The Top ODSC Europe 2024 Virtual Sessions to Watch for Free

Dreaming of ODSC Europe but can’t make the trip? No worries! While jet-setting to Europe might not be on your agenda this year, you can still immerse yourself in the rich culture, knowledge, and insights from the comfort of your home, and join us at ODSC Europe this September 5th-6th virtually! Here’s a rundown of some of the sessions we’re most excited about.

Data Morph: A Cautionary Tale of Summary Statistics

Stefanie Molin | Data Scientist, Software Engineer, Author of Hands-On Data Analysis with Pandas | Bloomberg

In the world of data analysis, it’s easy to fall into the trap of relying solely on summary statistics like the mean, median, or standard deviation to describe complex datasets. However, these numbers can be misleading when not accompanied by a visual representation of the data’s distribution. The talk “Data Morph: A Cautionary Tale of Summary Statistics,” explores this crucial concept through the lens of “Data Morph,” an innovative open-source package. Building on the fascinating “Datasaurus Dozen” project from Autodesk, “Data Morph” utilizes simulated annealing to transform datasets into various shapes while maintaining identical summary statistics.

Generative AI: The Supply Chain Alchemist, Turning Data into Superhuman Business Insights

Denis Rothman | Generative AI, Public Speaker, Instructor, Author | Rothman-AI

Companies face increasing complexity and volatility, requiring innovative solutions to stay competitive. Enter Generative AI — the supply chain’s new alchemist. This technology is revolutionizing the way businesses manage their operations, transforming raw data into superhuman business insights. In “Generative AI: The Supply Chain Alchemist, Turning Data into Superhuman Business Insights,” you’ll see how this cutting-edge tool is reshaping everything from demand forecasting to logistics optimization and risk management.

Through real-world examples and hands-on code demonstrations, this talk will reveal how Generative AI can solve everyday challenges, such as predicting equipment failures and improving customer support.

AI Development Lifecycle: Learnings of What Changed with LLMs

Noé Achache | Engineering Manager & Generative AI Lead | Sicara

The rise of Large Language Models has fundamentally altered the AI development landscape, making some aspects of the process easier while introducing new challenges in others. In the talk “AI Development Lifecycle: Learnings of What Changed with LLMs,” viewers will reflect on the significant differences between building models and pipelines with LLMs versus traditional machine learning and deep learning approaches. While creating Proof of Concepts has become remarkably simple, the evaluation phase has grown increasingly complex, often leading to its neglect. This oversight can result in inefficient iterations and a lack of clarity regarding the true performance of AI products, posing a major barrier to successful production deployment, especially when high accuracy is required.

You’ll also enjoy real-world case studies, such as automating financial document analysis and developing a Retrieval-Augmented Generation tool for a medical company, to illustrate these challenges.

Reinforcement Learning with Human Feedback

Luis Serrano, PhD | Author of Grokking Machine Learning and Creator of Serrano Academy

Over the last few years, Large Language Models have revolutionized the way we generate and interact with text. The thing though, is that fine-tuning these models to achieve high-quality, human-aligned outputs requires more than just raw computational power — it demands human insight. In the talk “Reinforcement Learning with Human Feedback,” you’ll get a bird’s eye view into the process of refining LLMs through human evaluation and feedback. Central to this process is Reinforcement Learning with Human Feedback or RLHF, a powerful method that leverages human judgment to enhance model performance.

The session will begin with a concise review of reinforcement learning, setting the stage for a deeper exploration of how RLHF is applied to fine-tune LLMs. A key focus will be on Proximal Policy Optimization (PPO), the reinforcement learning technique widely used in RLHF. Additionally, the talk will introduce Direct Preference Optimization (DPO), an alternative approach that fine-tunes LLMs using human feedback without relying on reinforcement learning and has shown impressive results.

Do Large Language Models Have a Duty to Tell the Truth?

Brent Mittelstadt, PhD | Associate Professor | University of Oxford

In an age where Large Language Models are increasingly relied upon for information and guidance, the question of their responsibility to convey truth becomes ever more critical. In the talk “Do Large Language Models Have a Duty to Tell the Truth?” you’ll explore the ethical and legal implications of LLMs’ outputs, particularly the concept of “careless speech” — a subtle but insidious form of harm that these models can generate. Unlike blatant falsehoods, careless speech involves plausible, confident, yet factually inaccurate or misleading responses that, over time, can erode the integrity of knowledge, science, and shared social truths in democratic societies.

