Although both AI and blockchain are probably at the peak of the'hype cycle' at the moment, that is where their similarities end; these two technologies actually represent contrasting ways of understanding and ordering the world. "In a way, blockchain is about certainty and transparency; a permanent and publicly verifiable way to record transactions," said Professor Steven Miller, Vice Provost of Research at Singapore Management University (SMU). "AI, on the other hand, involves making probabilistic statements and in that sense can be said to be about uncertainty." Professor Miller made these comments as moderator of a panel discussion titled'When Blockchain & AI Come Together: Possibilities & Impacts,' held on 24 January 2018 at SMU. Organised by SMU in conjunction with the National Research Foundation's Global Young Scientists Summit, the panel comprised three distinguished speakers: Mr Sopnendu Mohanty, Chief Financial Technology Officer of the Monetary Authority of Singapore; Professor John Hopcroft, IBM Professor of Engineering & Applied Mathematics in Computer Science at Cornell University (Turing Award recipient, 1986); and Professor Efim Zelmanov, Professor of Mathematics at the University of California, San Diego (Fields Medallist, 1994). Never the twain shall meet?

On February 28, Chinese tech giant Alibaba and the Nanyang Technological University (NTU) established a joint research institute in Singapore. The institute will focus on AI applications, including health care, smart home and urban transportation. According to Alibaba, the institute is the firm's first joint research center outside of China. It will be located on the NTU campus, and its first 50 researchers will be from Alibaba and NTU. AI applications developed by the institute will be tested within NTU, and across Singapore and Southeast Asia.

You are free to share this article under the Attribution 4.0 International license. Neural networks could pave the way for smarter, safer, and more sustainable cities by better predicting demand for taxi and ride-sharing services. In a study, the researchers used two types of neural networks--computational systems modeled on the human brain--that analyzed patterns of taxi demand. This deep learning approach, which lets computers learn on their own, could then predict the demand patterns significantly better than current technology. "Ride sharing companies, like Uber in the United States, and Didi Chuxing in China, are becoming more and more popular and have really changed the way people approach transportation," says Jessie Li, associate professor of information sciences and technology at Penn State.

Humans cannot see, smell or taste airborne asbestos fibers. Identifying them through a microscope requires the eye of a trained analyst -- but perhaps not for long. Australian engineer Jordan Gruber is working on technology that can automatically detect asbestos from the air around a worksite. Exposure to airborne asbestos fibers is the primary cause of mesothelioma, an aggressive form of cancer. The past use of asbestos in building materials has led to great suffering among Americans and Australians alike.

Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs). Furthermore, we show that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification. Our novel method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). We first exploit GAN architectures for synthesizing high quality liver lesion ROIs. Then we present a novel scheme for liver lesion classification using CNN. Finally, we train the CNN using classic data augmentation and our synthetic data augmentation and compare performance. In addition, we explore the quality of our synthesized examples using visualization and expert assessment. The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results increased to 85.7% sensitivity and 92.4% specificity. We believe that this approach to synthetic data augmentation can generalize to other medical classification applications and thus support radiologists' efforts to improve diagnosis.

Until rules and guidelines are written that govern how artificial intelligence software makes decisions, there will be grave risks to using it, including utter ineffectiveness, warns Nicolas Economou of The Future Society. The society is a nonprofit that began at the Harvard Kennedy School, and Economou is the founder of the society's Science, Law and Society Initiative, an international forum that works on AI governance and policy to ensure that humanity reaps the benefits of AI while mitigating its risks. Economou, who is also the CEO of the legal tech company H5, participated last week in a Global Governance of AI Roundtable in Dubai, at which policymakers crafted recommendations and began creating a road map for global cooperation on setting standards. Participants included execs from global tech companies including Microsoft and Facebook, as well as representatives from government and academia. Economou discussed the kinds of issues he and others raised at the roundtable. He urges that careful work be done to vet AI technology for accuracy and fairness and that a methodical, multidisciplinary approach be taken to establish a legal and moral framework for the powerful technology.

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China wants to become a country of innovation, and lead the world in artificial intelligence in 2030. China is outdoing the US in some kinds of AI-related intellectual property, according to a report published in mid-February by US business research firm CB Insights. The number of patents with the words "artificial intelligence" and "deep learning" published in China has grown faster than those published in the US, particularly in 2017, the firm found. Publication is a step that comes after applications are filed but before a patent is granted. The firm looked at data from the European patent office.

A team at Quid wrote an analysis on 5 innovation hotspots outside the U.S. which was published on the World Economic Forum. Using Quid, we found London, Paris, Singapore, Munich, and Tel Aviv to be five innovation hotspots and examined what makes them so attractive to entrepreneurs. To do this we analysed every company that has received funding in the last 10 years across these five hotspots. We then used Quid's artificial intelligence (AI) and Natural Language Processing (NLP) to "read" the business descriptions of the companies in order to segment each market by theme and derive further insights into the fastest growing segments, the largest exits and the top investors. We also interviewed Tech Pioneers, members of the World Economic Forum's global community of trailblazing companies, who are based in these hotspots.

He started his talk with a broader philosophical statement: "Anything is possible." Referring to SpaceX and Tesla as the most cutting edge companies he has funded, he continued, "The future of technology that we couldn't imagine 15–20 years ago is obviously now possible. There is no doubt that the future of cars will be electric and autonomous. He then reflected on the SpaceX story. "I remember Elon came back from Russia very disappointed with the realization that there is a very weird supply chain in this industry.