Microsoft's new Azure edge computing offerings are helping customers extend the reach of its cloud-based machine learning services, according to Clayton Fernandez, the company's global director, Internet of Things. In June this year, Azure IoT Edge hit general availability, offering customers of Microsoft's cloud service new capabilities including support for the Moby container management system at the edge, the Azure IoT Edge security manager, an Automatic Device Management (ADM) service, and a range of tools for developers. Coinciding with the announcement that it had been GAed, Microsoft open sourced the IoT Edge code and posted it on GitHub. "We started with putting a PC on everyone's desk," Fernandez told Computerworld. "Next came everyone having smartphones in their pockets -- but now things are going to start getting really interesting with this whole universe of interconnected devices that are coming together in the intelligent cloud. "Today we're accepting billions of signals securely; we ingest, we reason, we take action – we call it the intelligent edge because these devices are becoming so capable that they're helping power some of the most advanced algorithms.

AUT's Artificial Intelligence knows your choices before you do Picture standing in your local dairy, indecisively scanning the drinks fridge to choose which can you're going to buy. If you were hooked up to NeuCube, it would be able to predict which one you'd reach for – before you'd opened the fridge, and even before the thought was fully formed. Using artificial intelligence to predict a person's choices before they have even made up their mind is a world first - and it was developed at Auckland University of Technology (AUT). Professor Nikola Kasabov, who worked on the project, said they wanted to find out if there was brain activity before the conscious perception of an object. READ MORE: * Artificial intelligence presents us with an opportunity, and a challenge * Don't fear the new AI revolution * How computers'see' faces and other objects * How science may just end up killing crime There is, it turns out.

Interest in the use of Artificial Intelligence (AI) in schools is growing. More educators are participating in important conversations about it as understanding develops around how AI will impact the work of teachers and schools. In this post I want to add to the conversation by raising some issues and putting forward some questions that I believe are critical. To begin I want to suggest a definition of the term'Artificial Intelligence' or AI as it is commonly known. What do we mean by'Artificial Intelligence'?

Industry datasets used for text classification are rarely created for that purpose. In most cases, the data and target predictions are a by-product of accumulated historical data, typically fraught with noise, present in both the text-based document, as well as in the targeted labels. In this work, we address the question of how well performance metrics computed on noisy, historical data reflect the performance on the intended future machine learning model input. The results demonstrate the utility of dirty training datasets used to build prediction models for cleaner (and different) prediction inputs.

Forecasting multivariate time series data, such as prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate the task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism. Typical attention mechanism reviews the information at each previous time step and selects the relevant information to help generate the outputs, but it fails to capture the temporal patterns across multiple time steps. In this paper, we propose to use a set of filters to extract time-invariant temporal patterns, which is similar to transforming time series data into its "frequency domain". Then we proposed a novel attention mechanism to select relevant time series, and use its "frequency domain" information for forecasting. We applied the proposed model on several real-world tasks and achieved the state-of-the-art performance in all of them with only one exception. We also show that to some degree the learned filters play the role of bases in discrete Fourier transform.

We provide an efficient algorithm for the classical problem, going back to Galton, Pearson, and Fisher, of estimating, with arbitrary accuracy the parameters of a multivariate normal distribution from truncated samples. Truncated samples from a $d$-variate normal ${\cal N}(\mathbf{\mu},\mathbf{\Sigma})$ means a samples is only revealed if it falls in some subset $S \subseteq \mathbb{R}^d$; otherwise the samples are hidden and their count in proportion to the revealed samples is also hidden. We show that the mean $\mathbf{\mu}$ and covariance matrix $\mathbf{\Sigma}$ can be estimated with arbitrary accuracy in polynomial-time, as long as we have oracle access to $S$, and $S$ has non-trivial measure under the unknown $d$-variate normal distribution. Additionally we show that without oracle access to $S$, any non-trivial estimation is impossible.

Latent feature models (LFM)s are widely employed for extracting latent structures of data. While offering high, parameter estimation is difficult with LFMs because of the combinational nature of latent features, and non-identifiability is a particularly difficult problem when parameter estimation is not unique and there exists equivalent solutions. In this paper, a necessary and sufficient condition for non-identifiability is shown. The condition is significantly related to dependency of features, and this implies that non-identifiability may often occur in real-world applications. A novel method for parameter estimation that solves the non-identifiability problem is also proposed. This method can be combined as a post-process with existing methods and can find an appropriate solution by hopping efficiently through equivalent solutions. We have evaluated the effectiveness of the method on both synthetic and real-world datasets.

Amazon.com is in talks with Chile to house and mine massive amounts of data generated by the country's giant telescopes, which could prove fertile ground for the company to develop new artificial intelligence tools. The talks, which have been little reported on so far and which were described to Reuters by Chilean officials and an astronomer, are aimed at fuelling growth in Amazon.com President Sebastian Pinera's center-right government, which is seeking to wean Chile's $325 billion economy from reliance on copper mining, announced last week it plans to pool data from all its telescopes onto a virtual observatory stored in the cloud, without giving a timeframe. Amazon.com is in talks with Chile to house and mine massive amounts of data generated by the country's giant telescopes, which could prove fertile ground for the company to develop new artificial intelligence tools. The government talked of the potential for astrodata innovation, but did not give details.

McDonald's Monopoly finished in the UK back in May, but has now started in its fast food restaurants across Australia. The popular contest, available at participating McDonald's restaurants around the world, offers customers a chance to collect stickers and win special prizes. The top prizes include everything from video game consoles, to brand-new cars. However in an article for The Conversation, Sarah Belet, a postgraduate student from Monash University, and Jennifer Flegg, a senior lecturer in Applied Mathematics at the University of Melbourne, crunched the numbers to find out what chance McDonald's diners really have of winning. They found the chance of someone bagging a top prize is just 1 in 136 million – which is about as unlikely as winning at the EuroMillions jackpot.

How do we learn from biased data? Historical datasets often reflect historical prejudices; sensitive or protected attributes may affect the observed treatments and outcomes. Classification algorithms tasked with predicting outcomes accurately from these datasets tend to replicate these biases. We advocate a causal modeling approach to learning from biased data and reframe fair classification as an intervention problem. We propose a causal model in which the sensitive attribute confounds both the treatment and the outcome. Building on prior work in deep learning and generative modeling, we describe how to learn the parameters of this causal model from observational data alone, even in the presence of unobserved confounders. We show experimentally that fairness-aware causal modeling provides better estimates of the causal effects between the sensitive attribute, the treatment, and the outcome. We further present evidence that estimating these causal effects can help us to learn policies which are both more accurate and fair, when presented with a historically biased dataset.