AI pilots are progressing into production based on their combined contributions to improving customer experience, stabilizing and increasing revenues, and reducing costs. The most successful AI use cases contribute to all three areas and deliver measurable results. Of the many use cases where AI is delivering proven value in enterprises today, the ten areas discussed below are notable for the measurable results they are providing. What each of these ten use cases has in common is the accuracy and efficiency they can analyze and recommend actions based on real-time monitoring of customer interactions, production, and service processes. Enterprises who get AI right the first time build the underlying data structures and frameworks to support the advanced analytics, machine learning, and AI techniques that show the best potential to deliver value.

It raises the question of the replacement of humanity by artificial intelligence by taking the measure of the influence of algorithms on our lives and gives the floor to many researchers, sociologists, human rights lawyers, scientists or investigative journalists, supporters or not of artificial intelligence and its omnipotence. Presentation of the documentary: "The creation of an artificial intelligence would be the greatest event in the history of mankind. But it could also be the last," said Stephen Hawking. The famous cosmologist had predicted the infinite growth of computer science but shared with some pioneers the fear that it would become uncontrollable. Today, AI promises to help cure diseases, cope with climate change or fight poverty.

In late 2019, China reported a cluster of atypical pneumonia cases of unknown etiology in Wuhan. The causative agent was identified as a new betacoronavirus, called severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), that causes coronavirus disease 2019 (COVID-19) (1). The virus rapidly spread across the globe and caused a pandemic. Sequencing of the viral genome allowed for the development of nucleic acid–based tests that have since been widely used for the diagnosis of acute (current) SARS-CoV-2 infections (2). Development of serological assays, which measure the antibody responses induced by SARS-CoV-2 infection (past but not current infections), took longer.

I have covered more than 100 protests in my tenure as a general assignment news reporter, before turning my focus to tech. Most were peaceful, but I've been caught in the midst of the melee, too – pelted with rocks, tear-gassed, and attacked by extremists co-opting nonviolent marches to create chaos nonrelated to the cause. What I learned early on, long before the days of smartphones and social media, is that keeping track of what's going on – even along the same city block – can be next to impossible. Just gathering information, law enforcement would often tell me one thing, the protest organizers another, and dozens more leaders, marchers, and watchdog groups would say something totally different. Each new hour was often a tangled mess of conflicting information and I felt like – at the end of the day – the only truth I knew for certain, was what I had seen with my own two eyes.

Analytics Insight predicts that the global AI market is expected to reach US$53.2 billion in 2020 and will further grow on to reach US$152.9 billion in 2023. Beyond that, businesses are bound to become even more innovative with rising AI trends this year. According to a report, AI will help with the repetitive and labor extensive tasks that people carry mostly on their machines. The extensive form filling work, generating reports, and diagrams all can be done more quickly. According to Forbes, approximately 23% of businesses have implemented Artificial intelligence into processing and product services, and more than 60 businesses are still in process. However, this number will increase by 80-90% until 2022.

What do Ted Cruz, Neil deGrasse Tyson and Goldman Sachs all have in common? They predict that the world's first trillionaire will make their innumerable fortune in space. While Cruz is not precisely sure how this will come to be, Tyson and Goldman Sachs believe that the gateway to this immense wealth is through mining asteroids. The reason why space mining is so sought after is due to what is happening here on Earth. Based on known terrestrial reserves and estimates of the growing consumption in countries, essential elements needed for modern industry and food production (such as lead, phosphorus and gold) could be exhausted within the next 60 years.

A research group made up of academics from across the globe have published a paper arguing that "cross-cultural cooperation" on AI ethics and governance is vital if the technology is to "bring about benefit worldwide." The experts -- from Cambridge University's Leverhulme Centre for the Future of Intelligence, Peking University's Center for Philosophy and the Future of Humanity, and the Beijing Academy of Artificial Intelligence -- specifically want to see cooperation across different domains, disciplines, and cultures, as well as different nations. "Such cooperation will enable advances to be shared across different parts of the world, and will ensure that no part of society is neglected or disproportionately negatively impacted by AI," wrote researcher Jess Whittlestone in a blog post this week that summarizes the paper. "Without such cooperation, competitive pressures between countries may also lead to underinvestment in safe, ethical, and socially beneficial AI development, increasing the global risks from AI." AI is poised to change the world in the coming decades as machines become increasingly competent at a range of tasks, from driving cars to discovering new drugs. But some are concerned that AI could end up being a dangerous technology if it is developed in isolated silos across different labs in different countries. In the near term, there's a genuine risk that AI could be used in warfare to power autonomous weapons, and in the long term, some have speculated that "superintelligent" machines could decide humans are no longer necessary and wipe them out altogether.

In this paper, we~present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by a directed acyclic graph. Traditionally, heuristic algorithms have been widely used for many resource scheduling domains, and Heterogeneous Earliest Finish Time (HEFT) has been a dominating state-of-the-art technique across a broad range of heterogeneous resource scheduling domains over many years. Despite their long-standing popularity, HEFT-like algorithms are known to be vulnerable to a small amount of noise added to the environment. Our Deep Reinforcement Learning (DRL)-based SoC Scheduler (DeepSoCS), capable of learning the "best" task ordering under dynamic environment changes, overcomes the brittleness of rule-based schedulers such as HEFT with significantly higher performance across different types of jobs. We~describe a DeepSoCS design process using a real-time heterogeneous SoC scheduling emulator, discuss major challenges, and present two novel neural network design features that lead to outperforming HEFT: (i) hierarchical job- and task-graph embedding; and (ii) efficient use of real-time task information in the state space. Furthermore, we~introduce effective techniques to address two fundamental challenges present in our environment: delayed consequences and joint actions. Through an extensive simulation study, we~show that our DeepSoCS exhibits the significantly higher performance of job execution time than that of HEFT with a higher level of robustness under realistic noise conditions. We~conclude with a discussion of the potential improvements for our DeepSoCS neural scheduler.

Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers. Recent work has reported the state-of-the-art performance of PnP algorithms using pre-trained deep neural nets as denoisers in a number of imaging applications. However, current PnP algorithms are impractical in large-scale settings due to their heavy computational and memory requirements. This work addresses this issue by proposing an incremental variant of the widely used PnP-ADMM algorithm, making it scalable to large-scale datasets. We theoretically analyze the convergence of the algorithm under a set of explicit assumptions, extending recent theoretical results in the area. Additionally, we show the effectiveness of our algorithm with nonsmooth data-fidelity terms and deep neural net priors, its fast convergence compared to existing PnP algorithms, and its scalability in terms of speed and memory.

Transfer learning has been successfully applied across many high-impact applications. However, most existing work focuses on the static transfer learning setting, and very little is devoted to modeling the time evolving target domain, such as the online reviews for movies. To bridge this gap, in this paper, we study a novel continuous transfer learning setting with a time evolving target domain. One major challenge associated with continuous transfer learning is the potential occurrence of negative transfer as the target domain evolves over time. To address this challenge, we propose a novel label-informed C-divergence between the source and target domains in order to measure the shift of data distributions as well as to identify potential negative transfer. We then derive the error bound for the target domain using the empirical estimate of our proposed C-divergence. Furthermore, we propose a generic adversarial Variational Auto-encoder framework named TransLATE by minimizing the classification error and C-divergence of the target domain between consecutive time stamps in a latent feature space. In addition, we define a transfer signature for characterizing the negative transfer based on C-divergence, which indicates that larger C-divergence implies a higher probability of negative transfer in real scenarios. Extensive experiments on synthetic and real data sets demonstrate the effectiveness of our TransLATE framework.