This article was written by Kwon Sok Oh, a Financial Analyst at I Know First. Israel currently has over 7000 thousand startups, living up to its reputation as the "startup nation." Israel is considered the most venture capital intense country, with the highest investment funds per capita in the world. Israel's unique combination of innovative and entrepreneurial mentality, technology-oriented ecosystem, government support, and international investment has driven the proliferation of the startup environment. Over 350 multinational companies, including technology giants, such as Intel, Apple, Microsoft, Amazon, and Samsung, and global financial institutions, such as JP Morgan, Citibank, and Barclays, have established R&D Centers in Israel to take advantage of the advent of novel startups and innovative technologies that solve existing problems.

BEIJING (CHINA DAILY/ASIA NEWS NETWORK) - China may be ranked seventh globally when it comes to the number of professionals working in the cutting-edge industry of artificial intelligence (AI), but the country is predicted to climb the rankings in the next decade. More than 50,000 AI technical professionals are working in China. India, Britain, Canada, Australia and France took second to sixth place in the rankings, according to the report based on LinkedIn user data. "The core technique of AI is closely related to computer science, in which the US has maintained an absolute advantage in the past 20 years," said LinkedIn China's vice-president Wang Di. Researcher Li Hui at the Shanghai Institute for Science of Science said that AI talent in the US is mainly concentrated in such primary technical fields as chips, machine learning, natural language processing, computer vision and imaging, and far surpasses its Chinese counterparts in numbers. The LinkedIn report found that about one in six employees in the field in the US were born before 1970, compared with only one in 25 in China.

Amazon's Prime Day is July 16. Chinese tech IPOs are the talk of the town this week following Xiaomi's listing on the Hong Kong Stock Exchange. In spite of its less-than-ideal debut, analysts expect a flood of Chinese tech firms--including Jack Ma-backed Ant Financial and online food delivery service Meituan-Dianping--to go public this year. A month ago, another Chinese tech company with grand ambitions had already made the jump: Ecovacs Robotics, whose line of robot vacuum cleaners dominate its native Chinese market and on Amazon.com. Headquarted in Suzhou, Ecovacs raised 803 million yuan ($121 million) when it listed on the Shanghai Stock Exchange on May 28, making its founder, Qian Dongqi, a billionaire.

Surveillance companies are showing an increasing interest in hacking into IoT devices like the Amazon Echo. With an impressive seed raise of $12.5 million and ex-Israeli Prime Minister Ehud Barak as co-founder, alongside an "all-star" leadership team, Tel Aviv-based Toka Cyber can certainly claim to have nailed the definition of an auspicious beginning. But, as it comes out of stealth Monday, Toka is revealing itself as an atypical force in the digital security sphere, acting as a one-stop hacking shop for intelligence and law enforcement agencies. Whatever spy tool they need, Toka will try to craft it for them. Privacy activists are hoping the company follows through on its promise to operate ethically.

Top Pentagon official Michael Griffin sat down a few weeks ago with Air Force scientists at Wright Patterson Air Force Base in Ohio to discuss the future of quantum computing in the U.S. military. Griffin, the undersecretary of defense for research and engineering, has listed quantum computers and related applications among the Pentagon's must-do R&D investments. Quantum computing is one area where the Pentagon worries that it is playing catchup while China continues to leap ahead. The technology is being developed for many civilian applications and the military sees it as potentially game-changing for information and space warfare. The U.S. Air Force particularly is focused on on what is known as quantum information science.

For future robotics, knowing how a robot will respond under different conditions is necessary to strengthen safe operations. The complexity is with assessing understanding what might break a robot without actually carrying out the activity and damaging the machine. The answer lies in a a new machine learning method. The method, which comes from the Institute of Science and Technology Austria, can make use of observations gathered under safe conditions. The algorithm can then make accurate predictions across a range of possible conditions, based on the same physical dynamics, allowing a robot to predict which activities would be safe and which would be dangerous. This provides a resemblance of the cognition that human shave in sizing up risks before an action is taken.

Measuring similarities between strings is central for many established and fast growing research areas including information retrieval, biology, and natural language processing. The traditional approach for string similarity measurements is to define a metric over a word space that quantifies and sums up the differences between characters in two strings. The state-of-the-art in the area has, surprisingly, not evolved much during the last few decades. The majority of the metrics are based on a simple comparison between character and character distributions without consideration for the context of the words. This paper proposes a string metric that encompasses similarities between strings based on (1) the character similarities between the words including. Non-Standard and standard spellings of the same words, and (2) the context of the words. Our proposal is a neural network composed of a denoising autoencoder and what we call a context encoder specifically designed to find similarities between the words based on their context. The experimental results show that the resulting metrics succeeds in 85.4\% of the cases in finding the correct version of a non-standard spelling among the closest words, compared to 63.2\% with the established Normalised-Levenshtein distance. Besides, we show that words used in similar context are with our approach calculated to be similar than words with different contexts, which is a desirable property missing in established string metrics.

Convolutional Neural Networks (CNNs) are propelling advances in a range of different computer vision tasks such as object detection and object segmentation. Their success has motivated research in applications of such models for medical image analysis. If CNN-based models are to be helpful in a medical context, they need to be precise, interpretable, and uncertainty in predictions must be well understood. In this paper, we develop and evaluate recent advances in uncertainty estimation and model interpretability in the context of semantic segmentation of polyps from colonoscopy images. We evaluate and enhance several architectures of Fully Convolutional Networks (FCNs) for semantic segmentation of colorectal polyps and provide a comparison between these models. Our highest performing model achieves a 76.06\% mean IOU accuracy on the EndoScene dataset, a considerable improvement over the previous state-of-the-art.

Your computer is continuously executing programs, but does it really understand them? Not in any meaningful sense. That burden falls upon human knowledge workers, who are increasingly asked to write and understand code. They would benefit greatly from intelligent tools that reveal the connections between their code and its subject matter. Towards this prospect, we develop an AI system that forms semantic representations of computer programs, using techniques from knowledge representation and program analysis. We focus on code written for data science, although our method is more generally applicable. The semantic representations are created through a novel algorithm for the semantic enrichment of dataflow graphs. This algorithm is undergirded by a new ontology language for modeling computer programs and a new ontology about data science, written in this language.

Artificial Intelligence techniques such as agent-based modeling and probabilistic reasoning have shown promise in modeling complex biological systems and testing ecological hypotheses through simulation. We develop an agent-based model of Arctic foxes from Medniy Island while utilizing Probabilistic Graphical Models to capture the conditional dependencies between the random variables. Such models provide valuable insights in analyzing factors behind catastrophic degradation of this population and in revealing evolutionary mechanisms of its persistence in high-density environment. Using empirical data from studies in Medniy Island, we create a realistic model of Arctic foxes as agents, and study their survival and population dynamics under a variety of conditions.