10 of Our Favorite AI Slide Decks from ODSC East
We might have to wait another year to gather together live in Boston for another ODSC East, but here, you can check out the slide decks from some of the most popular sessions from the conference. You can also watch a number of virtual sessions for free here.
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ODSC East Keynotes
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.
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, Abeba: a) presents work that highlights numerous concerns arising from large-scale datasets, b) discusses the downstream impact of such datasets on models (including the exacerbation of societal biases and negative stereotypes) and c) reviews 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-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.
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.
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, we will give you a guided tour of each architecture to help you understand its pros and cons. We 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.
Ines Chami | Co-founder and Chief Scientist | NUMBERS STATION AI
Learn about recent research at Stanford University and Numbers Station AI on applying FMs to structured data and their applications in the modern data stack. You’ll start with ETL/ELT, then move up the stack and discuss work at Numbers Station to use FMs to accelerate data analytics workflows, finally, this talk concludes by discussing challenges and solutions for production deployment in the modern data stack.
James Bowkett | Technical Delivery Director | OpenCredo
To deal with an increasingly data-centric world, the 12-factor app helps define how to think about and design cloud-native applications. This session will take you through the 12 principles of designing data-centric applications that have been useful across 4 categories: Architecture & Design, Quality & Validation (Observability), Audit & Explainability, and Consumption.
Graphs: The Next Frontier of GenAI Explainability
Amy Hodler | Founder, Consultant | GraphGeeks.org
Michelle Yi | Board Member | Women In Data
What happens when you incorporate graphs into the realm of generative AI? This talk seeks to answer that question. You’ll learn about foundational concepts such as directed acrylic graphs (DAGs), Jedeau Pearl’s “do” operator, and keeping domain expertise in the loop. You’ll also hear how the explainability landscape is evolving, comparisons of graph-based models to other methods, and how we can evaluate the different fairness models available.
AI Resilience: Upskilling in an AI Dominant Environment
Leondra Gonzalez | Senior Data & Applied Scientist | Microsoft
Gen AI and LLMs have disrupted countless industries and roles, including data science. In this ever-changing environment, we may not be sure where to invest your time and energy. This talk aims to guide existing AI practitioners on how to maintain relevant skills in an increasingly automated world, and how to stand out in an oversaturated job market.
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Originally posted on OpenDataScience.com
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