LUGMan

Per info e prenotazioni visita l'indirizzo: lugman.org

Rome AI, Machine Learning and Computer Vision Meetup

Join our virtual meetup to hear talks from experts on MCP, agents and skills.

Date, Time and Location

Oct 08, 2026

9:00 AM - 11:00 AM PST

Online. Register for the Zoom!

Designing Multi‑Agent Systems: Sequential, Parallel, and Beyond with ADK

Multi‑agent systems are powerful but choosing the wrong interaction pattern can quickly lead to fragile, slow, or expensive AI systems.

In this talk, we explore the core multi‑agent design patterns enabled by ADK, including sequential, parallel, and more advanced coordination models. Rather than focusing on tools alone, we’ll look at how to think architecturally about agent collaboration.

You’ll learn:

• When sequential agents are the right choice and when they become a bottleneck
• How parallel agents improve speed and coverage (and the trade‑offs they introduce)
• Common failure modes in poorly designed agent interactions
• Practical criteria for choosing the right pattern based on task, latency, and reliability

By the end of the session, you’ll have a clear mental model for designing multi‑agent systems that are intentional, scalable, and production‑ready.

About the Speaker

Dr Roushanak Rahmat is an Enterprise AI Architect, Google Developer Expert (AI & Cloud), and recognized among the Top 100 Women in Tech (2025). With a PhD in Artificial Intelligence and over 15 years of experience, she specializes in designing and delivering enterprise-scale Generative AI, Agentic AI, and Deep Learning solutions that transform industries including healthcare, finance, energy, and public services.

Privacy by Deployment: Architecting Agent-Driven Localization Workflows for Regulated Environments

Most enterprise AI today is private by promise - a DPA, a SOC 2 report, or a contract clause that says, "we won't train on your data". For a regulated buyer, these are remedies after a breach, not controls that prevent or contain one. For organizations in healthcare, finance, defense, and government, privacy often requires stronger guarantees: data residency, customer-controlled execution, and, in some cases, operation within air-gapped environments.

This session demonstrates how agentic AI can automate a localization workflow while operating within these constraints. Using a real-world localization pipeline as an example, we will show how agentic systems can coordinate translation, review, quality assurance, and content preparation tasks while incorporating human checkpoints for approval and oversight.

We will also walk through the architectural patterns that enable these workflows to run inside customer-controlled and air-gapped environments without transferring sensitive content outside the customer boundary. The session includes a live product demonstration.

Key Takeaways

• Architectural patterns for deploying agentic AI in air-gapped and customer-controlled environments
• How agentic systems can automate localization workflows while preserving critical human review and approval processes
• Practical considerations for operating agentic workflows in regulated environments with auditability and governance requirements

About the Speaker

Shruti Joshi is building an AI powered secure localization stack for regulated industries such as healthcare, legal, finance that cannot send their content to a typical hosted SaaS. She brings 12+ years of engineering and architecture experience to the question this talk addresses: how do you make an agentic AI system deployable inside a regulated perimeter.

MCP Is the Interface; Skills Are the Operating Discipline

This talk shows how MCP and Agent Skills work together in practical agent systems. MCP gives agents a standard interface to tools, data, and workflows; skills encode the operating discipline that makes those connections reliable. Using a sanitized field-operations ledger as the case study, the talk walks through source intake, normalized state, uncertainty labels, role prompts, QA gates, and share-safe status drafting.

About the Speaker

Chuck Hernandez is an AI engineering and client-delivery leader with 10+ years across software, data platforms, and enterprise implementation, including 3+ years shipping production GenAI systems.

Agentic engineering is about good guidance.

Garbage Inn. Is garbage out? This is true. For many input and output processes. In biological life and in computer systems, and equally true when working with LLM’s. The better the prompt, the better the context, the better the focus, And the better the contextual awareness, the better the quality of the output the LLM’s generates.

This is the governance, art and practice of what we like to call agentic engineering, something I've been practicing over the last year.

About the Speaker

Dimitri Geelen builds things that don't need him once they're done. Frameworks, transitions, agentic systems — the measure of success is always the same: does it hold up when he leaves the room? He understands not just how to deploy, but what it takes for a new service to survive and scale inside a complex enterprise.

