AI is rapidly changing everything. It is transforming society, the very way we live and act as social creatures, how we behave, how we do business, and even the very fabric of our own human identity. Cities are managed by machine driven big data gathered by sensors, constructing together what we known as the Internet of Things. An escalating digital transformation is transforming our vast amounts of paper ledgers into digital records, from traffic to finance to medical records. These, which convey most of the data gathered about us, are now processed in the cloud and augmented with machine learning artificial intelligence's multiple tools, which are stored in blockchain distributed ledgers.

There are many characterisations of artificial intelligence (AI), such as Andrew Ng's in the World Intellectual Property Organization's (WIPO) report on Technology Trends 2019 regarding AI. He adds: "I can hardly imagine an industry which is not going to be transformed by AI." Precise definitions, however, are lacking. In order to come to grips with the term it is recommended to distinguish between AI techniques, such as machine learning, logic programming, fuzzy logic, probabilistic reasoning and ontology engineering, functional applications, and AI application fields. Computer vision, natural language processing and speech processing can be mentioned as examples of AI functional applications. The application fields are several, such as networks, life and medical sciences, telecommunications and transportation.

In 1969, artificial-intelligence pioneer and Nobel laureate Herbert Simon proposed a new science, one that approached the study of artificial objects just as one would study natural objects. "Natural science is knowledge about natural objects and phenomena," Simon wrote. "We ask whether there cannot also be'artificial' science -- knowledge about artificial objects and phenomena." Now, 50 years later, a team of researchers from Harvard, MIT, Stanford, the University of California, San Diego, Google, Facebook, Microsoft, and other institutions is renewing that call. In a recent paper published in the journal Nature, the researchers proposed a new, interdisciplinary field -- machine behavior -- that would study artificial intelligence through the lens of biology, economics, psychology, and other behavioral and social sciences.

Doctors could soon get some help from an artificial intelligence tool when diagnosing brain aneurysms -- bulges in blood vessels in the brain that can leak or burst open, potentially leading to stroke, brain damage or death. The AI tool, developed by researchers at Stanford and detailed in a paper published June 7 in JAMA Network Open, highlights areas of a brain scan that are likely to contain an aneurysm. "There's been a lot of concern about how machine learning will actually work within the medical field," said Allison Park, a graduate student in statistics and co-lead author of the paper. "This research is an example of how humans stay involved in the diagnostic process, aided by an artificial intelligence tool." This tool, which is built around an algorithm called HeadXNet, improved clinicians' ability to correctly identify aneurysms at a level equivalent to finding six more aneurysms in 100 scans that contain aneurysms.

The Western Australia Police Force (WAPF) has been piloting an Australian-developed cloud AI-based solution for managing the vast troves of data seized in cases involving digital evidence. Manually sorting through it all is a time-consuming task, and it can be easy to miss information and connections. WAPF has taken steps to address this issue by trialling a data and AI platform developed by Victoria's Modis. The trial, conducted in collaboration with Microsoft, has involved using Azure-based cognitive services to collect and efficiently analyse data, and to manage translations into English. The Data & AI solution is designed to use AI techniques to ingest and analyse large qualities of information collected during an investigation, helping investigators make sense of the evidence and find critical connections.

DUBAI, UNITED ARAB EMIRATES - Yemen's Houthi rebels said on Tuesday they launched at least two drones targeting a southwest Saudi city that's home to an air base. The Houthis' Al-Masirah satellite news channel reported the rebels launched Qasef-2K drones to strike the city of Khamis Mushait. The state-run Saudi Press Agency reported Tuesday, quoting military spokesman Col. Turki al-Maliki, that soldiers "intercepted" two drones launched by the Houthis. The Iranian-allied Houthis increasingly have targeted the kingdom with bomb-carrying drones. Khamis Mushait, some 815 km (510 miles) southwest of the capital, Riyadh, is near the kingdom's border with Yemen.

