The Top ODSC East 2024 Virtual Sessions to Watch for Free
ODSC East 2024 is done and we’ll have to wait another year to gather together live in Boston. In the meantime, recordings of our virtual sessions are now available on demand! Keynotes and talks are available for free, while deep dives into topics with training sessions and workshops are available for purchase. Check out a few of the highlights from each group below.
Get your ODSC Europe 2024 pass today!
In-Person and Virtual Conference
September 5th to 6th, 2024 — London
Featuring 200 hours of content, 90 thought leaders and experts, and 40+ workshops and training sessions, Europe 2024 will keep you up-to-date with the latest topics and tools in everything from machine learning to generative AI and more.
ODSC East 2024 Keynote Speakers
Setting Up Text Processing Models for Success: Formal Representations versus Large Language Models
Carolyn Rosé, PhD | Professor, Program Director for the Masters of Computational Data Science | Carnegie Mellon University
This talk explores techniques for injecting insight into data representations to increase effectiveness in model performance, especially in a cross-domain setting. More recently, Large Language Models (LLMs) have been used to produce textual augmentations to data representations, with more success. Couched within these tensions, this talk reports on recent work towards increased availability of both formal and informal representations of language and knowledge as well as explorations within the space of tensions to use this knowledge in effective ways.
Deep Reinforcement Learning in the Real World: From Chip Design to LLMs
Anna Goldie | Senior Staff Research Scientist | Google DeepMind
Reinforcement learning (RL) is famously powerful but difficult to wield, and until recently, had demonstrated impressive results on games, but little real-world impact. I will start the talk with a discussion of RL for Large Language Models (LLMs), including scalable supervision techniques to better align models with human preferences (Constitutional AI / RLAIF). Next, I will discuss RL for chip floorplanning, one of the first examples of RL solving a real-world engineering problem. This learning-based method can generate placements that are superhuman or comparable on modern accelerator chips in a matter of hours, whereas the strongest baselines require human experts in the loop and can take several weeks.
Social and Ethical Implications of Generative AI
Abeba Birhane | Senior Fellow in Trustworthy AI, Adjunct Lecturer/Assistant Professor | Mozilla Foundation, Trinity College Dublin
As Artificial Intelligence systems pervade day-to-day life, the need for these systems to be robust, fair, accurate, and just has become of urgent importance. As the foundational backbone of AI systems, large-scale datasets play a crucial role in the performance, accuracy, robustness, fairness, and trustworthiness of AI systems. In this talk, I: a) present work that highlights numerous concerns arising from large-scale datasets, b) discuss the downstream impact of such datasets on models (including the exacerbation of societal biases and negative stereotypes) and c) review some approaches to both incremental improvements as well as shepherding broader structural change.
Top ODSC East 2024 Virtual Talks
While there are dozens of virtual talks available, these are the top-five rated ones according to those who watched them live.
End-to-End Speech Recognition: The Journey from Research to Production
Tara Sainath, PhD | Principal Research Scientist | Google DeepMind
End-to-end (E2E) speech recognition has become a popular research paradigm in recent years, allowing the modular components of a conventional speech recognition system (acoustic model, pronunciation model, language model), to be replaced by one neural network. In this talk, we will discuss a multi-year research journey of E2E modeling for speech recognition at Google. We will also touch upon future research efforts with E2E models, including multi-lingual speech recognition.
Beyond Theory: Effective Strategies for Bringing Generative AI into Production
Heiko Hotz | Generative AI Global Blackbelt | Google
In the rapidly evolving and constantly advancing landscape of artificial intelligence, foundation models like GPT-4 and DALL-E 3 and the broader world of generative AI have emerged as potential game-changers, offering unprecedented and previously unimagined capabilities across a wide variety of domains and use cases. However, while these theoretical models showcase promising capabilities, the practical challenge of transitioning from conceptual research to full-scale production-level applications remains a major obstacle that many organizations and teams continue to face. This presentation aims to help bridge this gap by taking a deep dive into exploring pragmatic and actionable strategies and best practices for successfully integrating these cutting-edge AI technologies into real-world business environments.
The Promise of Edge ML: Bringing Your Model to Your Data
David Aronchick | CEO | Expanso
In the intersection of machine learning (ML) and edge computing, this talk will explore the new opportunity in processing data with ML where it’s generated. We’ll discuss the advantages of edge ML, including immediate insights, privacy preservation, and reduced network demands. Challenges like resource constraints and the need for efficient model management will be addressed, emphasizing solutions such as lightweight architectures and robust MLOps practices.
CodeLlama: Open Foundation Models for Code
Baptiste Roziere | Research Scientist | Meta
In this session, we will present the methods used to train Code Llama, the performance we obtained, and show how you could use Code Llama in practice for many software development use cases. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama — Python), and instruction-following models (Code Llama — Instruct) with 7B, 13B, 34B, and now 70B parameters each.
Deciphering Data Architectures (choosing between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh)
James Serra | Data & AI Architect | Microsoft
Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they’re also surrounded by a lot of hyperbole and confusion. In this presentation, I will give you a guided tour of each architecture to help you understand its pros and cons. I will also examine common data architecture concepts, including data warehouses and data lakes. You’ll learn what data lakehouses can help you achieve, and how to distinguish data mesh hype from reality. Best of all, you’ll be able to determine the most appropriate data architecture for your needs.
ODSC West 2024 tickets available now!
In-Person & Virtual Data Science Conference
October 29th-31st, 2024 — Burlingame, CA
Join us for 300+ hours of expert-led content, featuring hands-on, immersive training sessions, workshops, tutorials, and talks on cutting-edge AI tools and techniques, including our first-ever track devoted to AI Robotics!
How can I learn more at the next conferences?
While watching videos on-demand is a great way to learn about AI and data science, nothing beats the live conference experience. To learn more in person in a hands-on setting, or virtually with thousands of other data scientists from around the world, check out ODSC Europe this September 5th-6th in London, and ODSC West this October 29th-31st in San Francisco. Tickets for each conference are currently 70% off, so register now while tickets are the most affordable!
Originally posted on OpenDataScience.com
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