The "SP theory of intelligence", with its realisation in the "SP computer model", aims to simplify and integrate observations and concepts across AI-related fields, with information compression as a unifying theme. This paper describes how abstract structures and processes in the theory may be realised in terms of neurons, their interconnections, and the transmission of signals between neurons. This part of the SP theory -- "SP-neural" -- is a tentative and partial model for the representation and processing of knowledge in the brain. In the SP theory (apart from SP-neural), all kinds of knowledge are represented with "patterns", where a pattern is an array of atomic symbols in one or two dimensions. In SP-neural, the concept of a "pattern" is realised as an array of neurons called a "pattern assembly", similar to Hebb's concept of a "cell assembly" but with important differences. Central to the processing of information in the SP system is the powerful concept of "multiple alignment", borrowed and adapted from bioinformatics. Processes such as pattern recognition, reasoning and problem solving are achieved via the building of multiple alignments, while unsupervised learning -- significantly different from the "Hebbian" kinds of learning -- is achieved by creating patterns from sensory information and also by creating patterns from multiple alignments in which there is a partial match between one pattern and another. Short-lived neural structures equivalent to multiple alignments will be created via an inter-play of excitatory and inhibitory neural signals. The paper discusses several associated issues, with relevant empirical evidence.

Thank you to Rahul Iyer from Pivotal for contributing to the software and to this article. Path functions are a powerful capability in the data science toolkit, and they are now available in the newest release of the open source Apache MADlib (incubating) library. For example, path functions can be used to reason over website, shopping cart, and customer support clickstreams to identify the golden paths to purchase, multi-channel promotion effectiveness, or customer churn. In addition, they can be used in predictive analytics use cases, like analyzing millions of sensor logs from cars or other machines to identify common patterns in part failure. These scenarios can also improve safety and substantially lower operating costs.

Shutterstock is flexing its AI muscles with the news that the stock photo giant is introducing new computer-vision search smarts to its platform. The company, which is headquartered in New York's Empire State Building, went public back in 2012 and now offers more than 70 million images for bloggers and media outlets -- which can make searching for specific assets challenging. Of course, the trusty old keyword search tool is effective to an extent, but what if you want to find images that are similar to one you have in your possession? Or what if you want alternative images based on color schemes, mood, or shapes? This is where Shutterstock's new reverse image search comes into play.

Google is so dominant in the search engine market at large that it becomes hard to launch anything that remotly looks like a search tool. A team of Russian developers decided to still give it a go and focus on a niche market: image search. The team's objective seems very ambitious, create an artificial intelligence based image search engine to help designers find inspiration or resources in an easier way. They promise that SlashPixels will understand each image that it indexes, thus giving it a big advantage when it comes to sort the pictures. Unfortunatly, all this doesn't exist yet, but you can support the team's IndieGogo campaign to help them build this new tool.

Computer-supported authorship attribution provides tools for extracting stylistic features that can help verify or identify the author of text documents. In many situations finding the author of a document is very important, such as the detection of plagiarism for protecting copyrights and forensic support during criminal investigations. This paper, thus explores a novel stylistic feature with the aim of accurately characterizing an author's work. In particular, the use of part-of-speech skip-grams and an in-house top-k sequential pattern mining algorithm are considered for the task of authorship attribution. A study using a collection of of 30 texts, written by 10 authors, consisting of 2,615,856 words and 99,903 sentences, confirms that mining part-of-speech skip-grams in texts facilitates authorship inference.

As a species, we are excellent at imbuing life into the lifeless--just as we are proficient in giving meaning to the meaningless. One could argue that the ability of our brains to recognize patterns quickly is part of what gives us our humanity. Seeing faces on Mars, yelling at our cars for breaking down and giving animals more agency than they may possess are all results of our psyche. Our penchant for gestalt is important in the ever increasing world of social robots and machines. When it comes to technology and social robotics, the whole is often seen as more meaningful than the sum of the parts. The field of social robotics includes machines that use social behaviors and cues to interact with people.

Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

The role of the doctor could soon become redundant, overtaken by forms of Artificial Intelligence (AI) which will test, diagnose and treat disease just as well as any human medical professional could if not better, two doctors from New Zealand's Whangarei Hospital predict. This worldwide robotic vision of the future, estimated to be 10-to-20 years away, the editorial suggests doctors could be eradicated from the payroll with medical assistants, midwives and nurses filling the more human-based skill gaps that AI is unable to perform. An editorial, published in the New Zealand Medical Journal today, says the secret behind an AI takeover is a unique pattern-recognition algorithm that synthesises and compares a patient's data to predefined disease categories. Once a diagnosis is delivered, AI can then recommend an evidence-based treatment, specific to each patient. "Over the coming years, AI will challenge the traditional role of the doctor," the paper reads.

We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech. Some of the key advancements that contribute to our system include: a nearest-neighbor discriminant analysis (NDA) approach (as opposed to LDA) for intersession variability compensation in the i-vector space, the application of speaker and channel-adapted features derived from an automatic speech recognition (ASR) system for speaker recognition, and the use of a DNN acoustic model with a very large number of output units ( 10k senones) to compute the frame-level soft alignments required in the i-vector estimation process. We evaluate these techniques on the NIST 2010 SRE extended core conditions (C1-C9), as well as the 10sec-10sec condition. To our knowledge, results achieved by our system represent the best performances published to date on these conditions. For example, on the extended tel-tel condition (C5) the system achieves an EER of 0.59%. To garner further understanding of the remaining errors (on C5), we examine the recordings associated with the low scoring target trials, where various issues are identified for the problematic recordings/trials. Interestingly, it is observed that correcting the pathological recordings not only improves the scores for the target trials but also for the nontarget trials.

Network analysis offers a new set of techniques to tackle the persistent and growing problem of complex fraud. Network analysis supplements traditional techniques by providing a mechanism to bridge investigative and analytics methods. Beyond base visualization, network analysis provides a standardized platform for complex fraud pattern storage and retrieval, pattern discovery and detection, statistical analysis, and risk scoring. This article gives an overview of the main challenges and demonstrates a promising approach using a hands-on example. With swelling globalization, advanced digital communication technology, and international financial deregulation, fraud investigators face a daunting battle against increasingly sophisticated fraudsters.