She has no body, no face, no desire to drink coffee or eat pasta, or even watch cat videos on YouTube. She is, in fact, a specially designed AI juror. Created by Belgian AI engineering service provider Faction XYZ and advertising agency DDB, Pearl has one purpose – to impartially, and without bias, select the best advertising campaigns in the world. Designed to celebrate the 10th anniversary of the Belgian MIXX awards by selecting a winner in a new AI Intelligence Award category, Pearl is a fascinating experiment to determine whether or not creativity is measurable. Powered by Microsoft's Video Indexer Service – a tool which automatically analyses videos using machine learning to uncover insights – Pearl was fed with all international MIXX case studies from the past ten years, and given three months to analyse the texts, videos, music and results.

They were once seen as a bizarre fetish, but it seems that sex dolls are now so widely in demand that they could be going into mass production. Samantha, an eerily realistic sex bot, is currently on sale in London, but could soon be available for the masses. The robot's designer claims that he is looking to mass produce the head for Samantha in Wales in a bid to keep up with growing demands. They were once seen as a bizarre fetish, but it seems that sex dolls are now so widely in demand that they could be going into mass production. 'Silicon Samantha' is covered in sensors that respond to human touch and can switch between'family' and'sexy' mode.

The lives of elderly and the disabled will be transformed by self-driving cars, the Transport Secretary claimed today. The first autonomous cars are expected to be on Britain's roads by 2021. In a speech in London, Chris Grayling promoted the benefits of this new mode of transport to the economy and to society. The government has estimated that driverless cars could be worth £28 billion to the economy by 2035. The first autonomous cars are expected to be on Britain's roads by 2021 and increase mobility for nearly a third of the population It has also been claimed automated cars will make the roads safer, with 85 per cent of accidents last year caused by human error.

Millions of NHS patients are being offered an appointment with their GP by video on their smartphones under a controversial new scheme. The service, which has left experts concerned, is to be trialed at five surgeries in London but it is predicted it will become the standard way to'see' a doctor. Patients will be able to get a GP appointment within minutes using the new service, which will work 24 hours a day, seven days a week. The technology has been developed for use in private medicine by Babylon. It will be paid from the public purse to run the trial of the'GP at Hand' service.

Cutting Edge Deep Learning For Coders, Part 2 from fast.ai

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.

Mechanistic models of single-neuron dynamics have been extensively studied in computational neuroscience. However, identifying which models can quantitatively reproduce empirically measured data has been challenging. We propose to overcome this limitation by using likelihood-free inference approaches (also known as Approximate Bayesian Computation, ABC) to perform full Bayesian inference on single-neuron models. Our approach builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters. We learn a Bayesian mixture-density network approximating the posterior over multiple rounds of adaptively chosen simulations. Furthermore, we propose an efficient approach for handling missing features and parameter settings for which the simulator fails, as well as a strategy for automatically learning relevant features using recurrent neural networks. On synthetic data, our approach efficiently estimates posterior distributions and recovers ground-truth parameters. On in-vitro recordings of membrane voltages, we recover multivariate posteriors over biophysical parameters, which yield model-predicted voltage traces that accurately match empirical data. Our approach will enable neuroscientists to perform Bayesian inference on complex neuron models without having to design model-specific algorithms, closing the gap between mechanistic and statistical approaches to single-neuron modelling.

A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedded across multiple measurements. We propose an approach for extracting low-dimensional dynamics from multiple, sequential recordings. Our algorithm scales to data comprising millions of observed dimensions, making it possible to access dynamics distributed across large populations or multiple brain areas. Building on subspace-identification approaches for dynamical systems, we perform parameter estimation by minimizing a moment-matching objective using a scalable stochastic gradient descent algorithm: The model is optimized to predict temporal covariations across neurons and across time. We show how this approach naturally handles missing data and multiple partial recordings, and can identify dynamics and predict correlations even in the presence of severe subsampling and small overlap between recordings. We demonstrate the effectiveness of the approach both on simulated data and a whole-brain larval zebrafish imaging dataset.

Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are necessary to infer the true underlying spiking activity from fluorescence measurements. Bayesian model inversion can be used to solve this problem, but typically requires either computationally expensive MCMC sampling, or faster but approximate maximum-a-posteriori optimization. Here, we introduce a flexible algorithmic framework for fast, efficient and accurate extraction of neural spikes from imaging data. Using the framework of variational autoencoders, we propose to amortize inference by training a deep neural network to perform model inversion efficiently. The recognition network is trained to produce samples from the posterior distribution over spike trains. Once trained, performing inference amounts to a fast single forward pass through the network, without the need for iterative optimization or sampling. We show that amortization can be applied flexibly to a wide range of nonlinear generative models and significantly improves upon the state of the art in computation time, while achieving competitive accuracy. Our framework is also able to represent posterior distributions over spike-trains. We demonstrate the generality of our method by proposing the first probabilistic approach for separating backpropagating action potentials from putative synaptic inputs in calcium imaging of dendritic spines.

While all kinds of mixed data -from personal data, over panel and scientific data, to public and commercial data- are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult. Users spend significant amounts of time in identifying the parametric form of the random variables (Gaussian, Poisson, Logit, etc.) involved and learning the mixed models. To make this difficult task easier, we propose the first trainable probabilistic deep architecture for hybrid domains that features tractable queries. It is based on Sum-Product Networks (SPNs) with piecewise polynomial leave distributions together with novel nonparametric decomposition and conditioning steps using the Hirschfeld-Gebelein-R\'enyi Maximum Correlation Coefficient. This relieves the user from deciding a-priori the parametric form of the random variables but is still expressive enough to effectively approximate any continuous distribution and permits efficient learning and inference. Our empirical evidence shows that the architecture, called Mixed SPNs, can indeed capture complex distributions across a wide range of hybrid domains.