Per info e prenotazioni visita l'indirizzo: meetup.com/rome-ai-machine-lea…

Rome AI, Machine Learning and Computer Vision Meetup

This workshop explores the FiftyOne plugin framework to build custom computer vision applications. You’ll learn to extend the FiftyOne App with Python based panels and server side operators, as well as integrate external tools for labeling, vector search, and model inference into your dataset views.

Date, Time and Location

Sep 30, 2026

9 AM - 10 AM PST

Online. Register for the Zoom!

You’ll also automate repetitive tasks by writing custom workflows executing within the FiftyOne environment. Attendees will learn to transform FiftyOne from a visualization tool into a central hub for your vision stack.

What you'll learn:

• Build Python plugins. Define plugin manifests and directory structures to register custom functionality within the FiftyOne ecosystem.
• Develop server side operators. Write functions to execute model inference, data cleaning, or metadata updates from the App interface.
• Build interactive panels. Create custom UI dashboards using to visualize model metrics or specialized dataset distributions.
• Manage operator execution contexts. Pass data between the App front end and your backend to build dynamic user workflows.
• Implement delegated execution. Configure background workers to handle long running data processing tasks without blocking the user interface.
• Build labeling integrations. Streamline the flow of data between FiftyOne and annotation platforms through custom triggers and ingestion scripts.
• Extend vector database support. Program custom connectors for external vector stores to enable semantic search across large sample datasets.
• Package and share plugins. Distribute your extensions internally and externally

Per info e prenotazioni visita l'indirizzo: meetup.com/rome-ai-machine-lea…

ODSC AI Milano

Add to your calendar: streamyard.com/watch/YsJkDEA9N…

Speakers:

\- Karan Sachdeva\, Global Lead, IBM Google, Strategic Partnership, IBM

\- Sean Falconer\, Head of AI, Confluent

\- Sohrab Rahimi\, Director and AI/ML Lead, Google Cloud

This event is part of the ODSC AI Skills Accelerator, bringing together AI and data professionals for practical, expert-led sessions on building real-world AI systems.

Just like we don't put crude oil in a car—we refine it into gasoline—AI can't run on raw data. It needs continuous, refined context.

Serving fresh context is the missing architectural layer for reliable AI.

In this session, learn how IBM, Confluent Intelligence, and Google Cloud turn operational data into fresh context for AI agents to take the right action at the right time. See how Confluent’s data streaming platform turns operational data into automated decisions, and how Google Cloud adds a layer of real-time investigation and explanation on top—all grounded in context that reflects the current state of the business.

We'll demo a real-time architecture for anomaly detection and investigation—streaming racing telemetry through Confluent's Real-Time Context Engine, where a Confluent Streaming Agent detects anomalies and recommends pit timing in real time.

See a Gemini agent in Google Antigravity, connected via MCP, investigate those decisions and explain the reasoning behind them, with governed data synced to BigQuery for historical record and analysis.

You'll leave with practical, streaming-native patterns to move from brittle pipelines and demos to production-grade AI on Google Cloud while reducing TCO.

Join us on September 9 for a new ODSC AI Skills Accelerator session, bringing together experts from IBM, Confluent, and Google Cloud to explore how real-time data and fresh context can power more reliable AI agents.

\#\# ​Useful Links

• ​​​Free access to more talks/trainings: Ai+ Training platform
• ​​​ODSC blog: opendatascience.com/
• ​​​Slack Channel: hubs.li/Q03SyZP80
• ​​​Code of conduct: odsc.ai/code-of-conduct/

Per info e prenotazioni visita l'indirizzo: meetup.com/milano-data-science…

PyVenice

Carissimi, continuiamo il nostro viaggio itinerante per il #Veneto a #Treviso !

Ci troviamo in #presenza giovedì 15 ottobre alle ore 19:00.

La location sarà comunicata appena definita. E per chi non potesse raggiungerci, si terrà anche in #streaming su YouTube.

🗣 La scaletta non è ancora stata definita.

E dopo\, per chi vuole\, resteremo lì per \( 🍸 \| 🍺 \) e \( 🍕 \| 🍔 \)

e #networking ad oltranza ‼️

📽 E se avete piacere di condividere idee, progetti, argomenti, o proporvi come prossimi speaker, lascio un form Google e la pagina Sessionize !

