PyData Pittsburgh

Due to a medical issue, we'll have to cancel and reschedule this event. Please keep an eye out for communications about a new date and time! Sorry for the short notice and any inconvenience this may cause.

Please join PyData Pittsburgh for the talk When AI Goes Awry with AI ethics advisor and researcher Ravit Dotan! We'll be gathering in Room 102 in Benedum Hall at the University of Pittsburgh.

Please RSVP on the Meetup.com event listing here:

About the talk

What is "AI ethics" all about? And what can engineers, data scientists, and others do to develop AI in socially responsible ways? In this session, you will learn what AI ethics is through discussing cases in which AI has gone awry. For each case, we will talk about what went wrong, and what could have been done to prevent it from happening.

About Ravit

Ravit Dotan, PhD is an AI ethics advisor, researcher, speaker, and content creator. Her specialty is helping tech companies, investors, and procurement professionals develop responsible AI approaches. You can find Ravit's content on her website and Linkedin page. Ravit's recognition includes a PhD in philosophy from UC Berkeley, being named one of the "100 Brilliant Women in AI Ethics" by Women in AI Ethics, and frequent interviews in publications such as The New York Times, The Financial Times, CNBC, and TechCrunch.

Getting to Benedum Hall

If you're traveling by car, convenient parking is available at the Soldiers and Sailors Parking Garage for a flat rate of $5. Street parking is also free throughout the area after 6pm.

The University of Pittsburgh is served by many PRT bus routes. Any bus stop along Fifth Avenue between Bigelow Boulevard and Meyran Avenue will get you reasonably close.

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If you'd like to help spread the word about When AI Goes Awry, please share these links and posts on your favorite platforms!

• Meetup: https://www.meetup.com/pydata-pittsburgh/events/295326609/
• Forum: https://talk.pypgh.org/topic/10/when-ai-goes-awry-examples-and-mitigation-strategies
• LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7095083626706452480
• Mastodon: https://micro.hrsn.me/@patrick/110860753483486482
• Twitter: https://twitter.com/hrsndotme/status/1689323653060599814

Quick reminder that our AutoML as it Always Should Have Been event with Greg Michaelson is tomorrow. Remember to RSVP on Meetup.com if you haven't already. We look forward to seeing everyone there!

As an experiment, we made a handful of stickers featuring our black-and-gold PyData Pittsburgh logo.

Come out to our AutoML as it Always Should Have Been event with Greg Michaelson if you'd like to pick one up!

Please join PyData Pittsburgh on Wednesday, July 12 for the talk AutoML as it Always Should Have Been with Greg Michaelson, cofounder at Zerve and previously one of the early employees at DataRobot. We'll be gathering at COhatch Shadyside, a brand-new co-working space on Walnut Street.

Please RSVP on the Meetup.com event listing here:

https://www.meetup.com/pydata-pittsburgh/events/294435441/

About the talk

When AutoML was popularized during the 2010s, there was a great hope that the citizen data scientist would take over machine learning and that business analysts everywhere would soon be building thousands of advanced AI-based solutions, ushering in the age of AI in business. Not only did that not happen, but even the name “AutoML” has become sullied along with the myth of the citizen data scientist. In this talk, Greg will discuss the launch of a brand new open source project, Pypelines, that promises to deliver AutoML as it should have been: open, flexible, code-based, and targeted at the only people generating value from machine learning — data science experts.

About Greg

Greg Michaelson is Cofounder and Chief Product Officer at Zerve, a young, stealthy startup that’s rethinking the data science development experience. Previously, Greg was an early joiner at DataRobot, where he played many roles, including Chief Customer Officer. Prior to that, he worked as a data scientist in the financial sector after earning a PhD in applied statistics from the University of Alabama. In his spare time, Greg manufactures a line of flavored breakfast cereal toppings called Cerup. He lives in Spring Creek, Nevada, with his wife, four children, and two Clumber Spaniels.

This just came across my radar, thought it might be of interest to others. It's a plugin for Jupyter that lets you access generative AI models from a variety of different model providers (OpenAI, Anthropic, etc) directly within your notebook.

