Top 5 Talks from ODSC East 2024

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Talk #1

David Talby, PhD

Chief Technology Officer | John Snow Labs

David Talby is the Chief Technology Officer at John Snow Labs, helping companies apply artificial intelligence to solve real-world problems in healthcare and life science. David is the creator of Spark NLP – the world’s most widely used natural language processing library in the enterprise. He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft’s Bing in the US and Europe, and to scale Amazon’s financial systems in Seattle and the UK. David holds a Ph.D. in Computer Science and Master’s degrees in both Computer Science and Business Administration. He was named USA CTO of the Year by the Global 100 Awards in 2022 and Game Changers Awards in 2023.

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Applying Responsible Generative AI in Healthcare

This session surveys lessons learned from real-world projects in healthcare that created a compelling POC and only then uncovered major gaps from what a production-grade system will require:
1. Fragility and sensitivity of current LLMs in minor changes to both datasets and prompts and their accuracy impact.
2. Where guardrails and prompt engineering fall short in addressing critical bias, sycophancy, and stereotype risks.
3. The vulnerability of current LLM’s to known medical cognitive biases such as anchoring, ordering, and attention bias.

This session is intended for practitioners who are building Generative AI systems in Healthcare and need to be aware of the legal and reputation risks involved and what can be done to mitigate them.

Talk #2

James Serra

Data & AI Architect | Microsoft

James works at Microsoft as a big data and data warehousing solution architect where he has been for most of the last nine years. He is a thought leader in the use and application of Big Data and advanced analytics, including data architectures such as the modern data warehouse, data lakehouse, data fabric, and data mesh. Previously he was an independent consultant working as a Data Warehouse/Business Intelligence architect and developer. He is a prior SQL Server MVP with over 35 years of IT experience. He is a popular blogger (JamesSerra.com) and speaker, having presented at dozens of major events including SQLBits, PASS Summit, Data Summit and the Enterprise Data World conference. He is the author of the book “Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh”.

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Deciphering Data Architectures

(choosing between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh)

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. The content is derived from my book Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh.

Talk #3

Baptiste Roziere

Research Scientist | Meta

Baptiste is a research scientist at Meta AI in Paris working in the code generation team. He contributed to Llama and led Code Llama. At Meta, Baptiste conducted research on unsupervised translation of programming languages and model pre-training for code. His work was featured in dozens of news articles in more than ten languages. He also started a collaboration between the Fundamental AI Research department and production teams putting code models in production. Prior to Meta, Baptiste worked as an applied scientist in advertising at Amazon.

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Unlocking the Unstructured with Generative AI: Trends, Models, and Future Directions

The exponential growth in computational power, alongside the advent of powerful GPUs and advancements in cloud computing, has ushered in a new era of generative artificial intelligence (AI), transforming the landscape of unstructured data extraction. Traditional methods such as text pattern matching, optical character recognition (OCR), and named entity recognition (NER) have been plagued by challenges related to data quality, process inefficiency, and scalability. However, the emergence of large language models (LLMs) has provided a groundbreaking solution, enabling the automated, intelligent, and context-aware extraction of structured information from the vast oceans of unstructured data that dominate the digital world. This talk delves into the innovative applications of generative AI in natural language processing and computer vision, highlighting the technologies driving this evolution, including transformer architectures, attention mechanisms, and the integration of OCR for processing scanned documents. We will also talk about future of generative AI in handling complex datasets.

Talk #4

Jay Mishra

Chief Operating Officer | Astera

Jay Mishra is a Data Solution leader and COO at Astera, a leading provider of code-free data solutions. With a career spanning over two decades, Jay has focused on achieving excellence in data architecture and software solutions. His expertise includes solution design, development, technical leadership, and product innovation, demonstrated through successful collaborations with companies like Wells Fargo, Raymond James, and Farmers Mutual. Beyond product development, Jay has excelled in driving transformative business initiatives, optimizing operations, and achieving significant financial success for organizations. Leveraging technical acumen and strategic vision, Jay aims to empower organizations to maximize their data architecture potential and attain unparalleled financial success.

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Moving Beyond Statistical Parrots - Large Language Models and their Tooling

This talk explores frameworks and techniques to move beyond statistical mimicry. We discuss leveraging tools to retrieve knowledge, prompt engineering to steer models, monitoring systems to detect biases, and cloud offerings to deploy conversational agents. This talk explores the emerging ecosystem of frameworks, services, and tooling that propel large language models and enable developers to build impactful applications powered by large language models. Complex mechanisms like function calling and Retrieval Augmented Generation, navigating towards meaningful outputs and applications requires an overarching focus on strong model governance frameworks that can ensure that biases and harmful ideologies embedded in the training data are duly mitigated, paving the way towards beneficial application development.

Talk #5

Ben Auffarth, PhD

Author: Generative AI with LangChain | Lead Data Scientist | Hastings Direct

Ben Auffarth is a seasoned data science leader with a background and Ph.D. in computational neuroscience. Ben has analyzed terabytes of data, simulated brain activity on supercomputers with up to 64k cores, designed and conducted wet lab experiments, built production systems processing underwriting applications, and trained neural networks on millions of documents. He’s highly regarded in the London data science community and the best-selling author of the books Generative AI with LangChain, Machine Learning for Time Series, and Artificial Intelligence with Python Cookbook.

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CodeLlama: Open Foundation Models for Code

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. Code Llama is a family of open large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. 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. Code Llama reaches state-of-the-art performance among open models on several code benchmarks. Notably, Code Llama - Python 7B outperforms Llama 2 70B on HumanEval and MBPP, and all our models outperform every other open model on MultiPL-E. Code Llama was released under a permissive license that allows for both research and commercial use.