The U.S. and China have been getting lots of attention lately for the AI-focused startups, talent and investment coming out of these two countries. AI startups in the U.S. and China are garnering eyebrow-raising funding rounds. Case in point: SenseTime, a Chinese startup, is now the most valuable AI startup in the world having raised $1.6 billion to date. In 2017 alone, AI-focused startups raised a staggering $12 billion, and investment shows no signs of slowing down in the years ahead. The Chinese government sees AI as a strategic imperative and is investing heavily into developing the industry.

Results of last week's U.S. midterm elections were mixed, leaving a chasm in the U.S. political landscape. The Republicans not only retained control of the Senate, but also gained more seats. The Democrats, meanwhile, took back the House of Representatives. But in terms of American foreign policy, a veteran New York Times columnist who was recently in Japan says that the election results are unlikely to affect President Donald Trump so much, and that he is likely to keep challenging Japan and China -- especially on trade issues. I don't think much will change regarding trade.

In May, a video appeared on the internet of Donald Trump offering advice to the people of Belgium on the issue of climate change. "As you know, I had the balls to withdraw from the Paris climate agreement," he said, looking directly into the camera, "and so should you." The video was created by a Belgian political party, Socialistische Partij Anders, or sp.a, and posted on sp.a's Twitter and Facebook. It provoked hundreds of comments, many expressing outrage that the American president would dare weigh in on Belgium's climate policy. One woman wrote: "Humpy Trump needs to look at his own country with his deranged child killers who just end up with the heaviest weapons in schools."

Andrew Moore is the new head of Google's Cloud AI business, a unit that is striving to make machine-learning tools and techniques more accessible and useful for ordinary businesses. To that end, his team announced several new tools today. These include AI Hub, a modular framework for connecting different machine-learning components, and Kubeflow Pipelines, software that makes machine-learning projects more portable. Efforts to make AI more accessible are likely to define the technology's impact. They will also prove very important to the future of companies like Google.

Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be meaningful to the consumer. In contrast, this paper suggests a new approach to this problem. It introduces a simple, practical framework, called Teaching Explanations for Decisions ( TED), that provides meaningful explanations that match the mental model of the consumer. We illustrate the generality and effectiveness of this approach with two different examples, resulting in highly accurate explanations with no loss of prediction accuracy for these two examples.

The use of deep neural networks in edge computing devices hinges on the balance between accuracy and complexity of computations. Ternary Connect (TC) \cite{lin2015neural} addresses this issue by restricting the parameters to three levels $-1, 0$, and $+1$, thus eliminating multiplications in the forward pass of the network during prediction. We propose Generalized Ternary Connect (GTC), which allows an arbitrary number of levels while at the same time eliminating multiplications by restricting the parameters to integer powers of two. The primary contribution is that GTC learns the number of levels and their values for each layer, jointly with the weights of the network in an end-to-end fashion. Experiments on MNIST and CIFAR-10 show that GTC naturally converges to an `almost binary' network for deep classification networks (e.g. VGG-16) and deep variational auto-encoders, with negligible loss of classification accuracy and comparable visual quality of generated samples respectively. We demonstrate superior compression and similar accuracy of GTC in comparison to several state-of-the-art methods for neural network compression. We conclude with simulations showing the potential benefits of GTC in hardware.

Statistical Machine Learning (SML) refers to a body of algorithms and methods by which computers are allowed to discover important features of input data sets which are often very large in size. The very task of feature discovery from data is essentially the meaning of the keyword `learning' in SML. Theoretical justifications for the effectiveness of the SML algorithms are underpinned by sound principles from different disciplines, such as Computer Science and Statistics. The theoretical underpinnings particularly justified by statistical inference methods are together termed as statistical learning theory. This paper provides a review of SML from a Bayesian decision theoretic point of view -- where we argue that many SML techniques are closely connected to making inference by using the so called Bayesian paradigm. We discuss many important SML techniques such as supervised and unsupervised learning, deep learning, online learning and Gaussian processes especially in the context of very large data sets where these are often employed. We present a dictionary which maps the key concepts of SML from Computer Science and Statistics. We illustrate the SML techniques with three moderately large data sets where we also discuss many practical implementation issues. Thus the review is especially targeted at statisticians and computer scientists who are aspiring to understand and apply SML for moderately large to big data sets.

Two months into the launch of her dance studio, Natalie Borch needed a loan. The 34-year-old first-time business owner had opened the doors to The Pink Studio in February after she and her brother invested $40,000 of their own cash and took out a $100,000 loan from the Business Development Bank of Canada. "We just needed a small amount of money to expand our services for new teachers and classes," she said, from beginner Beyonce to Bollywood fusion. Finding no help from the main banks, she found Lendified Inc., a fintech startup that offers loans to small businesses based on artificial intelligence-powered screening assessments. After filling out a few online forms on cash flow and collateral, Borch received a $30,000 loan.

Imagine a country with an army of techies, a government that supports AI and blockchain by setting a mandate and investing billions, large scale tech companies that are rapidly experimenting and implementing at scale, and an abundance of data to feed the application of these technologies. This just about covers the AI and blockchain playground that is China. The gloves are off, and over the coming years some of the greatest advancements will emanate from the east. An ambitious AI strategic plan was laid by the China's State Council in July 2017, aiming to create a domestic 1 trillion yuan ($150 billion) AI industry by 2030. Following this, Chinese president Xi Jinping called upon his country to take the lead in developing new technologies like artificial intelligence, the internet of things, and blockchain.

Artificial intelligence is no longer just a futuristic technology. It is now being applied throughout the healthcare arena from imaging to triaging patients. But outside of the mainstream hospital uses the technology is also be deployed in apps, wearables and trackers. Here is five of the cutting edge ways AI is being used by health professionals today. One problem that innovators are looking to tackle with AI is paperwork.