Artificial intelligence (AI) continues to attract'buzz' and fascination in the business world, but it's fast becoming an essential tool is making sense and use of the masses of data we accumulate. In simple terms, AI is machines executing tasks based on smart algorithms; computers learning and acting from rich datasets without being explicitly programmed to do so. As consumers, or just people, we encounter this technology frequently in the form of predictive modelling, or machine learning, where models are built for making future decisions based on new data points. That takes form in the products and services we use daily, whether it's Netflix recommending what you want to watch next, Google Maps knowing you'll probably be going home at 6pm, or your Revolut account flagging an anomalous payment. It's the draw of smart products like these that have led to 93% of UK and US organizations considering AI to be a business priority according to a recent Vanson Bourne study commissioned by SnapLogic.

The Departments of Justice and Homeland Security on Friday announced plans to resume hearings for migrants who are seeking asylum in the U.S., as part of the Migrant Protection Protocols program during the COVID-19 pandemic. Why it matters: The MPP program requires migrants to wait in Mexico until their hearings can be completed. But the coronavirus outbreak has put immigration proceedings on hold since March, forcing hundreds of migrants to camp out at the border in the interim.

Think of artificial intelligence, and the mind often goes to industrial robots and benign surveillance systems. Increasingly, though, these are steppingstones for Big Brother to enhance capabilities in domestic security and international military warfare. China has co-opted a controversial big data policing program into law enforcement, both for racial profiling of its Uighur minority population and for broader citizen surveillance through facial recognition. Wuhan has an entirely AI-staffed police station. But experts say China's artificial intelligence research is also being adapted for unconventional military warfare in the country's bid to dominate the field over the next decade.

The United Nations Educational, Scientific and Cultural Organisation (Unesco) has launched a global online consultation on the ethics of artificial intelligence (AI), which will be used by the organisation's international group of AI experts to help draft a framework governing how the technology is applied globally. The multidisciplinary unit of 24 AI specialists, known as the Ad Hoc Expert Group (AHEG), was formed in March 2020, and has been tasked with producing a draft Unesco recommendation that takes into account the wide-ranging impacts of AI, including on the environment, labour markets and culture. The first draft text of its recommendation was published on 15 May 2020, which Unesco is now inviting the public to comment on until 31 July 2020. It outlined 11 principles for the "research, design, development, deployment and use of AI systems", including fairness, responsibility and accountability, human oversight and determination, sustainability, mutli-stakeholder and adaptive governance, and privacy, among others. The text also outlined six values that would provide the foundation for these principles, which are human dignity, human rights and fundamental freedoms, leaving no one behind, living in harmony, trustworthiness, and protection of the environment. "It is crucial that as many people as possible take part in this consultation, so that voices from around the world can be heard during the drafting process for the first global normative instrument on the ethics of AI," said Audrey Azoulay, director-general of Unesco.

We present a new data-driven model to reconstruct nonlinear flow from spatially sparse observations. The model is a version of a conditional variational auto-encoder (CVAE), which allows for probabilistic reconstruction and thus uncertainty quantification of the prediction. We show that in our model, conditioning on the measurements from the complete flow data leads to a CVAE where only the decoder depends on the measurements. For this reason we call the model as Semi-Conditional Variational Autoencoder (SCVAE). The method, reconstructions and associated uncertainty estimates are illustrated on the velocity data from simulations of 2D flow around a cylinder and bottom currents from the Bergen Ocean Model. The reconstruction errors are compared to those of the Gappy Proper Orthogonal Decomposition (GPOD) method.

In this paper, we study communication-efficient decentralized training of large-scale machine learning models over a network. We propose and analyze SQuARM-SGD, a decentralized training algorithm, employing momentum and compressed communication between nodes regulated by a locally computable triggering rule. In SQuARM-SGD, each node performs a fixed number of local SGD (stochastic gradient descent) steps using Nesterov's momentum and then sends sparisified and quantized updates to its neighbors only when there is a significant change in its model parameters since the last time communication occurred. We provide convergence guarantees of our algorithm for strongly-convex and non-convex smooth objectives. We believe that ours is the first theoretical analysis for compressed decentralized SGD with momentum updates. We show that SQuARM-SGD converges at rate $\mathcal{O}\left(\frac{1}{nT}\right)$ for strongly-convex objectives, while for non-convex objectives it converges at rate $\mathcal{O}\left(\frac{1}{\sqrt{nT}}\right)$, thus matching the convergence rate of \emph{vanilla} distributed SGD in both these settings. We corroborate our theoretical understanding with experiments and compare the performance of our algorithm with the state-of-the-art, showing that without sacrificing much on the accuracy, SQuARM-SGD converges at a similar rate while saving significantly in total communicated bits.

In terms of AI productivity, 2019 would be hard to beat. Twenty-nine new and updated political schemes were announced for domesticating artificial intelligence, according to the United Nations. But one announcement this year -- the formation of the Global Partnership on Artificial Intelligence -- obviates or overshadows most of the AI development plans published since 2016, when China pushed the world's first statement out. The global partnership, or GPAI, solidified last month. And it only happened after Western democracies (notably the United States) realized that all the go-it-alone development and control approaches in the world will not stop China from dominating the technology all by itself.

With the emergence of incredibly powerful machine learning technologies, such as Deepfakes and Generative Neural Networks, it has become easier to spread false information. In this article, we will briefly introduce deepfakes and generative neural networks, as well as a few ways to spot AI-generated content and protect yourself against misinformation. I have many relatives that just aren't well-versed with technology. Some of these people believe nearly anything they read, or at least believe it enough to share it on social media. While that doesn't sound so bad, it depends on what you are sharing.

The global Artificial Intelligence (AI) -based Drone Software market size is expected to continue its rapid growth through the next five years, according to several reports. A Research And Markets report said that: "Digital industries are now implementing AI in their devices to improve in their fields across the globe. Application of AI in drone is one such advancement which has brought a revolutionary change in the operations of the industries. AI enables storing and managing the data in bulk which enables the drones to give better performance. The application of AI can enable the drones to function as per the user's command and with longer distance coverage. In addition, AI integrated drone enables the industries to keep a bird-eye view of the land for vigilance & mapping purpose. The increased income levels have brought up new demands that have resulted in increasing supply of goods. Manufacturers are bringing in new features by implementing AI in their devices such as mobiles so ...

As exciting and thrilling as it is to watch all the historic footage from the Apollo moon landings, you have to admit, the quality is sometimes not all that great. Even though NASA has worked on restoring and enhancing some of the most popular Apollo footage, some of it is still grainy or blurry. But now, new developments in artificial intelligence have come to the rescue, providing viewers a nearly brand-new experience in watching historic Apollo video. A photo and film restoration specialist who goes by the name of DutchSteamMachine has worked some AI magic to enhance original Apollo film, creating strikingly clear and vivid video clips and images. "I really wanted to provide an experience on this old footage that has not been seen before," he told Universe Today.