries in terms of artificial intelligence and robotics, Indian companies and the government are working hard to incorporate new-age technology. Humanoid Robots are often built for a specific purpose like healthcare, education, and Humanoid Robots based on applications. According to IFR data, robot sales in India grew by 27% to a record high of 2,627 units, nearly identical to Thailand. According to another poll, India is ranked third in the world for robotic automation implementation.

As a result, the business was able to shut off the crypto miners as soon as they began digging. Machine learning is used to search for network vulnerabilities and automate actions, in addition to detecting early threats. Cybersecurity systems create massive amounts of data, so it's no surprise that this technology is so beneficial. As a result, in the domain of cybersecurity, this is proving to be a big benefit.

The year 2020 was unforgettable in many ways. It was a year of incredible stories of courage, optimism, but there were some sad ones too. While it challenged us in many ways, it also taught us the value of freedom and the importance of being digitally advanced. From startups to conglomerates, COVID-19 wreaked havoc on all types of businesses and industries. The only difference was in the impact.

When you were a kid, were you ever told not to look directly into the flaming eye of the Sun? It can be almost as dangerous for solar telescopes. The Atmospheric Imagery Assembly or AIA has been staring right into those flames for over a decade aboard the Solar Dynamic Observatory (SDO). AIA can see in 3 UV wavelengths and 7 extreme UV (EUV) wavelengths, and anything in the UV range is too short for the human eye. AIA has to suffer for science.

Why does Covid-19 present itself more severe in some patients but not in others? The question has puzzled researchers and clinicians since the start of the pandemic, but now new research from the EPFL Blue Brain Project may have found a major clue to solving the mystery thanks to machine learning. Analyzing data extracted from 240,000 open access scientific papers, the findings of a paper published in Frontiers revealed the previously undiscovered roles elevated blood glucose levels have in the severity of Covid-19. What makes one person more at risk of developing severe Covid-19 than someone else? While it is widely accepted that elderly people are the most at-risk during the current pandemic, many young, seemingly healthy people have also been hospitalized by the disease.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Troubling drone footage emerged online Sunday that reportedly showed up to 1,000 migrants being held by border patrol in Mission, Texas, prompted criticism from Republicans who said the footage underscores the crisis at the border. "An absolute catastrophe from Joe Biden, Kamala Harris, and House & Senate Democrats," Rep. Elise Stefanik, R-N.Y., posted on Twitter. He said the footage showed the "largest group of migrants we've ever seen being held by Border Patrol under Anzalduas Bridge in Mission, TX." "Looks like it could be up to 1,000 people," he posted.

Machine learning (ML) and artificial intelligence (AI) researchers play an important role in the ethics and governance of AI, including through their work, advocacy, and choice of employment. Nevertheless, this influential group's attitudes are not well understood, undermining our ability to discern consensuses or disagreements between AI/ML researchers. To examine these researchers' views, we conducted a survey of those who published in two top AI/ML conferences (N = 524). We compare these results with those from a 2016 survey of AI/ML researchers (Grace et al., 2018) and a 2018 survey of the US public (Zhang & Dafoe, 2020). We find that AI/ML researchers place high levels of trust in international organizations and scientific organizations to shape the development and use of AI in the public interest; moderate trust in most Western tech companies; and low trust in national militaries, Chinese tech companies, and Facebook. While the respondents were overwhelmingly opposed to AI/ML researchers working on lethal autonomous weapons, they are less opposed to researchers working on other military applications of AI, particularly logistics algorithms. A strong majority of respondents think that AI safety research should be prioritized and that ML institutions should conduct pre-publication review to assess potential harms. Being closer to the technology itself, AI/ML researchers are well placed to highlight new risks and develop technical solutions, so this novel attempt to measure their attitudes has broad relevance. The findings should help to improve how researchers, private sector executives, and policymakers think about regulations, governance frameworks, guiding principles, and national and international governance strategies for AI. This article appears in the special track on AI & Society.

The null space of the $k$-th order Laplacian $\mathbf{\mathcal L}_k$, known as the {\em $k$-th homology vector space}, encodes the non-trivial topology of a manifold or a network. Understanding the structure of the homology embedding can thus disclose geometric or topological information from the data. The study of the null space embedding of the graph Laplacian $\mathbf{\mathcal L}_0$ has spurred new research and applications, such as spectral clustering algorithms with theoretical guarantees and estimators of the Stochastic Block Model. In this work, we investigate the geometry of the $k$-th homology embedding and focus on cases reminiscent of spectral clustering. Namely, we analyze the {\em connected sum} of manifolds as a perturbation to the direct sum of their homology embeddings. We propose an algorithm to factorize the homology embedding into subspaces corresponding to a manifold's simplest topological components. The proposed framework is applied to the {\em shortest homologous loop detection} problem, a problem known to be NP-hard in general. Our spectral loop detection algorithm scales better than existing methods and is effective on diverse data such as point clouds and images.