Deep learning-based time series models are being extensively utilized in engineering and manufacturing industries for process control and optimization, asset monitoring, diagnostic and predictive maintenance. These models have shown great improvement in the prediction of the remaining useful life (RUL) of industrial equipment but suffer from inherent vulnerability to adversarial attacks. These attacks can be easily exploited and can lead to catastrophic failure of critical industrial equipment. In general, different adversarial perturbations are computed for each instance of the input data. This is, however, difficult for the attacker to achieve in real time due to higher computational requirement and lack of uninterrupted access to the input data. Hence, we present the concept of universal adversarial perturbation, a special imperceptible noise to fool regression based RUL prediction models. Attackers can easily utilize universal adversarial perturbations for real-time attack since continuous access to input data and repetitive computation of adversarial perturbations are not a prerequisite for the same. We evaluate the effect of universal adversarial attacks using NASA turbofan engine dataset. We show that addition of universal adversarial perturbation to any instance of the input data increases error in the output predicted by the model. To the best of our knowledge, we are the first to study the effect of the universal adversarial perturbation on time series regression models. We further demonstrate the effect of varying the strength of perturbations on RUL prediction models and found that model accuracy decreases with the increase in perturbation strength of the universal adversarial attack. We also showcase that universal adversarial perturbation can be transferred across different models.

Building surrogate models is one common approach when we attempt to learn unknown black-box functions. Bayesian optimization provides a framework which allows us to build surrogate models based on sequential samples drawn from the function and find the optimum. Tuning algorithmic parameters to optimize the performance of large, complicated "black-box" application codes is a specific important application, which aims at finding the optima of black-box functions. Within the Bayesian optimization framework, the Gaussian process model produces smooth or continuous sample paths. However, the black-box function in the tuning problem is often non-smooth. This difficult tuning problem is worsened by the fact that we usually have limited sequential samples from the black-box function. Motivated by these issues encountered in tuning, we propose a novel additive Gaussian process model called clustered Gaussian process (cGP), where the additive components are induced by clustering. In the examples we studied, the performance can be improved by as much as 90% among repetitive experiments. By using this surrogate model, we want to capture the non-smoothness of the black-box function. In addition to an algorithm for constructing this model, we also apply the model to several artificial and real applications to evaluate it.

Calithera is running registered clinical trials on its products to study their safety, whether they're effective in patients with specific gene mutations, and how well they work in combination with other therapies. The company must collect detailed data on hundreds of patients. While some of its trials are in early stages and involve only a small number of patients, others span more than 100 research centers across the globe. "In the life-sciences world, one of the biggest challenges we have is the enormous amount of data we generate, more than any other business," says Behrooz Najafi, Calithera's lead information technology strategist. Calithera must store and manage the data while making sure it's readily available when needed, even years from now.

Lee explains his research on marsupial robots, or carrier-passenger pairs of heterogeneous robot systems. They discuss the possible applications of marsupial robots including the DARPA Subterranean Competition, and some of the technical challenges including optimal deployment formulated as a stochastic assignment problem. Chris Lee is pursuing a Master of Science in Robotics at Oregon State University, having received a Bachelor of Science in Mechanical Engineering from the University of Buffalo. His research is in robotic exploration, frontier extraction, and stochastic assignment.

Artificial intelligence (AI) is sometimes referred to as a herald of the fourth industrial revolution. That revolution is already here. Whenever you say "Hey Siri" or glance at your phone in order to unlock it, you're using AI. Its current and potential applications are numerous, including medical diagnosis and predictive technologies that enhance user interactions. As chairwoman of the U.S. House Committee on Science, Space, and Technology, I am particularly interested in the potential for AI to accelerate innovation and discovery across the science and engineering disciplines.

Researchers mimic the animal kingdom's most basic signs of intelligence in quantum material WEST LAFAYETTE, Ind. -- For artificial intelligence to get any smarter, it needs first to be as intelligent as one of the simplest creatures in the animal kingdom: the sea slug. A new study has found that a material can mimic the sea slug's most essential intelligence features. The discovery is a step toward building hardware that could help make AI more efficient and reliable for technology ranging from self-driving cars and surgical robots to social media algorithms. The study, publishing this week in the Proceedings of the National Academy of Sciences, was conducted by a team of researchers from Purdue University, Rutgers University, the University of Georgia and Argonne National Laboratory. "Through studying sea slugs, neuroscientists discovered the hallmarks of intelligence that are fundamental to any organism's survival," said Shriram Ramanathan, a Purdue professor of materials engineering.

Artificial intelligence (AI) will smoothen the government operations and also accelerate the pace of developments of all the governments as AI's role increases and becomes more mainstream, say public and private executives. "It's going to be interesting how artificial intelligence will react on smoothing the governments challenges through adopting the behaviours and with creative model. The ultimate goal of AI is to increase the value of the work, cut the cost, and save time. Therefore, it will accelerate the pace of the developments in all governments," said Dr. Ebrahim Al Alkeem Al Zaabi, director of cybersecurity and artificial intelligence at Government of Abu Dhabi. A study by Oliver Wyman, a global management consulting firm, has estimated that the efficiencies generated by AI technology can support Middle Eastern government budgets by up to $7 billion (Dh25.7 billion) annually.

Trumpeting a message that he conceded might be different from peers, Stefan Golling, a member of Munich Re Board of Management, said that Munich Re remains bullish on the cyber insurance and reinsurance markets. In fact, "if insurers and reinsurers shy away from the cyber market, they will not survive," said Golling, Munich Re board member for Global Clients/North America, during a presentation the European reinsurer's virtual Rendez-Vous presentation. He noted media reports of a hardening cyber insurance market, reduced available capacity and narrower carrier and reinsurer appetites for cyber risk. Those shouldn't scare insurers and reinsurers away, Golling said. "If we want to remain relevant in this industry, relevant for our clients, then we need to find solutions for cyber. And we will," he said.

Taking a Zero Trust approach to managing every machine identity authentication on a network now ... [ ] could save thousands of hours and dollars in the future. Bottom Line: Bad actors quickly capitalize on the wide gaps in machine identity security, creating one of the most breachable threat surfaces today. Forrester's recent webinar on the topic, How To Secure And Govern Non-Human Identities, estimates that machine identities (including bots, robots and IoT) are growing twice as fast as human identities on organizational networks. Forrester defines machine, or non-human, identities as robotic process automation (bots), robots (industrial, enterprise, medical, military) and IoT devices. The webinar points out that one of the fastest-growing automation types is software bots, with 36% used in finance and accounting, 15% used in business line and 15% in IT.

Robotics have acquired the centerstage of Industrial activity and are making a way into day-to-day life. They are performing various impossible tasks in deep space, oceans, defence applications, working in radioactive environment, extreme temperature furnace, and many other areas, where human presence is challenging. In healthcare, robotics has applications in performing critical surgeries, in financial world, major financial institutions across the world are resorting to robotics-based process automation. Humanity has been long fascinated by the concept of robots and artificial intelligence. It has been a collective pipedream to automate mundane tasks which one would prefer to not have to do themselves.