Title & Speakers	Event
Beyond the Prototype: What It Takes to Build Enterprise-Grade AI 2026-01-28 · 19:00 The AI landscape is cluttered with impressive demos and promising proofs of concept—but turning those early wins into real, scalable impact is a different challenge entirely. This webinar dives deep into what it actually takes to evolve from experimentation to production in enterprise AI. Join a panel of experts who have built and deployed AI at scale to explore the operational, architectural, and organizational requirements that separate enterprise-ready AI from pilot projects that never leave the lab. We'll cover how to navigate infrastructure decisions, design for governance and observability, and build systems that are robust, compliant, and built to last. Key Takeaways: 1️⃣ From Idea to Impact: What separates successful enterprise AI deployments from stalled prototypes. 2️⃣ Architecting for Scale: Best practices for building AI pipelines that are modular, maintainable, and audit-ready. 3️⃣ Trust and Governance: How to bake in model observability, compliance, and responsible AI from day one. 4️⃣Collaboration Across Functions: Why cross-team alignment (ML, IT, data, product) is essential—and how to make it work in practice. 5️⃣ Lessons from the Field: Real-world insights from leaders who’ve scaled AI across industries. Panelists to be announced soon. Register here	Beyond the Prototype: What It Takes to Build Enterprise-Grade AI
Event Meetup ElasticFR #100 - BNP Paribas 2026-01-26
Well... You never know! 😅 2026-01-26 · 18:00 Let's hear about what is cooking at Elastic at this period. You know, Shay, for Search... ELK
Building secure, agent-powered intelligence 2026-01-26 · 18:00 Responsible AI with Microsoft & Elastic: Building secure, agent-powered intelligence. AI/ML ELK Microsoft
Troubleshooting and planning performance at scale in BNP Paribas 2026-01-26 · 18:00 In this session we will explain how we are tuning, analyzing and fixing Elasticsearch huge cluster's performance issues in BNP Paribas, and how we are sizing our infrastructure to cope our performance needs. elasticsearch