Per info e prenotazioni visita l'indirizzo: meetup.com/pyvenice/events/315…

Contattare in privato per partecipare alla riunione

Per info e prenotazioni visita l'indirizzo: firenze.linux.it/

Contattare in privato per partecipare alla riunione

Per info e prenotazioni visita l'indirizzo: firenze.linux.it/

Rome AI, Machine Learning and Computer Vision Meetup

Join our virtual meetup on September 24 to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Sep 24, 2026

9:00 AM - 11:00 AM PST

Online. Register for the Zoom!

How Do Mercedes-Benz AI Principles Drive our Innovation?

At Mercedes-Benz, our AI Principles guide every step of innovation, emphasizing responsible use, safety and reliability, explainability, and the protection of privacy. These principles go beyond statements and actively shape how we design, test, and deploy AI systems in real-world automotive and enterprise settings. In this talk, I will present how these principles inspired our recent research on when reusing LoRA (Low-Rank Adaptation) is effective. By combining theoretical analysis with synthetic data as a proxy for enterprise scenarios, we uncovered the strengths and limitations of modular AI components under constrained data access. Our findings provide practical guidance on when reused LoRAs could deliver high-quality results.

About the Speaker

Mei-Yen Chen is a Senior Data Scientist at Mercedes-Benz Tech Innovation GmbH in Germany with 10 years of industry experience in AI and data solutions. She leads early-stage AI projects across business functions and collaborates with research institutions on machine learning and responsible AI.

Region Tokens as the Visual Primitive: From Recognition to World Modeling

Patch-based tokenization has become the default interface between vision encoders and downstream models, yet patches carry no semantic structure and scale poorly with resolution and temporal extent. This talk presents a research program centered on replacing patch tokens with region-level representations — semantically dense tokens grounded in visual entities rather than arbitrary grid crops.

I will describe RELOCATE, REN, and T-REN, a progression of methods that produce region tokens via pooling, train them with region-level objectives, and extend them to video with temporal coherence. I will then present ongoing work integrating region tokens into VLMs to directly expand visual context capacity, and preliminary results on future region trajectory prediction as a foundation for world modeling.

The broader thesis is that region-level tokens are a more natural unit of visual computation than patches, and their advantage compounds as task complexity, resolution, and temporal horizon increase.

About the Speaker

Savya Khosla is a second-year Ph.D. student at the University of Illinois Urbana-Champaign, advised by Prof. Derek Hoiem and Prof. Alex Schwing.

Leveraging Text-To-Image Diffusion Models for Consistent Set-to-Set Generation

Image collections are humans' primary way of capturing the world, yet advances in generative editing remain largely inapplicable to this modality. We address this gap by introducing Match-and-Fuse - a zero-shot, training-free method for consistent set-to-set generation from image collections that share a common visual element but differ in viewpoint, capture time, and surrounding content.

Our key idea is a unified graph-based framework that combines dense correspondences with an emergent prior in text-to-image diffusion models to generate coherent canvases. We achieve state-of-the-art consistency and visual quality, and unlock new creative capabilities for content generation.

About the Speaker

Kate Feingold is a PhD student in Computer Vision at the Weizmann Institute of Science. Her research sits at the intersection of generative models, 3D/4D perception, and multimodal learning, focusing on problems where vision meets other modalities or paradigms in creative tasks.

Yield Estimation of a Coffee in a dense environment

This presentation provides a detailed workflow related to coffee yield estimation in a dense environment. With photos of pre-harvest coffee plants from a couple of coffee estates, details related to pre-processing, annotation to detect regions of interest (ROI), object detection training and inferencing results with various Yolo models and finally segmentation with SAM2 and Yolo\*-seg with training and inference results to determine the count of raw, pre-mature, mature and over-mature coffee berries and finally the yield of the entire estate. All this is based on real world data captured on iPhone and android phones.

About the Speaker

Raghu M. Rao is a consultant working on applications of computer vision AI models. He was previously with AMD and Xilinx. He has a Ph.D. in Wireless Communications from UCLA and is a Senior Member, IEEE. His current interests are in applications of AI for agriculture, health care and wireless communications.