Beyond Interpretability: An Interdisciplinary Approach to Communicate Machine Learning Outcomes

Merve Alanyali, PhD | Head of Data Science Research and Academic Partnerships | Allianz Personal

Explainable AI (XAI) has become a cornerstone of modern AI research, providing much-needed transparency into how machine learning models generate outcomes. However, traditional approaches to XAI often focus narrowly on technical explanations, which may not fully capture the broader implications of AI decision-making. In the talk “Beyond Interpretability: An Interdisciplinary Approach to Communicate Machine Learning Outcomes,” you will learn about a novel approach that extends the concept of XAI beyond technical interpretations.

Why Gaussian Splatting is a New Neurone Imagination Engine

Oles’ Petriv | Co-founder and CTO | Reface

In AI, new techniques are continually emerging to push the boundaries of what’s possible. One such innovation is Gaussian splatting, a method poised to revolutionize the representation of visual and semantic information. At ODSC EU, you’ll enjoy a talk titled “Why Gaussian Splatting is a New Neurone Imagination Engine,” Oles Petriv will shed light on why this technique is making waves in the AI community. By offering a more efficient and clear representation of data, Gaussian splatting not only reduces the cost of model training but also simplifies its integration into existing machine learning pipelines, 3D software, and game engines.

Advanced RAG Scenarios: Building Your Own Database Agents

Adrián González Sánchez | Data & AI Specialist at Microsoft | Book Author | LinkedIn Learning & DeepLearning.ai Instructor

In this talk you will learn how to develop an AI agent that interacts with databases using natural language, simplifying the process for querying and extracting insights. Designed for developers, data professionals, business analysts, and professionals who want more sophisticated interaction with their databases through natural language instead of advanced SQL queries.

By the end of the talk, you’ll be equipped with the technical knowledge and practical experience to implement similar systems in your projects or organizations, enabling more efficient and accessible data interaction and analysis.

Using Generative AI to better understand B2B audiences: from Topic Modelling to Text Classification

Lourens Walters | Senior Data Scientist | Informa

In the complex and data-rich world of B2B marketing, understanding audience interests and improving data quality is paramount for driving successful campaigns. The IIRIS team at Informa has been at the forefront of this challenge, supporting the promotion of 1,500 trade shows by collecting, enriching, and analyzing a staggering 2.5 billion customer interactions. In the talk “Using Generative AI to Better Understand B2B Audiences: From Topic Modelling to Text Classification,” you’ll see insights into how a fusion of traditional Machine Learning (ML) techniques and cutting-edge Generative AI, particularly Large Language Models (LLMs), is being used to overcome these challenges.

Gender Bias in Machine Learning

Shalvi Mahajan | Senior Data Scientist | SAP SE

Gender bias in machine learning is a pervasive issue with significant implications, as it often mirrors and amplifies societal stereotypes, affecting areas like product design and service delivery. Natural language processing models, including Large Language Models (LLMs), are particularly prone to this bias, as they are trained on vast datasets that reflect historical gender norms, leading to problematic assumptions — such as defaulting to female for “nurse” and male for “doctor.” This bias is largely fueled by the use of biased training data, which reinforces stereotypes in subtle yet impactful ways. Addressing this challenge requires improving dataset diversity, refining algorithms for greater transparency, and implementing fairness-aware techniques during model development. Additionally, the development of ethical guidelines and regulations is crucial to ensure responsible and accountable deployment of ML systems. This talk will explore these issues and potential solutions for creating more equitable AI systems.

Orchestrating LLM AI Agents with CrewAI

Alessandro Romano | Senior Data Scientist | Kuehne Nagel

This talk will explore the integration of Large Language Models using CrewAI, an open-source software platform designed for orchestrating multiple AI agents. As you watch, the range of topics will cover the fundamentals of LLMs, their integration challenges, and how CrewAI enhances their collaborative capabilities. Key themes include inter-LLM communication, dynamic task decomposition, adaptive learning, and ethical considerations. Attendees will learn how and when to use CrewAI, as well as how it compares to other modules. Through real-world examples, this session will provide insights into leveraging CrewAI to improve LLM efficiency and tackle complex problems across various industries.

Conclusion

Some great talks right? The best part, you’ll be able to experience to best in AI and data science from the comfort of your own home or even office. So what are you waiting for? Get your pass today before they run out!

Originally posted on OpenDataScience.com

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