Jan 22 - Women in AI 2026-01-22 · 23:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 23:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 23:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 23:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 23:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Jan 22 - Women in AI 2026-01-22 · 17:00 Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd. Date, Time and Location Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom! Align Before You Recommend The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements. While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning. By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items. Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage About the Speaker Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts. Generalizable Vision-Language Models: Challenges, Advances, and Future Directions Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings. About the Speaker Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM. Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved! About the Speaker Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist. FiftyOne Labs: Enabling experimentation for the computer vision community FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product. About the Speaker Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.	Jan 22 - Women in AI
Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 22:00 Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join: AI/ML developers working on real-world deployments Policy makers and legal experts exploring AI governance Data scientists interested in ethical model design Academics and researchers studying AI and society Ethicists, community leaders, and social impact advocates Product leaders balancing innovation with responsibility Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most: general – Start with introductions, announcements, and cross-topic conversation networking – Share your portfolio, white papers, startups, or ethical AI tools intros – Tell your story: what you work on and why it matters help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where? Second Wednesday of Every month 5:00 PM – 7:00 PM (your local time) Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh/source--me No login hurdles. No presentations. Just people-driven dialogue. Why Join? Global gathering of AI professionals shaping the future responsibly Self-led chats & 1-on-1 networking with values-aligned peers Exchange insights, frameworks, and use cases for applied AI Build your ethical AI toolkit and collaborative network Reconnect monthly to deepen the conversation Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links: Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY Support Page: https://noworkerleftbehind.org/event_support Hashtags for Discovery & Engagement AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity	Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility
Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 22:00 Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join: AI/ML developers working on real-world deployments Policy makers and legal experts exploring AI governance Data scientists interested in ethical model design Academics and researchers studying AI and society Ethicists, community leaders, and social impact advocates Product leaders balancing innovation with responsibility Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most: general – Start with introductions, announcements, and cross-topic conversation networking – Share your portfolio, white papers, startups, or ethical AI tools intros – Tell your story: what you work on and why it matters help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where? Second Wednesday of Every month 5:00 PM – 7:00 PM (your local time) Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh No login hurdles. No presentations. Just people-driven dialogue. Why Join? Global gathering of AI professionals shaping the future responsibly Self-led chats & 1-on-1 networking with values-aligned peers Exchange insights, frameworks, and use cases for applied AI Build your ethical AI toolkit and collaborative network Reconnect monthly to deepen the conversation Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links: Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY Support Page: https://noworkerleftbehind.org/event_support Hashtags for Discovery & Engagement AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity	Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility
Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 22:00 Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join: AI/ML developers working on real-world deployments Policy makers and legal experts exploring AI governance Data scientists interested in ethical model design Academics and researchers studying AI and society Ethicists, community leaders, and social impact advocates Product leaders balancing innovation with responsibility Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most: general – Start with introductions, announcements, and cross-topic conversation networking – Share your portfolio, white papers, startups, or ethical AI tools intros – Tell your story: what you work on and why it matters help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where? Second Wednesday of Every month 5:00 PM – 7:00 PM (your local time) Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh No login hurdles. No presentations. Just people-driven dialogue. Why Join? Global gathering of AI professionals shaping the future responsibly Self-led chats & 1-on-1 networking with values-aligned peers Exchange insights, frameworks, and use cases for applied AI Build your ethical AI toolkit and collaborative network Reconnect monthly to deepen the conversation Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links: Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY Support Page: https://noworkerleftbehind.org/event_support Hashtags for Discovery & Engagement AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity	Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility
Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 17:00 Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join: AI/ML developers working on real-world deployments Policy makers and legal experts exploring AI governance Data scientists interested in ethical model design Academics and researchers studying AI and society Ethicists, community leaders, and social impact advocates Product leaders balancing innovation with responsibility Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most: general – Start with introductions, announcements, and cross-topic conversation networking – Share your portfolio, white papers, startups, or ethical AI tools intros – Tell your story: what you work on and why it matters help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where? Second Wednesday of Every month 5:00 PM – 7:00 PM (your local time) Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh No login hurdles. No presentations. Just people-driven dialogue. Why Join? Global gathering of AI professionals shaping the future responsibly Self-led chats & 1-on-1 networking with values-aligned peers Exchange insights, frameworks, and use cases for applied AI Build your ethical AI toolkit and collaborative network Reconnect monthly to deepen the conversation Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links: Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY Support Page: https://noworkerleftbehind.org/event_support Hashtags for Discovery & Engagement AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity	Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility
Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 17:00 Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join: AI/ML developers working on real-world deployments Policy makers and legal experts exploring AI governance Data scientists interested in ethical model design Academics and researchers studying AI and society Ethicists, community leaders, and social impact advocates Product leaders balancing innovation with responsibility Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most: general – Start with introductions, announcements, and cross-topic conversation networking – Share your portfolio, white papers, startups, or ethical AI tools intros – Tell your story: what you work on and why it matters help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where? Second Wednesday of Every month 5:00 PM – 7:00 PM (your local time) Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh No login hurdles. No presentations. Just people-driven dialogue. Why Join? Global gathering of AI professionals shaping the future responsibly Self-led chats & 1-on-1 networking with values-aligned peers Exchange insights, frameworks, and use cases for applied AI Build your ethical AI toolkit and collaborative network Reconnect monthly to deepen the conversation Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links: Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY Support Page: https://noworkerleftbehind.org/event_support Hashtags for Discovery & Engagement AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity	Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility
Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 16:00 Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join: AI/ML developers working on real-world deployments Policy makers and legal experts exploring AI governance Data scientists interested in ethical model design Academics and researchers studying AI and society Ethicists, community leaders, and social impact advocates Product leaders balancing innovation with responsibility Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most: general – Start with introductions, announcements, and cross-topic conversation networking – Share your portfolio, white papers, startups, or ethical AI tools intros – Tell your story: what you work on and why it matters help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where? Second Wednesday of Every month 5:00 PM – 7:00 PM (your local time) Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh No login hurdles. No presentations. Just people-driven dialogue. Why Join? Global gathering of AI professionals shaping the future responsibly Self-led chats & 1-on-1 networking with values-aligned peers Exchange insights, frameworks, and use cases for applied AI Build your ethical AI toolkit and collaborative network Reconnect monthly to deepen the conversation Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links: Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY Support Page: https://noworkerleftbehind.org/event_support Hashtags for Discovery & Engagement AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity	Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility
Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 16:00 Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join: AI/ML developers working on real-world deployments Policy makers and legal experts exploring AI governance Data scientists interested in ethical model design Academics and researchers studying AI and society Ethicists, community leaders, and social impact advocates Product leaders balancing innovation with responsibility Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most: general – Start with introductions, announcements, and cross-topic conversation networking – Share your portfolio, white papers, startups, or ethical AI tools intros – Tell your story: what you work on and why it matters help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where? Second Wednesday of Every month 5:00 PM – 7:00 PM (your local time) Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh No login hurdles. No presentations. Just people-driven dialogue. Why Join? Global gathering of AI professionals shaping the future responsibly Self-led chats & 1-on-1 networking with values-aligned peers Exchange insights, frameworks, and use cases for applied AI Build your ethical AI toolkit and collaborative network Reconnect monthly to deepen the conversation Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links: Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY Support Page: https://noworkerleftbehind.org/event_support Hashtags for Discovery & Engagement AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity	Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility
Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 16:00 Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join: AI/ML developers working on real-world deployments Policy makers and legal experts exploring AI governance Data scientists interested in ethical model design Academics and researchers studying AI and society Ethicists, community leaders, and social impact advocates Product leaders balancing innovation with responsibility Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most: general – Start with introductions, announcements, and cross-topic conversation networking – Share your portfolio, white papers, startups, or ethical AI tools intros – Tell your story: what you work on and why it matters help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where? Second Wednesday of Every month 5:00 PM – 7:00 PM (your local time) Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh No login hurdles. No presentations. Just people-driven dialogue. Why Join? Global gathering of AI professionals shaping the future responsibly Self-led chats & 1-on-1 networking with values-aligned peers Exchange insights, frameworks, and use cases for applied AI Build your ethical AI toolkit and collaborative network Reconnect monthly to deepen the conversation Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links: Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY Support Page: https://noworkerleftbehind.org/event_support Hashtags for Discovery & Engagement AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity	Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility

Beyond the Prototype: What It Takes to Build Enterprise-Grade AI 2026-01-28 · 19:00

The AI landscape is cluttered with impressive demos and promising proofs of concept—but turning those early wins into real, scalable impact is a different challenge entirely. This webinar dives deep into what it actually takes to evolve from experimentation to production in enterprise AI.

Join a panel of experts who have built and deployed AI at scale to explore the operational, architectural, and organizational requirements that separate enterprise-ready AI from pilot projects that never leave the lab. We'll cover how to navigate infrastructure decisions, design for governance and observability, and build systems that are robust, compliant, and built to last.

Key Takeaways:

1️⃣ From Idea to Impact: What separates successful enterprise AI deployments from stalled prototypes.

2️⃣ Architecting for Scale: Best practices for building AI pipelines that are modular, maintainable, and audit-ready.

3️⃣ Trust and Governance: How to bake in model observability, compliance, and responsible AI from day one.

4️⃣Collaboration Across Functions: Why cross-team alignment (ML, IT, data, product) is essential—and how to make it work in practice.

5️⃣ Lessons from the Field: Real-world insights from leaders who’ve scaled AI across industries.

Panelists to be announced soon.

Register here

Beyond the Prototype: What It Takes to Build Enterprise-Grade AI

Jan 22 - Women in AI 2026-01-22 · 23:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 23:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 23:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 23:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 23:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Jan 22 - Women in AI 2026-01-22 · 17:00

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026 9 - 11 AM Pacific Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs. While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored. This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat \~ Ethical AI governance advocate\, pioneering AI frameworks that prioritize emergent AI behavior & consciousness\, R&D\, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.

