DeepMind has recently announced a fresh collaborative partnership with the UK's health service, with plans for the artificial intelligence firm to develop machine learning technology to research breast cancer. DeepMind, a Google subsidiary, is perhaps best known for successfully building AI that is now better than humans at the ancient game of Go. But in recent months – when attempting to apply this tech to serious healthcare issues – it has been on the sidelines of a data breach storm. In July, DeepMind's collaboration with London's Royal Free hospital led to the NHS trust violating the UK's data protection laws. The Information Commissioner's Office (ICO) found that Royal Free's decision to share 1.6m personally identifiable patient records with DeepMind for the development of Streams – an automated kidney injury detection software – was "legally inappropriate".

While just over a fifth of UK businesses have already invested in Artificial Intelligence (AI), more than eight in every ten executives will do so over the coming years. According to the latest analysis, half of all UK organisations will have sunk over £10 million into digital technology by 2020, as companies look to avoid being left by the wayside amid a period of major innovation. However, concerns regarding a digital skills shortage are hampering efforts to get the most out of AI. A new study from Deloitte has examined the opinions of 51 executives from the UK's most influential companies and public sector entities – worth a combined market value of £229 billion – regarding the implementation of AI in their businesses. The findings of Deloitte's Digital Disruption Index showed that 85% of the leaders responsible for digital technologies intend to invest in AI by the turn of the decade.

In a guest blogpost, Peter Pugh-Jones, head of technology at SAS UK & Ireland, reflects on how the analytics industry is evolving and what organisations need in a data-driven economy. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address. This email address doesn't appear to be valid.

Three very different robotics startups have been battling it out over the last week to win the "Robohub Choice" award in our annual startup competition. One was social, one was medical and one was agricultural! Also, one was from the UK, one was from the Ukraine and one was from Canada. Although nine startups entered the voting, it was clear from the start that it was a three horse race – thanks to our Robohub readers and the social media efforts of the startups. The most popular startup was UniExo with 70.6% of the vote, followed by BotsAndUs on 14.8% and Northstar Robotics on 13.2%.

My name is Alessia Nigretti and I am a Technical Evangelist for Unity. My job is to introduce Unity's new features to developers. My fellow evangelist Ciro Continisio and I developed the first demo game that uses the new Unity Machine Learning Agents system and showed it at DevGamm Minsk 2017. This post is based on our talk and explains what we learned making the demo. At the same time, we invite you to join the ML-Agents Challenge and show off your creative use-cases of the toolkit.

She's appeared on the cover of Britain's ELLE magazine and talk shows like Good Morning Britain and The Tonight Show. When Charlie Rose interviewed her on CBS's 60 Minutes this past June, the dangers of this humanoid hit me like a tidal wave. Sophia is the latest in a string of robotic humanoids. And, she is arguably the most powerful and threatening "woman" on the planet. Renowned scientist Stephen Hawking and hundreds of technology leaders are also worried about the potential risk of robots like Sophie being used as weaponry.

Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network can be increased by widening and adding layers. However, usually the former introduces additional parameters, while the latter increases the runtime. As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution. By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence. Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model.

Complex data-generating processes are often described using graph models [1], [2]. In such models, each node represents a component with a signal. Directed links between nodes represent their influence on each other. For example, in the case of sensor networks, a distance-based graph is often used to characterize the underlying process [3]. In this paper, we are interested in graph models that are useful for prediction and filtering tasks. In the former case, the goal is to predict the signal values at a subset of nodes using information from the remaining nodes. In the latter case, the observed signal has been subject to some unknown perturbation and the goal is to identify the magnitude and source nodes of the perturbation [4]. To address both tasks, we aim to learn partial correlation graph models from a finite set of training data. Such graphs can be viewed as the minimal-assumption counterparts of conditional independence graphs [5], [6].

Designing a logo for a new brand is a lengthy and tedious back-and-forth process between a designer and a client. In this paper we explore to what extent machine learning can solve the creative task of the designer. For this, we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web. Training Generative Adversarial Networks (GANs) for logo synthesis on such multi-modal data is not straightforward and results in mode collapse for some state-of-the-art methods. We propose the use of synthetic labels obtained through clustering to disentangle and stabilize GAN training. We are able to generate a high diversity of plausible logos and we demonstrate latent space exploration techniques to ease the logo design task in an interactive manner. Moreover, we validate the proposed clustered GAN training on CIFAR 10, achieving state-of-the-art Inception scores when using synthetic labels obtained via clustering the features of an ImageNet classifier. GANs can cope with multi-modal data by means of synthetic labels achieved through clustering, and our results show the creative potential of such techniques for logo synthesis and manipulation. Our dataset and models will be made publicly available at https://data.vision.ee.ethz.ch/cvl/lld/.

The problem of multi-speaker localization is formulated as a multi-class multi-label classification problem, which is solved using a convolutional neural network (CNN) based source localization method. Utilizing the common assumption of disjoint speaker activities, we propose a novel method to train the CNN using synthesized noise signals. The proposed localization method is evaluated for two speakers and compared to a well-known steered response power method.