The AI Conference™
Solving Multi-Tenancy In Vector Search Requires A Paradigm Shift, Etienne Dilocker, CTO, Weaviate
updated
The session will offer leaders and builders practical guidance on unlocking value, identifying bottlenecks, and embracing the organizational shift required for AI-powered futures.
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Featured workshops: (full sessions available on our channel)
Building Production-Ready AI Agents with Strands Agents — Du’An Lightfoot (AWS): youtu.be/hwWGx91hwY0?si=854Z9AE3-eefUfjA
From Ingestion to Query: Using Weaviate for Vector Search — Adam Chan (Weaviate): youtu.be/ouzIpY_F0bs?si=u_glGqzPcHivCKvs
Top Mistakes Developers Make When Building AI Voice Agents — Andrii Mazur (Vapi): youtu.be/AP-AwFnXc8c?si=iiInlRG8omngEC-N
Empowering Agents with Knowledge Graphs using Neo4j MCB Servers — Jason Koo (Neo4j): youtu.be/in-bbFGQz2U?si=khpmN9wby1_APiTx
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👉 See Ansh’s keynote live at The AI Conference™ 2025 in San Francisco.
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Getting Robots Deployed at Scale; Robot Demo Videos Are Not Sufficient
It is a golden age for robotics, driven by amazing amounts of compute available onboard, low cost hardware driven by smartphones, electric scooters, and smart appliances, and sixty years of AI advances. And it is a time where robots are desperately needed as labor is short in manufacturing, warehouse/fulfilment, farming, construction, and many other industries.
Rodney Brooks has been a pioneer in robotics and AI in both academia and in six startups over the last 40+ years. His companies have shipped more robots than any others, and in four major product lines. Now at Robust.AI he and his teammates have developed a new class of mobile robots, driven by AI advances, that radically increase the productivity of people employed in warehouses and manufacturing, by becoming their most valued tools and making their work much less physically demanding.
AI systems have to satisfy human users. Robots do too, but first they have to satisfy physics, and that is a much less forgiving requirement. Brooks will explain how critical a physics based understanding of robots is to figuring out what it is they need to know about the world, and what they can learn from the world. And he will lead you through what parts of that are long understood, what parts have just become possible to deploy with reliability, and what parts are still distant dreams.
Finally he will talk about why every physical robot class, from fully autonomous automobiles to home robot vacuums, has a much longer deployment ramp than products which are shipped over the network as ephemeral bits; ranging from reliability of operation, to supply chain vulnerability, to the costs of both underutilized or unavailable inventory.
Key Takeaways:
The evolving role of robots in reshoring U.S. manufacturing and warehouse operations.
How robots are already augmenting human workers in warehouses and what’s next for AI-powered automation.
Practical insights into deploying hybrid automation models to address labor shortages and improve operational efficiency.
Realistic timelines for fully automated warehouses and the gradual shift to more collaborative systems.
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leaders at AMD, Google
DeepMind, Robust.AI, Reltio,
and Replit to hands-on demos
and the always-energizing
Startup Showdown, Mission
Rock was buzzing with ideas
and innovation all day long.
Catch the highlights in this
30-second recap video — and
get ready, because Day 2 has
even more in store.
#theaiconference #AI #builders #TAIC2025 #taic #AIC25
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Ready to keep learning? Watch this session and explore all Hack Day talks on our channel.
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Ready to keep learning? Watch this session and explore all Hack Day talks on our channel.
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Ready to keep learning? Watch this session and explore all Hack Day talks on our channel.
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Ready to go deeper? Watch this session and see all Hack Day talks on our channel.
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From speaker panels to casual side chats, he tapped into real experience and future-shaping ideas.
He didn’t just attend. He leveled up.
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From transforming how public defenders access justice, to winning the Startup Showdown 2024, he proved that purpose-driven AI has a place at the forefront of innovation.
