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. 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 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.

Republican Senator John Thune, who chairs the Commerce Committee, Bill Nelson, the top Democrat on the panel, and Senator Gary Peters, a Michigan Democrat, said in a joint statement that existing federal vehicle regulations written over recent decades did not account for self-driving cars without a human driver behind the wheel.

In recent years, we have become increasingly good at training deep neural networks to learn a very accurate mapping from inputs to outputs, whether they are images, sentences, label predictions, etc. from large amounts of labeled data. What our models still frightfully lack is the ability to generalize to conditions that are different from the ones encountered during training. Every time you apply your model not to a carefully constructed dataset but to the real world. The real world is messy and contains an infinite number of novel scenarios, many of which your model has not encountered during training and for which it is in turn ill-prepared to make predictions. The ability to transfer knowledge to new conditions is generally known as transfer learning and is what we will discuss in the rest of this post. Over the course of this blog post, I will first contrast transfer learning with machine learning's most pervasive and successful paradigm, supervised learning. I will then outline reasons why transfer learning warrants our attention. Subsequently, I will give a more technical definition and detail different transfer learning scenarios. I will then provide examples of applications of transfer learning before delving into practical methods that can be used to transfer knowledge.

Multi-agent pathfinding (MAPF) is an area of expanding research interest. At the core of this research area, numerous diverse search-based techniques were developed in the past 6 years for optimally solving MAPF under the sum-of-costs objective function. In this paper we survey these techniques, while placing them into the wider context of the MAPF field of research. Finally, we provide analytical and experimental comparisons that show that no algorithm dominates all others in all circumstances. We conclude by listing important future research directions.

Depending upon which automaker you listen to, you could be riding in a self-driving car within one to five years. As cool as that sounds, the car will look pretty familiar, with a steering wheel and pedals and a dashboard just like you're used to. But in time, those features will change, if not vanish--an idea that excites automotive designers because it opens new opportunities. Seats that turn to face the rear passengers. This creates new problems, not the least of which is what to do with the airbags.

The government endorsed a plan Friday to prioritize investment in human resource development to buttress Japan's economic growth and improve its tattered finances. Japan will maintain its pledge to achieve a surplus in the primary balance by fiscal 2020, while the government's annual economic policy blueprint said that another indicator used to gauge fiscal health -- the debt to gross domestic product ratio -- is also important. Ballooning social security costs have made it imperative to rein in spending as the graying of its population picks up pace. One key step on the agenda is to make preschool education free, although questions remain over how to fund such a policy. The blueprint only states that a decision should be reached by the end of the year. With a public debt twice the size of its GDP, Japan's fiscal health is already the worst of the major economies.

There is no denying the fact that Artificial Intelligence is the breakthrough technology of recent times. The machines have come a long way from assisting humans in mechanical operations to performing smarter tasks using cognitive intelligence. Every day, we are coming across interesting applications of AI. The ability of Deep Learning algorithms to learn and predict efficiently has opened the doors of possibilities. Nowadays, AI is impacting many other areas as well. In this blog post, we will discuss some niche applications of AI.

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions.

The technological ecosystem needed to enable AI has finally formed. And, just like a perfect storm, AI's timeline and path is hard to predict and many business owners don't know whether to closely follow and obsess with it or hope it passes them by altogether. What comprises the ecosystem needed to set the growth curve sharply upwards, as in the classic hockey stick analogy we all know very well? First, vast and rich data sets are forming rapidly. Big data require hefty processing power and storage, which is becoming more cost-effective and accessible every day, even to small companies.

Measuring conditional dependencies among the variables of a network is of great interest to many disciplines. This paper studies some shortcomings of the existing dependency measures in detecting direct causal influences or their lack of ability for group selection to capture strong dependencies and accordingly introduces a new statistical dependency measure to overcome them. This measure is inspired by Dobrushin's coefficients and based on the fact that there is no dependency between $X$ and $Y$ given another variable $Z$, if and only if the conditional distribution of $Y$ given $X=x$ and $Z=z$ does not change when $X$ takes another realization $x'$ while $Z$ takes the same realization $z$. We show the advantages of this measure over the related measures in the literature. Moreover, we establish the connection between our measure and the integral probability metric (IPM) that helps to develop estimators of the measure with lower complexity compared to other relevant information theoretic based measures. Finally, we show the performance of this measure through numerical simulations.