Toyota will build a 175-acre hydrogen-powered test city beginning next year at the base of Japan's Mount Fuji to study the interactions of a number of cutting-edge technologies, including autonomous transportation, robotics and artificial intelligence. The huge project, called Woven City, is being personally championed by Toyota Motor Corp. CEO Akio Toyoda, who appeared Monday at CES here to discuss the plan. Woven City -- which will be roughly the size of Apple's circular campus in Cupertino, Calif., -- is being designed by renowned Danish architect Bjarke Ingels, CEO of Bjarke Ingels Group and designer of Google's new headquarters, 2 World Trade Center in New York City and a number of other high-profile projects globally. The cost of the project was not revealed, but it is expected to be in the billions of dollars. Toyota says an estimated 2,000 people -- employees and their families, retired couples, retailers, visiting scientists and industry partners -- are expected to inhabit Woven City initially when completed.

LAS VEGAS – Toyota Motor Corp. said Monday it plans to build a prototype "city of the future" at the base of Mount Fuji, powered by hydrogen fuel cells and functioning as a laboratory for autonomous cars, smart homes, artificial intelligence and other technologies. Toyota unveiled the plan at CES, the big technology industry show. The development, to be built at the site of a factory that is planned to be closed in Shizuoka Prefecture, will be called "Woven City" -- a reference to Toyota's start as a loom manufacturing company -- and will serve as a home to full-time residents and researchers. Toyota did not disclose costs for the project. Executives at many major automakers have talked about how cities of the future could be designed to cut climate-changing emissions from vehicles and buildings, reduce congestion and apply internet technology to everyday life.

Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms, but also energy-efficient computational models when implemented in VLSI circuits. In this paper, we propose a novel supervised learning algorithm for SNNs based on temporal coding. A spiking neuron in this algorithm is designed to facilitate analog VLSI implementations with analog resistive memory, by which ultra-high energy efficiency can be achieved. We also propose several techniques to improve the performance on a recognition task, and show that the classification accuracy of the proposed algorithm is as high as that of the state-of-the-art temporal coding SNN algorithms on the MNIST dataset. Finally, we discuss the robustness of the proposed SNNs against variations that arise from the device manufacturing process and are unavoidable in analog VLSI implementation. We also propose a technique to suppress the effects of variations in the manufacturing process on the recognition performance.

Recently, it has been widely accepted by the research community that interactions between humans and cyber-physical infrastructures have played a significant role in determining the performance of the latter. The existing paradigm for designing cyber-physical systems for optimal performance focuses on developing models based on historical data. The impacts of context factors driving human system interaction are challenging and are difficult to capture and replicate in existing design models. As a result, many existing models do not or only partially address those context factors of a new design owing to the lack of capabilities to capture the context factors. This limitation in many existing models often causes performance gaps between predicted and measured results. We envision a new design environment, a cyber-physical human system (CPHS) where decision-making processes for physical infrastructures under design are intelligently connected to distributed resources over cyberinfrastructure such as experiments on design features and empirical evidence from operations of existing instances. The framework combines existing design models with context-aware design-specific data involving human-infrastructure interactions in new designs, using a machine learning approach to create augmented design models with improved predictive powers.

LONDON – Global stock markets took another hit Monday while oil and gold prices surged in response to the escalating tensions in the Middle East following the U.S. killing of Iran's top general. The death of Qassem Soleimani in a U.S. drone strike has heightened geopolitical risks for financial markets, including concerns about potential disruptions to the global oil supply. The U.S. has reinforced its presence in the Middle East in preparation for reprisals from Iran, which has vowed revenge. Iraq, meanwhile, has called for the expulsion of American troops from its territory. The moves in financial markets illustrated the concerns of investors.

The business applications of artificial intelligence are boundless. Tony Nash realized AI's potential in an underserved niche. His startup, Complete Intelligence, uses AI to focus on decision support, which looks at the data and behavior of costs and prices within a global ecosystem in a global environment to help top-tier companies make better business decisions. "The problem that were solving is companies don't predict their costs and revenues very well," says Nash, the CEO and founder of Complete Intelligence. "There are really high error rates in company costs and revenue forecasts and so what we've done is built a globally integrated artificial intelligence platform that can help people predict their costs and their revenues with a very low error rate."

Today's article is about a particularly inspiring AGI Podcast revolving around decentralized efforts to achieve synoptical systems for social good, and which ties in with a new endeavor undertaken by the SingularityNET team. This week we interviewed a prominent figure in the European blockchain and AI innovation scene: Jan-Peter Doomernik. Jan-Peter is Nature 2.0's Lead Architect and a Senior Business Developer working in one of Holland's leading distribution service operators (DSO) Enexis Netbeheer. In the podcast, we discuss the "demystification of complexity", the upcoming Odyssey hackathon, and the efforts that civil society, academia and industry can make to introduce new autonomous systems imbued with humanitarianism. "In forests, you have big trees and little trees and those trees are connected like a network in which the big trees share resources of sunlight and water to the little trees so that the little trees do not have to become competitors."

In the decade ahead, waves of exponential technological advancements are stacking atop one another, eclipsing decades of breakthroughs in scale and impact. Emerging from these waves are 20 "Metatrends," likely to revolutionize entire industries (old and new), redefine tomorrow's generation of businesses and contemporary challenges, and transform our livelihoods from the bottom-up. Among these metatrends are augmented human longevity, the surging smart economy, AI-human collaboration, urbanized cellular agriculture, and high-bandwidth brain-computer interfaces, just to name a few. It is here that master entrepreneurs and their teams must see beyond the immediate implications of a given technology, capturing second-order, Google-sized business opportunities on the horizon. Welcome to a new decade of runaway technological booms, historic watershed moments, and extraordinary abundance.

Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM), to establish a technical basis to overcome these difficulties by exploring local patterns using machine learning. The underlying local patterns in multiscale data are represented by a set of physical features that embody the information from a physical system of interest, empirical correlations, and the effect of mesh size. After performing a limited number of high-fidelity numerical simulations and a sufficient amount of fast-running coarse-mesh simulations, an error database is built, and deep learning is applied to construct and explore the relationship between the local physical features and simulation errors. Case studies based on mixed convection have been designed for demonstrating the capability of data-driven models in bridging global scale gaps.

Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (eg, radiation effects) into an existing compact model is not trivial and may require redevelopment from scratch. Machine Learning (ML) techniques have the potential to automate and significantly speed up the development of compact models. In addition, ML provides a range of modeling options that can be used to develop hierarchies of compact models tailored to specific circuit design stages. In this paper, we explore three such options: (1) table-based interpolation, (2)Generalized Moving Least-Squares, and (3) feed-forward Deep Neural Networks, to develop compact models for a p-n junction diode. We evaluate the performance of these "data-driven" compact models by (1) comparing their voltage-current characteristics against laboratory data, and (2) building a bridge rectifier circuit using these devices, predicting the circuit's behavior using SPICE-like circuit simulations, and then comparing these predictions against laboratory measurements of the same circuit.