📣 If you’ve got a mission, and the tech to back it, this is your moment.
🔥 Your startup could be next.
aiconference.com/cfs
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Escaping AI Proof-of-Concept Purgatory:Techniques for Enterprise AI Engineers
Many organizations implementing generative AI find themselves trapped in “proof-of-concept purgatory,” unable to transform promising experiments into production-ready solutions. This talk provides a practical roadmap for overcoming critical challenges in AI engineering for enterprises through a “full-stack” approach to generative AI.
We’ll explore strategies for managing project scope, enhancing accuracy through domain-specific models and graph-based RAG, and navigating web development intricacies in AI applications. Using real-world examples, we’ll demonstrate how these elements come together.
Attendees will gain a clear framework for tackling core challenges in AI engineering for enterprises, empowering them to escape proof-of-concept purgatory and deliver AI systems that drive tangible business value.
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Generative AI Development for Software Engineers
For AI to be truly democratized, software engineers will have to be at the forefront of AI application development. What does the software development workflow look like for generative AI apps?
This talk will show how things like version control, evaluation and monitoring apply to generative AI, and the tools and processes we can use to bring rigor to managing prompts, evaluating gen AI apps and having AI as just another tool in the software developer’s toolkit.
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This panel brings together key voices to explore the bill’s implications for technology, business, and society: Senator Scott Wiener, the bill’s author Priya Anand, reporter from the SF Standard, and Ion Stoica, co-founder of Anyscale and Databricks. Our experts will look into the potential impacts of SB-1047 on AI development, regulation, and innovation in California and beyond.
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AMD x Hugging Face :Deploying large language models on AMD GPUs
Join this session and learn how simple it is to pull down and deploy Hugging Face models on AMD GPUs using ROCm software and discover the benefit of end-to-end workflows in an open-source development ecosystem.
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Unleashing the Power of Large Language Models at DoorDash for a Seamless Shopping Adventure
At DoorDash, our expansion into various services like groceries, convenience, and retail goes hand in hand with our commitment to advanced machine learning, especially the integration of Large Language Models (LLMs). In our presentation, we’ll highlight how LLMs are revolutionizing our approach to personalization, search optimization, and product knowledge graph building. These models are instrumental in managing complex scenarios, such as substituting low-in-stock items, and are pivotal in refining our product catalog and taxonomy.
We’ll provide an in-depth look at how LLMs, alongside traditional machine learning techniques, are enhancing user experience. This includes facilitating rapid basket building, simplifying the discovery of new products, and ensuring seamless order fulfillment. Our discussion will focus on the significant impact of LLMs, detailing the lessons learned, challenges encountered, and the practical implications of applying these state-of-the-art technologies in real-world applications at DoorDash.
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Building an AI-Driven Company with Agents
AI is unlocking new frontiers for businesses, and leveraging its potential is no longer optional. When we embarked on building CrewAI, a framework for creating AI Agents, we didn’t just anticipate a shift in how we work; we experienced a transformation. This talk will delve into the journey of how AI Agents have become a catalyst for growth, not only within our company but also for our customers.
Through concrete, real-world examples, we’ll explore how AI Agents autonomously navigate complex tasks, streamline workflows, and spark innovative solutions. You’ll gain insights into the foundational elements that make these agents reliable and powerful, and how you can implement them into your own projects. This is more than a narrative—it’s a guide to harnessing AI as a driving force in your business strategy.
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AI All the Way Down: Building Trust into Generative Applications with Model-Based Feedback
Generative AI promises to unlock unprecedented efficiency and enhance human expertise in collaborative workflows. However, this technology differs from predictive AI in significant ways, and that creates new challenges to safety and reliability. One important difference is that unlike their predictive predecessors, LLMs are trained on general purpose tasks and subsequently applied to different, specific problem spaces. This means that gathering performance feedback and root-causing problems is not as simple as comparing a prediction against a ground-truth label.
