In this workshop, we will take a historical journey through the ideas that eventually led to today's large language models, such as ChatGPT and Claude.

Modern LLMs can seem overwhelmingly complex, but they didn't appear all at once. Many of the basic ideas behind them have been developing for more than a century. By following this history step by step, we can better understand what language models do and how they evolved.

In part 1 of this workshop, we will begin with Andrey Markov's early work on sequences and probability in 1913 and Claude Shannon's influential work on information and language in 1948. From there, we will explore key ideas such as Markov chains, N-grams, and the next-word generation process using simple, accessible examples.

In part 2 of the workshop, which is not in this workshop, we will also look at how an artificial neuron can learn to predict the next word, an important step toward neural language models that later became the foundation of modern LLMs and we will see what perplexity is.

The workshop is designed for anyone who is curious about where today's AI came from and how a century of ideas about language, probability, and computation helped make modern language models possible. This workshop targets students of any level who are interested in language models or history. No background in computer science or artificial intelligence is required. The hardest part is converting counted numbers to probabilities.

Our historical journey will cover roughly the period from 1918 to 1970s.

Location: Melville Library, Central Reading Room, Lab A

Register for the event on SB Engaged.

Are you interested in understanding the challenges that lie ahead as Artificial Intelligence (AI) systems become increasingly autonomous, dynamically acquire information, and adapt behaviors?
 
Join us for an exciting afternoon of talks by visionaries and leaders from industry, government, and academia as we kickoff a three-part Trusted AI Challenge Series designed to Build the Vision - Formalize Challenges - Advance the Art of next generation of AI systems.
 
The Air Force Research Laboratory Information Directorate, The State University of New York, Innovare Advancement Center, NYSTEC, and Griffiss Institute invite you to join us for this half-day virtual event!
 
WHEN: Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT
 
Hosted by Innovare Advancement Center, this webinar is the first of a three-part series designed to cultivate, define and fund creative solutions to a set of challenge problems in trustworthy AI with a particular focus on dynamic, autonomous systems that learn and adapt behaviors.
 
Keynote speakers include Dr. David Goldstein of  Space X; Dr. Scott Hubbard of Stanford University; Dr. Pramod Khargonekar of UC Irvine, and more!
 
This event is designed for academic and government researchers, university students, and small businesses.
 
Would you like to understand some of the most formidable technical challenges in future autonomous systems?  Would you like to sponsor some of the brightest minds in AI to work on problems of interest to you? Would you like to learn more about AI in real systems?
 
If so, Save the Date! Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT.
 
Please see additional information on the three-part series here. Registration details to follow! 
 
Stay tuned: https://www.innovare.org/news-events  

The AI Community will be hosting our very first Datathon💡📊

Ready to turn data into groundbreaking insights? 🧠

Compete in our Datathon, where you'll analyze real-world data 📈 and share innovate solutions in these tracks:

🏫 Student Life

🌱 Environment & Sustainability

💉 Health & Wellness

💰 Finance & Economics

Whether you're a data pro or just starting out, this is your chance to network, learn, and win exciting prizes! 🏆🎉 Bring your creativity 🧩 collaborate with fellow students 🧑‍🤝‍🧑 and gain hands-on experience showcasing your analytical skills 💻

Submissions will be judged by professors 🧑‍🏫 so take this chance to impress them!

There will be free food ☕ and games 🎲 to fuel your brain and imagination! Don't miss out--register now and unleash the power of data! 🔥✨

Registration Form: https://forms.gle/6XYMfmhyAByzFpxz5

Time: Friday (4/4) 10:30am - 5pm ⏰

Location: Bauman Center 📍

Professor Nanpeng Yu from UC Riverside present Machine Learning and Big Data Analytics in Power Distribution Systems.

Abstract: The electric utility industry is being swamped by petabytes of data coming from various sources such as smart meters, phasor measurement units, SCADA systems, geographical information systems and customer management systems. The primary and secondary value embedded in the complex and heterogeneous data sets from power distribution systems is immense. However, algorithms and applications for unlocking the potential of big data in power systems are at an early stage of development. This talk discusses the recent advancement of machine learning algorithms and big data analytics methods in power distribution systems. In particular, we will explain how to develop hybrid algorithms, which synergistically combine the merits of state-of-the-art machine learning algorithms and physical model-based methods. We will take a deep dive into the following applications: network topology identification, electricity theft detection, estimation of behind-the-meter solar generation and data-driven distribution system controls.

Bio: Dr. Nanpeng Yu received his B.S. in Electrical Engineering from Tsinghua University, Beijing, China, in 2006. Dr. Yu received his M.S. degrees in Electrical Engineering and Economics and Ph.D. degree from Iowa State University in 2010. Before joining University of California, Riverside, Dr. Yu was a senior power system planner and project manager at Southern California Edison from Jan, 2011 to July 2014.

Currently, he is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Riverside, CA. Dr. Yu is the recipient of the Regents Faculty Fellowship and Regents Faculty Development award from University of California. He received multiple best paper awards from IEEE Power and Energy Society General Meeting, IEEE Power and Energy Society Grand International Conference and Exposition Asia and the Second International Conference on Green Communications, Computing and Technologies.

Dr. Yu is the director of Smart City Innovation Laboratory at UC Riverside. He currently serves as the vice chair of the distribution system operation and planning subcommittee of IEEE Power and Energy Society and the co-chair for IEEE Big Data Applications in Power Distribution Networks Task Force. Dr. Yu currently serves as the associate editor for IEEE Transactions on Smart Grid and International Transactions on Electrical Energy Systems.
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
Abstract: How is AI transforming pricing decisions, and what opportunities, challenges, and risks does this transformation bring? This book traces the evolution of pricing from traditional approaches to today's AI-powered strategies, showing how big data, machine learning, and generative AI are reshaping the way organizations set prices. Pricing decisions can now be adjusted in real time, personalized to individual consumers, and optimized at an unprecedented scale. The book examines the behavioral principles underlying modern pricing, explores how AI enables managers to automate and enhance pricing decisions, and discusses the ethical, legal, and societal challenges raised by these innovations, including concerns about competition, consumer protection, and discrimination. Ultimately, the book argues that AI is not merely changing how prices are set--it is redefining the role of pricing itself, requiring organizations to balance profitability, fairness, and consumer trust.

Speaker Bio: Maxime Cohen is the Scale AI Chair Professor and the Academic Director of the Bensadoun School of Retail Management at McGill University. He is the Chief of AI Strategy of the CIUSSS West-Central Montreal and the Scientific Director of the non-profit MyOpenCourt.org. He was a Visiting Professor of Operations Management and a Shubik Fellow at Yale School of Management. He has collaborated with many companies including Google, Microsoft, Meta, Uber, Waze, Amazon, Expedia, Oracle Retail, IBM Research, Via, Spotify, Aldo Group, Couche- Tard/Circle K, Loblaws, Canadian Tire, L'Oréal, and Staples and he serves on the advisory board of several startups. His research and teaching have received 40+ awards, including Poets&Quants Best 40-Under-40 MBA Professors, RETHINK Retail's Top Retail Influencers, MSOM Young Scholar Prize, and Best OM Paper in Management Science. Finally, he serves on the editorial board of several leading Operations journals (DE at M&SOM and POM, AE at Management Science, OR, and MBR).

Location: Harriman Hall 304