Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained (node-level) or holistic (graph-level) properties, respectively. To tackle this challenge we introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations. The random distillation is achieved by training one GNN to predict another GNN with randomly initialized network weights. Extensive experiments on 16 real-world graph datasets from diverse domains show that our model significantly outperforms seven state-of-the-art models. Code and datasets are available at https://git.io/GLocalKD.

Country officials and campaigners have expressed disappointment after United Nations talks on autonomous weapons systems – known as "killer robots" – stopped short of launching negotiations into an international treaty to govern their use following opposition from manufacturing states. Unlike existing semi-autonomous weapons such as drones, fully-autonomous weapons have no human-operated "kill switch" and instead leave decisions over life and death to sensors, software and machine processes. The regulation of the industry has taken on new urgency since a UN panel report in March said the first autonomous drone attack may have occurred in Libya. This week, UN Secretary-General Antonio Guterres encouraged the 125 parties to the Convention on Certain Conventional Weapons (CCW) to come up with an "ambitious plan" on new rules. But on Friday, the Sixth Review Conference of the CCW failed to schedule further talks around the development and use of the Lethal Autonomous Weapon Systems, or LAWS.

Developer tools that use a neural machine learning model to make predictions about previously unseen code.

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In order to address the increasing demands of real-world applications, the research for knowledge-intensive NLP (KI-NLP) should advance by capturing the challenges of a truly open-domain environment: web scale knowledge, lack of structure, inconsistent quality, and noise. To this end, we propose a new setup for evaluating existing KI-NLP tasks in which we generalize the background corpus to a universal web snapshot. We repurpose KILT, a standard KI-NLP benchmark initially developed for Wikipedia, and ask systems to use a subset of CCNet - the Sphere corpus - as a knowledge source. In contrast to Wikipedia, Sphere is orders of magnitude larger and better reflects the full diversity of knowledge on the Internet. We find that despite potential gaps of coverage, challenges of scale, lack of structure and lower quality, retrieval from Sphere enables a state-of-the-art retrieve-and-read system to match and even outperform Wikipedia-based models on several KILT tasks - even if we aggressively filter content that looks like Wikipedia. We also observe that while a single dense passage index over Wikipedia can outperform a sparse BM25 version, on Sphere this is not yet possible. To facilitate further research into this area, and minimise the community's reliance on proprietary black box search engines, we will share our indices, evaluation metrics and infrastructure.

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. You will go from zero to mastery!

As part of Australia's Artificial Intelligence (AI) Action Plan, the Australian Government is investing $53.8 million over four years to establish the National AI Centre and four AI and Digital Capability Centres to lay the foundations for an Australian AI and digital ecosystem. As the national science agency, and housing some of the country's leading capability in AI research and technology development, we're well positioned to act as lead in establishing the National AI Centre. We're bringing together partners from government, industry and the research sector to boost exploration and adoption of AI in Australia. We are uniquely positioned to represent the interests and capability of Australia's AI sector internationally, and to grow awareness of our AI capacity with global leaders in the field. By partnering with like-minded organisations, we aim to drive a new level of understanding, technology development, and adoption of AI in Australia in the years to come.

To fight the invasive fish, the international team, composed of biologists and engineers from Australia, the U.S., and Italy, turned to its natural predator -- the largemouth bass (Micropterus salmoides) -- for inspiration. They crafted a robotic fish that mimics the appearance and simulates the movements of the real predator. Aided by computer vision, the robot strikes when it spots the mosquitofish approaching tadpoles of an Australian species (Litoria moorei), which is threatened by mosquitofish in the wild. Scared and stressed, the mosquitofish showed fearful behaviors and experienced weight loss, changes in body shape, and a reduction in fertility, all of which impair their survival and reproduction. "Mosquitofish is one of the 100 world's worst invasive species, and current methods to eradicate it are too expensive and time-consuming to effectively contrast its spread," says first author Giovanni Polverino (@GioPolverino) of the University of Western Australia.

It may be smart, but it's not that clever. Artificial intelligence is nothing without human input. The algorithms that drive AI rely on the expertise of programmers and it's still no more than a tool – albeit a powerful one – that scientists and engineers can use to solve problems. Yet this is not to say that AI isn't the fastest-growing deep technology in the world, with the potential to transform people's lives and boost nations' economies. Facilitating AI innovation has even become a priority for the UK government, as laid out in the National AI Strategy it published in September.

Artificial intelligence (AI) technology can generate plausible, entertaining, and scientifically interesting titles for potential research articles, finds a study in the Christmas issue of The BMJ. A study of The BMJ's most popular Christmas research articles--which combine evidence based science with light hearted or quirky themes--finds that AI generated titles were as attractive to readers but that, as in other areas of medicine, performance was enhanced by human input. As such, the researchers say AI could have a role in generating hypotheses or directions for future research. AI is already used to help doctors diagnose conditions, based on the idea that computer systems can learn from data and identify patterns. But can AI be used to generate worthwhile hypotheses for medical research?