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.

The 57-year-old mystery of an infamous prison break from Alcatraz may have finally been solved using artificial-intelligence and facial-recognition technology. Rothco, the Irish creative agency owned by Accenture Interactive, teamed up with AI specialists at Identv to analyse a picture of two escapees and have, for the first time, confirmed their identities. On 11 June 1962, three prisoners – Frank Morris, along with brothers John and Clarence Anglin – broke out of their cells and escaped from the prison on Alcatraz Island, near San Francisco Bay. The trio's extraordinary escape, in which they used sharpened spoons to dig through the walls and made papier-mâché dummies to fool the guards, was made famous in the 1979 movie Escape from Alcatraz. The prison, which shut down in 1963, was famed for being supposedly impossible to escape from.

We need better ways to help people. What's the medical breakthrough that could save the most lives in the U.S. over the next ten years? In the 2020s, medical research will likely inch forward when it comes to major killers like heart disease and cancer. But the biggest potential to save lives could lie in learning to prevent suicide. The rates of reported suicides have been creeping up over the last two decades.

Anthony Levandowski makes an unlikely prophet. Dressed Silicon Valley-casual in jeans and flanked by a PR rep rather than cloaked acolytes, the engineer known for self-driving cars--and triggering a notorious lawsuit--could be unveiling his latest startup instead of laying the foundations for a new religion. But he is doing just that. Artificial intelligence has already inspired billion-dollar companies, far-reaching research programs, and scenarios of both transcendence and doom. Now Levandowski is creating its first church.

SAN FRANCISCO – The top diplomats of Japan, the United States and South Korea on Tuesday urged North Korea to refrain from military provocation and continue denuclearization talks, but ruled out any easing of crushing economic sanctions without progress in the stalled negotiations. Foreign Minister Toshimitsu Motegi held discussions with his U.S. and South Korean counterparts, Mike Pompeo and Kang Kyung-wha, in East Palo Alto, just outside San Francisco, two weeks after a deadline set by Pyongyang for progress by the end of 2019 passed. "We agreed on the importance of North Korea making positive efforts in talks with the United States rather than going through with provocative moves," Motegi told reporters. The statement appeared to contradict remarks in a New Year speech by South Korean President Moon Jae-in a day earlier in Seoul, where he said that he could seek exemptions of U.N. sanctions to bring about improved inter-Korean relations that he believes would help restart the deadlocked nuclear negotiations between Pyongyang and Washington. Moon has previously made similar comments, despite outside worries that any lifting of sanctions could undermine U.S.-led efforts to eliminate North Korea's nuclear arsenal.

Estimating temporal patterns in travel times along road segments in urban settings is of central importance to traffic engineers and city planners. In this work, we propose a methodology to leverage coarse-grained and aggregated travel time data to estimate the street-level travel times of a given metropolitan area. Our main focus is to estimate travel times along the arterial road segments where relevant data are often unavailable. The central idea of our approach is to leverage easy-to-obtain, aggregated data sets with broad spatial coverage, such as the data published by Uber Movement, as the fabric over which other expensive, fine-grained datasets, such as loop counter and probe data, can be overlaid. Our proposed methodology uses a graph representation of the road network and combines several techniques such as graph-based routing, trip sampling, graph sparsification, and least-squares optimization to estimate the street-level travel times. Using sampled trips and weighted shortest-path routing, we iteratively solve constrained least-squares problems to obtain the travel time estimates. We demonstrate our method on the Los Angeles metropolitan-area street network, where aggregated travel time data is available for trips between traffic analysis zones. Additionally, we present techniques to scale our approach via a novel graph pseudo-sparsification technique.