Women in AI Research (WiAIR) is a podcast dedicated to celebrating the remarkable contributions of female AI researchers from around the globe. Our mission is to challenge the prevailing perception that AI research is predominantly male-driven. Our goal is to empower early career researchers, especially women, to pursue their passion for AI and make an impact in this rapidly growing field. You will learn from women at different career stages, stay updated on the latest research and advancements, and hear powerful stories of overcoming obstacles and breaking stereotypes.
What Makes a Sentence Memorable? Inside Language, Memory and LLMs, with Dr. Greta Tuckute
Why don't bigger LLMs look more like the human brain? In the brain's language network, today's large models explain only slightly more variance than GPT-2 XL - and the reason says a lot about what those brain regions actually do.
Dr. Greta Tuckute (Research Fellow at the Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard) joins Jekaterina Novikova on Women in AI Research to unpack what brain-LLM alignment does and does not tell us. Greta works where neuroscience, cognitive science and AI meet - and when asked to keep only one of those three labels, AI is the first one she drops.
The conversation covers how brain alignment develops over training, how sparse autoencoders can turn the "you are just comparing one black box to another" critique into something testable, and what memory experiments reveal about how meaning is stored - from why "pineapple" sticks in memory and "light" does not, to which sentence embeddings predict what people remember.
In this episode:
Brain alignment tracks formal linguistic competence, not reasoning - and it emerges after not much more than a developmentally plausible ~100M tokens, not 300B
Why better next-word prediction stops meaning "more brain-like" once a model has mastered language
Sparse autoencoder features plus surprisal: for one frontal brain region, surprisal alone does almost as well as 32,000 SAE features, while some voxels are captured by just six features- Brains and LLMs share the main, high-variance features of language - not the idiosyncratic ones
Why learning from BPE tokens puts brain-LLM comparisons on "pretty shaky ground", and what should come next
A distinctive meaning makes words and sentences memorable - and SBERT predicts human sentence memory better than the other embedding models tested
From running a photography business at 14 to a PhD at MIT, and why trying the other path first was worth it
PAPERS DISCUSSED
From Language to Cognition: How LLMs Outgrow the Human Language Network (https://aclanthology.org/2025.emnlp-main.1237/)
Interpreting Brain Responses to Language with Sparse Features from Language Models (https://arxiv.org/abs/2606.06857)
Driving and suppressing the human language network using large language models (https://www.nature.com/articles/s41562-023-01783-7)
Intrinsically memorable words have unique associations with their meanings (https://doi.org/10.1037/xge0001742)
A distinctive meaning makes a sentence memorable (https://doi.org/10.1016/j.jml.2025.104700)
MENTIONED IN THIS EPISODE
Anna Ivanova on formal vs functional linguistic competence (https://open.spotify.com/episode/6RiUIebWWtlXeWlcrnkcfv?si=c_REnIqJSBeb7oiORQSzCg) (WiAIR)
GRETA TUCKUTE
Website: http://www.tuckute.com
Bluesky: https://bsky.app/profile/gretatuckute.bsky.social
X: https://x.com/GretaTuckute
Women in AI Research (WiAIR) is a podcast and YouTube channel where Jekaterina Novikova talks with women doing AI research about their work, the questions driving it, and the paths that brought them there.
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Bluesky (https://bsky.app/profile/wiair.bsky.social)
X (Twitter) (https://x.com/WiAIR_podcast)
WiAIR website (https://women-in-ai-research.github.io)
19 Aug 2026
Is an Image Worth a Thousand Words? Hidden Failures of Multimodal Metrics, with Dr. Elisa Kreiss
An image is not worth a thousand words - it's worth an indefinite number of them. So why do the metrics we use to evaluate AI-generated image descriptions still assume there's one correct answer?
In this episode of Women in AI Research, I talk with Elisa Kreiss (Assistant Professor of Communication at UCLA, director of the Coalas Lab) about what happens when you actually test the metrics the field relies on, and why CLIPScore, one of the most widely used measures for scoring image descriptions, stops correlating with human judgment the moment you introduce context. We also get into why longer descriptions aren't necessarily more informative, what happens when you just ask a model to "be concise," and whether AI models trip over charts and graphs the same way humans do.