Per info e prenotazioni visita l'indirizzo: meetup.com/rome-ai-machine-lea…

Python Pescara

🇮🇹

Ci mancava troppo organizzare dei talk, ma fa anche troppo caldo. Quindi, come risolviamo? Semplice: portiamo il talk direttamente in spiaggia!

Il mood sarà: piedi nella sabbia, bevanda in mano e orecchie rivolte alla nostra speaker, Lucia Coronel, e al suo talk:

Fitting the Lock: Mapping Protein Pockets with CNN

Lucia ci parlerà di come utilizzare il deep learning per accelerare la scoperta di nuovi farmaci.

E non preoccupatevi delle insolazioni: alla peggio, un farmaco ce lo facciamo creare direttamente durante il talk.

📍Dove: Zara Spiaggia Bar, Lungomare G. Matteotti, 90, 65122 Pescara.

📅Quando: Giovedì 19 Agosto, 18.30-22.00.

🇺🇸

We’ve really missed hosting talks, but it’s also way too hot. So, what’s the solution? Simple: we’re bringing the talk directly to the beach!

The mood: feet in the sand, a drink in hand, and all ears on our speaker, Lucia Coronel, and her talk:

Fitting the Lock: Mapping Protein Pockets with CNN

Lucia will explain how deep learning can be used to accelerate drug discovery.

And don’t worry about getting sunburnt in the worst-case scenario, we’ll just have a new treatment designed during the talk!

📍Where: Zara Spiaggia Bar, Lungomare G. Matteotti, 90, 65122 Pescara.

📅When: Thursday, August 19, 6:30-10:00 PM.
Per info e prenotazioni visita l'indirizzo: meetup.com/pythonpescara/event…

LUGMan

Per info e prenotazioni visita l'indirizzo: lugman.org

LUGMan

Per info e prenotazioni visita l'indirizzo: lugman.org

Esploreremo non solo le tecnologie della vita quotidiana, come PC, smartphone e Internet, ma anche il loro utilizzo e la loro manutenzione, con l'obiettivo di rendere l'utente capace di fruire di questi mezzi, di guidarlo e renderlo consapevole del mondo digitale attuale.

Giusto per chi parte da zero, interessante per chi è navigato.

Maggiori info: wiki.golem.linux.it/CorsoALPHABIT2026

A cura di Tiziano
Per info e prenotazioni visita l'indirizzo: golem.linux.it/

LUGMan

Per info e prenotazioni visita l'indirizzo: lugman.org

Rome AI, Machine Learning and Computer Vision Meetup

Join our virtual meetup to hear talks from experts on cutting-edge topics across MCP, Agents and Skills.

Date, Time and Location

Jul 29, 2026

9:00 AM - 11:00 AM PST

Online. Register for the Zoom!

The Agent Control Plane: Turning Coding Agents into Reliable Engineering Workflows

AI coding agents are powerful but often unreliable — they hallucinate, lose context, and produce inconsistent results across runs. In this talk, Alex introduces Atomic, an open-source control plane that adds persistent memory, deterministic workflow phases (Research → Specify → Implement → Ship), and human-in-the-loop gates around coding agents like Claude Code and GitHub Copilot. The result: repeatable, auditable engineering workflows that teams can actually trust in production.

About the Speaker

Alex Lavaee is an Applied AI engineer at Microsoft Research and the creator of Atomic, an open-source SDK that wraps deterministic, research-to-execution workflows around AI coding agents. He previously conducted AI research at Harvard Medical School and Boston University, and has worked as an MLE and data scientist at companies including Boeing and Themis AI, an MIT CSAIL spinoff.

UISurf: Toward Universal UI Automation with Cross-Environment Agents

In this talk, we introduce UISurf, an open-source multimodal agentic UI automation platform in which agents can perceive, reason, and collaborate across browser and desktop environments to complete end-to-end tasks that require interaction with multiple user interfaces.

UISurf comprises three main components: uisurf-agent, the runtime for UI automation agents; uisurf-admin, the session orchestration and management service; and uisurf-app, the full-stack user application. Its multi-agent architecture includes a planningagent that transforms natural-language requests into structured execution plans, specialized Browser and Desktop Agents for environment-specific interaction, an automationagent that coordinates execution and inter-agent handoff through Agent-to-Agent (A2A) communication, and a summarization_agent that produces the final task summary for the user. UISurf supports both autonomous execution and human-in-the-loop supervision, offering a practical and extensible framework for studying and deploying cross-environment UI automation.

About the Speaker

Dr. Henry Ruiz is a Research Scientist at Texas A&M University @ AgriLife Research, specializing in Artificial Intelligence (AI) and Remote Sensing. His work focuses on the development of advanced software systems and computational algorithms for analyzing multi-source remote sensing data, including satellite imagery, UAVs (Unmanned Aerial Vehicles), LiDAR (Light Detection and Ranging), and Ground Penetrating Radar (GPR).

From Manual Workflows to AI-Assisted Skills: Building Reliable Internal Automation

In this session, I will discuss how teams can turn repetitive manual workflows into reliable AI-assisted and automation-driven “skills.” I will share practical lessons from building internal tools for CAD and engineering workflows, including how automation can reduce manual effort, improve consistency, and support better process control. The talk will also cover why many AI/agent experiments fail when they are not connected to real team workflows, standards, and validation steps. Attendees will walk away with a practical framework for identifying repeatable workflows, designing useful internal tools, and adopting AI assistance without losing accuracy or trust.

About the Speaker

Janvi Vijaykumar Saddi \- Janvi Saddi is a Computer Science graduate and CAD/Data Automation professional with experience in data center design workflows\, AutoCAD automation\, process improvement\, and data analytics\. She currently works as a CAD Tech 2 at Astreya\, supporting Google data center design workflows by building internal tools that reduce manual effort\, improve accuracy\, and streamline engineering processes\. Her background also includes SQL\, Power BI\, market research analytics\, and AI\-assisted development\.

Building Safe Agent Sandboxes: Let Agents Act Without Breaking Production

AI agents become truly useful when they can take action, not just generate text. But giving agents access to code, data, and systems raises an important question: how do you let them explore, execute, fail, and improve without putting production at risk?

In this talk, we'll explore the sandbox pattern for agent systems and how to equip agents with tools to read, write, execute, and iterate within controlled environments while using permissions, human approval, and safety guardrails to keep them reliable. We'll cover practical architectures and lessons learned for building agents that can safely evolve from experimentation to production..

About the Speaker

Adonai Vera \- Adonai Vera \- Machine Learning Engineer & DevRel at Voxel51\. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV\.

Per info e prenotazioni visita l'indirizzo: meetup.com/rome-ai-machine-lea…

metto orario allargato perché non sappiamo ancora se una o l'altra

Per info e prenotazioni visita l'indirizzo: hacklabcormano.it/

Rome AI, Machine Learning and Computer Vision Meetup

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Aug 06, 2026

9:00 AM - 11:00 AM PST

Online. Register for the Zoom!

Do Speech Models Actually Understand Speech? Evaluating Speech LLMs Under Realistic Spoken Instruction Conditions

Speech Large Language Models (SLLMs) are increasingly capable; but are we evaluating them the right way? Most benchmarks rely on text prompts, yet real users interact with these systems through speech, a modality that introduces noise, disfluencies, and stylistic variation that text simply doesn't capture.

In this talk, we present findings from a systematic study across 11 tasks, 12 languages, and five prompt styles, examining how prompt modality, language, and task type shape SLLM performance.

About the Speaker

Maike Züfle is a PhD student at the Karlsruhe Institute of Technology (KIT), working in Prof. Jan Niehues's group on interactive speech systems for more natural human–machine communication. Her research focuses on instruction-following speech models with speech as both input and output, with a recent emphasis on full-duplex systems. Beyond her research, she co-organises the instruction-following and speech translation metrics shared tasks at IWSLT. She is a 2026 Apple Scholar in AI/ML.

AI based Audio Forensics

In this presentation, attendees will discover several modules developed by Gradiant for the detection and analysis of synthetically generated or manipulated audio. The session will be delivered by one of the developers involved in the design and implementation of these technologies, providing first-hand insight into their capabilities and underlying methodology.

The presentation will cover the traceability module, which helps identify the origin of AI-generated content. It will also cover the segment detection tool, designed to locate manipulated regions within an audio recording, as well as the complete audio detection tool, which assesses whether an entire recording has been synthetically generated.

