Chinese fishing is increasing security risks near South Korea's tense nautical border, said a top Cabinet member in Seoul, pledging to deploy advanced technology to crack down on illegal trawling. Minister of Oceans and Fisheries Moon Seong-hyeok said in an interview that illegal fishing must be "completely eradicated," joining in calls from across Asia to end what many see as Beijing's assertive push into regional waters. South Korea has long complained about Chinese trawlers operating in the Yellow Sea -- what Koreans call the West Sea -- near its islands off the coast of North Korea. "When it comes to illegal fishing, whether it be foreign or domestic vessels, we will crack down," Moon told Bloomberg News on Friday, saying South Korea will from next year increase its maritime surveillance systems using drones at sea and artificial intelligence. South Korea, which lists the U.S. as its main military ally and China as its biggest trading partner, turned up the pressure on Beijing over the weekend when it won from Washington a termination of bilateral missile guidelines that have long restricted Seoul's development of missiles to under the range of 800 kilometers (500 miles).

Advanced driver assistance systems (ADAS) hold immense promise. At times, the headlines about the autonomous vehicle (AV) industry seem ominous, with a focus on accidents, regulation or company valuations that some find undeserving. None of this is unreasonable, but it makes the amazing possibilities of a world of AVs seem opaque. One of the universally accepted upsides of AVs is the potential positive impact on the environment, as most AVs will also be electric vehicles (EVs). Industry analyst reports project that by 2023, 7.3 million vehicles (7% of the total market) will have autonomous driving capabilities requiring $1.5 billion of autonomous-driving-dedicated processors.

NanoSniff has partnered with Vehant Technologies, a pioneer in Artificial Intelligence/Machine Learning- based physical security, surveillance and traffic monitoring and junction enforcement solutions. Union Education Minister, Ramesh Pokhriyal'Nishank' has launched NanoSniffer, a Microsensor based Explosive Trace Detector (ETD) developed by NanoSniff Technologies, an IIT Bombay incubated startup. NanoSniffer has been marketed by Vehant Technologies, a company that was incubated at IIT Delhi as a startup in 2005. A 100 per cent'Made in India' product in terms of research, development & manufacturing, the core technology of NanoSniffer is protected by patents in the US and Europe. Given the constant threats, which our nation faces due to geo-political realities, explosives and contraband detection has become a norm at high security locations like airports, railways and metro stations, hotels, malls and other public places.

Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal discovery and designing interventions in real world systems. In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation. Building on recent advances in variational inference, we use DiBS to devise an efficient method for approximating posteriors over structural models. Contrary to existing work, DiBS is agnostic to the form of the local conditional distributions and allows for joint posterior inference of both the graph structure and the conditional distribution parameters. This makes our method directly applicable to posterior inference of nonstandard Bayesian network models, e.g., with nonlinear dependencies encoded by neural networks. In evaluations on simulated and real-world data, DiBS significantly outperforms related approaches to joint posterior inference.

Wikipedia, the largest open-collaborative online encyclopedia, is a corpus of documents bound together by internal hyperlinks. These links form the building blocks of a large network whose structure contains important information on the concepts covered in this encyclopedia. The presence of a link between two articles, materialised by an anchor text in the source page pointing to the target page, can increase readers' understanding of a topic. However, the process of linking follows specific editorial rules to avoid both under-linking and over-linking. In this paper, we study the transductive and the inductive tasks of link prediction on several subsets of the English Wikipedia and identify some key challenges behind automatic linking based on anchor text information. We propose an appropriate evaluation sampling methodology and compare several algorithms. Moreover, we propose baseline models that provide a good estimation of the overall difficulty of the tasks.

LENs is a Python module integrating a variety of state-of-the-art approaches to provide logic explanations from neural networks. This package focuses on bringing these methods to non-specialists. It has minimal dependencies and it is distributed under the Apache 2.0 licence allowing both academic and commercial use. Source code and documentation can be downloaded from the github repository: https://github.com/pietrobarbiero/logic_explainer_networks.

Our last AI post on this blog, the New (if Decidedly Not'Final') Frontier of Artificial Intelligence Regulation, touched on both the Federal Trade Commission's (FTC) April 19, 2021, AI guidance and the European Commission's proposed AI Regulation. The recent FTC guidance also relied on older FTC work on AI, including a January 2016 report, "Big Data: A Tool for Inclusion or Exclusion?," The Big Data workshop addressed data modeling, data mining and analytics, and gave us a prospective look at what would become an FTC strategy on AI. The FTC's guidance begins with the data, and the 2016 guidance on big data and subsequent AI development addresses this most directly. The 2020 guidance then highlights important principles such as transparency, explain-ability, fairness, accuracy and accountability for organizations to consider.

The U.S. Postal Service often gets a bad rap for slow deliveries and budget overruns but lately has been embracing use of an edge artificial intelligence platform across 195 processing centers nationwide. The AI benefits include the ability to track a missing package in a couple of hours instead of several days under a previous routine, said Todd Schimmel, a manager of letter technology at USPS, in a statement. Training the USPS system for computer vision of packages was also vastly simplified with AI capabilities. What might have taken two weeks on a network of servers with 800 CPUs was reduced to 20 minutes on four Nvidia V100 Tensor Core GPUs in a single HPE Apollo 6500 server, Nvidia said. Now, each edge server processes 20 terabytes of images a day from more than 1,000 mail processing machines.

Both'sustainability' and'artificial intelligence' can be hard concepts to grapple with. I do not believe I can pin down two incredibly complex terms in one article. Rather I think of this more as a short exploration of different ways to define sustainable artificial intelligence (AI). If you have comments or thoughts they would be very much appreciated. These thoughts come after a discussion on Sustainable AI I moderated on the 21st of May as part of my role at the Norwegian Artificial Intelligence Research Consortium.

Today, publicity around highly touted but underperforming AI solutions has placed the health sector at risk for another AI winter. To respond to this challenge, we propose that industry organizations consider implementing self-governance standards to better mitigate risks and encourage greater trust in AI capabilities. Building on the National Academy of Medicine's AI implementation lifecycle, we created a detailed organizational framework that identifies 10 groups of AI risks and 14 groups of mitigation practices across the four lifecycle phases. AI developers, implementers, and other stakeholders can use this analysis to guide collective, voluntary actions to select, establish, and track adherence to trust-enhancing AI standards. Without industry self-governance, government agencies may act to institute their own compliance requirements. However, industries that have proactively defined, adopted, and implemented standards complementary to government regulation have reduced the urgency of public-sector action while allowing for the appropriate use of available resources.