In this talk, we’ll explore closing the feedback loop for generative applications with model-based metrics. At Fiddler AI, we call them “Trust Models” and they’re used both for offline diagnostics and synchronous runtime-path use cases where unsafe or underperforming output is intercepted in real-time.
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Moderated by Kenn So, Director, Smartsheet
Panelist:
Alex Kayyal, Partner, Lightspeed
Kany Gulati, Partner, Kholsa Ventures
Shravan Narayen, Partner, ivp
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Beyond Black Boxes: Rigorous Evaluation of AI Agents
This talk addresses key challenges in evaluating LLM-powered AI agents: behavioral instability, comprehensive testing, and cascading failures.
Brain icon for The AI Conference 2023, a groundbreaking two-day event on AGI, LLMs, Infrastructure, Alignment, AI Startups, and Neural Architectures.
We’ll explore advanced techniques including multi-dimensional metrics and automated scenario generation. Gain insights gleaned from hundreds of AI engineering teams in production into implementing agent-specific observability systems and designing robust evaluation pipelines that can handle the complexities of modern AI agents, from detecting subtle regressions to quantifying performance across diverse, dynamically generated test cases.
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3 Steps to Making Well-Informed AI-Driven Decisions in Real Time
Given the ease with which AI-based initiatives can be executed nowadays, it is no surprise that more and more enterprises are considering adopting AI-powered autonomous analytics to make well-informed decisions more economically, improve operational efficiencies, and delight customers. Achieving the true promise of AI, however, requires access to data and the execution of such analytics in real time.
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Serverless in Real Life: Use Cases & Insights
Tim Pietrusky on X: https://x.com/NERDDISCO
Tim Pietrusky on LinkedIn: linkedin.com/in/timpietrusky
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Automating Improvement in GenAI Applications Through Feedback & Evaluations
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Architecting Hyper-Personalized LLM Applications using Rich Contextual Data
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ALLY: LLM-based Assistive Recommendation
Large Language Models (LLMs) have demonstrated impressive capabilities for natural language understanding and generation. What if we power recommender systems with LLMs that can reason through user activities on the platform, and can help plan recommendations accordingly? Equipped with UXR insights that people use nuanced language to describe their long-term interest journeys, and they want more user agency in their recommender platforms, we pinpoint a combination of LLMs and personalization as a key ingredient for the next generation of assistive recommendation.
In this talk, we will introduce a novel LLM-based journey service that allows to uncover the personalized nuanced user interests, needs and goals of users, i.e., their interest journeys, through fine-tuning and prompt-tuning LLMs. We will discuss research findings from this work on LLMs for better user understanding, and will provide insights on LLM-based long-term planning to assist user journeys. Finally, we will provide challenges and opportunities across our long quest of integrating LLMs into user facing products to transform the user experience.
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Azure Experiences and Ecosystems, Microsoft
AI-Powered Startups: How Microsoft Azure is Fueling the Next Wave of Innovation
Atom icon for The AI Conference 2023, a groundbreaking two-day event on AGI, LLMs, Infrastructure, Alignment, AI Startups, and Neural Architectures.Join Annie Pearl for an exclusive insight into how Microsoft is reshaping startup ecosystems with cutting-edge AI. In this session, she will explore key patterns in AI adoption among startups leveraging Azure services and reveal how Microsoft is empowering the next wave of AI-driven innovation.
Discover what Azure uniquely offers to startups at every stage of their journey, from ideation to scaling advanced AI solutions.
Session takeaways:
Microsoft enables startups to build AI at any stage, from inception to the most sophisticated models in the world.
How Microsoft works with Founders through Founders Hub, the premier destination for startups seeking personalized guidance, free credits, and services tailored to their business needs.
