Talk

Industrial insight and articles from Winder.AI, focusing on the topic Talk

AI Strategy for CEOs: Aligning Tech with Business Goals

Published
Author
Charles Humble
Associate Editor

Join Phil Winder and Charles Humble in this insightful interview as he delves into the critical components of an enterprise AI strategy, based upon his excellent article on the subject.

Gain actionable insights on aligning AI with your business goals, building robust data infrastructure, and prioritizing projects based on ROI. Learn about MLOps, AI security, and the role of generative AI and reinforcement learning across industries.

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Working with the EU AI Act - Interview with Kai Zenner

Published
Author
Dr. Phil Winder
CEO

In this webinar I’m pleased to present a recorded interview with Kai Zenner, Head of Office and Digital Policy Adviser for MEP Axel Voss. Kai is a key figure in the EU AI Act, responsible for authoring parts of the regulation, whilst advising and debating the rest.

In this interview we discuss the impact of the EU AI Act on the AI industry, the implications for AI ethics, and the future of AI in Europe. We talk about the potential challenges for AI companies, especially those that lie outside of the EU. We find that although there are holes in the regulation, it is a step in the right direction for the AI industry. Kai makes his suggestions as to how companies can prepare for and help to improve the regulation.

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Artificial Intelligence in the Future of Supply Chains

Published
Author
Dr. Phil Winder
CEO

The use of artificial intelligence (AI) in supply chains has shifted from being a buzzword to a powerful tool with transformative potential. AI is no longer just an emerging technology—it is actively reshaping how companies approach their supply chain operations. I recently presented how AI, including generative AI, is transforming the supply chain industry. This post dives into the key insights from my presentation, highlighting the role AI plays in improving supply chains and what the future may hold.

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Testing and Evaluating Large Language Models in AI Applications

Published
Author
Dr. Phil Winder
CEO

With the rapidly expanding use of large language models (LLMs) in downstream products, the need to ensure performance and reliability is crucial. But with random outputs and non-deterministic behaviour how do you know if you application performs, or works at all? This webinar offers a comprehensive, vendor-agnostic exploration of techniques and best practices for testing and evaluating LLMs, ensuring they meet the desired success criteria and perform effectively across varied scenarios.

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Retrieval-Augmented Generation (RAG) Examples and Use Cases

Published
Author
Dr. Phil Winder
CEO

Watch our webinar to explore Retrieval Augmented Generation (RAG) and its integration with Large Language Models (LLMs). Learn about RAG use cases, advanced LLM architectures, and techniques to enhance AI applications. Ideal for professionals utilizing or interested in RAG and LLM-powered systems.

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Interview: How The EU AI Act Was Born With Javier Campos

Published
Author
Dr. Phil Winder
CEO

In this Webinar our CEO Phil Winder sat down with Javier Campos to discuss the EU AI Act. He is chief innovation officer at Fenestra, and is the author of “Grow Your Business with AI: A First Principles Approach for Scaling Artificial Intelligence in the Enterprise”, published by Apress. Javier was involved in the development of the EU AI act, and was also involved in the development of the EU Cookie Law in the early 2010s.

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ChatGPT from Scratch: How to Train an Enterprise AI Assistant

Published
Author
Dr. Phil Winder
CEO

This is a video of a presentation investigating how large language models are built and how to use them, inspired by our large language model consulting work. First presented at GOTO Copenhagen in 2023, the video investigates the history, the technology, and the use of large language models. The demo at the end is borderline cringe, but it’s a fun and demonstrates how you would fine-tune a language model on your proprietary data.

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