Reid Hoffman is a prominent Silicon Valley billionaire entrepreneur and investor known for co-founding the professional social networking site LinkedIn, now owned by Microsoft. The longtime Democrat donor threw his support behind Kamala Harris in the race for the White House. Hoffman spoke to the Observer about technology in the new political milieu and his new book about our future with artificial intelligence, Superagency. The book, while not ignoring the problems that AI might cause, argues that the technology is poised to give us cognitive superpowers that will increase our individual and collective human agency, creating a state of widespread empowerment for society. You have a vested interest in being positive about AI, including a company focused on conversational AI for business, Inflection AI.

With the United States set to meet delegations from Russia Ukraine separately in Saudi Arabia on Monday in an ongoing bid to halt the three-year war, Russia has launched a drone attack on Friday night on the Ukrainian city of Zaporizhzhia, killing three people and wounding 12, Ukrainian officials said. The city was hit by 12 drones, police said. Regional Governor Ivan Fedorov said residential buildings, cars and communal buildings were set on fire. Photos from the scene showed emergency services scouring the rubble for survivors. Ukraine and Russia agreed this week in principle to a limited ceasefire after US President Donald Trump held separate calls on consecutive days with the countries' leaders, but what actual targets would be off limits to attack remains contentious.

When Elon Musk met Narendra Modi in Washington DC in February, the SpaceX and Tesla chief presented India's prime minister with a gift and introduced him to his family. Modi described the meeting as "very good". Modi was in the United States to see President Donald Trump. In Modi's meeting with Musk, the two talked about collaborating in the fields of artificial intelligence (AI), space exploration, innovation and sustainable development, according to India's Ministry of External Affairs. But almost a month later, Musk's social media platform X has filed a lawsuit against the Indian government, alleging that New Delhi is unlawfully censoring content online. The lawsuit comes as Musk edges closer to launching both Starlink and Tesla in India.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The United Arab Emirates (UAE) has agreed to a 10-year, 1.4 trillion investment framework, the White House announced on Friday, saying it will "substantially increase the UAE's existing investments in the U.S. economy." The White House said the investments would be in AI infrastructure, semiconductors, energy, American manufacturing and more. The White House said in a press release that the UAE agreed to the framework after President Donald Trump hosted the UAE National Security Advisor, HH Sheikh Tahnoon bin Zayed Al Nahyan, for a meeting in the Oval Office.

Language agents powered by large language models (LLMs) face significant deployment challenges in resource-constrained environments, particularly for specialized domains and less-common languages. This paper presents Tox-chat, a Korean chemical toxicity information agent devised within these limitations. We propose two key innovations: a context-efficient architecture that reduces token consumption through hierarchical section search, and a scenario-based dialogue generation methodology that effectively distills tool-using capabilities from larger models. Experimental evaluations demonstrate that our fine-tuned 8B parameter model substantially outperforms both untuned models and baseline approaches, in terms of DB faithfulness and preference. Our work offers valuable insights for researchers developing domain-specific language agents under practical constraints.

Sophisticated phishing attacks have emerged as a major cybersecurity threat, becoming more common and difficult to prevent. Though machine learning techniques have shown promise in detecting phishing attacks, they function mainly as "black boxes" without revealing their decision-making rationale. This lack of transparency erodes the trust of users and diminishes their effective threat response. We present EXPLICATE: a framework that enhances phishing detection through a three-component architecture: an ML-based classifier using domain-specific features, a dual-explanation layer combining LIME and SHAP for complementary feature-level insights, and an LLM enhancement using DeepSeek v3 to translate technical explanations into accessible natural language. Our experiments show that EXPLICATE attains 98.4 % accuracy on all metrics, which is on par with existing deep learning techniques but has better explainability. High-quality explanations are generated by the framework with an accuracy of 94.2 % as well as a consistency of 96.8\% between the LLM output and model prediction. We create EXPLICATE as a fully usable GUI application and a light Chrome extension, showing its applicability in many deployment situations. The research shows that high detection performance can go hand-in-hand with meaningful explainability in security applications. Most important, it addresses the critical divide between automated AI and user trust in phishing detection systems.

