Most people only think about the systems that power their cities when something goes wrong. Unfortunately, many people in the San Francisco Bay Area had a lot to think about recently when their utility company began scheduled power outages in an attempt to prevent wildfires. The decision came after devastating fires last year were found to be the result of faulty equipment, including transformers. Transformers are the links between power plants, power transmission lines, and distribution networks. If something goes wrong with a transformer, entire power plants can go dark.

Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science. Large-scale spatial data often contains complex higher-order correlations across features and locations. While tensor latent factor models can describe higher-order correlations, they are inherently computationally expensive to train. Furthermore, for spatial analysis, these models should not only be predictive but also be spatially coherent. However, latent factor models are sensitive to initialization and can yield inexplicable results. We develop a novel Multi-resolution Tensor Learning (MRTL) algorithm for efficiently learning interpretable spatial patterns. MRTL initializes the latent factors from an approximate full-rank tensor model for improved interpretability and progressively learns from a coarse resolution to the fine resolution for an enormous computation speedup. We also prove the theoretical convergence and computational complexity of MRTL. When applied to two real-world datasets, MRTL demonstrates 4 ~ 5 times speedup compared to a fixed resolution while yielding accurate and interpretable models.

The simplified series is obtained by first applying their univariate technique to each of the variables independently; that is, each univariate batch of data is separated into variable-length subsequences, and the obtained subsequences are then clustered as explained in Section 4.1. With this process, a set of representative univariate subsequences is obtained for each variable. Each new multivariate batch of data is then represented by a vector of distances, (d 1,d 2,...,d l), where d j represents the Euclidean distance between the j th variable-length subsequence of the new batch and its corresponding representative subsequence. As with their univariate technique, the reference of normality that is considered by this method is the same time series. The technique proposed by Hu et al. [2019] is also based on reducing the dimensionality of the time series and allows us to detect variable-length discords, while using the same time series as the reference of normality. This is based on the fact that the most unusual subsequences tend to have local regions with significantly different densities (points that are similar) in comparison to the other subsequences in the series. Each point in the new univariate time series describes the density of a local region of the input multivariate time series obtained by a sliding window. This series is also used to obtain the variable-length subsequences. Discords are identified using the Euclidean and Bhattacharyya distances simultaneously.

When people hear about the race for Artificial Intelligence (AI) dominance, they often think that the main competition is between the US and China. After all, the US and China have most of the largest and most well funded AI companies on the planet, and the pace of funding, company growth, and adoption doesn't seem to be slowing anytime soon. However, if you look closely, you'll see that many other countries have a stake in the AI race, and indeed, some countries have AI efforts, funding, technologies, and intellectual property that make them serious contenders in the jostling for AI dominance. In fact according to a recent report from analyst firm Cognilytica, France, Israel, United Kingdom, and the United States all are equally strong when it comes to AI, with China, Canada, Germany, Japan, and South Korea equally close in their AI strategic strength. AI startups are raising more money than ever.

"If somebody graduated from a great university, that may be an indication that they will be capable of great things, but it's not necessarily the case. If you look at, say, people like Bill Gates or Larry Ellison, Steve Jobs, these guys didn't graduate from college, but if you had a chance to hire them, of course that would be a good idea," Musk said. Instead, Musk said he looks for "evidence of exceptional ability. And if there is a track record of exceptional achievement, then it is likely that that will continue into the future," he told Auto Bild. Tesla needs artificial intelligence talent to work on its self-driving vehicle ambitions.

Can meta-learning discover generic ways of processing time-series (TS) from a diverse dataset so as to greatly improve generalization on new TS coming from different datasets? This work provides positive evidence to demonstrate this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms as specific cases. We further identify via theoretical analysis the meta-learning adaptation mechanisms within N-BEATS, a recent neural TS forecasting model. Our meta-learning theory predicts that N-BEATS iteratively generates a subset of its task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. Our empirical results emphasize the importance of meta-learning for successful zero-shot forecasting to new sources of TS, supporting the claim that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.

The world's tech powers are sending giant sums of money spinning into Canada, but while many see this as a sign of success, others are worried about researchers and intellectual property being swallowed wholesale. The country is in the midst of an artificial intelligence (AI) boom, with Google, Microsoft, Facebook, Huawei and other global heavyweights spending millions or even hundreds of millions of dollars on research hubs in Quebec, Ontario and Alberta. Canadian doors are open – some fear too open. Jim Hinton, an IP lawyer and founder of the Own Innovation consultancy, reckons that more than half of all AI patents in Canada end up being owned by foreign companies. What we need to be doing is getting money out of our ideas ourselves, instead of seeing foreign talent scoop it all up," said Hinton. "Otherwise we'll never have a Canadian champion." The country is home to hundreds of fledgling AI companies, including much-talked-about start-ups like Element AI and Deep Genomics, but they remain relatively small. "They don't have a strong market position yet," Hinton says. Deep learning pioneers such as Yoshua Bengio and Geoffrey Hinton (no relation to Jim) have nurtured top-notch talent in AI in Canada for years, back when AI was an emerging field. But despite Canadian inheriting this brilliant AI lead from the country's AI "godfathers", big foreign players have an unassailable advantage over homegrown efforts, Hinton said. "It's not an easy go for the average company to make a business out of AI.

You will focus on bringing state of the art machine learning technologies to many different platforms across the company by building state of the art prototypes, consulting on best practices and procedures, and mentoring all team members.

Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods. Existing works relied on either inter- or intra-region connectivities to generate neighborhood representations but failed to fully utilize the informative yet heterogeneous data within neighborhoods. In this work, we propose Urban2Vec, an unsupervised multi-modal framework which incorporates both street view imagery and point-of-interest (POI) data to learn neighborhood embeddings. Specifically, we use a convolutional neural network to extract visual features from street view images while preserving geospatial similarity. Furthermore, we model each POI as a bag-of-words containing its category, rating, and review information. Analog to document embedding in natural language processing, we establish the semantic similarity between neighborhood ("document") and the words from its surrounding POIs in the vector space. By jointly encoding visual, textual, and geospatial information into the neighborhood representation, Urban2Vec can achieve performances better than baseline models and comparable to fully-supervised methods in downstream prediction tasks. Extensive experiments on three U.S. metropolitan areas also demonstrate the model interpretability, generalization capability, and its value in neighborhood similarity analysis.

Recently, human being's curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomous Underwater Vehicle (AUV) which attracted many interests. Authors of this article have learned the necessity of platform upgrade from a previous AUV design project, and would like to share the experience of one task extension in the area of fish detection. Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. In an AUV, after acquiring surrounding information from sensors, how to perceive and analyse corresponding information for better judgement is one of the challenges. The processing procedure can mimic human being's learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. In this paper, a convolutional neural network (CNN) based fish detection method was proposed.