Elisa's research sits at the intersection of linguistics, accessibility, and multimodal AI, and this conversation covers the full arc of her work, from the theoretical question of why humans never describe images the same way twice, to the practical question of what that means for building systems that actually work for blind and low-vision users.In this episode:
Why "context matters" is more radical than it sounds for image description evaluation
The hidden reason CLIPScore breaks down once context enters the picture
Why length is a bad proxy for information density - and what to use instead
What happens when you prompt a model to just "be concise"
Why charts and photos need completely different evaluation approaches
Whether AI models make the same mistakes as humans when reading data visualizations
What NeurIPS's Top Reviewer Award taught her about writing a genuinely useful peer review
Resources & Links:
Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics (https://aclanthology.org/2022.emnlp-main.309)
When More Words Say Less: Decoupling Length and Specificity in Image Description Evaluation (https://aclanthology.org/2026.acl-short.34/)
CHART-6: Human-Centered Evaluation of Data Visualization Understanding in Vision-Language Models (https://arxiv.org/abs/2505.17202)
🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI.
Follow WiAIR at:
LinkedIn (https://www.linkedin.com/company/women-in-ai-research/ )
Bluesky (https://bsky.app/profile/wiair.bsky.social)
X (Twitter) (https://x.com/WiAIR_podcast)
WiAIR website (https://women-in-ai-research.github.io)
22 Jul 2026
Is Your AI Just Flattering You? Sycophancy, AI Policies, and More, with Dr. Malihe Alikhani
Only 19% of Americans say AI has actually improved their productivity - so why the gap between the hype and reality?
In this episode of Women in AI Research, Dr. Malihe Alikhani (Northeastern University, Contextual AI Lab) unpacks the hidden failures in how we build and deploy AI: why sycophancy is really a collapse of alignment, why bigger models aren't better aligned, and why "thin" alignment breaks down in the real world.
Key topics
The impact of moving across different AI contexts on system design
The role of language as performative and active in shaping reality
Interactive inference and uncertainty in AI systems
The importance of context in meaning and system design
AI policy, transparency, and societal impact
Sycophantic behavior in large language models
Measuring AI alignment: thin vs. thick
AI adoption across sectors and demographic groups
The role of policy in AI development and safety
Ethical considerations in AI research and deployment
Resources & Links:
Breaking the AI Mirror: Sycophancy, productivity, and the future of collaboration (https://www.brookings.edu/articles/breaking-the-ai-mirror/)
Hype and harm: Why we must ask harder questions about AI and its alignment with human values (https://www.brookings.edu/articles/hype-and-harm-why-we-must-ask-harder-questions-about-ai-and-its-alignment-with-human-values/)
How are Americans using AI? Evidence from a nationwide survey (https://www.brookings.edu/articles/how-are-americans-using-ai-evidence-from-a-nationwide-survey/)
Connect with Dr. Malihe Alikhani:
https://x.com/malihealikhani (https://x.com/malihealikhani)
🎧 Subscribe to stay updated on new episodes spotlighting brilliant women shaping the future of AI.
Follow WiAIR at:
LinkedIn (https://www.linkedin.com/company/women-in-ai-research/ )
Bluesky (https://bsky.app/profile/wiair.bsky.social)
X (Twitter) (https://x.com/WiAIR_podcast)
WiAIR website (https://women-in-ai-research.github.io)
Who has been a guest on Women in AI Research (WiAIR)
Names identified in recent episode analyses. Showing up to five guests.
Dr Swabha Swayamdipta
Maria Ryskina
Dr Maria Antoniak
Dr Annie En-Shiun Lee
Dr Vered Shwartz
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YouTube views
27,313
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4.3 / 5
6 ratings
Podcast Authority Score: 20 / 100
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10
Social presence
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YouTube
76
Engagement
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