The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data. The toolbox supports a flexible modeling language based on probabilistic graphical models with latent variables and temporal dependencies. The specified models can be learnt from large data sets using parallel or distributed implementa- tions of Bayesian learning algorithms for either streaming or batch data. These algorithms are based on a flexible variational message passing scheme, which supports discrete and continu- ous variables from a wide range of probability distributions. AMIDST also leverages existing functionality and algorithms by interfacing to software tools such as Flink, Spark, MOA, Weka, R and HUGIN. AMIDST is an open source toolbox written in Java and available at http://www.amidsttoolbox.com under the Apache Software License version 2.0.

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

This paper describes a new entropy-style of equation that may be useful in a general sense, but can be applied to a cognitive model with related processes. The model is based on the human brain, with automatic and distributed pattern activity. Methods for carrying out the different processes are suggested. The main purpose of this paper is to reaffirm earlier research on different knowledge-based and experience-based clustering techniques. The overall architecture has stayed essentially the same and so it is the localised processes or smaller details that have been updated. For example, a counting mechanism is used slightly differently, to measure a level of 'cohesion' instead of a 'correct' classification, over pattern instances. The introduction of features has further enhanced the architecture and the new entropy-style equation is proposed. While an earlier paper defined three levels of functional requirement, this paper re-defines the levels in a more human vernacular, with higher-level goals described in terms of action-result pairs.

A Ford Fusion laden with self-driving sensors does some winter weather testing. Ford Motor is in pole position when it comes to benefiting from the coming age of autonomous vehicles. That's the conclusion of a study released Monday by Navigant Research, which sells its in-depth surveys of energy and transportation markets to suppliers, policymakers and other industry stakeholders. The Dearborn-based automaker took the top spot by demonstrating that it has the strategic vision and execution capabilities to both develop automated driving systems as well as deploy them across a range of mobility platforms. Many automakers are targeting 2021 for a roll-out of autonomous vehicles that likely will be part of a ride-sharing network.

Australian researchers have revealed a new technique to combine lasers into a single beam using diamonds - similar to the'superlaser' seen on the Death Star in the hit film Star Wars. The researchers say their breakthrough could be used for everything from shooting down drones on Earth to blasting space junk in orbit. Australian researchers have revealed a new technique to combine lasers into a single beam using diamonds - similar to the'superlaser' seen on the Death Star in the hit film Star Wars. Researchers at Macquarie University say their research, published in Laser and Photonics Reviews, demonstrates a concept where the power of multiple laser beams is transferred into a single intense output beam that can be directed to the intended target. 'Researchers are developing high power lasers to combat threats to security from the increased proliferation of low-cost drones and missile technology,' said said co-author Associate Professor Rich Mildren.

This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. This lecture series provides a thorough introduction to the cutting-edge research in deep learning appli... more

Investing in technology-oriented insurance ventures (insurtech) is clearly a global trend and almost half of all the money being poured into them globally is going into artificial intelligence and internet of things startups, new research finds. The research from Accenture, which includes an analysis of CB Insights data on 450 insurtech deals over the last three years, appears in a new Accenture report titled The Rise of InsurTech. The CB Insights data reveals that global insurtech investment totaled $1.7 billion in 2016 and both the volume and value of deals have almost doubled since 2014. While more than half of all deals still take place in the U.S., insurtech has gone global with the United Kingdom, Germany, China and India now being significant markets and other countries coming on. Only about 14 percent of the insurtech deals in 2016 had an insurance industry investor or partner, although the industry's participation has been rising every year.

Cloud services offered via web API endpoints are an exploding and apparently relentless trend. The big players are exposing a huge (and increasing) spectrum of state-of-the-art technology, making it possible for developers all over the world to integrate it into their apps. Clearly, the field of artificial intelligence and machine learning is no exception, claiming a huge share of the most high-tech functions exposed by vendors like Amazon, Google and Microsoft. Be it recognizing the content of images (see previous blog posts about the Google Vision API, Amazon Rekognition and a comparison of the two), the words spoken in a piece of recorded speech (Getting Started with Google Cloud Speech API) or crunching data using robust standard algorithm (Amazon Machine Learning: Use Cases and a Real Example in Python), it's nowadays very quick and easy to get started with some ready-to-go solution where all the underlying complexity is conveniently hidden by the cloud. In our recent post we described our encounter with the Google Cloud Natural Language API.

A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox. March is a month for change and new beginnings. The new UK Budget promised hundreds of millions of pounds to scientists and researchers to develop solutions to hi-tech challenges, including artificial intelligence and robotics, next generation batteries and new techniques for manufacturing medicines. The government is expected to allocate more than £500 million from the National Productivity Investment Fund so that UK companies might lead the way in the new technologies set to transform the world.

Professor Ajay Agrawal feels like he's back in 1995. That year, the first major commercial Internet service providers like AOL went online and Yahoo's search engine became available to the public. It was an inflection point for the Internet and, Agrawal believes, artificial intelligence or AI may now be nearing that stage. Agrawal, 47, is a professor of entrepreneurship at the University of Toronto's Rotman School of Management, but he also happens to be the founder of the Creative Destruction Lab (CDL). He has emerged as a prominent figure in the field of machine learning, with CDL's AI stream.