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Date and Time Feb 7, 2025 from 5:30 PM to 8:30 PM

Location The Meetup will take place at MotionLab.Berlin, Bouchéstraße 12/Halle 20 in Berlin

Smart Data Loops: A New Paradigm for AI Development and Anomaly Detection

In the era of autonomous driving, the quality and efficiency of AI development hinge on the ability to manage data intelligently. This talk introduces the concept of Smart Data Loop, a novel paradigm that revolutionizes data handling by improving out-of-distribution detection, and leveraging trigger functions to refine AI models continuously. We will explore how these innovative approaches enhance anomaly detection and streamline AI workflows.

About the Speaker

Dr. Azarm Nowzad holds a PhD in Computer Science and serves as the Technical Project Lead and Product Owner for “Data for AI” at Continental Automotive. She is currently leading the publicly funded project “justbetterDATA”, which focuses on developing efficient and highly accurate data generation methods for AI applications, particularly in the field of autonomous driving. With her expertise in computer vision and AI, she plays a pivotal role in advancing data-driven solutions for next-generation mobility.

All About Agentic AI

Today, the concept of Agentic AI is shaping how we think about intelligent systems. These are AI systems designed to act autonomously, making decisions, completing tasks, and interacting with their environment—beyond traditional AI models. Understanding how to design and develop Agentic AI products is essential for staying ahead in the competitive landscape of AI-driven innovation. In this talk, Dr. Arman Nassirtoussi introduces Agentic AI. He’ll cover how these systems differ from standard AI, the evolving architectures that support them, and why they’re becoming critical.

About the Speaker

Dr. Arman Nassirtoussi earned his PhD in AI over a decade ago, focusing on predictive AI algorithms for intraday financial trading using Natural Language Processing (NLP), sentiment analysis, and text mining of online news. His main publication has quickly received over 1,200 citations on Google Scholar. Arman has led large data engineering, data science, and AI teams at companies like Henkel, Zalando, and T-Systems, helping build infrastructure, platforms, and products with a major focus on personalization and product analytics in e-commerce. Arman has also created a number of startups in multiple countries, and he is currently shaping a new one in the Agentic AI space.

Bridging Minds and Machines: Aligning Human Behavior and Machine Algorithm

As AI systems increasingly support human decision-making, integrating human-centered design principles into ML engineering has become essential. This talk bridges the foundational concepts of Human-Computer Interaction (HCI) with the complex demands of algorithmic decision-making, focusing on bidirectional Human-AI alignment, trust calibration, and Reciprocal Human-Machine Learning (RHML).

We explore the necessity of embedding human behavior and neurocognitive feedback loops into ML pipelines to enable adaptive and trustworthy systems. Addressing overtrust, undertrust, and trust miscalibration, we emphasize aligning ML systems with both high-performance metrics and user behavior, ensuring systems are effective and ethically aligned.

About the Speaker

Anke Borchers is an AI Strategist and Consultant specializing in Machine Learning (ML), Generative AI, and Trustworthy AI. With a background in Industrial and Communication Design and over 15 years of experience in innovation and business strategy, she bridges the gap between human-centered design and advanced AI systems.

Dedicated to crafting tailored solutions for the medical and business sectors, Anke highlights the critical importance of human-centered AI systems. She offers deep expertise in cognitive and machine decision-making, as well as AI Alignment, empowering organizations to develop AI solutions that are high-performing, ethically sound, and optimized to address user needs effectively.

Attention is All We Need: Using Transformers in Vision Tasks

Attention mechanism, initially developed for natural language processing, is now being effectively applied in Computer Vision. This talk will focus on how attention enables Visual Transformers to capture context and why they are overpowering the classical approaches to vision tasks.

About the Speaker

Kira Kravets is a Machine Learning engineer at Kertos, specializing in LLMs and the development of trustworthy AI systems. With experience in Computer Vision, particularly in the highly demanding medical field, she is passionate about building real-world AI applications with all the limitations and restrictions of production environments.

Speaker: Dr. Arman Nassirtoussi Dr. Nassirtoussi will discuss how Agentic AI differs from standard AI, the evolving architectures that support it, and its growing importance.

Speaker: Kira Kravets, Kertos An exploration of the application of transformers in visual tasks.

Speaker: Dan Gural, Voxel51 Join us for a live coding session demonstrating how to work with 2D and 3D medical images to optimize your Medical ML projects. Discover models such as MedSam2 and NVIDIA’s Vista3D and more

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