Mining corporation Rio Tinto says that an autonomous rail system called AutoHaul that it's been developing in the remote Pilbara region of Australia for several years is now entirely operational -- an accomplishment the company says makes the system the "world's largest robot." "It's been a challenging journey to automate a rail network of this size and scale in a remote location like the Pilbara," Rio Tinto's managing director Ivan Vella told the Sidney Morning Herald, "but early results indicate significant potential to improve productivity, providing increased system flexibility and reducing bottlenecks." The ore-hauling train is just one part of an ambitious automation project involving robotics and driverless vehicles that Rio Tinto wants to use to automate its mining operations. The company conducted its first test of the train without a human on board earlier this year, and it now claims that the system has completed more than a million kilometers (620,000 miles) of autonomous travel. In response to concerns from labor unions, Rio Tinto promised that the autonomous rail system will not eliminate any existing jobs in the coming year -- though it's difficult to imagine the project won't cut into human jobs in the long term.

IBM has teamed up with a Montreal-based Institute for Data Valorization (IVADO) to jointly develop artificial intelligence (AI) products, with plans to add another 100 technology professionals to its innovation center. The innovation center, launched in 2016 for providing digital transformation services, will henceforth focus on AI and Salesforce expertise, the American technology giant stated in a press release. "Research in AI is quickly expanding worldwide – and particularly in Montréal – but AI is far from having reached its full potential in delivering concrete results for businesses," said Claude Guay, General Manager, IBM Services, Canada. "IBM's CIC in Montréal will focus on delivering value to its clients through applied AI and bringing real solutions to real problems." The new recruits the company is looking for will work as machine learning engineers, data scientists, full stack developers, or data engineers.

Deep learning provides a powerful tool for machine perception when the observations resemble the training data. However, real-world robotic systems must react intelligently to their observations even in unexpected circumstances. This requires a system to reason about its own uncertainty given unfamiliar, out-of-distribution observations. Approximate Bayesian approaches are commonly used to estimate uncertainty for neural network predictions, but can struggle with out-of-distribution observations. Generative models can in principle detect out-of-distribution observations as those with a low estimated density. However, the mere presence of an out-of-distribution input does not by itself indicate an unsafe situation. In this paper, we present a method for uncertainty-aware robotic perception that combines generative modeling and model uncertainty to cope with uncertainty stemming from out-of-distribution states. Our method estimates an uncertainty measure about the model's prediction, taking into account an explicit (generative) model of the observation distribution to handle out-of-distribution inputs. This is accomplished by probabilistically projecting observations onto the training distribution, such that out-of-distribution inputs map to uncertain in-distribution observations, which in turn produce uncertain task-related predictions, but only if task-relevant parts of the image change. We evaluate our method on an action-conditioned collision prediction task with both simulated and real data, and demonstrate that our method of projecting out-of-distribution observations improves the performance of four standard Bayesian and non-Bayesian neural network approaches, offering more favorable trade-offs between the proportion of time a robot can remain autonomous and the proportion of impending crashes successfully avoided.

Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy, including estimation of relevant parameters can be time consuming and costly. In this paper we address this issue by proposing generic adaptation strategies based on approaches from earlier works. Experimental results after using the proposed strategies with three adaptive algorithms on 36 datasets confirm their viability. These strategies often achieve better or comparable performance with custom adaptation strategies and naive methods such as repeatedly using only one adaptive mechanism.

A year later, Barrick has parted ways with its chief innovation officer, chief digital officer and many of the team tasked with making this transformation a reality, according to people familiar with the matter. The revolution in machine learning, as predicted by Barrick Chairman John Thornton and other mining executives, has yet to come. Miners have said digital technologies like artificial intelligence, or AI, will revolutionize one of the world's oldest industries in the same way it has changed other businesses, from retail to hailing a cab. Some experts say the promise of AI in mining has been overhyped and progress has been slow. Companies, including Barrick and giants such as Rio Tinto PLC and BHP Group Ltd., are running some AI-led projects. But implementation at some companies has hit cultural hurdles.

The high-dimensionality and volume of large scale multistream data has inhibited significant research progress in developing an integrated monitoring and diagnostics (M&D) approach. This data, also categorized as big data, is becoming common in manufacturing plants. In this paper, we propose an integrated M\&D approach for large scale streaming data. We developed a novel monitoring method named Adaptive Principal Component monitoring (APC) which adaptively chooses PCs that are most likely to vary due to the change for early detection. Importantly, we integrate a novel diagnostic approach, Principal Component Signal Recovery (PCSR), to enable a streamlined SPC. This diagnostics approach draws inspiration from Compressed Sensing and uses Adaptive Lasso for identifying the sparse change in the process. We theoretically motivate our approaches and do a performance evaluation of our integrated M&D method through simulations and case studies.

We consider the torpedo scheduling problem in steel production, which is concerned with the transport of hot metal from a blast furnace to an oxygen converter. A schedule must satisfy, amongst other considerations, resource capacity constraints along the path and the locations traversed as well as the sulfur level of the hot metal. The goal is first to minimize the number of torpedo cars used during the planning horizon and second to minimize the time spent desulfurizing the hot metal. We propose an exact solution method based on Logic based Benders Decomposition using Mixed-Integer and Constraint Programming, which optimally solves and proves, for the first time, the optimality of all instances from the ACP Challenge 2016 within 10 minutes. In addition, we adapted our method to handle large-scale instances and instances with a more general rail network. This adaptation optimally solved all challenge instances within one minute and was able to solve instances of up to 100,000 hot metal pickups.

You are free to share this article under the Attribution 4.0 International license. A new system combines artificial neural networks with infrared thermal imaging to control and interpret chemical reactions with precision and speed that far outpace conventional methods. Machine learning algorithms can predict stock market fluctuations, control complex manufacturing processes, enable navigation for robots and driverless vehicles, and much more. Now, researchers are tapping a new set of capabilities in this field of artificial intelligence with their new technique. "This system can reduce the decision-making process about certain chemical manufacturing processes from one year to a matter of weeks…" The researchers developed and tested the new method on microreactors that allow chemical discoveries to take place quickly and with far less environmental waste than standard large-scale reactions.

Sensor drift is a well-known issue in the field of sensors and measurement and has plagued the sensor community for many years. In this paper, we propose a sensor drift correction method to deal with the sensor drift problem. Specifically, we propose a discriminative subspace projection approach for sensor drift reduction in electronic noses. The proposed method inherits the merits of the subspace projection method called domain regularized component analysis. Moreover, the proposed method takes the source data label information into consideration, which minimizes the within-class variance of the projected source samples and at the same time maximizes the between-class variance. The label information is exploited to avoid overlapping of samples with different labels in the subspace. Experiments on two sensor drift datasets have shown the effectiveness of the proposed approach. Keywords: Sensor drift; Electronic nose; Subspace projection method; Domain adaptation; Transfer learning.

The vastness of the archival chemistry literature is both a blessing and a curse. The reaction that you're looking for is probably in there, provided you take enough time to search for it. Gao et al. trained a neural network model on 10 million known reactions to speed up this process. Specifically, the model was charged with predicting a catalyst, reagents, solvents, and temperature to achieve a given transformation. When tested, the model's top-10 list of suggestions produced a close match to actual conditions nearly 70% of the time, with a 20 C error margin in temperature.