Somewhat overshadowed in recent years by China's rise as a regional and global power, Japan still extends considerable influence as a world leader in various fields, including investment, policy management and trade, according to experts who gathered at a Tokyo conference earlier this week. But at the same time, Japan lags behind other countries in incorporating into everyday life technological innovations, such as artificial intelligence and financial technology to improve efficiency, they said. "We want to talk about Japan as a role model, not Japan following, not Japan copying, but Japan actually and constructively led by the young generation," said Jesper Koll, who heads Wisdom Tree Japan KK, a Tokyo-based exchange-traded fund sponsor, in the opening session of the annual G1 Global Conference organized at Globis University on Sunday. Experts shared insights in a session titled "Can Japan be a Role Model for Global Economic Prosperity and Stability?" Hiromichi Mizuno, chief investment officer at Government Pension Investment Fund said the country can show leadership in the field of investment by educating on responsible investment, a concept that gained traction after the fall of Lehman Brothers in 2008 that triggered the global financial crisis, exemplifying the long-term failure of a capitalism that emphasizes the single-minded pursuit of short-term corporate profits.

Artificial Intelligence began as a philosophical conundrum in ancient times, developed into a science fiction forecast (and warning) in the Modern Era and is a practical reality today. This shows that from the earliest known period of human history to the present day it has been a subject of interest to some of the brightest minds and powerful personalities. Here's a run-down of some of the most insightful, important or accurate things which have been said: Alan Turing was a pioneer in bringing AI from the realm of philosophical prediction to reality. He realized in the 1950s it would need greater understanding of human intelligence before we could hope to build machines which would "think" like us. "I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted."

In the last 200 years, manufacturing [has brought] jobs. But today -- because of the artificial intelligence, because of the robots -- manufacturing is no longer the main engine of creating jobs,

The question seemed to baffle the head of Russia's biggest tech firm, who was giving Putin a tour on the company's Moscow HQ on Thursday. "I hope never", he replied after taking a pause to gather his thoughts. "It's not the first machine to be better than humans at something. An excavator digs better than we do with a shovel. But we don't get eaten by excavators. A car moves faster than we do…" "They don't think," he remarked.

Content-dense news report important factual information about an event in direct, succinct manner. Information seeking applications such as information extraction, question answering and summarization normally assume all text they deal with is content-dense. Here we empirically test this assumption on news articles from the business, U.S. international relations, sports and science journalism domains. Our findings clearly indicate that about half of the news texts in our study are in fact not content-dense and motivate the development of a supervised content-density detector. We heuristically label a large training corpus for the task and train a two-layer classifying model based on lexical and unlexicalized syntactic features. On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together. Our annotation and prediction experiments demonstrate that the concept of content density varies depending on the domain and that naive annotators provide judgement biased toward the stereotypical domain label. Domain-specific classifiers are more accurate for domains in which content-dense texts are typically fewer. Domain independent classifiers reproduce better naive crowdsourced judgements. Classification prediction is high across all conditions, around 80%.

Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better empirical performance. However, the resulting optimization problem is much more challenging. Recent state-of-the-art requires an expensive full SVD in each iteration. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, a cutoff can be derived to automatically threshold the singular values obtained from the proximal operator. This allows such operator being efficiently approximated by power method. Based on it, we develop a proximal gradient algorithm (and its accelerated variant) with inexact proximal splitting and prove that a convergence rate of O(1/T) where T is the number of iterations is guaranteed. Furthermore, we show the proposed algorithm can be well parallelized, which achieves nearly linear speedup w.r.t the number of threads. Extensive experiments are performed on matrix completion and robust principal component analysis, which shows a significant speedup over the state-of-the-art. Moreover, the matrix solution obtained is more accurate and has a lower rank than that of the nuclear norm regularizer.

Regularized empirical risk minimization using kernels and their corresponding reproducing kernel Hilbert spaces (RKHSs) plays an important role in machine learning. However, the actually used kernel often depends on one or on a few hyperparameters or the kernel is even data dependent in a much more complicated manner. Examples are Gaussian RBF kernels, kernel learning, and hierarchical Gaussian kernels which were recently proposed for deep learning. Therefore, the actually used kernel is often computed by a grid search or in an iterative manner and can often only be considered as an approximation to the "ideal" or "optimal" kernel. The paper gives conditions under which classical kernel based methods based on a convex Lipschitz loss function and on a bounded and smooth kernel are stable, if the probability measure $P$, the regularization parameter $\lambda$, and the kernel $k$ may slightly change in a simultaneous manner. Similar results are also given for pairwise learning. Therefore, the topic of this paper is somewhat more general than in classical robust statistics, where usually only the influence of small perturbations of the probability measure $P$ on the estimated function is considered.

File photo: Tesla Chief Executive Elon Musk smiles as he attends a forum on startups in Hong Kong, China January 26, 2016. Despite Elon Musk's continued warnings, evil machines won't take over the world, two experts said this week. Artificial intelligence (AI) could be destined to turn against humanity, Musk has argued. The tech exec, who in addition to running high-profile companies such as Tesla and SpaceX, is a co-founder of OpenAI, a non-profit AI research company "discovering and enacting the path to safe artificial general intelligence." However, other executives in Silicon Valley have taken issue with Musk's comments, including the leader of Google's artificial intelligence efforts. "I'm definitely not worried about the AI apocalypse," said Google's John Giannandrea, when speaking at TechCrunch Disrupt SF. "I just object to the hype and soundbites that some people are making," he added.

Those hoping to be among the first to own Apple's $999 iPhone X could be left empty-handed come November 3, a new report suggests. Apple initially targeted the November release date for the highly-anticipated device amid rumours of delays, with pre-orders to begin in October – but, the report claims Apple still has yet to begin final production of the handset. After meetings with supply chain companies in Asia, analyst Christopher Case says the iPhone X is facing an'incremental delay in the build plans,' with production now set to begin mid-way through next month. The devices will ship on November 3. The price will be $999 (£999) for the 64GB version and $1,149 (£1,149) for the 256GB version. In an investor's note obtained by Barron's, Raymond James chip analyst Christopher Caso reveals iPhone X production is, as many have suspected, about a month behind schedule.

Jack Ma: 'It's not made in China, it's made on the internet' Jack Ma knows artificial intelligence will change the world. The Alibaba founder and chairman doesn't think we should be scared. But he does think we should be prepared for major disruptions to the job market. "In the last 200 years, manufacturing [has brought] jobs. But today -- because of the artificial intelligence, because of the robots -- manufacturing is no longer the main engine of creating jobs," Ma said Wednesday in a speech at the Bloomberg Global Business Forum in New York City.