After years of complaints that there were no rules to determine what constitutes a "sustainable" investment, investors are now fretting that there will soon be too many to navigate easily. More than 30 taxonomies outlining what is and isn't a green investment are being compiled by governments across Asia, Europe and Latin America, each one reflecting national economic idiosyncrasies that can jar with a global capital market that has seen trillions pour into sustainable funds. The European Union will introduce its green investment taxonomy, or common framework, in January to help asset managers inside the bloc and make green activities more visible and attractive to investors. The rules also aim to stamp out "green washing," whereby organizations overstate their environmental credentials. The U.K., which hosts the COP26 climate change conference from Oct. 31, is set to finalize its own taxonomy next year but has already signaled it will not just replicate what is drawn up across the channel.

Yahoo Japan Corp. said Tuesday that it has introduced a function to automatically shut comment sections of its news article site when defamatory posts are detected. Using artificial intelligence, the unit of Z Holdings Corp. will make it impossible for users to view or submit new comments based on the number of posts violating the site's policies and other criteria. The new function was introduced to coincide with the start of the official campaign period for the Oct. 31 Lower House election. A message urging users to take care not to make illegal posts on political news articles will be displayed during the campaign period. So far, Yahoo Japan has deleted the posts it has judged to be defamatory and suspended posts by users who repeatedly violated the site's comment policy.

A hallmark of artificial intelligence is its ability to learn from the past. As researchers advance and refine AI applications, it could increasingly become part of routine research, too--the type of work that supported the advances toward tackling this pandemic and can support the response to a future one, too. Finding meaning in a sea of messy or incomplete data is precisely what data scientists at Pacific Northwest National Laboratory (PNNL) do. With expertise in applying graph-based machine learning, detailed molecular modeling, and explainable AI to questions of national security and basic science, PNNL researchers are now turning their artificial intelligence tools to the study of fundamental questions about treatments for COVID. What they are learning sharpens the tools available in the computational toolbox for responding quickly to a future pandemic.

A massive disruption now appears imminent in one of the world's largest – and most important – industries. In much the same way that Amazon disrupted the retail business – and how PayPal disrupted the payments industry – one under-the-radar health technology company now seeks to transform the $11.85 trillion global health industry. By moving healthcare away from brick and mortar, traditional medicine into an AI-driven tool that offers unprecedented speed, efficiency, and accuracy... Investors still have a brief window of opportunity to get in on this transformational investment opportunity while it still flies beneath Wall Street's radar. But as you'll soon discover, this company's technology is so powerful that it could become a valuable addition to hundreds of millions of households worldwide. Whether most patients, providers, or large healthcare companies realize it or not, the healthcare industry is already in the early stages of significant change. That's because patients now desire access to more information – and better information – in the blink of an eye. In a recent survey of U.S. health consumers, 71% reported facing major frustrations through their experience with healthcare providers. Concerns ranged from difficulties scheduling appointments to impersonal visits.

I have written previously that the Intelligence Community (IC) must rapidly advance its artificial intelligence (AI) capabilities to keep pace with our nation's adversaries and continue to provide policymakers with accurate, timely, and exquisite insights. The good news is that there is strong bipartisan support for doing so. The not-so-good news is that the IC is not well-postured to move quickly and take the risks required to continue to outpace China and other strategic competitors over the next decade. In addition to the practical budget and acquisition hurdles facing the IC, there is a strong cultural resistance to taking risks when not absolutely necessary. This is understandable given the life-and-death nature of intelligence work and the U.S. government's imperative to wisely execute national security funds and activities. However, some risks related to innovative and cutting-edge technologies like AI are in fact necessary, and the risk of inaction – the costs of not pursuing AI capabilities – is greater than the risk of action.

