Conference Schedule

If you’ve seen the news lately, the tech bros will have you believe that AI is both here to take your job and also your soul. On the other side, people are saying that we are in an AI-bubble, it’s all hype, and we don’t need to worry about it at all. Which is it? The truth is…it’s a little of BOTH. Dona will demystify what the latest buzzwords in AI (what is OpenClaw all about) and show you a roadmap for how you can AND SHOULD be using AI in your day-to-day life. You’ll learn how to set up an experiment framework for yourself, identify the workflows AI is actually good for, how to run the...Read more

AI in software delivery is no longer just about experimenting with copilots or generating code faster. It is about changing how software is imagined, specified, generated, validated, governed, and improved. As AI continues to be used for producing prototypes, requirements, code, tests, documentation, deployment artifacts, and operational insights, the software lifecycle shifts from a sequence of handoffs into a continuous learning loop. The real opportunity is therefore not only about leveraging AI tools, but adopting a new working model for transforming intent into trusted outcomes. Join...Read more

Despite record investment in artificial intelligence, the gender gap in AI remains staggering. Women make up less than 22% of AI and ML professionals globally (World Economic Forum, 2024) and hold under 15% of technical leadership roles in the field. Only 3% of angel investors in regions like the Pacific Northwest are women, so the earliest capital shaping AI’s future is overwhelmingly male. This session for leaders and managers explores why this workforce imbalance matters and how it’s holding back innovation both across the tech ecosystem and within individual organizations. First, Lana...Read more

Scientific discovery faces a major bottleneck: while generative AI has transformed information processing, it has not yet unlocked comparable acceleration in understanding the physical world. Traditional computational chemistry and materials modeling remain too slow, expensive, and limited to power large-scale innovation. SandboxAQ tackled this challenge by developing Large Quantitative Models (LQMs) — a new class of AI models that merge machine learning, physics-based simulation, and quantum-inspired algorithms to model molecular and material interactions at unprecedented speed...Read more

Advances in robot learning and affordable general-purpose hardware are changing physical work faster than any past wave of automation. Industrial robot arms transformed repetitive factory tasks, but they could only operate in tightly controlled settings. Now, new learning methods and off-the-shelf mobile robots make it possible to teach machines to perform many kinds of hands-on work in warehouses and logistics facilities. This session looks at where the technology stands today, what has changed, and what the next few years will bring. Attendees will learn how to spot real opportunities...Read more

Shipping AI into regulated, safety-critical mobile apps is more than “just call an LLM.” In automotive and medical contexts, we must meet strict privacy, latency, and auditability requirements while working with flaky connectivity and device constraints. In this talk, Ronak will share a field-tested architecture for agentic AI on the edge: a hybrid edge–cloud agent that uses tool-calling plugins, a policy engine for pre/post-prompt guardrails, offline fallbacks, and structured telemetry for audits. He will walk through how his team bounds agent behavior with capability cards, redacts PHI/...Read more

Artificial Intelligence is driving innovation across industries, but its growing energy demands pose critical challenges around cost, scalability, and sustainability. In this talk, Bhanu will share practical strategies for designing energy-efficient AI systems, focusing on: dynamic batching & KV caching for reducing inference overhead, sparse neural networks & structured pruning for lightweight models, carbon-aware scheduling to align compute with renewable energy, federated learning & edge deployments to reduce data transfer energy, and a sustainability maturity model for...Read more

The hype cycle is over. The pilot graveyard is full. MIT research suggests that 95% of Generative AI implementations have little to no measurable impact on P&L. So what separates the 5% that actually deliver? In this no-fluff panel, practitioners from the front lines of enterprise AI — spanning automotive, cloud platforms, and the messy middle of real-world deployment — will have a candid conversation about what's working, what consistently stalls, and what it actually takes to move from experimentation to results.

The panel will discuss: