Ai in Cloud Computing San Francisco

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About the Workshop

Visual AI is revolutionizing healthcare by enabling more accurate diagnoses, streamlining medical workflows, and uncovering valuable insights across various imaging modalities. Yet, building trustworthy AI in healthcare demands more than powerful models — it requires clean, curated data, strong visualizations, and human-in-the-loop understanding.

Join us for a free, 90-minute, hands-on workshop built for healthcare researchers, medical data scientists, and AI engineers working with real-world imaging data. Whether you're analyzing CT scans, radiology images, or multi-modal patient datasets, this session will equip you with the tools to design robust, transparent, and insight-driven computer vision pipelines — powered by FiftyOne, the open-source platform for Visual AI.

By the end of the workshop, you'll be able to:

• Load and organize complex medical datasets (e.g., ARCADE, DeepLesion) with FiftyOne.
• Explore medical imaging data using embeddings, patches, and metadata filters.
• Curate balanced datasets and fine-tune models using Ultralytics YOLOv8 for tasks like stenosis detection.
• Analyze segment CT scans using MedSAM2.
• Analyze results from VLMs and foundation models like MedGEMMA, NVIDIA VISTA, and NVIDIA CRADIO.
• Evaluate model predictions and uncover failure cases using real-world clinical examples.

Why Attend?

This healthcare edition of our "Getting Started with FiftyOne" workshop connects foundational tools with real-world impact. Through curated datasets and clinical use cases, you'll see how to harness Visual AI responsibly, building data-centric pipelines that promote accuracy, interpretability, and trust in medical AI systems.

Prerequisites

Basic knowledge of Python and computer vision is recommended. No prior experience in healthcare is required — just curiosity and a commitment to building meaningful AI.

All participants will receive access to workshop notebooks, code examples, and extended resources to continue their journey in healthcare AI.

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Exploring Vision-Language-Action (VLA) Models: From LLMs to Embodied AI

This talk will explore the evolution of foundation models, highlighting the shift from large language models (LLMs) to vision-language models (VLMs), and now to vision-language-action (VLA) models. We'll dive into the emerging field of robot instruction following—what it means, and how recent research is shaping its future. I will present insights from my 2024 work on natural language-based robot instruction following and connect it to more recent advancements driving progress in this domain.

About the Speaker

Shreya Sharma is a Research Engineer at Reality Labs, Meta, where she works on photorealistic human avatars for AR/VR applications. She holds a bachelor’s degree in Computer Science from IIT Delhi and a master’s in Robotics from Carnegie Mellon University. Shreya is also a member of the inaugural 2023 cohort of the Quad Fellowship. Her research interests lie at the intersection of robotics and vision foundation models.

Farming with CLIP: Foundation Models for Biodiversity and Agriculture

Using open-source tools, we will explore the power and limitations of foundation models in agriculture and biodiversity applications. Leveraging the BIOTROVE dataset. The largest publicly accessible biodiversity dataset curated from iNaturalist, we will showcase real-world use cases powered by vision-language models trained on 40 million captioned images. We focus on understanding zero-shot capabilities, taxonomy-aware evaluation, and data-centric curation workflows.

We will demonstrate how to visualize, filter, evaluate, and augment data at scale. This session includes practical walkthroughs on embedding visualization with CLIP, dataset slicing by taxonomic hierarchy, identification of model failure modes, and building fine-tuned pest and crop monitoring models. Attendees will gain insights into how to apply multi-modal foundation models for critical challenges in agriculture, like ecosystem monitoring in farming.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry.

Multi-modal AI in Medical Edge and Client Device Computing

In this live demo, we explore the transformative potential of multi-modal AI in medical edge and client device computing, focusing on real-time inference on a local AI PC. Attendees will witness how users can upload medical images, such
as X-Rays, and ask questions about the images to the AI model. Inference is executed locally on Intel's integrated GPU and NPU using OpenVINO, enabling developers without deep AI experience to create generative AI applications.

About the Speaker

Helena Klosterman is an AI Engineer at Intel, based in the Netherlands, Helena enables organizations to unlock the potential of AI with OpenVINO, Intel's AI inference runtime. She is passionate about democratizing AI, developer experience, and bridging the gap between complex AI technology and practical applications.

The Business of AI

The talk will focus on the importance of clearly defining a specific problem and a use case, how to quantify the potential benefits of an AI solution in terms of measurable outcomes, evaluating technical feasibility in terms of technical challenges and limitations of implementing an AI solution, and envisioning the future of enterprise AI.