A new long-term funding programme to support farmers, growers, foresters and other businesses to embrace innovative ways to maximise productivity and drive sustainability has opened for applications today, 20 October. The Farming Innovation Programme, one of the new measures set out in the Government's Agricultural Transition Plan, will support ambitious projects to transform productivity and enhance environmental sustainability in England's agricultural and horticultural sectors, whilst driving the sectors towards net zero. In partnership with UK Research & Innovation (UKRI), Defra is today making £17.5 million available for the first round of the three funds which make up the Programme. The first fund to open is the'Industry-led R&D Partnerships Fund', where farmers, growers, foresters and businesses can bid for funding to develop new technologies and practices that will help them overcome challenges and exploit new opportunities in the sector such as the use of artificial intelligence and low-emission machineries to optimise the production process, and the development of climate-resilient crops. Early next year, Defra will launch the'Farming Futures R&D Fund', for strategic projects aimed at tackling climate change by reducing the environmental impact of farming.

Socialbots are software-driven user accounts on social platforms, acting autonomously (mimicking human behavior), with the aims to influence the opinions of other users or spread targeted misinformation for particular goals. As socialbots undermine the ecosystem of social platforms, they are often considered harmful. As such, there have been several computational efforts to auto-detect the socialbots. However, to our best knowledge, the adversarial nature of these socialbots has not yet been studied. This begs a question "can adversaries, controlling socialbots, exploit AI techniques to their advantage?" To this question, we successfully demonstrate that indeed it is possible for adversaries to exploit computational learning mechanism such as reinforcement learning (RL) to maximize the influence of socialbots while avoiding being detected. We first formulate the adversarial socialbot learning as a cooperative game between two functional hierarchical RL agents. While one agent curates a sequence of activities that can avoid the detection, the other agent aims to maximize network influence by selectively connecting with right users. Our proposed policy networks train with a vast amount of synthetic graphs and generalize better than baselines on unseen real-life graphs both in terms of maximizing network influence (up to +18%) and sustainable stealthiness (up to +40% undetectability) under a strong bot detector (with 90% detection accuracy). During inference, the complexity of our approach scales linearly, independent of a network's structure and the virality of news. This makes our approach a practical adversarial attack when deployed in a real-life setting.

We study the problem of adversarially robust learning in the transductive setting. For classes $\mathcal{H}$ of bounded VC dimension, we propose a simple transductive learner that when presented with a set of labeled training examples and a set of unlabeled test examples (both sets possibly adversarially perturbed), it correctly labels the test examples with a robust error rate that is linear in the VC dimension and is adaptive to the complexity of the perturbation set. This result provides an exponential improvement in dependence on VC dimension over the best known upper bound on the robust error in the inductive setting, at the expense of competing with a more restrictive notion of optimal robust error.

Requirements driven search-based testing (also known as falsification) has proven to be a practical and effective method for discovering erroneous behaviors in Cyber-Physical Systems. Despite the constant improvements on the performance and applicability of falsification methods, they all share a common characteristic. Namely, they are best-effort methods which do not provide any guarantees on the absence of erroneous behaviors (falsifiers) when the testing budget is exhausted. The absence of finite time guarantees is a major limitation which prevents falsification methods from being utilized in certification procedures. In this paper, we address the finite-time guarantees problem by developing a new stochastic algorithm. Our proposed algorithm not only estimates (bounds) the probability that falsifying behaviors exist, but also it identifies the regions where these falsifying behaviors may occur. We demonstrate the applicability of our approach on standard benchmark functions from the optimization literature and on the F16 benchmark problem.

Colosseum is an open-access and publicly-available large-scale wireless testbed for experimental research via virtualized and softwarized waveforms and protocol stacks on a fully programmable, "white-box" platform. Through 256 state-of-the-art Software-defined Radios and a Massive Channel Emulator core, Colosseum can model virtually any scenario, enabling the design, development and testing of solutions at scale in a variety of deployments and channel conditions. These Colosseum radio-frequency scenarios are reproduced through high-fidelity FPGA-based emulation with finite-impulse response filters. Filters model the taps of desired wireless channels and apply them to the signals generated by the radio nodes, faithfully mimicking the conditions of real-world wireless environments. In this paper we describe the architecture of Colosseum and its experimentation and emulation capabilities. We then demonstrate the effectiveness of Colosseum for experimental research at scale through exemplary use cases including prevailing wireless technologies (e.g., cellular and Wi-Fi) in spectrum sharing and unmanned aerial vehicle scenarios. A roadmap for Colosseum future updates concludes the paper.