Repainting the Past with Distributed Machine Learning and Docker
Explore cutting-edge machine learning techniques to colorize black and white videos using distributed computing with Docker, revolutionizing historical footage preservation at reduced costs.
How to Build Machine Learning Pipelines with Docker
Explore practical applications of Docker containers for rapid ML project prototyping, including face detection, sentiment analysis, and smart bot creation.
PyTorch: A Foundation for Open Source AI/ML - Live Webinar
Explore PyTorch's evolution, usage, and future with its creator. Learn about the new PyTorch Foundation and discover opportunities in open-source AI/ML development.
ChatGPT, Stable Diffusion, and the Future of Open Source AI
Explore the impact of generative AI on open source ethics, data usage, and individual rights, with insights from a leading AI scientist on the future of this technology.
Trusted and Responsible AI - Explainability, Adversarial Attacks, Bias, and Fairness
Explore key aspects of responsible AI: explainability, adversarial challenges, bias mitigation, and fairness. Learn strategies for building trustworthy and ethical AI systems in real-world applications.
How to Build Trustworthy AI with Open Source
Explore open-source tools for building trustworthy AI systems, focusing on robustness, fairness, and explainability to ensure ethical and responsible AI development.
Training AI to Code Using Project CodeNet - The Largest Code Dataset
Explore Project CodeNet, the largest AI-ready dataset for code, and its potential to revolutionize AI-assisted programming and software development automation.
How We Built Ubuntu Distroless Containers
Explore the creation of Ubuntu distroless containers, understanding principles, benefits, and techniques for building secure, minimal-footprint images from scratch using standard tools.
Security Vulnerabilities in AI Assistant Based Applications
Explores security vulnerabilities in AI assistants, covering physical attacks, adversarial techniques, and defense strategies. Provides insights for developers and security professionals.
What Are Machines Really Learning to Do? - Advancements in AI and Machine Learning
Explore the rapid advancements in machine learning, its impact on various industries, and the complex tasks AI systems are now capable of performing.
Artificial Intelligence and the Future of Humanity
Explore AI's impact on society, work, and technology. Learn about neural networks, job automation, ethical concerns, and future possibilities from AI expert Andrew Ng.
Use Dmabuf to Streamline Sensor Data Handling in Automotive Systems - Lecture
Streamlining sensor data handling in self-driving vehicles using Dmabuf, exploring efficient memory management and zero-copy data pipelines for modern automotive systems.
AMD Hardware, Software and Libraries for Developing AI/ML Applications Using PyTorch
Explore AMD's ecosystem for developing AI/ML applications with PyTorch, including hardware, software, and libraries optimized for high-performance machine learning workflows.
Generative AI and Stable Diffusion - Lightning Talks
Explore cutting-edge AI topics: generative models, Stable Diffusion, and Intel's optimizations for PyTorch 2.0 in these concise, informative lightning talks.
How to Utilize Profiles for AI Use Cases
Explore effective strategies for leveraging profiles in AI applications, enhancing decision-making and personalization capabilities.
libcamera: The Future of Cameras on Linux
Explore libcamera, the future of Linux camera technology. Learn about CMOS sensors, image processing algorithms, and integrating libcamera into applications.
AI/ML, Data Analytics, and Closed Loop Control in Network Management
Explore AI/ML, data analytics, and closed loop control concepts with industry experts from Aarna Networks in this insightful presentation.
Trustworthy AI in Network Operations
Explore symbolic reinforcement learning, formal verification, and explainability in AI-based models for network operations, addressing challenges and advancements in trustworthy AI.
Federated Learning All-in-One Box
Explore federated learning architecture, deployment workflows, and practical demonstrations using Microshift for decentralized machine learning applications.
Machine Learning Applications in Particle Physics, Astronomy, and Cosmology
Explore machine learning applications in cosmology and astronomy with NYU's Kyle Cranmer, featuring insights from the 2018 Machine Learning Physics Next workshop.
Making Quantum Algorithms Learn from Data
Explore cutting-edge research on integrating machine learning with quantum algorithms, enhancing their ability to process and learn from data for advanced computational tasks.