Live Interactions with Jan Chorowski and Adrian Kosowski | Bonus Resource

As a part of this bootcamp, we hosted a captivating Fireside Chat featuring Jan Chorowski, CTO of Pathway. Jan is a renowned figure in Artificial Intelligence with a Ph.D. in Neural Networks and a portfolio that includes more than 10k+ citations and collaborations on research papers with AI pioneers like Geoff Hinton and Yoshua Bengio.

During this captivating Fireside Chat, our host, Anup Surendran, engaged in a deep exploration of the captivating realm of Large Language Models (LLMs) alongside Jan. Their dynamic discussion spanned a wide spectrum of LLM topics, encompassing their diverse applications, the operational hurdles they face, and the intriguing concept of 'learning to forget.' Moreover, the conversation delved into the real-time capabilities of LLMs and illuminated their paramount importance in the ever-evolving landscape of modern technology.

Key Highlights:

• Gain insights into the evolution of LLMs and their practical applications.

• Explore the operational challenges faced by LLMs in real-time scenarios.

• Understand the concept of 'learning to forget' and its role in LLM development.

• Dive into the discussion on the real-time nature of LLMs and their relevance.

• Get valuable answers to audience questions about LLMs, document versioning, and more.

Don't miss out on this illuminating Fireside Chat that offers a unique perspective on the evolving world of Large Language Models. Dive into the past conversation to uncover the mysteries and possibilities of LLMs in today's tech-driven world.

Another Bonus Resource: Recorded Interaction on Real-time Data Processing

This is a session from Pathway's Archives. It's a live interaction between Jon Krohn (Chief Data Scientist at Nebula | GitHub) and Adrian Kosowski (CPO at Pathway | Google Scholar). Adrian Kosowski, notable for his early PhD at and co-founding Spoj.com, brings over 15 years of diverse research experience to the discussion. It delves into real-time data processing, contrasting stream versus batch processing, and exploring practical ML applications.

Key Highlights:

• Gain a deep understanding of real-time data processing nuances through a discussion on reactive data processing.

• Explore the key differences and practical applications of stream versus batch processing.

• Understand the role of transformers in data engineering, especially in managing and streaming data.

• Discover emerging machine learning tools and approaches that are particularly beneficial for startups.

Don't miss these illuminating Fireside Chats that offer unique perspectives on the fast-evolving domains of Large Language Models and Real-time Data Processing. These sessions provide valuable insights into the wonderful world of AI and machine learning.