Digital twin technology has significant promise, relevance and potential of widespread applicability in various industrial sectors such as aerospace, infrastructure and automotive. However, the adoption of this technology has been slower due to the lack of clarity for specific applications. A discrete damped dynamic system is used in this paper to explore the concept of a digital twin. As digital twins are also expected to exploit data and computational methods, there is a compelling case for the use of surrogate models in this context. Motivated by this synergy, we have explored the possibility of using surrogate models within the digital twin technology. In particular, the use of Gaussian process (GP) emulator within the digital twin technology is explored. GP has the inherent capability of addressing noise and sparse data and hence, makes a compelling case to be used within the digital twin framework. Cases involving stiffness variation and mass variation are considered, individually and jointly along with different levels of noise and sparsity in data. Our numerical simulation results clearly demonstrate that surrogate models such as GP emulators have the potential to be an effective tool for the development of digital twins. Aspects related to data quality and sampling rate are analysed. Key concepts introduced in this paper are summarised and ideas for urgent future research needs are proposed.

The boolean satisfiability problem is a famous NP-complete problem in computer science. An effective way for this problem is the stochastic local search (SLS). However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers. To address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a solution prediction model, which boosts SLS by changing initialization assignments with a neural network. We evaluated NLocalSAT on five SLS solvers (CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with problems in the random track of SAT Competition 2018. The experimental results show that solvers with NLocalSAT achieve 27%~62% improvement over the original SLS solvers.

Since the first Graphical User Interface (GUI) prototype was invented in the 1970s, GUI systems have been deployed into various personal computer systems and server platforms. Recently, with the development of artificial intelligence (AI) technology, malicious malware powered by AI is emerging as a potential threat to GUI systems. This type of AI-based cybersecurity attack, targeting at GUI systems, is explored in this paper. It is twofold: (1) A malware is designed to attack the existing GUI system by using AI-based object recognition techniques. (2) Its defensive methods are discovered by generating adversarial examples and other methods to alleviate the threats from the intelligent GUI attack. The results have shown that a generic GUI attack can be implemented and performed in a simple way based on current AI techniques and its countermeasures are temporary but effective to mitigate the threats of GUI attack so far.

The idea behind GPT-2 is similar to the predictive text function on a smartphone.

Great video to show how machine learning can help automate supply chain. "Technology, through automation and artificial intelligence, is definitely one of the most disruptive sources." "The higher the minimum wage goes, the lower the threshold will go for robots to replace humans in many minimum-wage roles." "Machine learning and deep learning will create a new set of hot jobs in the next 5 years."

His research career took him to AT&T Bell Laboratories, AT&T Labs Research, NEC Labs America and Microsoft. He joined Facebook AI Research in 2015. The long term goal of Léon's research is to understand how to build human-level intelligence. Although reaching this goal requires conceptual advances that cannot be anticipated at this point, it certainly entails clarifying how to learn and how to reason. Leon Bottou best known contributions are his work on neural networks in the 90s, his work on large scale learning in the 00's, and possibly his more recent work on causal inference in learning systems.

Every week we bring to you best AI research papers, articles and videos that we have found interesting, cool or simply weird that week. Rubik's Code is a boutique data science and software service company with more than 10 years of experience in Machine Learning, Artificial Intelligence & Software development. Check out the services we provide. Eager to learn how to build Deep Learning systems using Tensorflow 2 and Python? Get our'Deep Learning for Programmers' ebook here!

Director of Data Science, CLARA analytics -- Ji Li has leadership responsibility for organizing and directing the CLARA data science team in building optimized machine learning solutions, creating artificial intelligence applications, and drivin… (show all) Ji Li has leadership responsibility for organizing and directing the CLARA data science team in building optimized machine learning solutions, creating artificial intelligence applications, and driving innovation. Dr. Li is well-published in fields related to computational theory and big data applications. His specific expertise and interests include machine learning, deep learning, text mining, and natural language processing and understanding. Dr. Li received his Ph.D. in mathematics from the University of Connecticut.

As AI continues to make headlines by automating anything from warehouses to calls centres, creative professionals find solace in the claim that the technology will never be smart enough to be a truly original thinker. Jean-Baptiste Le Divelec, creative copywriter at Fred&Farid Shanghai, has always been fascinated by neural networks and deep learning, so with some friends he fed a neural network a decade's worth of Nike commercials to see if it could write something original and inspiring. The result -- which JB named'AI and Kennedy' -- is a erratic journey of motivation bordering on the unintelligible. "But between these absurd funny lines, I observed, with a chill, a touch of genius," says JB.

We used Adam as the optimizer and train the model for 6 epochs. We use transfer learning to then train the model on the training data set while measuring loss and accuracy on the validation set. As shown by the loss and accuracy numbers below, the model trains very quickly. After the 1st epoch, train accuracy is 87% and validation accuracy is 97%!. This is the power of transfer learning. Our final model has a validation accuracy of 98.4%.