In a statement, CLDC says that it will work with Rui Xin to develop a consumer financial platform. CLDC expects to provide value-added consumer financial services to insurance consumers of Rui Xin and its partners. In addition, CLDC and Rui Xin will explore opportunities for collaboration in areas such as insurance consumer acquisition, development of insurance products, expansion of insurance business, and customisation of consumer financial solutions. Moreover, CLDC will benefit from Rui Xin and its partners' advanced technological capabilities in big data and artificial intelligence to improve its risk management and enhance its customer experience. In its turn, Rui Xin will be able to explore new business opportunities and increase its competency to eventually expand its customer base in the insurance industry by benefiting from CLDC's financial service expertise, bank credit facility resources, and client base in certain regional markets.

On July 15, 2019 Paige, the leader in computational pathology focused on building artificial intelligence (AI) to transform the clinical diagnosis and treatment of cancer, reported the publication of an article in Nature Medicine, a leading monthly journal publishing original peer-reviewed research in all areas of medicine, describing an AI system for computational pathology that achieves clinical-grade accuracy levels (Press release, Paige AI, JUL 15, 2019, View Source [SID1234537535]). The paper provides further scientific evidence that pathologists' work in diagnosing and treating cancer can be complemented and aided through the deployment of computational decision-support systems to improve patient care. NEW REPORT: Immuno-Oncology Drug Development: Analytical Tool Immuno-Oncology Drug Development: Analytical Tool is the most up to date and comprehensive commercial pipeline review and competitive assessment available on this hot and fast moving area in oncology. This unique product is truly the only one of its kind and is designed to give you a competitive edge in your I-O drug intelligence. Covers more than 1204 companies plus partners who are today developing 3691 I-O drugs in cancer across 617 different targets.

An artificial intelligence system created by researchers at the University of California has solved the Rubik's Cube in just over a second. DeepCubeA, as the algorithm was called, completed the 3D logic puzzle which has been taxing humans since it was invented in 1974. "It learned on its own," said report author Prof Pierre Baldi. The researchers noted that its strategy was very different from the way humans tackle the puzzle. "My best guess is that the AI's form of reasoning is completely different from a human's," said Prof Baldi, who is professor of computer science at University of California, Irvine.

Thiel's criticism appears to refer to Google's 2018 decision not to renew its contract with the Department of Defense, which allowed the agency to review drone footage with the company's artificial intelligence tools. The same year, Google faced backlash for working on "Dragonfly," a project to create a censored search engine for China. However, in December, CEO Sundar Pichai announced there were no plans to launch it.

Drone delivery service Wing is launching its own air-traffic control app to keep its craft safe in the skies. The company, owned by Google-parent Alphabet, recently started making deliveries in parts of Australia and Finland. Wing's new iOS and Android app aims to'help users comply with rules and plan flights more safely and effectively,' providing a rundown of airspace restrictions and hazards as well as events nearby that could interfere. The new app, Open Sky, is being released to drone flyers in Australia this month according to Wing. 'The design of our software has required a detailed understanding of flight rules -- along with buildings, roads, trees, and other terrain -- that allow aircraft to navigate safely at low altitudes, and we've used it to complete tens of thousands of flights on three continents,' Wing said in a blog post.

Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures accuracy between predicted values and ground truth values. Decision-focused learning explicitly integrates the downstream decision problem when training the predictive model, in order to optimize the quality of decisions induced by the predictions. It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization. However, these previous applications have uniformly focused on problems from specific classes with simple constraints. Here, we enable decision-focused learning for the broad class of problems that can be encoded as a Mixed Integer Linear Program (MIP), hence supporting arbitrary linear constraints over discrete and continuous variables. We show how to differentiate through a MIP by employing a cutting planes solution approach, which is an exact algorithm that iteratively adds constraints to a continuous relaxation of the problem until an integral solution is found. We evaluate our new end-to-end approach on several real world domains and show that it outperforms the standard two phase approaches that treat prediction and prescription separately, as well as a baseline approach of simply applying decision-focused learning to the LP relaxation of the MIP.