Jan 22 - Women in AI

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 22:00

Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join:

AI/ML developers working on real-world deployments
Policy makers and legal experts exploring AI governance
Data scientists interested in ethical model design
Academics and researchers studying AI and society
Ethicists, community leaders, and social impact advocates
Product leaders balancing innovation with responsibility

Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most:

general – Start with introductions, announcements, and cross-topic conversation
networking – Share your portfolio, white papers, startups, or ethical AI tools
intros – Tell your story: what you work on and why it matters
help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment
industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where?

Second Wednesday of Every month
5:00 PM – 7:00 PM (your local time)
Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh/source--me

No login hurdles. No presentations. Just people-driven dialogue. Why Join?

Global gathering of AI professionals shaping the future responsibly
Self-led chats & 1-on-1 networking with values-aligned peers
Exchange insights, frameworks, and use cases for applied AI
Build your ethical AI toolkit and collaborative network
Reconnect monthly to deepen the conversation

Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links:

Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY
Support Page: https://noworkerleftbehind.org/event_support

Hashtags for Discovery & Engagement

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 22:00

Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join:

AI/ML developers working on real-world deployments
Policy makers and legal experts exploring AI governance
Data scientists interested in ethical model design
Academics and researchers studying AI and society
Ethicists, community leaders, and social impact advocates
Product leaders balancing innovation with responsibility

Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most:

general – Start with introductions, announcements, and cross-topic conversation
networking – Share your portfolio, white papers, startups, or ethical AI tools
intros – Tell your story: what you work on and why it matters
help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment
industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where?

Second Wednesday of Every month
5:00 PM – 7:00 PM (your local time)
Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh

No login hurdles. No presentations. Just people-driven dialogue. Why Join?

Global gathering of AI professionals shaping the future responsibly
Self-led chats & 1-on-1 networking with values-aligned peers
Exchange insights, frameworks, and use cases for applied AI
Build your ethical AI toolkit and collaborative network
Reconnect monthly to deepen the conversation

Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links:

Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY
Support Page: https://noworkerleftbehind.org/event_support

Hashtags for Discovery & Engagement

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 22:00

Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join:

AI/ML developers working on real-world deployments
Policy makers and legal experts exploring AI governance
Data scientists interested in ethical model design
Academics and researchers studying AI and society
Ethicists, community leaders, and social impact advocates
Product leaders balancing innovation with responsibility

Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most:

general – Start with introductions, announcements, and cross-topic conversation
networking – Share your portfolio, white papers, startups, or ethical AI tools
intros – Tell your story: what you work on and why it matters
help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment
industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where?

Second Wednesday of Every month
5:00 PM – 7:00 PM (your local time)
Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh

No login hurdles. No presentations. Just people-driven dialogue. Why Join?

Global gathering of AI professionals shaping the future responsibly
Self-led chats & 1-on-1 networking with values-aligned peers
Exchange insights, frameworks, and use cases for applied AI
Build your ethical AI toolkit and collaborative network
Reconnect monthly to deepen the conversation

Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links:

Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY
Support Page: https://noworkerleftbehind.org/event_support

Hashtags for Discovery & Engagement

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 17:00

Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join:

AI/ML developers working on real-world deployments
Policy makers and legal experts exploring AI governance
Data scientists interested in ethical model design
Academics and researchers studying AI and society
Ethicists, community leaders, and social impact advocates
Product leaders balancing innovation with responsibility

Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most:

general – Start with introductions, announcements, and cross-topic conversation
networking – Share your portfolio, white papers, startups, or ethical AI tools
intros – Tell your story: what you work on and why it matters
help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment
industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where?

Second Wednesday of Every month
5:00 PM – 7:00 PM (your local time)
Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh

No login hurdles. No presentations. Just people-driven dialogue. Why Join?