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On-Device LLMs: Unleashing the Power of Conversational AI for Mobile and Wearable Devices
Large Language Models (LLMs) have captured the attention of the tech world with their remarkable common-sense reasoning and generalizability. However, their large size and server transfer requirements can make them resource-intensive and slow, which is problematic for use in mobile or wearable devices like smart glasses and smart watches. Moreover, on-device computing could offer a solution to privacy concerns by keeping sensitive data, such as text messages or photos, on the device itself. To tackle these challenges, we’ve developed a more compact language model, ranging from 0.5B to 1.4B parameters. This model is designed to run on-device, providing a competitive performance for conversational grounded tasks, while also managing latency and memory usage effectively.
In this presentation, I’ll delve into our work on creating a versatile, on-device LLM, which is a distilled version of the LLAMA model, specifically tailored for conversational reasoning tasks. I’ll discuss our pretraining framework and our innovative approach to finetuning, which involves using LLM synthesized, task-specific data in a fresh dialogue format. Finally, I’ll share our strategy for scaling our text-based conversational model to a multimodal model, enhancing generative experiences such as composing text replies, document summarization, image captioning, and visual question-answering on wearables or mobile devices.
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Empowering Sales with LLM-Powered Data Insights and Workflow Automation
Snowflake leverages Large Language Models (LLMs) to empower the sales team with efficient search and execution capabilities for customer-related metrics and business performance data.
By using data-to-text and task execution chatbots, salespeople can easily understand and interact with their data.
These chatbots enable users to search complex datasets, receive clear summaries, and execute tasks efficiently, all within a single platform.
This integration ensures that sales professionals have quick access to vital information and can manage their workflows seamlessly.
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From Concept to Reality:Mastering LLMs from POC to Production
As businesses move from LLM experimentation to real-world deployments in 2024, they face a critical balancing act between quality, speed, and cost.
Drawing from a decade of NLP experience with Fortune 100 companies, this talk navigates the complexities of moving from POC to production-ready LLM solutions.
Strategic LLM applications transforming industries from legal to healthcare and eCommerce
Best practices for minimizing costs and risks such as hallucination issues
Scaling for growing demands
Techniques for future-proofing deployments as next-gen models like Llama 3 and GPT-5 emerge
The crucial role of regression testing in safeguarding existing applications
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Effects of scaling in world model training
The bitter lesson teaches us that simple techniques at scale are the most effective. This has been proven with LLMs and has similarly shown true in video models, where scale has unlocked truly emergent behaviors in 3D and is showing early glimpses of the future of multimodal intelligence.
We’ll discuss the approach to, and findings of training Dream Machine, Luma’s SOTA video generation model.
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Observability in LLMOps pipeline - different levels of scale
The term LLMOps has been around for just under 2 years. In the meantime, we have gone from using fine-tuned foundation models to running complex Agentic AI systems in production. Many breakthroughs are yet to be made and hardware limitations to be overcome.
In this talk I will walk you through the LLMOps pipeline as if we were building an AI system from scratch. From building foundation models and fine-tuning them to observing complex Agentic AI systems in production. I will emphasize the different levels of scalability requirements for observability infrastructure and tooling at each step and why you might want to invest into it.
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Building the first Safety-focused Conversational AI for Healthcare
Healthcare is facing a massive, global staffing crisis. The World Health Organization predicts a 10 million healthcare worker shortage by 2030, but stretched systems and underserved patients are already feeling the effects. Generative AI is the key to closing this staffing gap and ensuring more people can receive a level of care that has never existed before. With generative AI and large language models (LLM) working alongside healthcare workers of all kinds, we are looking to improve access, equity, and outcomes.
Hippocratic AI is building the industry’s first safety-focused LLM for healthcare, specifically focused on non-diagnostic, patient-facing, real-time healthcare conversation. We present Polaris — an agentic trillion-parameter constellation system with collaborative specialization and safety guardrails for human-like conversation. We will talk about the architecture, specialized training and alignment, and the first comprehensive clinician evaluation of an LLM system for healthcare with thousands of U.S. licensed nurses and hundreds of U.S. licensed physicians. We will also share industry use-cases to showcase the best and safest ways to deploy this technology, and explore what AI- and LLM-powered healthcare could look like in the future.
