This paper presents an initial design concept and specification of a civilian Unmanned Aerial Vehicle (UAV) management simulation system that focuses on explainability for the human-in-the-loop control of semi-autonomous UAVs. The goal of the system is to facilitate the operator intervention in critical scenarios (e.g. avoid safety issues or financial risks). Explainability is supported via user-friendly abstractions on Belief-Desire-Intention agents. To evaluate the effectiveness of the system, a human-computer interaction study is proposed.

The traditional paradigm for developing machine prognostics usually relies on generalization from data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this way assumes that future field data will have a very similar distribution to the experiment data. However, many complex machines operate under dynamic environmental conditions and are used in many different ways. This makes collecting comprehensive data very challenging, and the assumption that pre-deployment data and post-deployment data follow very similar distributions is unlikely to hold. Transfer Learning (TL) refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain). In this work, we present a TL method for predicting Remaining Useful Life (RUL) of equipment, under the assumption that labels are available only for the source domain and not the target domain. This setting corresponds to generalizing from a limited number of run-to-failure experiments performed prior to deployment into making prognostics with data coming from deployed equipment that is being used under multiple new operating conditions and experiencing previously unseen faults. We employ a deviation detection method, Consensus Self-Organizing Models (COSMO), to create transferable features for building the RUL regression model. These features capture how different target equipment is in comparison to its peers. The efficiency of the proposed TL method is demonstrated using the NASA Turbofan Engine Degradation Simulation Data Set. Models using the COSMO transferable features show better performance than other methods on predicting RUL when the target domain is more complex than the source domain.

There's an old saying that wars are easy to get into but hard to get out of. President Trump understands this, which is why he wisely resisted the temptation to launch a military strike against Iran after that nation launched a missile and drone attack last week against Saudi Arabian oil facilities. When he was running for president, Trump promised the American people he would not jump into endless conflicts in the greater Middle East, where thousands of members of the U.S. military have been killed and wounded in wars in Iraq and Afghanistan. Fighting began in 2001 in Afghanistan and 2003 in Iraq and still continues in both countries. U.S. forces have also fought on a smaller scale in Syria to strike at terrorist targets.

At the SIS Global Forum 2019, Chevron, Microsoft and Schlumberger, announced the industry's first three-party collaboration to accelerate the creation of innovative digital and petro-technical technologies. The three companies will work together to build Azure-native applications in the DELFI cognitive E&P environment initially for Chevron, which will enable companies to process, visualize, interpret and eventually obtain meaningful and vital insights from numerous data sources. The collaboration will be completed in three phases, starting with the deployment of the Petrotechnical Suite in the DELFI environment. This will be followed by the development of cloud-native applications on Azure and the co-innovation of a suite of cognitive computing native compatibilities across the E&P value chain tailored Chevron's objectives. "Combining the expertise of these three global enterprises creates vastly improved and digitally-enabled Petrotechnical workflows. Never before has our industry seen a collaboration of this kind, and of this scale. Working together will accelerate faster innovation with better results, marking the beginning of a new era in our industry that will enable us to elevate performance across our industry's value chain," said Olivier Le Peuch, Chief Executive Officer, Schlumberger.

In this work, we propose a robust approach to design distributed controllers for unknown-but-sparse linear and time-invariant systems. By leveraging modern techniques in distributed controller synthesis and structured linear inverse problems as applied to system identification, we show that near-optimal distributed controllers can be learned with sub-linear sample complexity and computed with near-linear time complexity, both measured with respect to the dimension of the system. In particular, we provide sharp end-to-end guarantees on the stability and the performance of the designed distributed controller and prove that for sparse systems, the number of samples needed to guarantee robust and near optimal performance of the designed controller can be significantly smaller than the dimension of the system. Finally, we show that the proposed optimization problem can be solved to global optimality with near-linear time complexity by iteratively solving a series of small quadratic programs.

Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-state operating points and road and/or track performance data. As the parameter space of control variables, design variable constraints, and objective functions increases, the cost and duration for optimal calibration become prohibitively large. In order to reduce the number of dynamometer tests required for calibrating modern engines, a large-scale simulation-driven machine learning approach is presented in this work. A parallel, fast, robust, physics-based reduced-order engine simulator is used to obtain performance and emission characteristics of engines over a wide range of control parameters under various transient driving conditions (drive cycles). We scale the simulation up to 3,906 nodes of the Theta supercomputer at the Argonne Leadership Computing Facility to generate data required to train a machine learning model. The trained model is then used to predict various engine parameters of interest. Our results show that a deep-neural-network-based surrogate model achieves high accuracy for various engine parameters such as exhaust temperature, exhaust pressure, nitric oxide, and engine torque. Once trained, the deep-neural-network-based surrogate model is fast for inference: it requires about 16 micro sec for predicting the engine performance and emissions for a single design configuration compared with about 0.5 s per configuration with the engine simulator. Moreover, we demonstrate that transfer learning and retraining can be leveraged to incrementally retrain the surrogate model to cope with new configurations that fall outside the training data space.

When Built Robotics emerged out of stealth in October 2017, the company's self-driving excavators had completed a couple simple projects that included digging and moving dirt at a community garden and a California mountain bike trail. Since then, giant autonomous robots have been deployed on large commercial projects, such as digging the foundations for wind farms. The technology has also expanded to include bulldozers and skid steers, in addition to excavators. Today Built announced a $33 million Series B led by Next47, the new global venture fund backed by Siemens. This brings the company's total funding to $48 million.

When Built Robotics emerged out of stealth in October 2017, the company's self-driving excavators had completed a couple simple projects that included digging and moving dirt at a community garden and a California mountain bike trail. Since then, giant autonomous robots have been deployed on large commercial projects, such as digging the foundations for wind farms. The technology has also expanded to include bulldozers and skid steers, in addition to excavators. Today Built announced a $33 million Series B led by Next47, the new global venture fund backed by Siemens. This brings the company's total funding to $48 million.

Tech billionaires Elon Musk and Jack Ma put artificial intelligence in the spotlight when they staged a public debate on the future of the technology earlier this month. Musk reiterated his concern regarding the potentially negative consequences AI could unleash on society, while Ma took a markedly more optimistic tone. Both agreed, however, that the technology will inevitably change the way we live and work, perhaps like no other technology has. Speculation aside, the reality is that AI and machine learning technologies are already transforming the world of business. And the transformation has only just begun.

Consumers and activists are rebelling against Silicon Valley titans, and all levels of government are probing how they operate. Much of the concern is over vast quantities of data that tech companies gather--with and without our consent--to fuel artificial intelligence models that increasingly shape what we see and influence how we act. If "data is the new oil," as boosters of the AI industry like to say, then scandal-challenged data companies like Amazon, Facebook, and Google may face the same mistrust as oil companies like BP and Chevron. Vast computing facilities refine crude data into valuable distillates like targeted advertising and product recommendations. But burning data pollutes as well, with faulty algorithms that make judgments on who can get a loan, who gets hired and fired, even who goes to jail.