Welcome to
Women in AI Research

Do large language models truly understand language—or are they sophisticated pattern matchers?In this conversation, Dr. Anna Ivanova (Asst. Prof. at Georgia Tech) explores one of the important questions in AI: the relationship between language, thought, and intelligence. Drawing from neuroscience, cognitive science, and AI research, Anna explains why language understanding is harder to define than most people realize, why reasoning and language are not the same thing, and what today's LLMs can and cannot tell us about human cognition.Key Topics:Do LLMs understand language or merely generate convincing text?The difference between formal and functional linguistic competenceWhat LLMs can learn from language alone—and what they cannotWhy human cognition and AI cognition may be fundamentally differentTheory of mind, reasoning, and common misconceptions about AI capabilitiesHow cognitive scientists evaluate the "thinking" abilities of LLMsWhat neuroscience can teach AI researchers about interpretabilityWhy understanding AI requires studying both behavior and internal representationsThe future of multimodal models and AI cognitionResources & Links:What does it mean to understand language?Dissociating language and thought in large language modelsHow to evaluate the cognitive abilities of LLMsHow Do LLMs Use Their Depth?True LensConnect with Dr. Anna Ivanova:https://bsky.app/profile/neuranna.bsky.socialhttps://x.com/neuranna🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI.Follow WiAIR at:LinkedInBlueskyX (Twitter)⁠WiAIR website⁠

What actually happens when AI systems fail in the real world?In this final part of our conversation with Saadia Gabriel (UCLA), we unpack one of the most urgent challenges in modern AI: why even the most advanced models remain vulnerable to manipulation - and what that means for safety, fairness, and society.From multi-turn jailbreaking attacks with near 100% success rates to misinformation shaping human beliefs, this conversation goes beyond surface-level concerns and dives into how harms actually emerge in deployed systems.We explore:Why current guardrails are not enoughHow realistic attack scenarios differ from academic benchmarksThe connection between model vulnerabilities and societal harmWhat AI can (and cannot) do about misinformation and persuasionThe open research problems that still don’t have solutionsResources & Links:Generative AI in the Era of 'Alternative Facts'ModelCitizens: Representing Community Voices in Online SafetyTranslation as a Scalable Proxy for Multilingual EvaluationConnect with Dr. Saadia Gabriel:https://x.com/GabrielSaadiahttps://bsky.app/profile/skgabrie.bsky.social

What does it mean to build AI systems we can actually trust?In this first part of our conversation with Saadia Gabriel (UCLA), we explore the deeply personal and technical journey behind her work on AI safety, misuse, and responsible NLP.From experiencing targeted hate speech firsthand to receiving a best paper nomination, Saadia shares how her lived experience shaped her research — and why language models must be designed with both capability and risk in mind.🧠 In this episode, we cover:How personal experiences influence AI research directionsThe intersection of NLP, security, and privacyWhy LLMs can be both powerful and dangerousWhat it means to build trustworthy AI systemsLessons from working across multiple research paradigmsHow to pursue high-impact research as a PhD or early-career scientistResources & Links:X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-AgentsConnect with Dr. Saadia Gabriel:https://x.com/GabrielSaadiahttps://bsky.app/profile/skgabrie.bsky.social