Global gathering of AI professionals shaping the future responsibly
Self-led chats & 1-on-1 networking with values-aligned peers
Exchange insights, frameworks, and use cases for applied AI
Build your ethical AI toolkit and collaborative network
Reconnect monthly to deepen the conversation

Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links:

Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY
Support Page: https://noworkerleftbehind.org/event_support

Hashtags for Discovery & Engagement

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 17:00

Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join:

AI/ML developers working on real-world deployments
Policy makers and legal experts exploring AI governance
Data scientists interested in ethical model design
Academics and researchers studying AI and society
Ethicists, community leaders, and social impact advocates
Product leaders balancing innovation with responsibility

Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most:

general – Start with introductions, announcements, and cross-topic conversation
networking – Share your portfolio, white papers, startups, or ethical AI tools
intros – Tell your story: what you work on and why it matters
help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment
industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where?

Second Wednesday of Every month
5:00 PM – 7:00 PM (your local time)
Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh

No login hurdles. No presentations. Just people-driven dialogue. Why Join?

Global gathering of AI professionals shaping the future responsibly
Self-led chats & 1-on-1 networking with values-aligned peers
Exchange insights, frameworks, and use cases for applied AI
Build your ethical AI toolkit and collaborative network
Reconnect monthly to deepen the conversation

Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links:

Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY
Support Page: https://noworkerleftbehind.org/event_support

Hashtags for Discovery & Engagement

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 16:00

Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join:

AI/ML developers working on real-world deployments
Policy makers and legal experts exploring AI governance
Data scientists interested in ethical model design
Academics and researchers studying AI and society
Ethicists, community leaders, and social impact advocates
Product leaders balancing innovation with responsibility

Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most:

general – Start with introductions, announcements, and cross-topic conversation
networking – Share your portfolio, white papers, startups, or ethical AI tools
intros – Tell your story: what you work on and why it matters
help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment
industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where?

Second Wednesday of Every month
5:00 PM – 7:00 PM (your local time)
Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh

No login hurdles. No presentations. Just people-driven dialogue. Why Join?

Global gathering of AI professionals shaping the future responsibly
Self-led chats & 1-on-1 networking with values-aligned peers
Exchange insights, frameworks, and use cases for applied AI
Build your ethical AI toolkit and collaborative network
Reconnect monthly to deepen the conversation

Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links:

Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY
Support Page: https://noworkerleftbehind.org/event_support

Hashtags for Discovery & Engagement

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 16:00

Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join:

AI/ML developers working on real-world deployments
Policy makers and legal experts exploring AI governance
Data scientists interested in ethical model design
Academics and researchers studying AI and society
Ethicists, community leaders, and social impact advocates
Product leaders balancing innovation with responsibility

Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most:

general – Start with introductions, announcements, and cross-topic conversation
networking – Share your portfolio, white papers, startups, or ethical AI tools
intros – Tell your story: what you work on and why it matters
help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment
industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where?

Second Wednesday of Every month
5:00 PM – 7:00 PM (your local time)
Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh

No login hurdles. No presentations. Just people-driven dialogue. Why Join?

Global gathering of AI professionals shaping the future responsibly
Self-led chats & 1-on-1 networking with values-aligned peers
Exchange insights, frameworks, and use cases for applied AI
Build your ethical AI toolkit and collaborative network
Reconnect monthly to deepen the conversation

Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links:

Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY
Support Page: https://noworkerleftbehind.org/event_support

Hashtags for Discovery & Engagement

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility 2026-01-14 · 16:00

Join a global, open-format gathering for those building and questioning AI — from real-world use cases to critical conversations around ethics, bias, and transparency. Every month, we create space for developers, researchers, and policymakers to connect in meaningful, unmoderated dialogue — with flexible chats, peer learning, and 1-on-1 video networking. Watch how it works: https://www.youtube.com/watch?v=bOYkxBjrAYY What Is the Applied AI & Ethics #MonthlyMixer? The Applied AI & Ethics MonthlyMixer is a self-directed online networking event where professionals from across the AI spectrum come together to collaborate, question, and connect. This isn’t a webinar or panel — it’s a structured-free, chat-first environment hosted on a Slack-like virtual platform, featuring topic-specific channels and private video or text chat options. Whether you're designing algorithms, drafting policy, or advocating for responsible tech, this is your space to exchange ideas, showcase projects, or meet others who care about building AI with integrity and impact. Who Should Join:

