It's a question that Diana Akrong found herself asking last year. Diana is a UX researcher based in Accra, Ghana, and the founding member of Google's Accra UX team. Across the world, her manager Dr. Courtney Heldreth, was equally interested in answering this question. Courtney is a social psychologist and a staff UX researcher based in Seattle, and both women work as part of Google's People Artificial Intelligence Research (PAIR) group. "Looking back on history, we can see how the industrial revolution played a significant role in creating global inequality," she says.

Games have long been a fertile testing ground for the artificial intelligence community, and not just because of their accessibility to the popular imagination. Games also enable researchers to simulate different models of human intelligence, and to quantify performance. No surprise, then, that the 2016 victory of DeepMind's AlphaGo algorithm--developed by 2019 ACM Computing Prize recipient David Silver, who leads the company's Reinforcement Learning Research Group--over world Go champion Lee Sedol generated excitement both within and outside of the computing community. As it turned out, that victory was only the beginning; subsequent iterations of the algorithm have been able to learn without any human data or prior knowledge except the rules of the game and, eventually, without even knowing the rules. Here, Silver talks about how the work evolved and what it means for the future of general-purpose AI.

How do we deal with very large data sets of high resolution images, in particular in the field of radio astronomy? This question encompasses the scope of a joint PhD project between the University of Groningen (The Netherlands), the University of Stellenbosch (South Africa), and ASTRON, which is the Netherlands Institute for Radio Astronomy. Modern radio telescopes typically consist of 100 to a few hundred receiving elements, whose signals are pairwise correlated producing tens of thousands correlations for tens of thousands of frequency channels simultaneously. For a system like the Square Kilometre Array (SKA) this produces a data deluge of 1 TByte/s. This data may be affected by man-made radio frequency interference (RFI), instrumental failures and other effects that make the data unsuitable for scientific analysis.

Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multi-level Context and Uncertainty aware (MCUa) dynamic deep learning ensemble model.MCUamodel consists of several multi-level context-aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model.MCUamodelhas achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models.

The rights associated with intellectual property are of immense importance to those involved in the development, exploitation and use of computer hardware and software, and information technology generally. Trademarks do not protect technology, but the names or symbols used to distinguish a product in the marketplace. This means that these intellectual property rights accord different types of legal protection on software programmes. The idea must be fixed in definite medium of expression and it must be ascertained that it's the author's own intellectual creation. There are two right or benefits that accrue to a computer programmer with respect to his software programme, which are Economic Right and Moral Right.

The Patent Office of South Africa has issued the world's first patent for an invention created by artificial intelligence. The DABUS system, which simulates human mental activity, has created a food container based on fractal geometry and has improved characteristics compared to containers of standard shapes. The application was submitted to the agency on September 17, 2019, indicating that the invention was generated by an autonomous artificial intelligence. The" author " of the invention is DABUS (Device for Autonomous Bootstrapping of Unified Sentience), an artificial intelligence system that simulates the human thought process to generate new ideas and inventions. DABUS was able to create a food container based on fractal geometry with improved structural strength and reduced heat transfer compared to conventional containers.

This transcript has been edited for clarity. This is Eric Topol with Medicine and the Machine, with my co-host, Abraham Verghese. This is a special edition for us, to speak with one of the leading lights of artificial intelligence (AI) in the world, Jeff Dean, who heads up Google AI. Jeff Dean, PhD: Thank you for having me. Topol: You have now been at Google for 22 years. In a recent book by Cade Metz (a New York Times tech journalist) called Genius Makers, you are one of the protagonists. I didn't know this about you, but you grew up across the globe. Your parents took you from Hawaii, where you were born, to Somalia, where you helped run a refugee camp during your middle school years. As a high school senior in Georgia where your father worked at the CDC, you built a software tool for them that helped researchers collect disease data, and nearly four decades later it remains a staple of epidemiology across the developing world.

Low-tubal-rank tensor approximation has been proposed to analyze large-scale and multi-dimensional data. However, finding such an accurate approximation is challenging in the streaming setting, due to the limited computational resources. To alleviate this issue, this paper extends a popular matrix sketching technique, namely Frequent Directions, for constructing an efficient and accurate low-tubal-rank tensor approximation from streaming data based on the tensor Singular Value Decomposition (t-SVD). Specifically, the new algorithm allows the tensor data to be observed slice by slice, but only needs to maintain and incrementally update a much smaller sketch which could capture the principal information of the original tensor. The rigorous theoretical analysis shows that the approximation error of the new algorithm can be arbitrarily small when the sketch size grows linearly. Extensive experimental results on both synthetic and real multi-dimensional data further reveal the superiority of the proposed algorithm compared with other sketching algorithms for getting low-tubal-rank approximation, in terms of both efficiency and accuracy.

Deep learning's unprecedented success raises several ethical concerns ranging from biased predictions to data privacy. Researchers tackle these issues by introducing fairness metrics, or federated learning, or differential privacy. A first, this work presents an ethical federated learning model, incorporating all three measures simultaneously. Experiments on the Adult, Bank and Dutch datasets highlight the resulting ``empirical interplay" between accuracy, fairness, and privacy.

The past decade has seen a substantial rise in the amount of mis- and disinformation online, from targeted disinformation campaigns to influence politics, to the unintentional spreading of misinformation about public health. This development has spurred research in the area of automatic fact checking, from approaches to detect check-worthy claims and determining the stance of tweets towards claims, to methods to determine the veracity of claims given evidence documents. These automatic methods are often content-based, using natural language processing methods, which in turn utilise deep neural networks to learn higher-order features from text in order to make predictions. As deep neural networks are black-box models, their inner workings cannot be easily explained. At the same time, it is desirable to explain how they arrive at certain decisions, especially if they are to be used for decision making. While this has been known for some time, the issues this raises have been exacerbated by models increasing in size, and by EU legislation requiring models to be used for decision making to provide explanations, and, very recently, by legislation requiring online platforms operating in the EU to provide transparent reporting on their services. Despite this, current solutions for explainability are still lacking in the area of fact checking. This thesis presents my research on automatic fact checking, including claim check-worthiness detection, stance detection and veracity prediction. Its contributions go beyond fact checking, with the thesis proposing more general machine learning solutions for natural language processing in the area of learning with limited labelled data. Finally, the thesis presents some first solutions for explainable fact checking.