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| Title & Speakers | Event |
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Observability Data Talks Back: AI Agents That Reason Over Infrastructure
2026-01-14 · 17:00
In this workshop, you’ll see how AI agents can reason over telemetry data, detect AWS infrastructure issues, and trigger automated remediation to keep your systems healthy and resilient. ⚠️ Important: to attend, you MUST register at https://www.honeycomb.io/go/honeycomb-aws-pulumi-ai-agents-workshop Speakers:
Join us to learn:
⚠️ Don’t forget: You must register to attend: https://www.honeycomb.io/go/honeycomb-aws-pulumi-ai-agents-workshop Explore more workshops here: https://pulumip.us/UpcomingWorkshops |
Observability Data Talks Back: AI Agents That Reason Over Infrastructure
|
|
Observability Data Talks Back: AI Agents That Reason Over Infrastructure
2026-01-14 · 17:00
In this workshop, you’ll see how AI agents can reason over telemetry data, detect AWS infrastructure issues, and trigger automated remediation to keep your systems healthy and resilient. ⚠️ Important: to attend, you MUST register at https://www.honeycomb.io/go/honeycomb-aws-pulumi-ai-agents-workshop Speakers:
Join us to learn:
⚠️ Don’t forget: You must register to attend: https://www.honeycomb.io/go/honeycomb-aws-pulumi-ai-agents-workshop Explore more workshops here: https://pulumip.us/UpcomingWorkshops |
Observability Data Talks Back: AI Agents That Reason Over Infrastructure
|
|
Observability Data Talks Back: AI Agents That Reason Over Infrastructure
2026-01-14 · 17:00
In this workshop, you’ll see how AI agents can reason over telemetry data, detect AWS infrastructure issues, and trigger automated remediation to keep your systems healthy and resilient. ⚠️ Important: to attend, you MUST register at https://www.honeycomb.io/go/honeycomb-aws-pulumi-ai-agents-workshop Speakers:
Join us to learn:
⚠️ Don’t forget: You must register to attend: https://www.honeycomb.io/go/honeycomb-aws-pulumi-ai-agents-workshop Explore more workshops here: https://pulumip.us/UpcomingWorkshops |
Observability Data Talks Back: AI Agents That Reason Over Infrastructure
|
|
Observability Data Talks Back: AI Agents That Reason Over Infrastructure
2026-01-14 · 17:00
In this workshop, you’ll see how AI agents can reason over telemetry data, detect AWS infrastructure issues, and trigger automated remediation to keep your systems healthy and resilient. ⚠️ Important: to attend, you MUST register at https://www.honeycomb.io/go/honeycomb-aws-pulumi-ai-agents-workshop Speakers:
Join us to learn:
⚠️ Don’t forget: You must register to attend: https://www.honeycomb.io/go/honeycomb-aws-pulumi-ai-agents-workshop Explore more workshops here: https://pulumip.us/UpcomingWorkshops |
Observability Data Talks Back: AI Agents That Reason Over Infrastructure
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The Deepfake Problem: Real Attacks & Real Risk For this month’s Berlin Cybersecurity Social, Jonny Smyth (CTO @ Ceartas) walks us through what’s changing in the deepfake landscape, why the technology has become so accessible, and what this could mean for security, trust, and day-to-day operations. Jonny Smyth is the CTO and Co-Founder of Ceartas, where he works on AI-driven tools that help creators and brands safeguard their content online by spotting misuse, issuing takedowns, and understanding how their work is being copied or manipulated. With experience across engineering and security roles, he focuses on making emerging threats, like deepfakes, easier for organisations to understand and respond to. Deepfakes have evolved fast, and they’re starting to show up in places that matter. This will be a relaxed, high-level session designed for the whole community—technical or not—focused on understanding the shift rather than diving into the weeds. This session is part of the Berlin Cybersecurity Social community’s monthly meetup, where security professionals, leaders, and enthusiasts come together to share knowledge and connect. So are you a cybersecurity professional looking to connect with like-minded professionals, share experiences, and make friends? Look no further! Agenda:
***Please note***: **We will be sharing the venue with other tech communities bringing more opportunity for collaboration, but will also be able to have a space for our community too as well as the common networking areas. **Registration at the venue is required (also for a free drink!), for privacy and PII concerns you can feel free to share minimal info but at minimum an email and first name and last initial is required (any of your choosing ;) About Venture Café Berlin: Venture Café Berlin connects a community of innovators and entrepreneurs with free high-impact programming and events. Venture Café is a part of the CIC network, whose mission is to fix the world through innovation. About Berlin Cybersecurity Social: This meetup is open to cybersecurity professionals of all levels, from beginners to experts. Whether you're a seasoned pro or just starting your journey in the field, this event is the perfect opportunity to connect with others who share your passion for cybersecurity. |
Berlin Cybersecurity Social #22 - The Deepfake Problem: Real Attacks & Real Risk
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Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 20:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Date and Time Dec 4, 2025 9:00 - 11:00 AM Pacific Benchmarking Vision-Language Models for Autonomous Driving Safety This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving. 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. TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection. Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation. About the Speaker Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025. WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability. Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation. About the Speaker Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction. Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive. About the Speaker Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI. |
Dec 4 - AI, ML and Computer Vision Meetup
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Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 20:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Date and Time Dec 4, 2025 9:00 - 11:00 AM Pacific Benchmarking Vision-Language Models for Autonomous Driving Safety This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving. 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. TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection. Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation. About the Speaker Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025. WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability. Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation. About the Speaker Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction. Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive. About the Speaker Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI. |
Dec 4 - AI, ML and Computer Vision Meetup
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Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Date and Time Dec 4, 2025 9:00 - 11:00 AM Pacific Benchmarking Vision-Language Models for Autonomous Driving Safety This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving. 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. TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection. Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation. About the Speaker Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025. WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability. Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation. About the Speaker Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction. Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive. About the Speaker Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI. |
Dec 4 - AI, ML and Computer Vision Meetup
