The Indian government yesterday stated the National Artificial Intelligence (AI) Portal, formed by National Association of Software and Service Companies (Nasscom) and backed from National e-Governance Division of the Ministry of Electronics and Information Technology (MeitY), is now live. The portal's objective is to function as a one-stop platform for all AI-related advancements in India, with sharing of useful resources like articles, investment funding news for AI startup, AI companies and educational firms on AI in India. The portal will also distribute documents, case studies, research reports etc, and provide learning and new job roles related to AI. The portal brings together ideas and thought leadership from Indian government stakeholders including Ministry of Electronics and Information Technology (MeitY), NITI Aayog, NASSCOM, along with state governments. The National AI Portal INDIAai is now live!

After the discovery of adversarial examples and their adverse effects on deep learning models, many studies focused on finding more diverse methods to generate these carefully crafted samples. Although empirical results on the effectiveness of adversarial example generation methods against defense mechanisms are discussed in detail in the literature, an in-depth study of the theoretical properties and the perturbation effectiveness of these adversarial attacks has largely been lacking. In this paper, we investigate the objective functions of three popular methods for adversarial example generation: the L-BFGS attack, the Iterative Fast Gradient Sign attack, and Carlini & Wagner's attack (CW). Specifically, we perform a comparative and formal analysis of the loss functions underlying the aforementioned attacks while laying out large-scale experimental results on ImageNet dataset. This analysis exposes (1) the faster optimization speed as well as the constrained optimization space of the cross-entropy loss, (2) the detrimental effects of using the signature of the cross-entropy loss on optimization precision as well as optimization space, and (3) the slow optimization speed of the logit loss in the context of adversariality. Our experiments reveal that the Iterative Fast Gradient Sign attack, which is thought to be fast for generating adversarial examples, is the worst attack in terms of the number of iterations required to create adversarial examples in the setting of equal perturbation. Moreover, our experiments show that the underlying loss function of CW, which is criticized for being substantially slower than other adversarial attacks, is not that much slower than other loss functions. Finally, we analyze how well neural networks can identify adversarial perturbations generated by the attacks under consideration, hereby revisiting the idea of adversarial retraining on ImageNet.

Knowledge Graphs (KGs) have gained considerable attention recently from both academia and industry. In fact, incorporating graph technology and the copious of various graph datasets have led the research community to build sophisticated graph analytics tools. Therefore, the application of KGs has extended to tackle a plethora of real-life problems in dissimilar domains. Despite the abundance of the currently proliferated generic KGs, there is a vital need to construct domain-specific KGs. Further, quality and credibility should be assimilated in the process of constructing and augmenting KGs, particularly those propagated from mixed-quality resources such as social media data. This paper presents a novel credibility domain-based KG Embedding framework. This framework involves capturing a fusion of data obtained from heterogeneous resources into a formal KG representation depicted by a domain ontology. The proposed approach makes use of various knowledge-based repositories to enrich the semantics of the textual contents, thereby facilitating the interoperability of information. The proposed framework also embodies a credibility module to ensure data quality and trustworthiness. The constructed KG is then embedded in a low-dimension semantically-continuous space using several embedding techniques. The utility of the constructed KG and its embeddings is demonstrated and substantiated on link prediction, clustering, and visualisation tasks.

In such networks, symbiosis leads to socioeconomic and environmental benefits for involved industrial agents and the society (see [14, 39]). One barrier against stable ISN implementations is the lack of frameworks able to secure such networks against unfair and unstable allocation of obtainable benefits among the involved industrial firms. In other words, although in general ISNs result in the reduction of the total cost, a remaining challenge for operationalization of ISNs is to tailor reasonable mechanisms for allocating the total obtainable cost reductions--in a fair and stable manner--among the contributing firms. Otherwise, even if economic benefits are foreseeable, lack of stability and/or fairness may lead to non-cooperative decisions. This will be the main focus of what we call the industrial symbiosis implementation problem. Reviewing recent contributions in the field of industrial symbiosis research, we encounter studies focusing on the necessity to consider interrelations between industrial enterprises [43, 47] and the role of contract settings in the process of ISN implementation [1, 44]. We believe that a missed element for shifting from theoretical ISN design to practical ISN implementation is to model, reason about, and support ISN decision processes in a dynamic way (and not by using snapshotbased modeling frameworks). For such a multiagent setting, the mature field of cooperative game theory provides rigorous methodologies and established solution concepts, e.g. the core of the game and the Shapley allocation [15, 30, 34, 7]. However, for ISNs modeled as a cooperative game, these established solution concepts may be either non-feasible (due to properties of the game, e.g.

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of black box or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine learning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.

Galaxies are spinning in different directions, scientists have found – a discovery that could change our undertstanding of the structure of the universe. The spin of those galaxies seem to suggest there are unexpected and unexplained links between the directions of their spin. The structure formed by those unusual links could suggest that the early universe was also spinning, according to the new study. That is in conflict with previous understandings of the structure of the universe at the largest scale. For decades, scientists have believed that the universe is expanding in no particular direction, with the galaxies inside of it distributed with no particular structure.

IIT-Ropar, one of the eight new IITs established by the Ministry of Human Resource Development (MHRD), Government of India, and TSW, the executive education division of Times Professional Learning (a part of The Times of India Group), have launched a Post Graduate Certificate Programme in Artificial Intelligence & Deep Learning. The programme will be coordinated by The Indo-Taiwan Joint Research Centre (ITJRC) on Artificial Intelligence (AI) and Machine Learning (ML), at IIT-Ropar. Supported by the Ministry of Science and Technology, Taiwan, ITJRC is a bilateral centre for collaborative research in disruptive technologies like AI and ML. The programme, with its focus on Artificial Intelligence and Deep Learning, has an eligibility criterion of a minimum of 2 years of work experience in the IT industry. Though an engineering degree is a desirable prerequisite for this programme, one does not need a coding or mathematics background to be eligible.

Drawing on the records of nearly 600,000 Chinese patients who had visited a pediatric hospital over an 18-month period, the vast collection of data used to train this new system highlights an advantage for China in the worldwide race toward artificial intelligence. Because its population is so large -- and because its privacy norms put fewer restrictions on the sharing of digital data -- it may be easier for Chinese companies and researchers to build and train the "deep learning" systems that are rapidly changing the trajectory of health care. On Monday, President Trump signed an executive order meant to spur the development of A.I. across government, academia and industry in the United States. As part of this "American A.I. Initiative," the administration will encourage federal agencies and universities to share data that can drive the development of automated systems. Pooling health care data is a particularly difficult endeavor.

U.S. national security officials have approved an investor group's purchase of gay-dating app Grindr that is being sold by a Chinese company after the Trump administration raised concerns about the potential theft of Americans' personal data. In investor documents released Friday, China's Beijing Kunlun Tech Co. said that the buyer has secured approval from the Committee on Foreign Investment in the United States, a panel of national security experts who ordered that Beijing Kunlun Tech sell its ownership last year.

The government has launched a new national artificial intelligence (AI) portal which will serve as a knowledge hub for all those who are engaged in this domain. The portal – www.ai.gov.in was launched by Union Minister for Electronics and IT, Law and Justice and Communications Ravi Shankar Prasad and it was also on the occasion of the first anniversary of the second tenure of the Narendra Modi government. This portal has been jointly developed by the Ministry of Electronics and IT and the IT Industry. The National e-Governance Division of Ministry of Electronics and IT and NASSCOM from the IT industry will jointly run this portal. According to the government, the portal will work as a one-stop digital platform for AI-related developments in India, sharing of resources such as articles, startups, investment funds in AI, resources, companies and educational institutions related to AI in India.