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.

The goal of regression analysis is to predict the value of a numeric outcome variable y given a vector of joint values of other (predictor) variables x. Usually a particular x-vector does not specify a repeatable value for y, but rather a probability distribution of possible y--values, p(y|x). This distribution has a location, scale and shape, all of which can depend on x, and are needed to infer likely values for y given x. Regression methods usually assume that training data y-values are perfect numeric realizations from some well behaived p(y|x). Often actual training data y-values are discrete, truncated and/or arbitrary censored. Regression procedures based on an optimal transformation strategy are presented for estimating location, scale and shape of p(y|x) as general functions of x, in the possible presence of such imperfect training data. In addition, validation diagnostics are presented to ascertain the quality of the solutions.

Japanese railway companies are turning to artificial intelligence to help tackle potential problems for their shinkansen bullet trains caused by accumulations of snow. West Japan Railway Co. is developing an AI system to gauge the amount of snow attached to Hokuriku Shinkansen trains that cut through Niigata, Toyama and Ishikawa prefectures adjacent to the Sea of Japan. The railway operator currently decides how many personnel to deploy for snow clearance a day beforehand, based on information from meteorological data providers and past experience, but it is often not very accurate. AI will gather data from images of trains that have accumulated snow while traveling, study weather conditions and predict the number of personnel necessary for clearance work. Test operations have proved positive so far and the system is set for full introduction next winter.