Open Source Summit + AI_dev Japan 2024

Love developing AI/ML systems, but don’t want to become the next front-page cyberattack? We got you! In this fast-paced, meme-a-liscious, hands-on workshop, we’ll take a deep dive into the murky waters of the AI/ML supply chain, explore its many threats and terrors, and then – with our trusty box of supply chain security tools – build an island of safety for our AI/ML systems! Participants will come away with the skills and knowledge to significantly improve AI/ML supply chain security at their organization, as well as the unpleasant awareness about what might happen if the industry doesn’t do the same. Sorry! Hands-on activities will include vulnerability scanning, creating/consuming SBOMs/AIBOMs, digital signing using Sigstore tools, and provenance tracking. We will also provide a conceptual background on AI/ML supply chain components, attack categories, and global regulation and standards related to AI/ML security. Led by software supply chain and AI deployment experts at Chainguard, this workshop will enable participants to harden their AI/ML systems and evangelize others to do the same.

Due to the ever-increasing adoption of AI into the lives of daily users, trustworthy AI is of utmost priority. Even though advocates of AI globally have started talking about ethical considerations during ML model building, in reality, very few people know how to create robust, privacy-preserving, and fair AI models. In this talk, I'll explore 2 concrete technical concepts of trustworthy AI, namely ensuring robustness and fairness in ML models. Robustness: 1. Attendees will go through an in-depth understanding of critical vulnerabilities of common AI models and how to exploit them to adversarially attack the model (e.g., inference attacks, data poisoning). 2. This will be followed by simple defence strategies to increase robustness (e.g., gradient obfuscation, transformations). 3. This will be further followed by adaptive attacks on previous defence strategies thereby motivating the concept of certified robustness of AI models. Fairness: 1. Attendees will get to know how they can unconsciously encode bias (representational bias, model bias, etc) during training AI models. 2. This is followed by strategies to correct this bias using domain knowledge to create fair AI models.

Ever feel like contributing to groundbreaking AI projects but unsure where to start? Have a cool project that you are looking to find a neutral entity for? Through the session learn how the LF AI & Data foundation empowers open-source projects. Discover the framework to support for open development, governance models, and resources like legal assistance, marketing, and events. Gain exclusive updates from the LF AI and Data Technical Advisory Council (TAC). Learn about their vision, technical roadmap, success stories and how you can contribute. The session will cover practical tips and tricks to dive into project contributions, navigate the initiation process, and ultimately guide you towards project graduation. Also discover the diverse work streams within LF AI and Data foundation and how you can leverage your skills to make a real difference in the open-source AI community.

Step into the future of AI as we break free from the constraints of cloud-based processing and unlock the immense potential of edge computing. This cutting-edge talk explores the revolutionary shift of AI from centralized data centers to the devices in your pocket, on your wrist, and all around you. Discover how this paradigm shift is not just changing the game – it's rewriting the rules. We'll journey through the landscape of on-device AI, revealing how it's transforming user experiences, supercharging privacy, and pushing the boundaries of what's possible in real-time applications. From smart homes to autonomous vehicles, from augmented reality to personalized healthcare – learn how on-device AI is the key to unlocking a world of intelligent, responsive, and secure applications.

Bitol is one of the newest LF AI & Data projects focusing on open standards and open source tools for modern data engineering. Bitol's flagship standard is ODCS (Open Data Contract Standard). In this talk, as the chair of the Bitol TSC, I will first explain what a data contract is, the work of the Bitol project, and focus on the benefits of data contracts for AI & analytics. I will conclude with an end-to-end demo of creating and enforcing data contracts using open-source and free tools.

As global regulations on AI software tighten, developers face a complex set of new, ambiguous rules. The AI Software Bill of Materials (AI BOM), especially the new SPDX 3.0 with AI and dataset profiles, offers a promising solution for compliance, providing detailed, machine-readable documentation of AI systems. Despite its benefits, adoption has been slow, hindered by gaps in developer knowledge and the complex nature of AI systems. Many AI BOMs are incomplete or inaccurate, limiting their utility for compliance. Our talk will tackle these issues, drawing on our experience with SPDX 3.0 and AI BOM implementation. We'll share best practices and strategies to improve AI BOM accuracy and utility, equipping professionals with the insights to ensure their AI applications are compliant and prepared for future regulations.