Differentiable architecture search (DARTS) is widely considered to be easy to overfit the validation set which leads to performance degradation. We first employ a series of exploratory experiments to verify that neither high-strength architecture parameters regularization nor warmup training scheme can effectively solve this problem. Based on the insights from the experiments, we conjecture that the performance of DARTS does not depend on the well-trained supernet weights and argue that the architecture parameters should be trained by the gradients which are obtained in the early stage rather than the final stage of training. This argument is then verified by exchanging the learning rate schemes of weights and parameters. Experimental results show that the simple swap of the learning rates can effectively solve the degradation and achieve competitive performance. Further empirical evidence suggests that the degradation is not a simple problem of the validation set overfitting but exhibit some links between the degradation and the operation selection bias within bilevel optimization dynamics. We demonstrate the generalization of this bias and propose to utilize this bias to achieve an operation-magnitude-based selective stop.

Accurate forecasts of photovoltaic power generation (PVPG) are essential to optimize operations between energy supply and demand. Recently, the propagation of sensors and smart meters has produced an enormous volume of data, which supports the development of data based PVPG forecasting. Although emerging deep learning (DL) models, such as the long short-term memory (LSTM) model, based on historical data, have provided effective solutions for PVPG forecasting with great successes, these models utilize offline learning. As a result, DL models cannot take advantage of the opportunity to learn from newly-arrived data, and are unable to handle concept drift caused by installing extra PV units and unforeseen PV unit failures. Consequently, to improve day-ahead PVPG forecasting accuracy, as well as eliminate the impacts of concept drift, this paper proposes an adaptive LSTM (AD-LSTM) model, which is a DL framework that can not only acquire general knowledge from historical data, but also dynamically learn specific knowledge from newly-arrived data. A two-phase adaptive learning strategy (TP-ALS) is integrated into AD-LSTM, and a sliding window (SDWIN) algorithm is proposed, to detect concept drift in PV systems. Multiple datasets from PV systems are utilized to assess the feasibility and effectiveness of the proposed approaches. The developed AD-LSTM model demonstrates greater forecasting capability than the offline LSTM model, particularly in the presence of concept drift. Additionally, the proposed AD-LSTM model also achieves superior performance in terms of day-ahead PVPG forecasting compared to other traditional machine learning models and statistical models in the literature.

As the applications of artificial intelligence (AI) and robotics emerge and with their ever-growing socio-economic influence in various fields of research and practice, there is an imminent need to study trust in such systems. With the opaque work mechanism of AI-based systems and the prospect of intelligent robots as workers' companions, context-specific interdisciplinary studies on trust are key in increasing their adoption. Through a thorough systematic literature review on (1) trust in AI and robotics (AIR) and (2) AIR applications in the architecture, engineering, and construction (AEC) industry, this study identifies common trust dimensions in the literature and uses them to organize the paper. Furthermore, the connections of the identified dimensions to the existing and potential AEC applications are determined and discussed. Finally, major future directions on trustworthy AI and robotics in AEC research and practice are outlined.

Today artificial intelligence (AI) driven digital technologies are giving us new pathways to always have your loved ones with you, 7x24. Not really, despite the eeriness from Black Mirror episodes, or Carrie Fisher digitally created to carry on as Princess Leia in Star Wars, and Microsoft securing a patent for software that could reincarnate people as a chat bot, opening the door to more uses of AI contemplating how to bring the dead back to life are rapidly accelerating. Are we ready for death resurrections? Is this the right thing for us to be doing? From my research, we don't have all the answers to this complex question yet, but what we have are many innovators, academics, researchers shaping the answer to this question that will enable richer immersive digital learning experiences – and others that bringing grandma back to life – and persisting forever – may feel positively therapeutic to ease a deep grief, or feel like you are immersed in a Stephen King movie.

Robots are sweeping the world, from amazon's Alexa to full functioning human-like androids. The internet seems all buzzed at a promise of a future where humans and robots will happily work together. However, there is a dark side to robots that many people are still unaware of. BINA48 employs a mix of off-the-shelf software and customized artificial intelligence algorithms, using a microphone to hear, voice recognition software, dictation software which allows improvement in the ability to listen and retain information during a conversation. This human look-like robot is one of the most advanced robots on this planet.

Dr. Sam Browd is a Seattle neurosurgeon who is taking telemedicine and virtual reality technology to a different, unexpected place – the operating room. Browd is professor of neurological surgery at the University of Washington, an attending neurosurgeon at Seattle Children's Hospital, and cofounder and chief medical officer at health IT vendor Proprio. He has spent the last few years working with engineers and other surgeons to bring the operating room out of the analog world and into the digital. What they've created is a new technology that provides surgeons a 360-view of surgery by combining virtual reality and artificial intelligence, enabling surgeons to integrate information in new ways. Out of this, too, comes work on telesurgery – the ability to do live surgery in different locations or mentorship and proctorship.

According to a survey conducted by PwC, 72% of business leaders use AI for their business advantage. The Digital marketing world has been restructured immensely since the emergence of AI. It helps companies develop powerful digital strategies, optimizes campaigns, and improves return on investment. Teleflora, a floral company in the US, used AI marketing to build new customers' profiles and improve customer loyalty. Using these historical data, Teleflora used AI marketing to predict the future customer behavior of different audience segments.

Transfer learning where the behavior of extracting transferable knowledge from the source domain(s) and reusing this knowledge to target domain has become a research area of great interest in the field of artificial intelligence. Probabilistic graphical models (PGMs) have been recognized as a powerful tool for modeling complex systems with many advantages, e.g., the ability to handle uncertainty and possessing good interpretability. Considering the success of these two aforementioned research areas, it seems natural to apply PGMs to transfer learning. However, although there are already some excellent PGMs specific to transfer learning in the literature, the potential of PGMs for this problem is still grossly underestimated. This paper aims to boost the development of PGMs for transfer learning by 1) examining the pilot studies on PGMs specific to transfer learning, i.e., analyzing and summarizing the existing mechanisms particularly designed for knowledge transfer; 2) discussing examples of real-world transfer problems where existing PGMs have been successfully applied; and 3) exploring several potential research directions on transfer learning using PGM.

This paper considers a large class of problems where we seek to recover a low rank matrix and/or sparse vector from some set of measurements. While methods based on convex relaxations suffer from a (possibly large) estimator bias, and other nonconvex methods require the rank or sparsity to be known a priori, we use nonconvex regularizers to minimize the rank and $l_0$ norm without the estimator bias from the convex relaxation. We present a novel analysis of the alternating proximal gradient descent algorithm applied to such problems, and bound the error between the iterates and the ground truth sparse and low rank matrices. The algorithm and error bound can be applied to sparse optimization, matrix completion, and robust principal component analysis as special cases of our results.

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles the spatial interactions using a graph convolutional network (GCN), and captures the temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Besides, we propose a weighted adjacency matrix to describe the intensities of mutual influence between vehicles, and the ablation study demonstrates the effectiveness of our proposed scheme. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM).Comparisons in three aspects, including prediction errors, model sizes, and inference speeds, show that our network can achieve state-of-the-art performance.