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Honeywell buys Sparta Systems for $1.3 billion, plots life sciences expansion

ZDNet

Infrastructure around the world is being linked together via sensors, machine learning and analytics. We examine the rise of the digital twin, the new leaders in industrial IoT (IIoT) and case studies that highlight the lessons learned from production IIoT deployments. Honeywell will acquire life sciences software company Sparta Systems for $1.3 billion in a move that will expand the reach for the Honeywell Forge platform. Sparta Systems features quality management software delivered as a service with artificial intelligence. Honeywell's plan is to leverage Sparta Systems and combine it with the Forge platform and Experian Process Knowledge System to expand more into life sciences.


How Japanese auto parts makers made masks and beds during coronavirus outbreak

The Japan Times

In March, Japan's largest auto parts maker, Denso Corp., was facing the urgent task of how to secure enough face masks for its workers given the mass shortage that was occurring amid the spread of COVID-19 infections. While the company, located in Kariya, Aichi Prefecture, had sufficient stocks of masks back then, executives were getting worried that if the company ran short, its production might be affected, since each factory worker needs five masks a day. At an executive meeting March 2, all eyes turned to Yasuhiko Yamazaki, 56, senior executive officer in charge of production, when he said, "How about making them ourselves?" After returning home, Yamazaki cut a mask he had with a pair of scissors, looked at its three-layered structure with nonwoven material used as a middle layer, and felt certain it could be made by Denso. The following day, he gathered seven to eight employees who were well-versed in auto parts production technology and were engaged in the designing and manufacturing of machinery and equipment.


Machine Learning Panel Data Regressions with an Application to Nowcasting Price Earnings Ratios

arXiv.org Machine Learning

This paper introduces structured machine learning regressions for prediction and nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the empirical problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and we find that it empirically outperforms the unstructured machine learning methods. We obtain oracle inequalities for the pooled and fixed effects sparse-group LASSO panel data estimators recognizing that financial and economic data exhibit heavier than Gaussian tails. To that end, we leverage on a novel Fuk-Nagaev concentration inequality for panel data consisting of heavy-tailed $\tau$-mixing processes which may be of independent interest in other high-dimensional panel data settings.


Quantum computers will disrupt multiple businesses, says Honeywell India President

#artificialintelligence

Artificial Intelligence (AI) and Machine Learning (ML) based analytics solutions require aggregating and analysing data to train them to mimic real-world …


Honeywell claims it has built the most powerful quantum computer ever

New Scientist

Honeywell, a company best known for making control systems for homes, businesses and planes, claims to have built the most powerful quantum computer ever. Other researchers are sceptical about its power, but for the company it is a step toward integrating quantum computing into its everyday operations. Honeywell measured its computer's capabilities using a metric invented by IBM called quantum volume. It takes into account the number of quantum bits – or qubits – the computer has, their error rate, how long the system can spend calculating before the qubits stop working and a few other key properties. Measuring quantum volume involves running about 220 different algorithms on the computer, says Tony Uttley, the president of Honeywell Quantum Solutions.


Honeywell launches new business unit to capture drone market

Reuters: Technology News

Stéphane Fymat, the head of that new business, said Honeywell expects the hardware and software market for urban air taxis, drone cargo delivery, and other drone businesses to reach $120 billion by 2030 and Honeywell's market opportunity would be about 20% of that. He declined to say how much of that market Honeywell was targeting to capture, adding only that the unit has hundreds of employees with many engineers. Honeywell doesn't build drones itself but provides autonomous flight controls systems and aviation electronics. The new business creation comes as the coronavirus pandemic creates a surge of interest in drone deliveries; Fymat said it's accelerating the drone cargo delivery programs of some of its partners. Some of Honeywell's customers include Intel-backed Volocopter, Slovenia-based small aircraft maker Pipistrel, which is developing an electric vertical take-off and landing aircraft for cargo delivery, and UK-based Vertical Aerospace, which has test flown a prototype vehicle last year that can carry 250 kilograms and fly at 80 kilometers an hour.


How Goodyear is using Data, AI to create tyres of future

#artificialintelligence

Goodyear has developed a'concept tire' which uses AI to learn from driver behaviour. The smart tyre releases a synthetic material to alter the composition of the tire to adapt the way the vehicle is driven to make it more "personalised" to the driver. Goodyear's tech could enable predictions based on data gathered from these smart tires for the use of authorities to maintain roads effectively, leading to less vehicle wear-and-tear and reducing accident rates.


How Goodyear Is Using Data, Artificial Intelligence And Digital Twins To Create The Tyres Of The Future

#artificialintelligence

The way we drive is changing. Globally, trends like urbanization, carpooling, and eventually, autonomous vehicles will mean that the demands we place on our vehicles will change, too. To meet this challenge, the design and function of every vehicular part must be re-imagined to fit these needs, and this includes the tire. How Goodyear Is Using Data, Artificial Intelligence And Digital Twins To Create The Tyres Of The ... [ ] Future Goodyear is a world-leading supplier of tires for cars as well as every type of commercial, industrial and agricultural vehicle. The US manufacturer has built its reputation by leading the development of tire technology since the late 19th century.


A hybrid optimization procedure for solving a tire curing scheduling problem

arXiv.org Artificial Intelligence

This paper addresses a lot-sizing and scheduling problem variant arising from the study of the curing process of a tire factory. The aim is to find the minimum makespan needed for producing enough tires to meet the demand requirements on time, considering the availability and compatibility of different resources involved. To solve this problem, we suggest a hybrid approach that consists in first applying a heuristic to obtain an estimated value of the makespan and then solving a mathematical model to determine the minimum value. We note that the size of the model (number of variables and constraints) depends significantly on the estimated makespan. Extensive numerical experiments over different instances based on real data are presented to evaluate the effectiveness of the hybrid procedure proposed. From the results obtained we can note that the hybrid approach is able to achieve the optimal makespan for many of the instances, even large ones, since the results provided by the heuristic allow to reduce significantly the size of the mathematical model.


ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

#artificialintelligence

This post describes our recent work on unsupervised domain adaptation for semantic segmentation presented at CVPR 2019. ADVENT is a flexible technique for bridging the gap between two different domains through entropy minimization. Our work builds upon a simple observation: models trained only on source domain tend to produce over-confident, i.e., low-entropy, predictions on source-like images and under-confident, i.e., high-entropy, predictions on target-like ones. Consequently by minimizing the entropy on the target domain, we make the feature distributions from the two domains more similar. We show that our approach achieves competitive performances on standard semantic segmentation benchmarks and that it can be successfully extended to other tasks such as object detection.