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Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Date and Time Dec 4, 2025 9:00 - 11:00 AM Pacific Benchmarking Vision-Language Models for Autonomous Driving Safety This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving. 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. TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection. Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation. About the Speaker Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025. WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability. Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation. About the Speaker Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction. Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive. About the Speaker Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI. |
Dec 4 - AI, ML and Computer Vision Meetup
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Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Date and Time Dec 4, 2025 9:00 - 11:00 AM Pacific Benchmarking Vision-Language Models for Autonomous Driving Safety This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving. 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. TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection. Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation. About the Speaker Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025. WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability. Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation. About the Speaker Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction. Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive. About the Speaker Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI. |
Dec 4 - AI, ML and Computer Vision Meetup
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Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Date and Time Dec 4, 2025 9:00 - 11:00 AM Pacific Benchmarking Vision-Language Models for Autonomous Driving Safety This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving. 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. TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection. Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation. About the Speaker Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025. WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability. Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation. About the Speaker Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction. Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive. About the Speaker Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI. |
Dec 4 - AI, ML and Computer Vision Meetup
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Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Date and Time Dec 4, 2025 9:00 - 11:00 AM Pacific Benchmarking Vision-Language Models for Autonomous Driving Safety This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving. 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. TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection. Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation. About the Speaker Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025. WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability. Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation. About the Speaker Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction. Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive. About the Speaker Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI. |
Dec 4 - AI, ML and Computer Vision Meetup
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Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Date and Time Dec 4, 2025 9:00 - 11:00 AM Pacific Benchmarking Vision-Language Models for Autonomous Driving Safety This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving. 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. TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection. Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation. About the Speaker Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025. WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability. Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation. About the Speaker Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction. Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive. About the Speaker Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI. |
Dec 4 - AI, ML and Computer Vision Meetup
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The $2B migration consulting industry is over: Can Agents actually do it?
2025-11-20 · 18:00
Your organization manages migrations manually via spreadsheets, emails, and reminders. Agents now appear capable of automating CVE remediation, language migrations, and platform updates. But can enterprises scale autonomous code changes safely? This session explores the infrastructure, governance, and organizational readiness needed, and whether now is the right time. Learn what infrastructure, guardrails, and organizational practices are required to actually run agent-driven migrations safely at scale. This session unpacks the realities of running scaled coding agents, including provisioning secure execution environments, maintaining governance and audit, ensuring data sovereignty, verifying agent quality and accuracy, designing human-in-the-loop and understanding which changes are ready for autonomous execution. Speaker: Lou Bichard - Field CTO @ Ona Lou is a product manager at Gitpod, a platform for secure standardized and automated development environments used by 1.5M+ users and large enterprises. Previously Principal Engineer of Developer Experience at DAZN, where he led their internal developer platform serving 13M global users. |
The $2B migration consulting industry is over: Can Agents actually do it?
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The $2B migration consulting industry is over: Can Agents actually do it?
2025-11-20 · 18:00
Your organization manages migrations manually via spreadsheets, emails, and reminders. Agents now appear capable of automating CVE remediation, language migrations, and platform updates. But can enterprises scale autonomous code changes safely? This session explores the infrastructure, governance, and organizational readiness needed, and whether now is the right time. Learn what infrastructure, guardrails, and organizational practices are required to actually run agent-driven migrations safely at scale. This session unpacks the realities of running scaled coding agents, including provisioning secure execution environments, maintaining governance and audit, ensuring data sovereignty, verifying agent quality and accuracy, designing human-in-the-loop and understanding which changes are ready for autonomous execution. Speaker: Lou Bichard - Field CTO @ Ona Lou is a product manager at Gitpod, a platform for secure standardized and automated development environments used by 1.5M+ users and large enterprises. Previously Principal Engineer of Developer Experience at DAZN, where he led their internal developer platform serving 13M global users. |
The $2B migration consulting industry is over: Can Agents actually do it?
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Closing Remarks
2025-11-06 · 17:00
Angelica Malin
– Entrepreneur & Host
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AI Agent 2 Agent Summit (NYC)
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Closing Remarks
2025-11-06 · 17:00
Angelica Malin
– Entrepreneur & Host
Closing remarks by Angelica Malin |
AI Agent 2 Agent Summit (NYC)
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AI Agent 2 Agent Summit (NYC)
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AI Agent 2 Agent Summit (NYC)
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Keynote 7: Salesforce
2025-11-06 · 16:00
Samuel Sharaf
– Senior Director, Frontier AI
@ Salesforce
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AI Agent 2 Agent Summit (NYC)
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