To find the path that minimizes the time to navigate between two given points in a fluid flow is known as the Zermelo's problem. Here, we investigate it by using a Reinforcement Learning (RL) approach for the case of a vessel which has a slip velocity with fixed intensity, V_s, but variable direction and navigating in a 2D turbulent sea. We use an Actor-Critic RL algorithm, and compare the results with strategies obtained analytically from continuous Optimal Navigation (ON) protocols. We show that for our application, ON solutions are unstable for the typical duration of the navigation process, and are therefore not useful in practice. On the other hand, RL solutions are much more robust with respect to small changes in the initial conditions and to external noise, and are able to find optimal trajectories even when V_s is much smaller than the maximum flow velocity. Furthermore, we show how the RL approach is able to take advantage of the flow properties in order to reach the target, especially when the steering speed is small.

Digital Rock Imaging is constrained by detector hardware, and a trade-off between the image field of view (FOV) and the image resolution must be made. This can be compensated for with super resolution (SR) techniques that take a wide FOV, low resolution (LR) image, and super resolve a high resolution (HR), high FOV image. The Enhanced Deep Super Resolution Generative Adversarial Network (EDSRGAN) is trained on the Deep Learning Digital Rock Super Resolution Dataset, a diverse compilation 12000 of raw and processed uCT images. The network shows comparable performance of 50% to 70% reduction in relative error over bicubic interpolation. GAN performance in recovering texture shows superior visual similarity compared to SRCNN and other methods. Difference maps indicate that the SRCNN section of the SRGAN network recovers large scale edge (grain boundaries) features while the GAN network regenerates perceptually indistinguishable high frequency texture. Network performance is generalised with augmentation, showing high adaptability to noise and blur. HR images are fed into the network, generating HR-SR images to extrapolate network performance to sub-resolution features present in the HR images themselves. Results show that under-resolution features such as dissolved minerals and thin fractures are regenerated despite the network operating outside of trained specifications. Comparison with Scanning Electron Microscope images shows details are consistent with the underlying geometry of the sample. Recovery of textures benefits the characterisation of digital rocks with a high proportion of under-resolution micro-porous features, such as carbonate and coal samples. Images that are normally constrained by the mineralogy of the rock (coal), by fast transient imaging (waterflooding), or by the energy of the source (microporosity), can be super resolved accurately for further analysis downstream.

We formalize and study the natural approach of designing convex surrogate loss functions via embeddings for problems such as classification or ranking. In this approach, one embeds each of the finitely many predictions (e.g. classes) as a point in R^d, assigns the original loss values to these points, and convexifies the loss in between to obtain a surrogate. We prove that this approach is equivalent, in a strong sense, to working with polyhedral (piecewise linear convex) losses. Moreover, given any polyhedral loss $L$, we give a construction of a link function through which $L$ is a consistent surrogate for the loss it embeds. We go on to illustrate the power of this embedding framework with succinct proofs of consistency or inconsistency of various polyhedral surrogates in the literature.

Rectified Linear Units (ReLU) have become the main model for the neural units in current deep learning systems. This choice has been originally suggested as a way to compensate for the so called vanishing gradient problem which can undercut stochastic gradient descent (SGD) learning in networks composed of multiple layers. Here we provide analytical results on the effects of ReLUs on the capacity and on the geometrical landscape of the solution space in two-layer neural networks with either binary or real-valued weights. We study the problem of storing an extensive number of random patterns and find that, quite unexpectedly, the capacity of the network remains finite as the number of neurons in the hidden layer increases, at odds with the case of threshold units in which the capacity diverges. Possibly more important, a large deviation approach allows us to find that the geometrical landscape of the solution space has a peculiar structure: while the majority of solutions are close in distance but still isolated, there exist rare regions of solutions which are much more dense than the similar ones in the case of threshold units. These solutions are robust to perturbations of the weights and can tolerate large perturbations of the inputs. The analytical results are corroborated by numerical findings.