Artificial Intelligence (AI) is the most important ethical issue of our age. It's certain that it will continue to play an ever-increasing role in all our lives. Last year, Facebook claimed it would be able to predict when we will die, along with other key life events, from marriages to deaths, based on social media activity. It's encouraging that some leading international data scientists are now keen to engage with philosophers and faith leaders as they start to deal with the ethical issues raised by AI. They know that people involved in faith have had thousands of years of practice discussing issues such as AI, which are hard to define but have a great impact on humanity.

Conversations about AI often focus on technology, but we shouldn't forget that most business outcomes are linked to relationships with people, said Warren Hero, chief digital officer at Webber Wentzel, a law firm headquartered in Johannesburg Webber Wentzel – recently recognized as the African law firm of the year – aims to build a completely digital practice. Hero was speaking at AfricaCom about his experience of applying AI in the legal profession to increase both efficiency and productivity. He revealed that in the first AI experiment the company ran, it was able to decrease the time required to deliver a specific service from seven months to just three weeks. "in the process, we were able to give our clients certainty about, first of all, the quality of the outcome. And, second of all, about the cost of that outcome," Hero said.

"I say this to everyone in the media world who I talk to," says Darren Atkins, wrapping up our phone interview: "Please, absolutely do not portray this as a hidden agenda to get rid of staff." Atkins is the Chief Technology Office for AI automation at East Suffolk and North Essex NHS Foundation Trust – group of hospitals employing more than 10,000 staff, who serve a quarter of a million people in the South East of England. "If this technology is applied in the wrong way, it can be very threatening," Atkins says. "Our main priority is to free up time for staff to do the work that they should be doing, rather than the work that has no value." Just over a year ago, Atkins led the deployment of virtual workers across his group of NHS hospitals – and according to him, it's been an unqualified success. Patients are missing fewer appointments and staff are happier.

Between 2013 and 2016, U.S. farmers and ranchers weathered a 45% dip in net farm income -- the largest since the Great Depression -- while the number of mouths to feed grew sharply by the day. The global population is expected to increase by 2.2 billion by 2050, and the world's farmers will have to grow about 70% more food than is now produced. If you ask Microsoft, the solution lies in technology. The tech giant's FarmBeats program, which launched in preview today on Azure Marketplace ahead of Ignite 2019, is a multi-year effort to bring robust data analytics to the agriculture sector. With a backend built on Azure and compatibility with hardware from a range of top manufacturers, it aims to promote what Ranveer Chandra, FarmBeats project lead and chief scientist at Azure Global, calls "data-driven" farming techniques. The International Food Policy Research Institute claims these can boost farm productivity by as much as 67% while reducing resource usage.

"Banning killer robots is both politically savvy and morally necessary," said Mary Wareham, the Arms Division advocacy director at Human Rights Watch and coordinator of the Campaign to Stop Killer Robots. "European states should take the lead and open ban treaty negotiations if they are serious about protecting the world from this horrific development." Countries attending the annual meeting of states parties to the Convention on Conventional Weapons (CCW) at the United Nations in Geneva will decide on November 15 whether to continue diplomatic talks on killer robots, also known as lethal autonomous weapons systems or fully autonomous weapons. Since 2014, these states have held eight meetings on lethal autonomous weapons systems under the auspices of the Convention on Conventional Weapons (CCW), a major disarmament treaty. Over the course of those meetings, states have built a shared understanding of concern, but they have struggled to reach agreement on credible recommendations for multilateral action due to the objections of a handful of military powers, most notably Russia and the United States.

Streets swamped by muddy water with garbage floating by, roads impassable. As in previous years, Diamniadio Lake City has not escaped the series of floods that affect some cities in Senegal each rainy season. Indeed, this urban centre is preparing to test, thanks to Artificial Intelligence (AI), a new way of managing urban development. "By taking the Digital Technologies Park of Diamniadio as a reference site, we have carried out modelling and worked on water runoff scenarios in order to channel them and solve these flood problems," Bassirou Abdoul Ba, coordinator of the Digital Technologies Park, told Scidev.Net. This park, covering 25 hectares, is the first experimental phase of the "smart city" under construction 35km from Dakar, the Senegalese capital.

Four years ago, I stood in the darkened operations center in front of a wall of blinking screens, arms crossed and squinting at video footage on one of them. The commander asked me for the second time, signaling toward the figure on the screen. I looked over and reviewed a mental checklist of the individual's pattern of life over more than a decade. I weighed this against his latest movements, reflected on the screen in real time. The commander took a step toward me and started again, "Kara. We are running out of time. I had a decision to make. Using a machine to determine the validity of the target and take action is a nonstarter. But not everyone agrees on the details. Though the machines I dealt with that day were only semi-autonomous, it is not difficult to imagine a world where fully autonomous weapons are programmed to make a lethal decision. Institutions, countries, industry, and society must choose when and how to govern this technology in today's world, where semi-autonomous ...

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This work provides a review of this literature. The motivation for this summary arose from our companion paper Ruf and W ang [2019]. There we continue th e discussions of this note; in particular, of potentially problematic data leakage when training ANNs to historic financial data. This paper is organised in the following way. Section 2 featu res Table 1, a summary of the literature that concerns the use of ANNs for nonparametric pricing (and hedging) of options. Section 3 provides a list of recommended papers from Table 1. Section 4 provides a n overview of related work where ANNs are applied in the context of option pricing and hedging, but not necessarily as nonparametric estimation tools. Section 5 briefly discusses various regularisation techniq ues used in the reviewed literature.

Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective tool for phenotyping. Most of the existing works assume either a static patient representation with aggregate data or only model temporal data. However, real EHR data contain both temporal (e.g., longitudinal clinical visits) and static information (e.g., patient demographics), which are difficult to model simultaneously. In this paper, we propose Temporal And Static TEnsor factorization (TASTE) that jointly models both static and temporal information to extract phenotypes. TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor. To fit the proposed model, we transform the original problem into simpler ones which are optimally solved in an alternating fashion. For each of the sub-problems, our proposed mathematical reformulations lead to efficient sub-problem solvers. Comprehensive experiments on large EHR data from a heart failure (HF) study confirmed that TASTE is up to 14x faster than several baselines and the resulting phenotypes were confirmed to be clinically meaningful by a cardiologist. Using 80 phenotypes extracted by TASTE, a simple logistic regression can achieve the same level of area under the curve (AUC) for HF prediction compared to a deep learning model using recurrent neural networks (RNN) with 345 features.