Here's a blog that documents what I'm learning about knowledge graphs & LLMs.
I have specific interests & objectives re: AI.
I've been very skeptical of many of the claims being made about the nature of AI tools and their relationship to human consciousness (& I often write about these topics on LinkedIn)—but that doesn't mean that many of these AI tools aren't promising.
I just want them to do the dishes, not pretend to be "smart" or "creative" while doing poorly what humans must strive to do well.
I work in healthcare, and standard LLMs simply aren't going to cut it from a quality perspective. When it comes to generative AI in heatlhcare, an AI tool's output needs to be both verifiable (meaning it meets your requirements, including requirements for truthfulness and reliability) and validatable (meaning it meets your user's needs).
Hence my interest in the combination of LLMs and knowledge graphs, or what Microsoft and others are calling "GraphRAG."
I'm not a great coder; mostly just a technical writer starting from some beginner-level Python & JS. I'll share my code snippets here from the various mostly Python-based AI/ML projects I'm working on—but be warned, things are gonna get messy. I do try to annotate my code carefully to make sure I know what's happening where. I also type out all code snippets myself when working through books, rather than copying & pasting—if you're following along at home, I encourage you do to this too, along with annotating as you go. These practices, recommended by Zed Shaw, have helped me greatly in learning what little I know.
To join in the fun, see what I'm currently up to, and get some sense of where I'm heading next, check out the roadmap.
I'm creating a general glossary for all the concepts I'm learning, and different collections of posts for the different books and projects I work through, each of which will be documented in the bibliography.
I'd be very happy to hear your thoughts about what I'm up to, as well as your corrections & suggestions. Thanks in advance!