CONNECT + COLLABORATE ON EVOLVING AI QUALITY

Gerrit De Vynck from the Washington Post will be moderating a panel that will delve into NIST, government-implemented standards, and their roles in developing AI. 

Natasha will be moderating a panel of leading VCs who have backed the top AI companies and understand the correction within the boom, flight to quality and what happens when OpenAI eats your lunch, how founders should think about giving big tech a spot on their cap tables, & generally how to invest at the speed of innovation right now.

Kodiak is on a mission to automate the driving of every commercial vehicle in the world. Today, Kodiak operates a nationwide autonomous trucking network 24x7x365, on the highway, in the dirt, and everywhere in between. We also release and deploy software about 30 times per day across this fleet that is not just mission critical, but also safety critical. Our AI development process must match this criticality and speed, providing fast engineering iteration while guaranteeing a high level of quality that is the requirement of safety. In this talk, we’ll share the details of that process, from how the system is architected, trained, and evaluated, to the validation CICD pipeline, which is the lifeblood of the development flywheel. We’ll talk about how we collect cases, how we iterate models, and how we do quality assurance, data, and release management - all in a way that seamlessly keeps our robots truckin’ across the US.

Retrieval-Augmented-Generations (RAG) is a powerful technique to reduce hallucinations from Large Language Models (LLMs) in GenAI applications. However, large context windows (e.g. 1M tokens for Gemini 1.5 pro) can be a potential alternative to the RAG approach. This talk contrasts both approaches and highlights when Large Context Window is a better option thank RAG, and vice-versa.

Weights &Biases CEO and Co-Founder Lukas Biewald will share his perspective on the Generative AI industry: where we've come from, where we are today, and where we're headed.

Enterprise AI leaders continue to explore the best productivity solutions that solve business problems, mitigate risks and increase efficiency. Building reliable and secure AI/ML systems requires following industry standards, an operating framework, and best practices that can accelerate and streamline the scalable architecture that can produce expected business outcomes.
This session, featuring veteran practitioners, focuses on building scalable, reliable and quality AI and ML systems for the enterprises.

-An operating framework for AI/ML use cases
-Standards and best practices in building scalable and automated AI systems
-Governance workflow, modernization tools and total experience journey

What do AI models see when the read and generate text and images? What are the units of meaning they use to understand the world? I’ll share some encouraging updates from my continuing exploration of how models process its input and generate data, enabled by recent breakthroughs in interpretability research. I’ll also discuss and share some demos of how this work opens up possibilities of radically different, more natural interfaces for working with generative AI models.

How has the ML engineering stack changed with foundation models? While the generative AI landscape is still rapidly evolving, some patterns have emerged. This talk discusses these patterns. Spoilers: the principles of deploying ML models into production remain the same, but we’re seeing many new challenges and new approaches. This talk is the result of Chip Huyen's survey of 900+ open source AI repos and discussions with many ML platform teams, both big and small.

In this talk, Shreya will share a candid look back at a year dedicated to developing reliable AI tools in the open-source community. The talk will explore which tools and techniques have proven effective and which ones have not, providing valuable insights from real-world experiences. Additionally, Shreya will offer predictions on the future of AI tooling, identifying emerging trends and potential breakthroughs. This presentation is designed for anyone interested in the practical aspects of AI development and the evolving landscape of open-source technology, offering both reflections on past lessons and forward-looking perspectives.

Today, Machine Learning (ML) plays a key role in Uber’s business, being used to make business-critical decisions like ETA, rider-driver matching, Eats homefeed ranking, and fraud detection. As Uber’s centralized ML platform, Michelangelo has been instrumental in driving Uber’s ML evolution since it was first introduced in 2016. It offers a set of comprehensive features that cover the end-to-end ML lifecycle, empowering Uber’s ML practitioners to develop and productize high-quality ML applications at scale. 

Learn about best practices when integrating Large Language Models (LLMs) into product development. We will discuss the strengths of modern LLMs like Claude and how they can be leveraged to enable and enhance various applications. The presentation will cover simple prompting strategies and design patterns that facilitate the effective incorporation of LLMs into products.

The balance between AI innovation and data security and privacy is a major challenge for ML practitioners today. In this talk, I’ll discuss policy and ethical considerations that matter for those of us building ML and AI solutions, in particular around data security, and describe ways to make sure your work doesn’t create unnecessary risks for your organization. It is possible to create incredible advances in AI without risking breaches of sensitive data or damaging customer confidence, by using planning and thoughtful development strategies.