About the Speaker

Milica Cvetkovic is an AI engineer and consultant driving the development and deployment of production-ready AI systems for diverse organizations. Her expertise spans custom machine learning, generative AI, and AI operationalization. With degrees in mathematics and statistics, she possesses a decade of experience in education and edtech, including curriculum design and machine learning instruction for technical and non-technical audiences. Prior to Google, Milica held a data scientist role in biotechnology and has a proven track record of advising startups, demonstrating a deep understanding of AI's practical application.

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Foundational capabilities and models for generalist agents for computers

As we move toward a future where language agents can operate software, browse the web, and automate tasks across digital environments, a pressing challenge emerges: how do we build foundational models that can act as generalist agents for computers? In this talk, we explore the design of such agents—ones that combine vision, language, and action to understand complex interfaces and carry out user-intent accurately.

We present OmniACT as a case study, a benchmark that grounds this vision by pairing natural language prompts with UI screenshots and executable scripts for both desktop and web environments. Through OmniACT, we examine the performance of today’s top language and multimodal models, highlight the limitations in current agent behavior, and discuss research directions needed to close the gap toward truly capable, general-purpose digital agents.

About the Speaker

Raghav Kapoor is a machine learning at Adobe, where he works on the Brand Services team, contributing to cutting-edge projects in brand intelligence. His work blends research with machine learning, reflecting his deep expertise in both areas. Prior to joining Adobe, Raghav earned his Master’s degree from Carnegie Mellon University, where his research focused on multimodal machine learning and web-based agents. He also brings industry experience from his experience as a strategist at Goldman Sachs India.

BEARCUBS: Evaluating Web Agents' Real-World Information-Seeking Abilities

The talk focuses on the challenges of evaluating AI agents in dynamic web settings, the design and implementation of the BEARCUBS benchmark, and insights gained from human and agent performance comparisons. In the talk, we will discuss the significant performance gap between human users and current state-of-the-art agents, highlighting areas for future improvement in AI web navigation and information retrieval capabilities.

About the Speaker

Yixiao Song is a Ph.D. candidate in Computer Science at the University of Massachusetts Amherst. Her research focuses on enhancing the evaluation of natural language processing systems, particularly in assessing factuality and reliability in AI-generated content. Her work encompasses the development of tools and benchmarks such as VeriScore, an automatic metric for evaluating the factuality of long-form text generation, and BEARCUBS, a benchmark for assessing AI agents' ability to identify factual information from web content.

Visual Agents: What it takes to build an agent that can navigate GUIs like humans

We’ll examine conceptual frameworks, potential applications, and future directions of technologies that can “see” and “act” with increasing independence. The discussion will touch on both current limitations and promising horizons in this evolving field.

About the Speaker

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

Implementing a Practical Vision-Based Android AI Agent

In this talk I will share with you practical details of designing and implementing Android AI agents, using deki.

From theory we will move to practice and the usage of these agents in
industry/production.

For end users - remote usage of Android phones or for automation of standard tasks. Such as:

• "Write my friend 'some_name' in WhatsApp that I'll be 15 minutes late"
• "Open Twitter in the browser and write a post about 'something'"
• "Read my latest notifications and say if there are any important ones"
• "Write a linkedin post about 'something'"

And for professionals - to enable agentic testing, a new type of test that only became possible because of the popularization of LLMs and AI agents that use them as a reasoning core.

About the Speaker

Rasul Osmanbayli is a senior Android developer at Kapital Bank, Baku/Azerbaijan. It is the largest private bank in Azerbaijan. He created deki, an Image Description model that was used as a foundation for an Android AI agent that achieved high results on 2 different benchmarks: Android World and Android Control.

He previously worked in Istanbul/Türkiye for various companies as an
Android and Backend developer. He is also a MS at Istanbul Aydin University in Istanbul/Türkiye.

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Part 1: Navigating the GUI Agent Landscape

Understanding the Foundation Before Building

The GUI agent field is evolving rapidly, but success requires an understanding of what came before. In this opening session, we'll map the terrain of GUI agent research—from the early days of MiniWoB's simplified environments to today's complex, multimodal systems tackling real-world applications. You'll discover why standard vision models fail catastrophically on GUI tasks, explore the annotation bottlenecks that make GUI datasets so expensive to create, and understand the platform fragmentation that makes "click a button" mean twenty different things across datasets.

We'll dissect the most influential datasets (Mind2Web, AITW, Rico) and models that have shaped the field, examining their strengths, limitations, and the research gaps they reveal. By the end, you'll have a clear picture of where GUI agents excel, where they struggle, and, most importantly, where the opportunities lie for your own contributions.

About the Instructor

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.