Google has been busy updating Lens, its AI-powered image recognition tool, over the past year. It can recognize dog and cat breeds, is available on iOS, and on non-Pixel Android phones. Now, at I/O, Google is rolling out the latest Lens update: It will now be integrated directly into the stock camera app. It'll start with the Pixel, but will also roll out to other Android phones like the recently announced LG G7. The company is also rolling out several new features to Lens: smart text selection, style match, and real-time results.

Gait recognition technology, a method to identify people by characteristics shown unconsciously in the ways they walk, is being utilized in criminal investigations in Japan. The technology enables the identification of individuals even from images taken from a distance and low-resolution footage. According to advocates, a video image of only two strides is sufficient to identify a person with a high rate of accuracy, based on arm swings, length of stride and other characteristics. Researchers are working to improve the accuracy of the technology with the use of artificial intelligence. In a brazen daytime attack in Tokyo's upscale Ginza district in April 2017, a man was robbed of some ¥40 million on a street after he had converted gold into cash.

During the opening F8 2018 keynote, Facebook CEO Mark Zuckerberg showed off the company's latest Instagram updates: Spotify integration, AI-based anti-bullying comment filters, AR camera effects and four-way video chat. During the Day 2 keynote, Facebook revealed how your daily Instagram updates are giving its AI technology a deep-learning crash course in image recognition--one that's apparently made its AI even smarter than Google's at categorizing objects in photos. Facebook pulled this off, amazingly enough, by instructing its AI to read photo hashtags and interpret photos' subject matter. Using this strategy, called "weakly supervised training", Facebook's AI achieved a record 85.4% accuracy rating on an industry-wide test of image recognition, beating out Google's previous record. A Facebook Engineering blog post went into detail on the methods.

In the race to continue building more sophisticated AI deep learning models, Facebook has a secret weapon: billions of images on Instagram. In research the company is presenting today at F8, Facebook details how it took what amounted to billions of public Instagram photos that had been annotated by users with hashtags and used that data to train their own image recognition models. They relied on hundreds of GPUs running around the clock to parse the data, but were ultimately left with deep learning models that beat industry benchmarks, the best of which achieved 85.4 percent accuracy on ImageNet. If you've ever put a few hashtags onto an Instagram photo, you'll know doing so isn't exactly a research-grade process. There is generally some sort of method to why users tag an image with a specific hashtag; the challenge for Facebook was sorting what was relevant across billions of images. When you're operating at this scale -- the largest of the tests used 3.5 billion Instagram images spanning 17,000 hashtags -- even Facebook doesn't have the resources to closely supervise the data.

For thousands of years, humans have recognized patterns, gathered and analyzed data. Identifying patterns and using this information has been key to our evolution, as well as playing an important role in our pastime activities. Whether it is making a difference between animals that want to kill us and those who don't, categorizing plants based on their edibility, seeing patterns in the stars, or creating algorithms that know exactly which cat video we want to see next on Youtube. Although data and pattern recognition have been around forever, the way we use, store and spread this information has changed drastically. While at the beginning we had to rely on word-of-mouth and cave drawings to spread knowledge, nowadays we can store and spread trillions of gigabytes of information without much effort.

Journal of Parallel and Distributed Computing 104, 19-35.

We illustrate pattern recognition techniques applied to an interesting mathematical problem: The representation of a number in non-conventional systems, generalizing the familiar base-2 or base-10 systems. The emphasis is on data science rather than mathematical theory, and the style is that of a tutorial, requiring minimum knowledge in mathematics or statistics. However, some off-the-beaten-path, state-of-the-art number theory research is discussed here, in a way that is accessible to college students after a first course in statistics. This article is also peppered with mathematical and statistical oddities, for instance the fact that there are units of information smaller than the bit. You will also learn how the discovery process works, as I have included research that I thought would lead me to interesting results, but did not. In all scientific research, only final, successful results are presented, while actually most of the research leads to dead-ends, and is not made available to the reader.

Artificial intelligence (AI) has taken huge leaps forward in recent years, driven by a combination of factors. Firstly, Web 2.0 paved the way for "big data" – vast amounts of quantitative and qualitative data covering many aspects of human behavior, collected by companies such as Google. Secondly, developments in cloud computing led to the rapid evolution of neural networks that have the processing capacity for such huge volumes of data. Jointly, these factors have enabled the development of machine learning, which is one major application of AI. Machine learning is rooted in statistical pattern recognition – essentially using large volumes of data to predict a particular outcome, based on precedent. This progression has led to various potential applications for AI technologies.

Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cut costs or both. Web Mining is the application of data mining techniques to discover patterns from the World Wide Web. It can be divided into three different types - Web usage mining, Web content mining and Web structure mining. Web usage mining itself can be classified further depending on the kind of usage data considered: Web Server Data, Application Server Data, and Application Level Data. Web log Mining includes three main stages: Data Pre-Processing, Pattern Discovery and Pattern Analysis. A) Data Pre-Processing: Web Server Data contains information such as who accessed the web site, what pages were accessed, Time of request etc. In pre-processing [3] stage, irrelevant data fields are removed and unique users are identified [4]. Transaction table is created through the user sessions.

Temporal Pattern Mining (TPM) is the problem of mining predictive complex temporal patterns from multivariate time series in a supervised setting. We develop a new method called the Fast Temporal Pattern Mining with Extended Vertical Lists. This method utilizes an extension of the Apriori property which requires a more complex pattern to appear within records only at places where all of its subpatterns are detected as well. The approach is based on a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for TMP. However, the speed-up comes at the expense of memory usage.