BANGKOK- As the coronavirus pandemic leads to anxiety over the strength of the world's food supply chains, everyone from governments to banks are turning to the skies for help. Orbital Insight, a California-based big data company that uses satellites, drones, balloons and mobile phone geolocation data to track what's happening on Earth, has seen inquiries about monitoring food supplies double in the past two months, according to James Crawford, founder and chief executive officer of the company. "We're helping supply chain managers, financial institutions, and government agencies answer questions they never thought they would have to ask," Crawford said in a phone interview. The coronavirus outbreak has triggered a fresh surge in demand for alternative data to shed light on how the pandemic is impacting industries and trade across the globe. That is especially important as multiple government lockdowns and tighter restrictions on the movement of people and goods upend supply chains and logistics everywhere from Asia to Europe and the Americas. Orbital customers have been asking for data such as when cargo ships leave ports, when plants close, and the number of passengers travelling through airports.

Apple Inc. unveiled the new iPhone SE, its first low-cost smartphone in four years, seeking to boost sales while consumers wait for the launch of new high-end models with 5G later this year. To get to the lower cost, Apple is using an iPhone 8 design that debuted in 2017 along with a less advanced camera system, a smaller and older display and a Touch ID fingerprint scanner instead of 3-D facial recognition. The new model comes in black, white or red with storage options ranging from 64GB to 256GB, Cupertino, California-based Apple said on Wednesday. While many of the device's specifications have been surpassed by newer technology at this point, the iPhone SE does use the same A13 processor as the latest flagship iPhone. This also gives Apple a more competitive model in countries such as India that are flooded with cheaper Android phones.

U.S. President Donald Trump's broadside against the World Health Organization is another blow to international institutions designed to help nations confront global crises -- and may leave countries even less prepared for the next one. Trump's move on Tuesday to suspend WHO funding amid a pandemic that has cost at least 130,000 lives is the latest salvo in a broader struggle between the U.S. and China over global leadership. Both countries are courting other nations and public opinion as they cover up their own shortcomings in the pandemic and position themselves for the post-virus world. China -- widely criticized for missteps early in the outbreak -- has ramped up efforts to send medical supplies to hard-hit nations, even as reports emerged that much of that gear was faulty or expired. The U.S., meanwhile, announced $300 million in aid to countries fighting the virus but rebuffed requests for the most essential gear while receiving donations from the governments of Egypt, Taiwan and Vietnam among others.

Two British firms have partnered to accelerate the adoption of binarized neural networks (BNNs), a technology that will drastically reduce memory footprint for AI models in endpoint applications such as voice control and person detection. The adoption of BNNs, which reduce parameters to 1-bit numbers, requires both new neural network models and special hardware that can support the 1-bit operations. Xcore.ai is one of the first non-ASIC parts with native support for the 1-bit vector arithmetic required for BNN inference. "We're making deep learning tiny and computationally radically more efficient," Roeland Nusselder, CEO of Plumerai told EETimes. "For this, we have been developing software for the most efficient form of deep learning, which is binarized neural networks."

Preferred Networks, as a research-oriented AI startup, participates every year in NeurIPS, the world's biggest machine learning conference. This post highlights our accomplishments and activities at NeurIPS 2019. We are very excited to be a part of it & looking forward to seeing top ML researchers from all over the world there! This year, four papers from Preferred Networks have been accepted for poster presentation. Three of them are based on ex-intern's work and we are very proud of their dedication and high-quality research.

Rabat – Moroccan-born professor of computer science at New York University (NYU) Dr. Anasse Bari has designed an artificial intelligence (AI) tool to analyze and curb the evolution of the COVID-19 pandemic. Managing a team of researchers at NYU, Bari helped create and study the efficacy of an AI instrument to predict patients vulnerable to coronavirus and determine the seriousness of COVID-19 infections. "Our goal was to design and deploy a decision-support tool using AI capabilities--mostly predictive analytics--to flag future clinical coronavirus severity," Bari said. "We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds and who can safely go home, with hospital resources stretched thin," the computer scientist added, in light of the fact that hospital resources are limited as the COVID-19 outbreak continues. The Moroccan professor holds a bachelor's degree in Computer Engineering from Al Akhawayn University in Ifrane (AUI), and is establishing negotiations between NYU and AUI to use the newly developed technology in tackling the spread of COVID-19 in Morocco.

Social distancing is a new fact of life for most, but not long-ago technical conferences brought experts together. In early February, Underwater Intervention provided a series of presentations on developments in untethered vehicles. A powerful trend was the increasing level of "autonomy" found in the sector. While not all the presentations addressed this theme, several helped clarify the many ways this "buzzword" is advancing the capabilities of untethered marine vehicles. Kongsberg Maritime offered two presentations: "How Autonomous is Your AUV" presented by Richard Mills and "Autonomous Technology for Ocean Exploration," by Arnt Olsen.

As the number of people infected with the new coronavirus continues to climb in Qatar and neighboring countries, the Biomedical Research Center of Qatar University is accelerating work to support efforts to better understand the virus. The center recently started several research projects related to the new virus, whose official name is SARS-CoV-2, for severe acute respiratory syndrome coronavirus 2. The center's projects include a detailed analysis of cases of infection with the virus inside and outside Qatar, a study of the body's immune responses to the virus, and a study evaluating the response of the virus to some antiviral drugs and natural compounds. "We use computer modeling and artificial intelligence for genetic sequencing of the virus and to look for inhibitors that can prevent the infection of the cell," said Hadi Yassine, an associate professor of epidemiology and infectious diseases at Qatar University who is research projects manager at the Biomedical Research Center. "This type of research enables us to predict the response of the virus to some drugs." As of April 14, more than 3,400 cases of Covid-19, the disease caused by the new coronavirus, had been reported in Qatar, with seven deaths.

Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.

This paper proposes a robust control design method using reinforcement-learning for controlling partially-unknown dynamical systems under uncertain conditions. The method extends the optimal reinforcement-learning algorithm with a new learning technique that is based on the robust control theory. By learning from the data, the algorithm proposed actions that guarantees the stability of the closed loop system within the uncertainties estimated from the data. Control policies are calculated by solving a set of linear matrix inequalities. The controller was evaluated using simulations on a blood glucose model for patients with type-1 diabetes. Simulation results show that the proposed methodology is capable of safely regulates the blood glucose within a healthy level under the influence of measurement and process noises. The controller has also significantly reduced the post-meal fluctuation of the blood glucose. A comparison between the proposed algorithm and the existing optimal reinforcement learning algorithm shows the improved robustness of the closed loop system using our method.