🌍 How Far Are AI Scientists from Changing the World? A must-read empirical study that goes beyond the hype to ask a grounded question: Do AI scientists really believe they’re changing the world? Based on a comprehensive survey of 2,778 AI researchers across academia and industry, this paper offers a rare, inside-out look at the hopes, fears, and expectations shaping the future of artificial intelligence. 🔑 Key Insights: • Reality Check: While the media leans into AI doomerism, the majority of researchers are optimistic — but grounded. • Top AI scientists’ views: • Expect significant progress in AGI this century • See real-world impact as increasingly tangible, especially in science & medicine • Not All Aligned: There’s substantial variation in how researchers perceive the timelines, risks, and responsibilities of AGI development. • Ethics & Regulation: Most respondents agree that AI poses real risks, and that technical work alone isn’t enough. Policy, safety research, and interdisciplinary collaboration are critical. 🧠 The big question isn’t if AI will change the world — it’s how, when, and who gets to shape that future. At the Agentic AI Bootcamp, we dig into the real-world implications of agentic systems and the people building them. Join the conversation that’s actually shaping the future: 🔗 hubs.la/Q03BybR-0 #AgenticAI #AGI #AIResearch #FutureOfAI #AIEthics #AIRegulation #LLMBootcamp #AIImpact #ResponsibleAI #MultidisciplinaryAI

Red Teaming AI Red Teaming - arxiv.org/pdf/2507.05538v1 Red teaming has evolved from its origins in military applications to become a widely adopted methodology in cybersecurity and AI. In this paper, we take a critical look at the practice of AI red teaming. We argue that despite its current popularity in AI governance, there exists a significant gap between red teaming’s original intent as a critical thinking exercise and its narrow focus on discovering model-level flaws in the context of generative AI. Current AI red teaming efforts focus predominantly on individual model vulnerabilities while overlooking the broader sociotechnical systems and emergent behaviors that arise from complex interactions between models, users, and environments. To address this deficiency, we propose a comprehensive framework operationalizing red teaming in AI systems at two levels: macro-level system red teaming spanning the entire AI development lifecycle, and micro-level model red teaming. Drawing on cybersecurity experience and systems theory, we further propose a set of recommendations. In these, we emphasize that effective AI red teaming requires multifunctional teams that examine emergent risks, systemic vulnerabilities, and the interplay between technical and social factors. #AIRedTeaming #RedTeaming #AIsecurity #AIgovernance #SystemicRisks #EmergentBehavior #SociotechnicalSystems #ModelVulnerabilities #AIThreatModeling #MacroRedTeaming #MicroRedTeaming #CyberSecurity #ResponsibleAI #AIrisks #AdversarialTesting #AIresilience #SystemSecurity #MultidisciplinaryAI #AIdevelopment #AIoversightAsk

"Multidisciplinary AI Applications" @NASA #event #ai #robotic #MultidisciplinaryAI