About the Speaker

Daniel Paniagua Ares is a research engineer at Gradiant. Graduated in computer engineering from the FIC and with a master's degree in AI from the VIU.

Curating, Searching, and Evaluating Audio Datasets in FiftyOne

In this talk, we'll start with the ESC-50 environmental-sound dataset to show how FiftyOne represents audio: browsing clips in the tabular view, rendering spectrograms directly in the sample grid with a custom renderer, and turning sounds into searchable vectors with CLAP embeddings. Then we'll demo a similarity-search panel that lets you query an entire audio collection by example clip or a natural-language prompt to quickly find matching sounds.

We'll conclude with a live research problem: Audio Moment Retrieval from the DCASE 2026 Challenge, where the goal is to localize the exact moment in a long recording that matches a text query. We'll frame this as temporal detection, evaluate predictions, and visualize ground-truth vs. predicted moments on an interactive timeline to intuitively expose model failure modes.

Attendees will leave with a concrete blueprint and open code for applying visual data-centric AI practices to their own audio and multimodal datasets.

About the Speaker

John Duncan is a Machine Learning Engineer, Customer Success at Voxel51. His research interests include vision, LiDAR, and audio perception for robots and intelligent systems.

Per info e prenotazioni visita l'indirizzo: meetup.com/rome-ai-machine-lea…

Rome AI, Machine Learning and Computer Vision Meetup

Join us on Aug 6 for a special edition of the AI, ML, and Computer Vision Meetup focused on audio use cases!

Date, Time and Location

Aug 06, 2026

9:00 AM - 11:00 AM PST

Online. Register for the Zoom

Do Speech Models Actually Understand Speech? Evaluating Speech LLMs Under Realistic Spoken Instruction Conditions

Speech Large Language Models (SLLMs) are increasingly capable; but are we evaluating them the right way? Most benchmarks rely on text prompts, yet real users interact with these systems through speech, a modality that introduces noise, disfluencies, and stylistic variation that text simply doesn't capture.

In this talk, we present findings from a systematic study across 11 tasks, 12 languages, and five prompt styles, examining how prompt modality, language, and task type shape SLLM performance.

About the Speaker

Maike Züfle is a PhD student at the Karlsruhe Institute of Technology (KIT), working in Prof. Jan Niehues's group on interactive speech systems for more natural human–machine communication.

AI based Audio Forensics

In this presentation, attendees will discover several modules developed by Gradiant for the detection and analysis of synthetically generated or manipulated audio. The session will be delivered by one of the developers involved in the design and implementation of these technologies, providing first-hand insight into their capabilities and underlying methodology.

The presentation will cover the traceability module, which helps identify the origin of AI-generated content. It will also cover the segment detection tool, designed to locate manipulated regions within an audio recording, as well as the complete audio detection tool, which assesses whether an entire recording has been synthetically generated.

About the Speaker

Daniel Paniagua Ares is a research engineer at Gradiant. Graduated in computer engineering from the FIC and with a master's degree in AI from the VIU.

Curating, Searching, and Evaluating Audio Datasets in FiftyOne

In this talk, we'll start with the ESC-50 environmental-sound dataset to show how FiftyOne represents audio: browsing clips in the tabular view, rendering spectrograms directly in the sample grid with a custom renderer, and turning sounds into searchable vectors with CLAP embeddings. Then we'll demo a similarity-search panel that lets you query an entire audio collection by example clip or a natural-language prompt to quickly find matching sounds.

We'll conclude with a live research problem: Audio Moment Retrieval from the DCASE 2026 Challenge, where the goal is to localize the exact moment in a long recording that matches a text query. We'll frame this as temporal detection, evaluate predictions, and visualize ground-truth vs. predicted moments on an interactive timeline to intuitively expose model failure modes.

Attendees will leave with a concrete blueprint and open code for applying visual data-centric AI practices to their own audio and multimodal datasets.

About the Speaker

John Duncan is a Machine Learning Engineer, Customer Success at Voxel51. His research interests include vision, LiDAR, and audio perception for robots and intelligent systems.

Per info e prenotazioni visita l'indirizzo: meetup.com/rome-ai-machine-lea…

Rome AI, Machine Learning and Computer Vision Meetup

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Date, Time and Location

Aug 06, 2026

9:00 AM - 11:00 AM PST

Online. Register for the Zoom!

