Some of My Talks
Can We Make Model Alignment a Software Engineering Process?
The AI Conference, San Francisco, September 10, 2024, Chicago Meetups, September 25 and 26, 2024
This talk explores two unique challenges for software developers that were created by Gen AI: 1) How the non-determinism of model outputs makes testing harder, and 2) Ways we can make model tuning and related refinements more incremental and iterative.
Where Is AI Headed?
Private Event, Chicago, July 22, 2024. Significant update July 25.
This talk combines and updates the two AI talks below, What Issues Are Blocking AI Adoption? and AI in the Open: Why It Matters. How to Achieve It. I prepared it for a longer session at an IBM in-house event in Chicago, July 22, 2024.
What Issues Are Blocking AI Adoption?
1871 AI Innovation Summit, Chicago, June 27 2024
Despite the promise of AI, several challenges block many deployments. What are those challenges and what do we do about them?
AI in the Open: Why It Matters. How to Achieve It.
AI Camp, Chicago, February 2024
To maximize availability and safety of AI, we should follow the path of open-source software, while recognizing what is new.
Reinforcement Learning: ChatGPT, Games, and More
GOTO Chicago, May 2023, and IBM Research, October 2023
Things move fast; an update to January's RL talk that expands the coverage of Reinforcement Learning from Human Feedback, a key element in training ChatGPT.
Lessons Learned from 15 Years of Scala in the Wild
Several Conferences, 2021-2022
Since I joined the Scala community roughly 15 years ago, the Scala community has learned a lot to make the language more robust and easier to use effectively. I've also learned lots of lessons about effective "enterprise" programming using Scala. Finally, I see warning signs for FP's future growth.
Modularity: A Retrospective
GOTO Chicago Nights, February 18, 2020 and Scala in the City, May 28, 2020
A look at what we've accomplished in making software modular and
where we need to go.
Cluster-wide Scaling of ML with Ray
YOW! Data, July 1, 2020, and CodeMesh, Nov., 2020
Ray is a distributed computing system that offers a concise, intuitive API, with excellent performance for distributed workloads. It emerged out of the AI community at U.C. Berkeley.
All Talks