Many multi-agent systems (MASs) are situated in stochastic environments. Some such systems that are based on the partially observable Markov decision process (POMDP) do not take the benevolence of other agents for granted. We propose a new POMDP-based framework which is general enough for the specification of a variety of stochastic MAS domains involving the impact of agents on each other's reputations. A unique feature of this framework is that actions are specified as either undirected (regular) or directed (towards a particular agent), and a new directed transition function is provided for modeling the effects of reputation in interactions. Assuming that an agent must maintain a good enough reputation to survive in the network, a planning algorithm is developed for an agent to select optimal actions in stochastic MASs. Preliminary evaluation is provided via an example specification and by determining the algorithm's complexity.

South Africa is facing a shortage of data scientists – a new breed of analytical data experts with the technical skills to solve complex problems. They're part mathematician, part computer scientist and part trend-spotter. And, because they straddle both the business and IT worlds, they're highly sought-after and well paid. The demand for data scientists is being driven by the emergence of big data – that unwieldy mass of unstructured information that can no longer be ignored and forgotten. It's a potential gold mine for companies – as long as there's someone who can dig in and unearth the business insights that no one thought to look for before.

A link has been posted to your Facebook feed. Question: What kind of hardware and software technology do pilots use when flying planes? Answer: Hardware and software vary by manufacturer and operator. Flight management computers use very complex software to perform a myriad of tasks. The interface is improving with airplanes such as the Boeing 787 and Airbus A350.

IsiXhosa is an interesting language that has over 9 million speakers. It is a language often associated with clicks. Our famous musician, the late Mama Africa, Miriam Makeba, made isiXhosa famous by introducing the Click Song, also called Qongqothwane to the world. Despite the stereotype, isiXhosa is not a clicking language but a Bantu language. Joseph Greenberg, the US linguist classified African languages into four stocks, one of which is the Bantu language that is spoken from Tanzania to South Africa.

Security threats are major concerns to healthcare organizations due to the value and vulnerability of clinical data that is being recorded and distributed. The value of the data comes from the fact that it is historical in nature; it directly affects our ability to safely treat patients, it takes a long time to rebuild, and it contains more than just clinical data, but also a lot of personal, financial, and demographic data, which allows it to be used for wider identity theft. It is persistent, whereas you can change credit cards and their passwords, PINs and account numbers in the event of a breach, you cannot change your mother's maiden name. The vulnerability comes from the fact that there has been a revolution in healthcare with the interconnection of systems, cloud computing, Internet of Health Things (IoHT) and mobile devices and the changes in working practices of clinicians, such as remote monitoring, telemedicine, and working from home. This revolution has not always been matched with the security awareness, policies, practices, and budgets of health care organizations.

Maven is also only one of hundreds of AI initiatives being pursued across the Pentagon. So many programs spread out over such a large organization can be the stuff of nightmares for military planners, but the building's hard-charging new undersecretary for research and engineering, Michael Griffin, said recently that he is setting up a Joint Artificial Intelligence Center that will will tie together the military's efforts with those of the Intelligence Community, allowing them to combine efforts in a breakneck push to move government's AI initiatives forward.

Chatbots and artificial intelligence (AI) are becoming business essentials. If utilised correctly, the continent will be able to benefit from the wealth of data its citizens generate on a daily basis. You recently participated on a panel titled Entrepreneurial Perspectives on Emerging Technologies in the Field of Artificial Intelligence and Autonomous Systems in Switzerland. With the power of big data, companies in Africa are looking for opportunities to provide more personalised customer experiences, improve core business processes and spark innovation through the use of AI. Businesses use chatbots to collect meaningful data through conversation.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

We introduce a new neural network model, together with a tractable and monotone online learning algorithm. Our model describes feed-forward networks for classification, with one output node for each class. The only nonlinear operation is rectification using a ReLU function with a bias. However, there is a rectifier on every edge rather than at the nodes of the network. There are also weights, but these are positive, static, and associated with the nodes. Our "rectified wire" networks are able to represent arbitrary Boolean functions. Only the bias parameters, on the edges of the network, are learned. Another departure in our approach, from standard neural networks, is that the loss function is replaced by a constraint. This constraint is simply that the value of the output node associated with the correct class should be zero. Our model has the property that the exact norm-minimizing parameter update, required to correctly classify a training item, is the solution to a quadratic program that can be computed with a few passes through the network. We demonstrate a training algorithm using this update, called sequential deactivation (SDA), on MNIST and some synthetic datasets. Upon adopting a natural choice for the nodal weights, SDA has no hyperparameters other than those describing the network structure. Our experiments explore behavior with respect to network size and depth in a family of sparse expander networks.

In this paper, we investigate the tendency of end-to-end neural Machine Reading Comprehension (MRC) models to match shallow patterns rather than perform inference-oriented reasoning on RC benchmarks. We aim to test the ability of these systems to answer questions which focus on referential inference. We propose ParallelQA, a strategy to formulate such questions using parallel passages. We also demonstrate that existing neural models fail to generalize well to this setting.