Do Speech Models Actually Understand Speech? Evaluating Speech LLMs Under Realistic Spoken Instruction Conditions

Speech Large Language Models (SLLMs) are increasingly capable; but are we evaluating them the right way? Most benchmarks rely on text prompts, yet real users interact with these systems through speech, a modality that introduces noise, disfluencies, and stylistic variation that text simply doesn't capture.

In this talk, we present findings from a systematic study across 11 tasks, 12 languages, and five prompt styles, examining how prompt modality, language, and task type shape SLLM performance.

About the Speaker

Maike Züfle is a PhD student at the Karlsruhe Institute of Technology (KIT), working in Prof. Jan Niehues's group on interactive speech systems for more natural human–machine communication. Her research focuses on instruction-following speech models with speech as both input and output, with a recent emphasis on full-duplex systems. Beyond her research, she co-organises the instruction-following and speech translation metrics shared tasks at IWSLT. She is a 2026 Apple Scholar in AI/ML.

AI based Audio Forensics

In this presentation, attendees will discover several modules developed by Gradiant for the detection and analysis of synthetically generated or manipulated audio. The session will be delivered by one of the developers involved in the design and implementation of these technologies, providing first-hand insight into their capabilities and underlying methodology.

The presentation will cover the traceability module, which helps identify the origin of AI-generated content. It will also cover the segment detection tool, designed to locate manipulated regions within an audio recording, as well as the complete audio detection tool, which assesses whether an entire recording has been synthetically generated.

About the Speaker

Daniel Paniagua Ares is a research engineer at Gradiant. Graduated in computer engineering from the FIC and with a master's degree in AI from the VIU.

Curating, Searching, and Evaluating Audio Datasets in FiftyOne

In this talk, we'll start with the ESC-50 environmental-sound dataset to show how FiftyOne represents audio: browsing clips in the tabular view, rendering spectrograms directly in the sample grid with a custom renderer, and turning sounds into searchable vectors with CLAP embeddings. Then we'll demo a similarity-search panel that lets you query an entire audio collection by example clip or a natural-language prompt to quickly find matching sounds.

We'll conclude with a live research problem: Audio Moment Retrieval from the DCASE 2026 Challenge, where the goal is to localize the exact moment in a long recording that matches a text query. We'll frame this as temporal detection, evaluate predictions, and visualize ground-truth vs. predicted moments on an interactive timeline to intuitively expose model failure modes.

Attendees will leave with a concrete blueprint and open code for applying visual data-centric AI practices to their own audio and multimodal datasets.

About the Speaker

John Duncan is a Machine Learning Engineer, Customer Success at Voxel51. His research interests include vision, LiDAR, and audio perception for robots and intelligent systems.

Per info e prenotazioni visita l'indirizzo: meetup.com/rome-ai-machine-lea…

Rome AI, Machine Learning and Computer Vision Meetup

In this session, you’ll learn how to build production-ready AI agents that can reason over your data, automate complex tasks, and integrate seamlessly into your existing stack using tools, skills, and the Model Context Protocol (MCP).

Date, Time and Location

Aug 13, 2026

9:00 AM - 10:00 AM PST

Online. Register for the Zoom!

We’ll walk through how modern agentic systems move beyond simple prompts—leveraging structured tools like dataset operations, embeddings, evaluation pipelines, and model execution to take real action. You’ll see how these agents can tag data, run inference, evaluate performance, and surface insights automatically, all within a unified workflow.

By combining natural language interfaces with programmable building blocks, teams can dramatically reduce manual effort, accelerate experimentation, and unlock faster decision-making across the ML lifecycle.

Whether you're building data-centric AI systems, managing large-scale vision datasets, or exploring agentic workflows for the first time, this session will give you a practical blueprint for getting started.

About the Speaker

Adonai Vera \- Machine Learning Engineer & DevRel at Voxel51\. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV\. I started as a software developer\, moved into AI\, led teams\, and served as CTO\. Today\, I connect code and community to build open\, production\-ready AI\, making technology simple\, accessible\, and reliable\.

Per info e prenotazioni visita l'indirizzo: meetup.com/rome-ai-machine-lea…

Rome AI, Machine Learning and Computer Vision Meetup

Join our virtual meetup to hear talks from experts on cutting-edge topics at the intersection of manufacturing, AI, ML, and computer vision.

