The United States appears poised to heighten scrutiny of Chinese investment in Silicon Valley to better shield sensitive technologies seen as vital to U.S. national security, current and former U.S. officials tell Reuters. Of particular concern is China's interest in fields such as artificial intelligence and machine learning, which have increasingly attracted Chinese capital in recent years. The worry is that cutting-edge technologies developed in the United States could be used by China to bolster its military capabilities and perhaps even push it ahead in strategic industries. The U.S. government is now looking to strengthen the role of the Committee on Foreign Investment in the United States (CFIUS), the inter-agency committee that reviews foreign acquisitions of U.S. companies on national security grounds. An unreleased Pentagon report, viewed by Reuters, warns that China is skirting U.S. oversight and gaining access to sensitive technology through transactions that currently don't trigger CFIUS review.

Pandas offers some methods to get information of a data structure: info, index, columns, axes, where you can see the memory usage of the data, information about the axes such as the data types involved, and the number of not-null values. To make a boolean query, you need to pass the DataFrame a True/False Series whose index aligns with the DataFrame being queried's index. Merging is another way of combining DataFrames, but unlike concat it combines them looking for matching values in columns of said DataFrames (you can merge by index too). The main method to perform merging in Pandas is merge which lives both in the main pandas namespace and in the DataFrame namespace (unlike concat).

Amazon's drone delivery program stopped being a joke a while ago, but the company still has to overcome serious challenges to make the technology actually work. One of these is getting drones near enough to large populations so they're more efficient than regular road delivery. Amazon has an idea for that though: Huge.

Google'machine learning' - I dare you. Machine learning is the'big data' of our time, hopelessly hyped up with outlandish applications for any and every application and business sector under the sun. Machine learning is ranked as a buzzword by Gartner, at the very top of their peak of inflated expectations; in this excitable environment how can businesses cut through the noise and find practical applications for these novel technologies? The key is to realise that'machine learning' by itself doesn't actually mean very much: it's a stand-in phrase for a diverse array of technologies which, broadly speaking, mean computer systems learn from feedback loops and improve themselves. Much ink has been spilled on predictions for how machine learning technologies can be applied, ranging from calculating the best time to serve advertisements to predicting the risk of heart failure, but this isn't exactly helpful for the average business.

While it may be some time before we commute to work in flying cars or seek a transfer to our company's lunar outpost, another concept once thought outside the realm of modern reality is now increasingly ordinary in the contemporary workplace: working side-by-side with robots and machines capable of artificial intelligence. This article provides an overview of some of the ways in which these once-futuristic technologies are being integrated in today's work environment, and offers best practice suggestions for human resources professionals and in-house counsel adapting to these developments. We have reached the point of "minimum viability" when it comes to artificial intelligence (AI) – we can now count on the reliable use of AI products to perform meaningful work. Long past are the days when AI was little more than a novelty (remember asking iPhone's Siri whether it was raining outside?). The technology to integrate AI into necessary functions is now available, the data needed to power AI has been accumulated, and investors are pouring money into AI systems to make them a worthwhile part of everyday life. Having reached minimum viability, we now stand on the cusp of revolution.

Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery. This article aims to give a general overview of MTL, particularly in deep neural networks. It introduces the two most common methods for MTL in Deep Learning, gives an overview of the literature, and discusses recent advances. In particular, it seeks to help ML practitioners apply MTL by shedding light on how MTL works and providing guidelines for choosing appropriate auxiliary tasks.

U.S. officials reportedly are rethinking the advisability of allowing the Chinese to invest in sensitive technologies seen as vital to national security. Reuters reported Wednesday U.S. officials are concerned such cutting-edge technologies as artificial intelligence and machine learning could be used by the Chinese to augment their military capabilities and achieve greater advancements in strategic industries. Technology is the fastest growing industry in the United States, and China has funneled $45.6 billion into U.S. acquisitions and Greenfield investments in the last year, Rhodium Group found. That investment is expected to double this year. Read: What Is Artificial Intelligence?

WASHINGTON – The United States appears poised to heighten scrutiny of Chinese investment in Silicon Valley to better shield sensitive technologies seen as vital to U.S. national security, current and former U.S. officials have told Reuters. Of particular concern is China's interest in fields such as artificial intelligence and machine learning, which have increasingly attracted Chinese capital in recent years. The worry is that cutting-edge technologies developed in the United States could be used by China to bolster its military capabilities, and perhaps even push it ahead in strategic industries. The U.S. government is now looking to strengthen the role of the Committee on Foreign Investment in the United States (CFIUS), the inter-agency committee that reviews foreign acquisitions of U.S. companies on national security grounds. An unreleased Pentagon report, viewed by Reuters, warns that China is skirting U.S. oversight and gaining access to sensitive technology through transactions that currently don't trigger CFIUS reviews. Such deals would include joint ventures, minority stakes and early-stage investments in start-ups.

In this work, we investigate a multi-period Home Health Care Scheduling Problem (HHCSP) under stochastic service and travel times. We first model the deterministic problem as an integer linear programming model that incorporates real-world requirements, such as time windows, continuity of care, workload fairness, inter-visit temporal dependencies. We then extend the model to cope with uncertainty in durations, by introducing chance constraints into the formulation. We propose efficient solution approaches, which provide quantifiable near-optimal solutions and further handle the uncertainties by employing a sampling-based strategy. We demonstrate the effectiveness of our proposed approaches on instances synthetically generated by real-world dataset for both deterministic and stochastic scenarios.

For example, we may need to (i) perform inverse learning of the cost parameters of a multi-objective reward based on observed agent behavior; (ii) perform sensitivity analyses of policies to various parameter settings; or (iii) analyze and optimize policy performance as a function of policy parameters. When such problems have mixed discrete and continuous state and/or action spaces, this leads to parameterized hybrid MDPs (PHMDPs) that are often approximately solved via discretization, sampling, and/or local gradient methods (when optimization is involved). In this paper we combine two recent advances that allow for the first exact solution and optimization of PHMDPs. We first show how each of the aforementioned use cases can be formalized as PHMDPs, which can then be solved via an extension of symbolic dynamic programming (SDP) even when the solution is piecewise nonlinear. Secondly, we can leverage recent advances in non-convex solvers that require symbolic forms of the objective function for non-convex global optimization in (i), (ii), and (iii) using SDP to derive symbolic solutions for each PHMDP formalization. We demonstrate the efficacy and scalability of our optimal analytical framework on nonlinear examples of each of the aforementioned use cases.