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Unpacking Graph RAG:An overview of history, terminologies and examples
This talk provides an overview of the current state of Graph RAG (Retrieval-Augmented Generation) and its components. Graph RAG has become an incredible buzz term recently, but at its core, it represents an extension to RAG by combining the power of graphs with vector search to enhance information access and response relevance. The talk presents a pragmatic perspective by uncovering the various stages involved in building and using Graph RAG, and highlights some examples from recent studies that show tangible improvements when using graph retrieval in combination with vector search.
You will also learn about some ongoing open source projects in Graph RAG that can help you get started with building, experimenting, and engaging with the rapidly growing community of practitioners who are interested in this space.
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Text Revision with Large Language Models -- Challenges and Opportunities
Text revision is a complex, iterative process. It is no surprise that human writers are unable to simultaneously comprehend multiple demands and constraints of the task of text revision when producing well-written texts, as they are required to cover the content, follow linguistic norms, set the right tone, follow discourse conventions, etc. This presents a massive challenge and opportunity for intelligent writing assistants, which have undergone an enormous shift in their abilities in the past few years and months via large language models and human-feedback learning. In addition to the quality of editing suggestions, writing assistance has undergone a monumental shift in terms of being prescriptive systems to being human-feedback-driven, collaborative systems. However, writing assistants still lack in terms of their quality, personalization, and overall usability, limiting the value they provide to users.
In this talk, I will present my research, challenges, and insights on building intelligent and interactive writing assistants for effective communication and navigating challenges pertaining to quality, personalization, and usability.
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Securing the New Attack Surface:Navigating the Security Risks and Protection of LLMs
Large Language Models (LLMs) have recently emerged as a cornerstone technology, reshaping every industry and revolutionizing how we interact with digital systems. Much like the advent of network computing and cloud technology before it, the widespread adoption of LLMs heralds a new era of innovation and, with it, a novel attack surface ripe for exploitation. This talk aims to shed light on the inherent security risks associated with LLMs with the real-world examples of LLM-targeted exploitation, and emphasize the importance of proactive threat management in safeguarding the future of LLM-driven applications. Since LLMs will be widely used in future applications, we will first give an overview of the Open Worldwide Application Security Project (OWASP) Top 10 Application Risks and Top 10 API Risks which are still applicable to LLMs. Next we will focus on the OWASP Top 10 LLM Specific Risks, and delve into three detailed examples of Prompt Injection, Sensitive Information Disclosure, and Training Data Poisoning. Each example is accompanied by a demonstration of the potential for exploitation, and also the strategies for mitigating these threats and protecting against attacks.
The goal of this talk is to educate the broad AI developer community with the potential risks and vulnerabilities of LLMs and also the knowledge to protect against them, and foster a culture of security that keeps pace with the rapid advancements in AI technology.
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Unpacking Graph RAG: An overview of history, terminologies and examples
This talk provides an overview of the current state of Graph RAG (Retrieval-Augmented Generation) and its components. Graph RAG has become an incredible buzz term recently, but at its core, it represents an extension to RAG by combining the power of graphs with vector search to enhance information access and response relevance. The talk presents a pragmatic perspective by uncovering the various stages involved in building and using Graph RAG, and highlights some examples from recent studies that show tangible improvements when using graph retrieval in combination with vector search. You will also learn about some ongoing open-source projects in Graph RAG that can help you get started with building, experimenting, and engaging with the rapidly growing community of practitioners who are interested in this space.
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Moderated by Alex Chao, Product Lead, Microsoft
Panelists:
Anna Marie Clifton, Head of Product, Zapier Central
Dr. Shelby Heinecke, Senior AI Research Manager, Salesforce
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