The government is considering introducing an artificial intelligence-based big data analysis system developed by an American firm in order to enable speedier policy decisions, according to government sources. It has started basic research on the matter, the sources said. The move reflects progress in AI technologies and plans by the administration of Prime Minister Yoshihide Suga to promote digitalization. The big data analysis system in question was developed by Palantir Technologies Inc., which was founded in 2003. "We are holding talks with Palantir in the fields of defense, national security and trade management," an official said.

Faced with the challenge of reinventing themselves into a digital business, organizations are scrambling for AIOps and DevOps expertise to work on multiple platforms. Gartner's latest Hype Cycle for Artificial Intelligence, 2020 found that the faster the democratization of AI occurs, the greater the importance of developers and DevOps to create enterprise-grade applications. The 2020 BMC Mainframe Survey provides new insights into just how quickly enterprises rejuvenate mainframes as part of their broader AIOps and DevOps initiatives. Most noteworthy about the study's results is how enterprises pursuing next-generation business models find value in rejuvenating mainframes as part of their digital business strategies. They are now core to DevOps and AIOps integration company-wide, further solidifying their value.

Faced with the challenge of reinventing themselves into a digital business, organizations are scrambling for AIOps and DevOps expertise to work on multiple platforms. Gartner's latest Hype Cycle for Artificial Intelligence, 2020 found that the faster the democratization of AI occurs, the greater the importance of developers and DevOps to create enterprise-grade applications. The 2020 BMC Mainframe Survey provides new insights into just how quickly enterprises rejuvenate mainframes as part of their broader AIOps and DevOps initiatives. Most noteworthy about the study's results is how enterprises pursuing next-generation business models find value in rejuvenating mainframes as part of their digital business strategies. They are now core to DevOps and AIOps integration company-wide, further solidifying their value.

The recent global outbreak of the coronavirus disease (COVID-19) has spread to all corners of the globe. The international travel ban, panic buying, and the need for self-quarantine are among the many other social challenges brought about in this new era. Twitter platforms have been used in various public health studies to identify public opinion about an event at the local and global scale. To understand the public concerns and responses to the pandemic, a system that can leverage machine learning techniques to filter out irrelevant tweets and identify the important topics of discussion on social media platforms like Twitter is needed. In this study, we constructed a system to identify the relevant tweets related to the COVID-19 pandemic throughout January 1st, 2020 to April 30th, 2020, and explored topic modeling to identify the most discussed topics and themes during this period in our data set. Additionally, we analyzed the temporal changes in the topics with respect to the events that occurred during this pandemic. We found out that eight topics were sufficient to identify the themes in our corpus. These topics depicted a temporal trend. The dominant topics vary over time and align with the events related to the COVID-19 pandemic.

This paper explores the hypothesis that the diversity of human languages, right now a barrier to interoperability in communication and trade, will become significantly less of a barrier as machine translation technologies are deployed over the next several years.But this new boundary-breaking technology does not reduce all boundaries equally, and it creates new challenges for the distribution of ideas and thus for innovation and economic growth.

Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting. Our analysis show that the initial KL-divergence between the Markov Chain and the target posterior distribution is exponentially decreasing while the error contributions to the overall KL-divergence from the additive noise is decreasing in polynomial time. We further show that the polynomial-term experiences speed-up with number of agents and provide sufficient conditions on the time-varying step-sizes to guarantee convergence to the desired distribution. The performance of the proposed algorithm is evaluated on a wide variety of machine learning tasks. The empirical results show that the performance of individual agents with locally available data is on par with the centralized setting with considerable improvement in the convergence rate.

While deep learning has resulted in major breakthroughs in many application domains, the frameworks commonly used in deep learning remain fragile to artificially-crafted and imperceptible changes in the data. In response to this fragility, adversarial training has emerged as a principled approach for enhancing the robustness of deep learning with respect to norm-bounded perturbations. However, there are other sources of fragility for deep learning that are arguably more common and less thoroughly studied. Indeed, natural variation such as lighting or weather conditions can significantly degrade the accuracy of trained neural networks, proving that such natural variation presents a significant challenge for deep learning. In this paper, we propose a paradigm shift from perturbation-based adversarial robustness toward model-based robust deep learning. Our objective is to provide general training algorithms that can be used to train deep neural networks to be robust against natural variation in data. Critical to our paradigm is first obtaining a model of natural variation which can be used to vary data over a range of natural conditions. Such models may be either known a priori or else learned from data. In the latter case, we show that deep generative models can be used to learn models of natural variation that are consistent with realistic conditions. We then exploit such models in three novel model-based robust training algorithms in order to enhance the robustness of deep learning with respect to the given model. Our extensive experiments show that across a variety of naturally-occurring conditions and across various datasets, deep neural networks trained with our model-based algorithms significantly outperform both standard deep learning algorithms as well as norm-bounded robust deep learning algorithms.

The National Science Foundation Convergence Accelerator addresses national-scale societal challenges through use-inspired convergence research. Leveraging a convergence approach the Convergence Accelerator builds upon basic research and discovery to make timely investments to strengthen the Nations innovation ecosystem associated with several key R&D priority areas and practices to include the coronavirus disease 2019, harnessing the data revolution, the future of work, and quantum technology. Artificial Intelligence is a key underlying theme across all of these areas.

The Royal Navy and the US Navy are working on ways to establish links between their digital delivery teams, test methods for international collaboration and develop deeper technical collaboration around artificial intelligence (AI) and machine learning (ML). The work, which started in August 2020, is part of a wider initiative to establish better technology cooperations between the US and the UK. It follows a mandate from senior leaders in digital and AI at both organisations, with the objective to "aggressively explore, develop and demonstrate" how the two countries make applications work together in an interoperable way, and how they can interchangeably use each other's technology. A shared long-term vision is that US-UK development squadrons will be created, to develop AI and ML to support fleet operations centres to tactical-level units, and interoperability with joint service partners. Under that mandate, a collaboration plan was devised by Royal Navy Digital Services and the US Navy to look at specific pieces of technology and the methods the organisations use to research, design and build software.

University of California San Diego School of Medicine and Moores Cancer Center say they have created a new artificial intelligence (AI) system called DrugCell that reportedly matches tumors to the best drug combinations, but does so in way that clearly makes sense. "That's because right now we can't match the right combination of drugs to the right patients in a smart way," said Trey Ideker, PhD, professor at University of California San Diego School of Medicine and Moores Cancer Center. "And especially for cancer, where we can't always predict which drugs will work best given the unique, complex inner workings of a person's tumor cells." Currently, Only four percent of all cancer therapeutic drugs under development earn final approval by the FDA. In a paper "Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells" published in Cancer Cell, Ideker, Brent Kuenzi, PhD, and Jisoo Park, PhD, postdoctoral researchers in his lab, published a paper on their work.