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More people believe misinformation about electric vehicles (EVs) than disagree with it, according to surveys of four countries, including Australia, Germany, Austria, and the US. The survey found having a conspiracy mentality was the main factor influencing such beliefs, the authors say.

The main -related concerns for Australians included that EVs are more likely to catch fire, that EVs are intentionally complex to prevent DIY, and that batteries are deliberately non-upgradeable. The authors also found that fact sheets and dialogues with AI-chatbots helped reduce belief in misinformation and increased pro-EV policy support and purchase intentions.

A University of Queensland-led study published in the journal Nature Energy has found misinformation about (EVs) has taken root in society and is primarily fueled by mistrust and .

Stating that Bitter frequently singles out an “exceedingly small subset of targets,” Proofpoint said the attacks are aimed at governments, diplomatic entities, and defense organizations so as to enable intelligence collection on foreign policy or current affairs.

Attack chains mounted by the group typically leverage spear-phishing emails, with the messages sent from providers like 163[.]com, 126[.]com, and ProtonMail, as well as compromised accounts associated with the governments of Pakistan, Bangladesh, and Madagascar.

The threat actor has also been observed masquerading as government and diplomatic entities from China, Madagascar, Mauritius, and South Korea in these campaigns to entice recipients into malware-laced attachments that trigger the deployment of malware.

On May 20, during her speech at the Annual EU Budget Conference 2025, Ursula von der Leyen, President of the European Commission, stated:

When the current budget was negotiated, we thought AI would only approach human reasoning around 2050. Now we expect this to happen already next year. It is simply impossible to determine today where innovation will lead us by the end of the next budgetary cycle. Our budget of tomorrow will need to respond fast.

This is remarkable coming from the highest-ranking EU official. It suggests the Overton window for AI policy has shifted significantly.

Using global land use and carbon storage data from the past 175 years, researchers at The University of Texas at Austin and Cognizant AI Labs have trained an artificial intelligence system to develop optimal environmental policy solutions that can advance global sustainability initiatives of the United Nations.

The AI tool effectively balances various complex trade-offs to recommend ways of maximizing carbon storage, minimizing economic disruptions and helping improve the environment and people’s everyday lives, according to a paper published today in the journal Environmental Data Science.

The project is among the first applications of the UN-backed Project Resilience, a team of scientists and experts working to tackle global decision-augmentation problems—including ambitious sustainable development goals this decade—through part of a broader effort called AI for Good.

Neuroscientists at the Sainsbury Wellcome Center (SWC) at UCL have discovered that the brain uses a dual system for learning through trial and error. This is the first time a second learning system has been identified, which could help explain how habits are formed and provide a scientific basis for new strategies to address conditions related to habitual learning, such as addictions and compulsions.

Published in Nature, the study in mice could also have implications for developing therapeutics for Parkinson’s. The study is titled “Dopaminergic action prediction errors serve as a value-free teaching signal.”

“Essentially, we have found a mechanism that we think is responsible for habits. Once you have developed a preference for a certain action, then you can bypass your value-based system and just rely on your default policy of what you’ve done in the past. This might then allow you to free up cognitive resources to make value-based decisions about something else,” explained Dr. Marcus Stephenson-Jones, Group Leader at SWC and lead author of the study.

Countries in the Global South risk being left out of the quantum revolution — along with its economic, technological and security benefits — due to growing export controls, siloed research initiatives and national security concerns, a new policy analysis argues.

In the first of a series of articles on quantum technologies published by the policy journal Just Securit y, researchers Michael Karanicolas, of Dalhousie University, and Alessia Zornetta, of UCLA Law, examine how the geopolitics of emerging quantum technologies are replicating long-standing patterns of technological exclusion. The authors argue that absent meaningful interventions, quantum could become another engine of global inequality, one that threatens to lock poorer nations out of the next era of technological and economic development.

The authors trace the roots of this divide to export control regimes that are quickly expanding in response to the strategic potential of quantum systems. Since 2020, governments in the U.S., EU and China have implemented targeted restrictions on quantum-enabling hardware, software, and communications systems.

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Given the complexity of multi-tenant cloud environments and the growing need for real-time threat mitigation, Security Operations Centers (SOCs) must adopt AI-driven adaptive defense mechanisms to counter Advanced Persistent Threats (APTs). However, SOC analysts face challenges in handling adaptive adversarial tactics, requiring intelligent decision-support frameworks. We propose a Cognitive Hierarchy Theory-driven Deep Q-Network (CHT-DQN) framework that models interactive decision-making between SOC analysts and AI-driven APT bots. The SOC analyst (defender) operates at cognitive level-1, anticipating attacker strategies, while the APT bot (attacker) follows a level-0 policy. By incorporating CHT into DQN, our framework enhances adaptive SOC defense using Attack Graph (AG)-based reinforcement learning. Simulation experiments across varying AG complexities show that CHT-DQN consistently achieves higher data protection and lower action discrepancies compared to standard DQN. A theoretical lower bound further confirms its superiority as AG complexity increases. A human-in-the-loop (HITL) evaluation on Amazon Mechanical Turk (MTurk) reveals that SOC analysts using CHT-DQN-derived transition probabilities align more closely with adaptive attackers, leading to better defense outcomes. Moreover, human behavior aligns with Prospect Theory (PT) and Cumulative Prospect Theory (CPT): participants are less likely to reselect failed actions and more likely to persist with successful ones. This asymmetry reflects amplified loss sensitivity and biased probability weighting — underestimating gains after failure and overestimating continued success. Our findings highlight the potential of integrating cognitive models into deep reinforcement learning to improve real-time SOC decision-making for cloud security.