10 Can’t-Miss Sessions Coming to ODSC Europe 2024
ODSC Europe 2024 is coming up soon on September 5th and 6th in London, and there are tons of sessions that you can learn from. While the schedule is packed, it can be hard to pick out what sessions you want to see live in person. Here are our picks for 10 ODSC Europe 2024 sessions that you won’t want to miss based on AI trends, influential speakers, or existing interest.
Generative AI: The Supply Chain Alchemist, Turning Data into Superhuman Business Insights
Denis Rothman | Generative AI, Public Speaker, Instructor, Author | Rothman-AI
Generative AI is a revolutionary tool that is transforming how companies manage their supply chains, optimize KPIs, and unlock hidden value. Dive into the real-world applications of Generative AI, from hyper-accurate demand forecasting to dynamic logistics optimization and proactive risk mitigation, and explore how this cutting-edge technology is slashing costs, boosting revenues, and enhancing agility for businesses across industries with concrete code examples.
Reinforcement Learning with Human Feedback
Luis Serrano, PhD | Author of Grokking Machine Learning and Creator of Serrano Academy
Although LLMs are tremendously successful at generating text, fine-tuning a model still relies on human feedback, commonly through reinforcement learning with human feedback. In this session, you’ll explore A very important step in their fine-tuning, which involves humans evaluating the output. In order to improve the model with human feedback, RLHF is a widely used method. In this talk, we’ll explore several aspects, including:
- How RLHF is used to fine-tune large language models
- Proximal Policy Optimization (PPO)
- Direct Preference Optimization (DPO)
Crafting the Perfect Model: Fine-Tuning and Merging LLMs
Maxime Labonne, PhD | Senior Staff Machine Learning Scientist | Liquid AI
Explore the best practices for fine-tuning and merging LLMs with a focus on the popular framework techniques for parameter-efficient fine-tuning, key concepts, supervised fine-tuning techniques, such as LoRA and QLoRA, and preference alignment methods with DPO.
Building with Mistral
Sophia Yang, PhD | Head of Developer Relations | Mistral AI
In this session, you’ll get a detailed overview of Mistral AI’s language models, including their open-source availability and customization options. The talk will highlight Mistral’s key models, such as the Codestral model, and discuss the performance benchmarks, including the efficient fine-tuning processes that enable tailored model adjustments for specific use cases.
Engineering Trust: The Technical Expert’s Role in Building Trustworthy AI
Maria Axente | Head of AI Public Policy and Ethics | PwC United Kingdom
The need for trustworthy AI has never been more crucial. This keynote will delve into the technical foundations of trustworthy AI, examining how developers and engineers can embed trust at every stage of the AI lifecycle. You’ll explore advanced techniques for ensuring AI reliability, discuss the implementation of robust institutional processes, and consider innovative approaches to meaningful stakeholder engagement.
Tailoring Small Language Models for Enterprise Use Cases
Julien Simon | Chief Evangelist | Arcee.ai
The fantastic pace of innovation of the open-source community has quickly made it possible to match and even exceed the accuracy of the best-closed LLMs with nimble and cost-effective small language models (SLMs). In this session, you’ll discuss the latest techniques to tailor SLMs to specific domains and company knowledge, starting with best practices to prepare training datasets for SLMs. You’ll also about the end-to-end model adaptation process with continuous pre-training, model merging, instruction fine-tuning, quantization, and inference.
Building RAG Applications with Databricks: End-to-End Implementation
Derar Alhussein | Data Engineer | O’Reilly Author | Instructor | Udemy
This workshop is designed to provide you with the skills and knowledge for building complete RAG applications using Databricks. You will gain a comprehensive understanding of augmenting generative AI models with external context information to improve output relevancy.
Multi-agent Systems in the Era of LLMs: Progress and Prospects
Michael Wooldridge, PhD | Professor of Computer Science | University of Oxford
The field of multi-agent systems was once understood as“Cooperative distributed problem-solving”. Since then, it has broadened to consider all issues that arise when multiple AI systems interact. Now, the emergence and dramatic success of Large Language Models (LLMs) has given new life to the old dream. A raft of LLM-powered agent frameworks have become available, and multi-agent LLMs are increasingly being adopted. In this talk, we’ll survey the main approaches, opportunities, and outstanding challenges for multi-agent systems in the new world of LLM-based AI.
AI for the Public Good
Dr. Laura Gilbert | Director, Data Science & Analytics | Director, Incubator for Artificial Intelligence (i.AI) | Chief Analyst for the Cabinet Office | 10 Downing Street
For many governments, the narrative around AI is predominantly that it is a risk to manage, leaving the implementation to Industry. However, the flip side of the threat coin is the incredible opportunity to use AI for public good. The mission of the new Incubator for AI is to harness the potential of AI to improve lives and deliver better public services. We are building a new world-class capability in the UK government, enabling us to build modern solutions to challenging problems. Here we will share learnings, and showcase some of our work, much of which is open-sourced and available to the public.
Safety Evaluation of Generative AI
Laura Weidinger | Senior Research Scientist | Google DeepMind
How do we know when an AI system is “safe”? Several ethical and social risks from generative AI have been observed in real-world applications, or are anticipated as these systems become more capable. In response, the research community and the public are building approaches to measuring and evaluating these risks. In this talk, You’ll explore the current state of safety evaluation of generative AI, key gaps, and propose a way forward.
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Originally posted on OpenDataScience.com
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