Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised. As their size and expressivity increases, so too does the variance of the model, yielding a nearly ubiquitous overfitting problem. Although mitigated by a variety of model regularisation methods, the common cure is to seek large amounts of training data--which is not necessarily easily obtained--that sufficiently approximates the data distribution of the domain we wish to test on. In contrast, logic programming methods such as Inductive Logic Programming offer an extremely data-efficient process by which models can be trained to reason on symbolic domains. However, these methods are unable to deal with the variety of domains neural networks can be applied to: they are not robust to noise in or mislabelling of inputs, and perhaps more importantly, cannot be applied to non-symbolic domains where the data is ambiguous, such as operating on raw pixels. In this paper, we propose a Differentiable Inductive Logic framework, which can not only solve tasks which traditional ILP systems are suited for, but shows a robustness to noise and error in the training data which ILP cannot cope with. Furthermore, as it is trained by backpropagation against a likelihood objective, it can be hybridised by connecting it with neural networks over ambiguous data in order to be applied to domains which ILP cannot address, while providing data efficiency and generalisation beyond what neural networks on their own can achieve.

Two years ago, roboticists from Davide Scaramuzza's lab at the University of Zurich used a set of pictures taken by cameras mounted on a hiker's head to train a deep neural network, which was then able to fly an inexpensive drone along forest paths without running into anything. This is cool, for two reasons: The first is that you can use this technique to make drones with minimal on-board sensing and computing fully autonomous, and the second is that you can do so without collecting dedicated drone-centric training datasets first. In a new paper appearing in IEEE Robotics and Automation Letters, Scaramuzza and one of his Ph.D. students, Antonio Loquercio, along with collaborators Ana I. Maqueda and Carlos R. del-Blanco from Universidad Politécnica de Madrid, in Spain, present some new work in which they've trained a drone to autonomously fly through the streets of a city, and they've done it with data collected by cars and bicycles. Most autonomous drones (and most autonomous robots in general) that don't navigate using a pre-existing map rely on some flavor of simultaneous localization and mapping, or (as the researchers put it), "map-localize-plan." Building a map, localizing yourself on that map, and then planning safe motion is certainly a reliable way to move around, but it requires big, complex, and of course very expensive, power-hungry sensors and computers.

Big data and artificial intelligence are two exciting issues that will be top of mind with ECR 2018 attendees, says Bernd Hamm, ECR president(1). And applications for both, along with other innovative diagnostic imaging solutions, will be front and center at the Carestream stand during the annual meeting of the European Society of Radiology in Vienna. See us in our new location – booth #405 in Hall X4! Highlights that you won't want to miss at the ECR 2018 congress include: See Carestream's medical imaging and healthcare IT systems at ECR 2018 See us in our new location at ECR 2018- booth #405 in Hall X4! Finally at ECR 2018, Carestream is presenting a Symposium on the OnSight 3D Extremity System that uses cone beam technology. The symposium will be 2.00pm on Friday 2nd March in Room Z, Second level ACV. Speakers will cover a range of subjects including the Evolution of CBCT; Use of OnSight 3D in emergency settings; clinical Indications for weight-bearing CBCT, workflow, and usability.

Technology developed at the University of Waterloo is paving the way for artificial intelligence (AI) to break free of the internet and cloud computing. New deep-learning AI software produced with that technology is compact enough to fit on mobile computer chips for use in everything from smartphones to industrial robots. That would allow devices to operate independent of the internet while using AI that performs almost as well as tethered neural networks. "We feel this has enormous potential," said Alexander Wong, a systems design engineering professor and Waterloo and co-creator of the technology. "This could be an enabler in many fields where people are struggling to get deep-learning AI in an operational form."

On Friday, Apple acquired another AI company: VocalIQ, a UK-based startup developing technology to help computers understand human speech. Google, Facebook, and Microsoft are all engaged in an AI arms race, hiring experts in the field from academia and acquiring specialized startups.

Theresa May is on her way to Davos, to speak at the World Economic Forum - and the papers are already stating that she is going to call for'safe and ethical' artificial intelligence. In my opinion, before we can even start to talk about'safe and ethical' artificial intelligence, we have to have ethical roboticists. Without a clear understanding of the ethical challenges that roboticists face, how can we establish standards for ethical artificial intelligence development – standards which are absolutely vital? But, 'Safe and ethical' artificial intelligence is a narrow approach. Whose moral compass will guide us through this revolution of technology?

Technology is getting smarter, faster. Experts including the authors of The Second Machine Age, Erik Brynjolfsson and Andrew McAfee, examine the impact that "thinking" machines may have on top-management roles.

As we move into 2018, the monthly Datasets Publishing Awards has concluded. We're pleased to have recognized many publishers of high-quality, original, and impactful datasets. It was only a little over a year ago that we opened up our public Datasets platform to data enthusiasts all over the world to share their work. We've now reached almost 10,000 public datasets, making choosing winners each month a difficult task! These interviews feature the stories and backgrounds of the November and December winners of the prize.

Recently, my application for insurance for a classic car I'd bought was refused. It was a first for me and when I inquired why, I was told that the insurance company was concerned that I associate with'high-value individuals'. I don't, but even if I did, how could this possibly impact my access to insurance? The broker kindly investigated on my behalf and discovered that a robot -- or more accurately an'automated decision-making machine' -- used by the insurance company had scoured the internet and discovered that in the distant past I'd been the motoring editor of a national newspaper. I was no wiser as to why this might suddenly have made me a liability.

Microsoft president Brad Smith, Princeton University professor Jennifer Rexford, and Mckinsey Global Institute chairman James Manyika discuss how to ensure AI can be a source for good.