AI/ML developers working on real-world deployments
Policy makers and legal experts exploring AI governance
Data scientists interested in ethical model design
Academics and researchers studying AI and society
Ethicists, community leaders, and social impact advocates
Product leaders balancing innovation with responsibility

Whether you're launching applied AI tools or questioning their societal effects, this mixer welcomes your perspective. Explore These Channels Inside: The platform provides themed group discussions to help you navigate the topics that matter most:

general – Start with introductions, announcements, and cross-topic conversation
networking – Share your portfolio, white papers, startups, or ethical AI tools
intros – Tell your story: what you work on and why it matters
help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment
industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

You’ll also be able to chat directly and launch 1-on-1 video calls for deeper conversations. When & Where?

Second Wednesday of Every month
5:00 PM – 7:00 PM (your local time)
Register from the platform here - https://events.tao.ai/pod/analytics.club/cont-r8tfbvicrqsh

No login hurdles. No presentations. Just people-driven dialogue. Why Join?

Global gathering of AI professionals shaping the future responsibly
Self-led chats & 1-on-1 networking with values-aligned peers
Exchange insights, frameworks, and use cases for applied AI
Build your ethical AI toolkit and collaborative network
Reconnect monthly to deepen the conversation

Questions or Suggestions? We’re always listening. Contact us anytime at: https://noworkerleftbehind.org/event_support Quick Links:

Explainer Video: https://www.youtube.com/watch?v=bOYkxBjrAYY
Support Page: https://noworkerleftbehind.org/event_support

Hashtags for Discovery & Engagement

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

Applied AI Ethics MonthlyMixer - Where Innovation Meets Responsibility

People (80 results)

Companies (2 results)

Activities & events

general – Start with introductions, announcements, and cross-topic conversation

networking – Share your portfolio, white papers, startups, or ethical AI tools

intros – Tell your story: what you work on and why it matters

help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment

industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

general – Start with introductions, announcements, and cross-topic conversation

networking – Share your portfolio, white papers, startups, or ethical AI tools

intros – Tell your story: what you work on and why it matters

help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment

industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

general – Start with introductions, announcements, and cross-topic conversation

networking – Share your portfolio, white papers, startups, or ethical AI tools

intros – Tell your story: what you work on and why it matters

help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment

industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

general – Start with introductions, announcements, and cross-topic conversation

networking – Share your portfolio, white papers, startups, or ethical AI tools

intros – Tell your story: what you work on and why it matters

help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment

industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

general – Start with introductions, announcements, and cross-topic conversation

networking – Share your portfolio, white papers, startups, or ethical AI tools

intros – Tell your story: what you work on and why it matters

help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment

industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

general – Start with introductions, announcements, and cross-topic conversation

networking – Share your portfolio, white papers, startups, or ethical AI tools

intros – Tell your story: what you work on and why it matters

help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment

industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

general – Start with introductions, announcements, and cross-topic conversation

networking – Share your portfolio, white papers, startups, or ethical AI tools

intros – Tell your story: what you work on and why it matters

help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment

industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity

general – Start with introductions, announcements, and cross-topic conversation

networking – Share your portfolio, white papers, startups, or ethical AI tools

intros – Tell your story: what you work on and why it matters

help-wanted – Request or offer guidance around data ethics, fairness, or responsible deployment

industry-room-tech – Dive deep into real-world cases, AI misuse, explainability, and bias mitigation

AppliedAI #EthicalAI #AIEthics #ResponsibleTech #MonthlyMixer #AIGovernance #AIandSociety #FairAI #BiasInAI #TechForGood #AINetworking #AIProfessionals #GlobalAICommunity