Among other things, it lets you use an IPython magics command (%%ai) to send the contents of a notebook cell as a prompt to a model of your choice — and the model's response appears right in your notebook as the cell's output.

Looks like this could be useful for coding and debugging support.

If you want to dig deeper, check out this talk from PyData Seattle:

One topic that came up in Jay's talk was reinforcement learning with human feedback (RLHF). For anyone who would like to go deeper on the topic, here are two sources I'd recommend:

Andrej Karpathy's "State of GPT" talk

Chip Huyen's RLHF explainer

Thanks again to everyone who braved the pouring rain and intermittent Meetup.com outages to come to the event! I thought we had a great turnout and a lively discussion.

Jay has made the slides from his presentation available here:

Please join PyData Pittsburgh for a tour of large language models (LLMs) with Jay Palat. We'll be gathering at COhatch Waterfront, a brand-new coworking space at the Waterfront in Homestead.

Please RSVP on the Meetup.com event listing here:

https://www.meetup.com/pydata-pittsburgh/events/293765117/

About the talk

Every day, the landscape of large language models (LLMs) expands with the unveiling of new models from OpenAI, Google, Microsoft, Anthropic, Meta and others. These companies are making substantial investments in harnessing the capabilities of LLMs, as evidenced by the array of cutting-edge features showcased at their developer conferences. The applications of LLMs span from assisting in code writing to generating awe-inspiring narratives and enabling versatile chatbots.

Yet, amidst this wave of excitement, one question lingers: How do these Large Language Models actually work? In this talk, we aim to provide a comprehensible overview of the inner workings of LLMs, tracing their origins and delving into the challenges associated with their training and deployment. By attending this talk, you will gain an understanding of these transformative tools, empowering you to strategically employ them in solving the problems you encounter. Join us on an accessible journey into the functionality and potential of Large Language Models.

(*In the spirit of the talk, this abstract was enhanced with ChatGPT.)

About Jay

Jay Palat is a seasoned technical leader with expertise in human-centered emerging technologies. His recent work includes working to build the discipline of AI Engineering for safety and mission critical AI systems as the Technical Director of AI for Mission at the CMU's Software Engineering Institute AI Division. Jay has built a career helping teams engineer good solutions that solve complex problems with companies like IBM, UPMC Enterprises, Rhiza and BCG. When he's not working or with his family, Jay's often walking the parks and streets of Pittsburgh.

Thanks for visiting the PyData Pittsburgh Forum! PyData Pittsburgh is a community of data scientists, data engineers, and the simply data-curious, with a special focus on open source tools and methods for data analysis, data visualization, machine learning, software development, and all kinds of scientific computing. Our members include data novices, professional machine learning engineers, students, academic researchers, marketing managers who've pushed Excel past its breaking point one too many times — and everyone in between.

If this sounds at all interesting to you, you're in the right place! Feel free to create an account and join the conversation. All are welcome.

Be sure to join the PyData Pittsburgh group on Meetup.com to receive notifications about our upcoming events, too.

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Community Guidelines¹

Share things that add value. Lessons you've learned, resources you've seen, or new ideas that you've been thinking about. Self-promotion and spamming is out of bounds.

Support each other with curiosity and patience. PyData Pittsburgh members come from a broad range of backgrounds and experience levels. Let's engage and learn from one another with kindness and empathy. Be constructive. Disrespectful speech and posting objectionable content won't be tolerated.

Keep private information private. The PyData Pittsburgh Forum is a public place. Please don't share any private information, or any information that doesn't belong to you without permission.

¹ Inspired by the guidelines for the Basecamp Community.

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Code of Conduct

PyData Pittsburgh follows the NumFOCUS Code of Conduct.

Here's the short version:

Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate for NumFOCUS.

All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate.

NumFOCUS is dedicated to providing a harassment-free community for everyone, regardless of gender, sexual orientation, gender identity and expression, disability, physical appearance, body size, race, or religion. We do not tolerate harassment of community members in any form.

Thank you for helping make this a welcoming, friendly community for all.

The full version is available here. If anything on the PyData Pittsburgh Forum makes you uncomfortable for any reason, please flag the post (if applicable) and reach out to one of our moderators.