Image recognition is a popular technology that can detect, understand, and distinguish images from one another. Technologies such as text recognition and facial recognition are all specific applications of image recognition. Understanding the way we perceive objects and images has always been a hot topic for research. Researchers globally have observed that the human eye is very sensitive to the edges of an object. Typically, a person identifies an object by first determining the outline of the object and then processing this information in the visual cortex.

Even with today's frequent technological breakthroughs in speech-interactive devices (think Siri and Alexa), few companies have tried their hand at enabling multi-user profiles. Google Home has been the most ambitious in this area, allowing up to six user profiles. The recent boom of this technology is what made the potential for this project very exciting to our team. We also wanted to engage in a project that is still a hot topic in deep-learning research, create interesting tools, learn more about neural network architectures, and make original contributions where possible. We sought to create a system able to quickly add user profiles and accurately identify their voices with very little training data, a few sentences as most! This learning from one to only a few samples is known as One Shot Learning.

What do tomorrow's automakers have to do with net-zero buildings? Why it's important: This will transform the design and technology requirements for buildings in order to accommodate personal EVs and even electric fleets What It Is: Drillinginfo, a SaaS provider for the energy industry, has acquired Pattern Recognition Technologies (PRT), an energy forecasting software player. Why It Matters: Adding PRT's machine learning capabilities to predict energy consumption will allow Drillinginfo to enter horizontal markets in energy data analytics. This maneuver also bolsters Drillinginfo's North American customer base, particularly in clean energy data analytics. Why It Matters: Incumbents are reacting to the transition towards smart products by picking up smart home specialists.

Recent advancements in deep learning algorithms and hardware performance have enabled researchers and companies to make giant strides in areas such as image recognition, speech recognition, recommendation engines, and machine translation. Six years ago, the first superhuman performance in visual pattern recognition was achieved. Two years ago, the Google Brain team unleashed TensorFlow, deftly slinging applied deep learning to the masses. TensorFlow is outpacing many complex tools used for deep learning. The keystone of its power is TensorFlow's ease of use.

Aurora has been at the forefront of deploying computer vision, machine learning and pattern recognition solutions for over 15 years; its solutions for authentication of individuals have been installed worldwide. In fact, it is Aurora technology that enables many automated passenger validation systems at some of the world's largest airports. Underpinning our world leading accuracy are AI solutions developed through our proprietary Deep Learning technology. Our team of PhDs, world-class experts in the field, can work closely with your team to analyse requirements and tailor your solution. Our extensive experience in deploying application software in the most challenging of real world environments, means you will have a complete solution, simple to install, easy to use and fully supported.

Back in May, Google announced AIY Projects -- do-it-yourself hardware kits for experimenting with artificial intelligence. Today, Google followed up the first Voice Kit with a new Vision Kit for image recognition and TensorFlow development. These AIY Kits are crude speakers -- and now cameras -- housed in simple cardboard boxes. Builders also need to supply their own Raspberry Pi Zero W and a Raspberry Pi Camera 2 for this latest project. Otherwise, the kit includes everything needed from lenses, wires, and a VisionBonnet board with an Intel Movidius MA2450 that connects to the Raspberry Pi.

LOS ANGELES – Facebook on Monday said stepping up the use of artificial intelligence to identify members of the leading social network who may be thinking of suicide. Software will look for clues in posts or even in videos being streamed at Facebook Live, then fire off reports to human reviewers and speed up alerts to responders trained to help, according to the social network. "This approach uses pattern recognition technology to help identify posts and live streams as likely to be expressing thoughts of suicide," Facebook vice president of product management Guy Rosen said in a blog post. Signs watched for were said to include texts by people or comments to them, such as someone asking if they are troubled. Facebook already has tools in place for people to report concerns about friend's who may be considering self-harm, but the software can speed the process and even detect signs people may overlook.

Machine learning is the name of the game for Amazon Web Services Inc. this week, as it spills out a couple of major announcements ahead of its big re:Invent conference next week in Las Vegas. The public cloud computing giant said Wednesday it's planning to open a new machine learning laboratory, called the ML Solutions Lab, that will see its experts work alongside customers looking to build new artificial intelligence-based technologies. In addition, Amazon said it's adding new features to Amazon Rekognition, a deep-learning-powered image recognition platform, including real-time face recognition and the ability to spot text in images. The announcements underline the importance Amazon is placing on its AI efforts, which can benefit both its cloud computing arm and also its main retail business. The company has made a number of announcements in the field in recent months.

This article presents the use of Answer Set Programming (ASP) to mine sequential patterns. ASP is a high-level declarative logic programming paradigm for high level encoding combinatorial and optimization problem solving as well as knowledge representation and reasoning. Thus, ASP is a good candidate for implementing pattern mining with background knowledge, which has been a data mining issue for a long time. We propose encodings of the classical sequential pattern mining tasks within two representations of embeddings (fill-gaps vs skip-gaps) and for various kinds of patterns: frequent, constrained and condensed. We compare the computational performance of these encodings with each other to get a good insight into the efficiency of ASP encodings. The results show that the fill-gaps strategy is better on real problems due to lower memory consumption. Finally, compared to a constraint programming approach (CPSM), another declarative programming paradigm, our proposal showed comparable performance.

Many countries are suffering from severe air pollution. Understanding how different air pollutants accumulate and propagate is critical to making relevant public policies. In this paper, we use urban big data (air quality data and meteorological data) to identify the \emph{spatiotemporal (ST) causal pathways} for air pollutants. This problem is challenging because: (1) there are numerous noisy and low-pollution periods in the raw air quality data, which may lead to unreliable causality analysis, (2) for large-scale data in the ST space, the computational complexity of constructing a causal structure is very high, and (3) the \emph{ST causal pathways} are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present \emph{p-Causality}, a novel pattern-aided causality analysis approach that combines the strengths of \emph{pattern mining} and \emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST causal pathways}. First, \emph{Pattern mining} helps suppress the noise by capturing frequent evolving patterns (FEPs) of each monitoring sensor, and greatly reduce the complexity by selecting the pattern-matched sensors as "causers". Then, \emph{Bayesian learning} carefully encodes the local and ST causal relations with a Gaussian Bayesian network (GBN)-based graphical model, which also integrates environmental influences to minimize biases in the final results. We evaluate our approach with three real-world data sets containing 982 air quality sensors, in three regions of China from 01-Jun-2013 to 19-Dec-2015. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.