This paper introduces two straightforward, effective indices to evaluate the input data and the data flowing through layers of a feedforward deep neural network. For classification problems, the separation rate of target labels in the space of dataflow is explained as a key factor indicating the performance of designed layers in improving the generalization of the network. According to the explained concept, a shapeless distance-based evaluation index is proposed. Similarly, for regression problems, the smoothness rate of target outputs in the space of dataflow is explained as a key factor indicating the performance of designed layers in improving the generalization of the network. According to the explained smoothness concept, a shapeless distance-based smoothness index is proposed for regression problems. To consider more strictly concepts of separation and smoothness, their extended versions are introduced, and by interpreting a regression problem as a classification problem, it is shown that the separation and smoothness indices are related together. Through four case studies, the profits of using the introduced indices are shown. In the first case study, for classification and regression problems , the challenging of some known input datasets are compared respectively by the proposed separation and smoothness indices. In the second case study, the quality of dataflow is evaluated through layers of two pre-trained VGG 16 networks in classification of Cifar10 and Cifar100. In the third case study, it is shown that the correct classification rate and the separation index are almost equivalent through layers particularly while the serration index is increased. In the last case study, two multi-layer neural networks, which are designed for the prediction of Boston Housing price, are compared layer by layer by using the proposed smoothness index.

To solve tasks with sparse rewards, reinforcement learning algorithms must be equipped with suitable exploration techniques. However, it is unclear what underlying objective is being optimized by existing exploration algorithms, or how they can be altered to incorporate prior knowledge about the task. Most importantly, it is difficult to use exploration experience from one task to acquire exploration strategies for another task. We address these shortcomings by learning a single exploration policy that can quickly solve a suite of downstream tasks in a multi-task setting, amortizing the cost of learning to explore. We recast exploration as a problem of State Marginal Matching (SMM): we learn a mixture of policies for which the state marginal distribution matches a given target state distribution, which can incorporate prior knowledge about the task. Without any prior knowledge, the SMM objective reduces to maximizing the marginal state entropy. We optimize the objective by reducing it to a two-player, zero-sum game, where we iteratively fit a state density model and then update the policy to visit states with low density under this model. While many previous algorithms for exploration employ a similar procedure, they omit a crucial historical averaging step, without which the iterative procedure does not converge to a Nash equilibria. To parallelize exploration, we extend our algorithm to use mixtures of policies, wherein we discover connections between SMM and previously-proposed skill learning methods based on mutual information. On complex navigation and manipulation tasks, we demonstrate that our algorithm explores faster and adapts more quickly to new tasks.

The calibration of a reservoir model with observed transient data of fluid pressures and rates is a key task in obtaining a predictive model of the flow and transport behaviour of the earth's subsurface. The model calibration task, commonly referred to as "history matching", can be formalised as an ill-posed inverse problem where we aim to find the underlying spatial distribution of petrophysical properties that explain the observed dynamic data. We use a generative adversarial network pretrained on geostatistical object-based models to represent the distribution of rock properties for a synthetic model of a hydrocarbon reservoir. The dynamic behaviour of the reservoir fluids is modelled using a transient two-phase incompressible Darcy formulation. We invert for the underlying reservoir properties by first modeling property distributions using the pre-trained generative model then using the adjoint equations of the forward problem to perform gradient descent on the latent variables that control the output of the generative model. In addition to the dynamic observation data, we include well rock-type constraints by introducing an additional objective function. Our contribution shows that for a synthetic test case, we are able to obtain solutions to the inverse problem by optimising in the latent variable space of a deep generative model, given a set of transient observations of a non-linear forward problem.

Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned. With the advent of massive online markets powered by data-driven matching platforms, it has become necessary to better understand the interplay between learning and market objectives. We propose a statistical learning model in which one side of the market does not have a priori knowledge about its preferences for the other side and is required to learn these from stochastic rewards. Our model extends the standard multi-armed bandits framework to multiple players, with the added feature that arms have preferences over players. We study both centralized and decentralized approaches to this problem and show surprising exploration-exploitation trade-offs compared to the single player multi-armed bandits setting.