Date, Time and Location

Aug 04, 2026

9:00 AM - 11:00 AM PST

Online. Register for the Zoom!

Enabling Multimodal Agents on the Edge

The next generation of AI agents is moving beyond cloud-based text-only models and will interact with the physical multimodal world in real-time. For example in the vision domain, AI agents rely on Vision-Language Models (VLMs) in their backbone. However, deploying massive VLMs with billions of parameters on the edge devices remains a significant engineering hurdle.

Drawing on our recent ICML and CVPR research papers, this session explores advancements in agentic model optimizations, specifically how distillation and pruning transform 'heavyweight' models into lean, edge-ready engines. Lastly, I present our UI agent running on the actual phone that is being developed by our lab's team.

About the Speaker

Denis Gudovskiy is a Distinguished AI Engineer at Panasonic North America where he conducts R&D activities of various core AI methods, including multimodal and hardware-efficient agents, supervised and RL training pipelines, and robustness to out-of-distribution scenarios.

When the Camera Can’t Be Trusted: Health-Aware Visual AI for Reliable Near-Miss Detection

Near-miss detection systems are often evaluated as though every camera frame is equally trustworthy, even though blur, poor exposure, occlusion, contamination, and changing lighting can silently degrade the visual evidence used to make safety decisions. This talk presents an online camera-health framework that estimates visual reliability before downstream perception performance significantly deteriorates.

I will discuss how camera-health signals can support condition-aware evaluation, prioritize human review, reduce unreliable alerts, and trigger appropriate fallback behavior. Drawing from research in safety-critical visual perception, the talk will demonstrate how these principles can be adapted to industrial video systems operating across different cameras, shifts, layouts, and environmental conditions.

The presentation will also connect camera-health monitoring with rare-event discovery and failure-driven dataset improvement for more trustworthy near-miss detection.

About the Speaker

Shiva Aher is a computer vision researcher with a graduate background in computer science from the Georgia Institute of Technology, specializing in artificial intelligence.

Agentic VLM applications in manufacturing

Vision Language Models (VLMs) introduce net-new functionality to vision workloads in manufacturing that traditional computer vision models simply do not offer (e.g., open-vocabulary detection, in-context-learning). Even so, fine-tuned models like YOLO offer a level of precision and recall that today's VLMs struggle to match out-of-the-box.

Through agentic harnesses that coordinate calls to VLMs, we can start to deliver similar reliability on manufacturing-relevant tasks (e.g., many-class, many-instance detection), while also supporting the net new functionalities (e.g., multimodal search) that make VLMs distinct. In this talk, we walk through the design of these harnesses, how you serve them efficiently, and how they deliver value in manufacturing.

About the Speaker

Subraiz Ahmed is a member of the Technical Staff at Perceptron AI. He builds the infrastructure to serve frontier vision models. He previously founded a series of startups.

Per info e prenotazioni visita l'indirizzo: meetup.com/rome-ai-machine-lea…

Rome AI, Machine Learning and Computer Vision Meetup

Join Voxel51 for a live workshop on how multimodal data workflows in FiftyOne help teams inspect, search, and debug complex Physical AI datasets and explain black-box model behavior at scale. We’ll show how teams can work with synchronized video and sensor data, query for similar scenarios across their datasets, and uncover patterns behind model failures faster than playback-only visualization tools allow.

Date, Time and Location

Aug 11, 2026

9:00 AM - 10:00 AM PST

Online. Register for the Zoom!

As robotics and autonomous vehicle teams move from traditional perception models to end-to-end Physical AI systems, understanding model behavior is becoming harder than ever. These models ingest synchronized inputs from cameras, sensors, and other data streams, but their decisions can be difficult to explain, reproduce, and improve.

You’ll learn how to use multimodal data to investigate questions like: when did the model swerve, miss an object, misinterpret a scene, or behave unexpectedly — and how can you find every similar moment across your dataset?

Designed for robotics, AV, and machine learning teams, this session will show how FiftyOne helps turn multimodal data into a scalable workflow for model evaluation, debugging, and improvement.

Per info e prenotazioni visita l'indirizzo: meetup.com/rome-ai-machine-lea…