In my book'Rise of the Moral Machine: Exploring Virtue Through a Robot's Eyes', I include a short fictional story about a couple (Mr and Mrs Morales) who are in the process of purchasing their first autonomous vehicle. Having chosen the model, the colour and the trim of the car, the last set of choices they are required to make concern the vehicle's'ethical alignment': i.e. the alignment of the vehicle's autonomous decisions on how it should drive with the Morales' social and ethical preferences. Without giving too much of the story away, the Morales' are presented with a series of situations each of which requires the autonomous vehicle to make a moral decision. These decisions are presented in terms of choices of who should be the casualties of an unavoidable collision, such as "should the vehicle run over the pensioner on the pedestrian crossing, or the child on the pavement?" (Figure 1).

To further strengthen our commitment to providing industry-leading coverage of data technology, VentureBeat is excited to welcome Andrew Brust and Tony Baer as regular contributors. Confidential computing focuses on potentially revolutionary technology, in terms of impact on data security. In confidential computing, data remains encrypted, not just at rest and in transit, but also in use, allowing analytics and machine learning (ML) to be performed on the data, while maintaining its confidentiality. The capability to encrypt data in use opens up a massive range of possible real-world scenarios, and it has major implications and potential benefits for the future of data security. VentureBeat spoke with Raluca Ada Popa about her research and work in developing practical solutions for confidential computing.

In this special guest feature, Dave DeCaprio, CTO and Co-founder, ClosedLoop.ai, discusses what it really takes to make AI that physicians trust. Dave has more than 20 years of experience transitioning advanced technology from academic research labs into successful businesses. His experience includes genome research, pharmaceutical development, health insurance, computer vision, sports analytics, speech recognition, transportation logistics, operations research, real time collaboration, robotics, and financial markets. Dave has been involved in several successful startups as well as consulting and advising both small and large organizations on how to innovate using technology with maximum impact. Dave graduated from MIT with a degree in Electrical Engineering and Computer Science and currently lives in Austin, TX.

Graph generative models are a highly active branch of machine learning. Given the steady development of new models of ever-increasing complexity, it is necessary to provide a principled way to evaluate and compare them. In this paper, we enumerate the desirable criteria for comparison metrics, discuss the development of such metrics, and provide a comparison of their respective expressive power. We perform a systematic evaluation of the main metrics in use today, highlighting some of the challenges and pitfalls researchers inadvertently can run into. We then describe a collection of suitable metrics, give recommendations as to their practical suitability, and analyse their behaviour on synthetically generated perturbed graphs as well as on recently proposed graph generative models.

On 1st October 2019, the UKRI Centre for Doctoral Training in Artificial Intelligence for Healthcare opens its doors to their first cohort. The United Kingdom Research and Innovation Centre for Doctoral Training in Artificial Intelligence for Healthcare (AI4Health) will open its doors to the first cohort of PhD students in October. Director Dr Aldo Faisal and the whole AI4Health Team are looking forward to getting started and turning vision into practice. Imperial College London understand the term "AI" as meaning the development of intelligent systems that embody a practical solution. However, practical solutions involving AI will require a broader approach and the College will drive technical innovation by providing broad training for exploitation of multiple technological strategies within the broader realm of AI.

Over the last few years machine learning has become embedded in a wide variety of day-to-day business, nonprofit, and government operations. As the popularity of machine learning increased, a cottage industry of high-quality literature that taught applied machine learning to practitioners developed. This literature has been highly successful in training an entire generation of data scientists and machine learning engineers. This literature also approached the topic of machine learning from the perspective of providing a learning resource to teach an individual what machine learning is and how it works. However, while fruitful, this approach left out a different perspective on the topic: the nuts and bolts of doing machine learning day to day.

Several kernel based methods for multi-task learning have been proposed, which leverage relations among tasks as regularization to enhance the overall learning accuracies. These methods assume that the tasks share the same kernel, which could limit their applications because in practice different tasks may need different kernels. The main challenge of introducing multiple kernels into multiple tasks is that models from different Reproducing Kernel Hilbert Spaces (RKHSs) are not comparable, making it difficult to exploit relations among tasks. This paper addresses the challenge by formalizing the problem in the Square Integrable Space (SIS). Specially, it proposes a kernel based method which makes use of a regularization term defined in the SIS to represent task relations. We prove a new representer theorem for the proposed approach in SIS. We further derive a practical method for solving the learning problem and conduct consistency analysis of the method. We discuss the relations between our method and an existing method. We also give an SVM based implementation of our method for multi-label classification. Experiments on two real-world data sets show that the proposed method performs better than the existing method.