The rapid growth of social media platforms has led to concerns about radicalization, filter bubbles, and content bias. Existing approaches to classifying ideology are limited in that they require extensive human effort, the labeling of large datasets, and are not able to adapt to evolving ideological contexts. This paper explores the potential of Large Language Models (LLMs) for classifying the political ideology of online content in the context of the two-party US political spectrum through in-context learning (ICL). Our extensive experiments involving demonstration selection in label-balanced fashion, conducted on three datasets comprising news articles and YouTube videos, reveal that our approach significantly outperforms zero-shot and traditional supervised methods. Additionally, we evaluate the influence of metadata (e.g., content source and descriptions) on ideological classification and discuss its implications. Finally, we show how providing the source for political and non-political content influences the LLM's classification.

Intrusion Detection Systems (IDS) are crucial for identifying malicious traffic, yet traditional signature-based methods struggle with zero-day attacks and high false positive rates. AI-driven packet-capture analysis offers a promising alternative. However, existing approaches rely heavily on flow-based or statistical features, limiting their ability to detect fine-grained attack patterns. This study proposes Xavier-CMAE, an enhanced Convolutional Multi-Head Attention Ensemble (CMAE) model that improves detection accuracy while reducing computational overhead. By replacing Word2Vec embeddings with a Hex2Int tokenizer and Xavier initialization, Xavier-CMAE eliminates pre-training, accelerates training, and achieves 99.971% accuracy with a 0.018% false positive rate, outperforming Word2Vec-based methods. Additionally, we introduce LLM-CMAE, which integrates pre-trained Large Language Model (LLM) tokenizers into CMAE. While LLMs enhance feature extraction, their computational cost hinders real-time detection. LLM-CMAE balances efficiency and performance, reaching 99.969% accuracy with a 0.019% false positive rate. This work advances AI-powered IDS by (1) introducing a payload-based detection framework, (2) enhancing efficiency with Xavier-CMAE, and (3) integrating LLM tokenizers for improved real-time detection.

This paper develops a critical theory of artificial intelligence, within a historical constellation where computational systems increasingly generate cultural content that destabilises traditional distinctions between human and machine production. Through this analysis, I introduce the concept of the algorithmic condition, a cultural moment when machine-generated work not only becomes indistinguishable from human creation but actively reshapes our understanding of ideas of authenticity. This transformation, I argue, moves beyond false consciousness towards what I call post-consciousness, where the boundaries between individual and synthetic consciousness become porous. Drawing on critical theory and extending recent work on computational ideology, I develop three key theoretical contributions, first, the concept of the Inversion to describe a new computational turn in algorithmic society; second, automimetric production as a framework for understanding emerging practices of automated value creation; and third, constellational analysis as a methodological approach for mapping the complex interplay of technical systems, cultural forms and political economic structures. Through these contributions, I argue that we need new critical methods capable of addressing both the technical specificity of AI systems and their role in restructuring forms of life under computational capitalism. The paper concludes by suggesting that critical reflexivity is needed to engage with the algorithmic condition without being subsumed by it and that it represents a growing challenge for contemporary critical theory.

Cybersecurity in robotics stands out as a key aspect within Regulation (EU) 2024/1689, also known as the Artificial Intelligence Act, which establishes specific guidelines for intelligent and automated systems. A fundamental distinction in this regulatory framework is the difference between robots with Artificial Intelligence (AI) and those that operate through automation systems without AI, since the former are subject to stricter security requirements due to their learning and autonomy capabilities. This work analyzes cybersecurity tools applicable to advanced robotic systems, with special emphasis on the protection of knowledge bases in cognitive architectures. Furthermore, a list of basic tools is proposed to guarantee the security, integrity, and resilience of these systems, and a practical case is presented, focused on the analysis of robot knowledge management, where ten evaluation criteria are defined to ensure compliance with the regulation and reduce risks in human-robot interaction (HRI) environments.