Plutonic Rainbows

I couldn’t get out to exercise today because it was raining on and off, so I spent many hours working on prompting templates for Flux. I finally managed to send my fleece off for repairs. It’s still a brilliant piece of winter clothing, so letting a few rusty zips stop me from using it seemed rather wasteful.

Inspired by Walpole Bay; seaweed, white flowers and salt water collected on 25th March 2014, (sunny day).

Reminiscent of an uninhabited bay; man-made structures that bridge land and sea and a crisp fragrance of salt, sweet pea and musk.

In the next five years, the landscape of AI-related jobs will evolve significantly as artificial intelligence becomes more deeply integrated into industries. Traditional roles in IT and data science will increasingly focus on AI-specific tasks, such as training, fine-tuning, and managing machine learning models. Positions like AI Ethics Officers and AI Compliance Specialists will emerge to address ethical concerns and ensure regulatory compliance in AI development and deployment. Additionally, roles such as Prompt Engineers will become more prominent, focusing on crafting precise instructions to guide generative AI systems. As businesses strive to leverage AI responsibly and effectively, demand for professionals skilled in interdisciplinary collaboration—bridging AI technology with areas like healthcare, finance, and education—will grow.

Creative and design roles will also transform as AI takes on more of the heavy lifting in content creation. Positions like AI-Assisted Creators or AI Content Curators will focus on guiding and enhancing AI-generated outputs rather than starting from scratch. Similarly, technical roles like AI Maintenance Specialists will focus on troubleshooting and optimising AI systems in real-time. New jobs like Synthetic Data Engineers will become crucial for generating high-quality data to train AI systems while maintaining privacy. Overall, there will be a significant shift towards roles that combine technical expertise with creativity, critical thinking, and ethical considerations to ensure AI is deployed to its fullest potential while mitigating risks.

Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.

AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.