دانلود کتاب مهندسی هوش مصنوعی -ایجاد اپلیکیشن با مدل‌های بنیادی

When ChatGPT came out, like many of my colleagues, I was disoriented. What surprised me wasn’t the model’s size or capabilities. For over a decade, the AI community has known that scaling up a model improves it. In 2012, the AlexNet authors noted in their landmark paper that: “All of our experiments suggest that our results can be improved simply by waiting for faster GPUs and bigger datasets to become available.” What surprised me was the sheer number of applications this capability boost unlocked. I thought a small increase in model quality metrics might result in a modest increase in applications. Instead, it resulted in an explosion of new possibilities. Not only have these new AI capabilities increased the demand for AI applications, but they have also lowered the entry barrier for developers. It’s become so easy to get started with building AI applications. It’s even possible to build an application without writing a single line of code. This shift has transformed AI from a specialized discipline into a powerful development tool everyone can use. Even though AI adoption today seems new, it’s built upon techniques that have been around for a while. Papers about language modeling came out as early as the 1950s. Retrieval-augmented generation (RAG) applications are built upon retrieval technology that has powered search and recommender systems since long before the term RAG was coined. The best practices for deploying traditional machine learning applications— systematic experimentation, rigorous evaluation, relentless optimization for faster and cheaper models—are still the best practices for working with foundation model-based applications. The familiarity and ease of use of many AI engineering techniques can mislead people into thinking there is nothing new to AI engineering. But while many principles for building AI applications remain the same, the scale and improved capabilities of AI models introduce opportunities and challenges that require new solutions. This book covers the end-to-end process of adapting foundation models to solve realworld problems, encompassing tried-and-true techniques from other engineering fields and techniques emerging with foundation models. I set out to write the book because I wanted to learn, and I did learn a lot. I learned from the projects I worked on, the papers I read, and the people I interviewed. During the process of writing this book, I used notes from over 100 conversations and interviews, including researchers from major AI labs (OpenAI, Google, Anthropic, …), framework developers (NVIDIA, Meta, Hugging Face, Anyscale, LangChain, LlamaIndex, …), executives and heads of AI/data at companies of different sizes, product managers, community researchers, and independent application developers (see “Acknowledgments” on page xx). I especially learned from early readers who tested my assumptions, introduced me to different perspectives, and exposed me to new problems and approaches. Some sections of the book have also received thousands of comments from the community after being shared on my blog, many giving me new perspectives or confirming a hypothesis.