Last week I shared a summary of what I've been learning w.r.t. AI. Since I've been on paternity leave I've been making more of an investment into learning. I've always subscribe to the idea of being a lifelong learner, but admittedly I've let myself slide a little in those investments. AI is such a large field and being aware of my own resource constraints (mostly time) I'm trying to be more deliberate with my learning. Part of that is sharing some nights and resources in public on a regular basis. I'm maintaining a Notion page with all the raw notes as I work through materials. At the end of the week I can revisit to summarize and draw connections that I may have missed initially.

The other part is defining my objective. What do I want to achieve from this time I'm investing? Ultimately I want to legitimately be able to put AI engineer on my resume. I don't mean just making API calls to OpenAI. I want to have a deeper understanding of how AI systems work, how they're built to scale, and the intuition behind them. I think there are a few high-level intermediate objectives:

1. Understanding deep learning archetectures (especially Transformers).
2. Build with third party LLMs like OpenAI. There is value in demonstrating compentency with these tools, but my initial thought is the value lies in developing competency in a few core skills. Beyond that I think there are diminishing returns, so I view this step as a good way to quickly prototype and get some early wins on the learning path.
3. Learn to build my own AI models with PyTorch. Combine parts of 1 and 2 with learning some PyTorch to build my own models. I can envision a scenario where relying on third party LLM providers is not the future of AI for a variety of reasons. Having the knowledge of how to build custom AI models for a diverse set of use cases will be valuable.
4. and Hugging Face. This could be 3a I suppose. Hugging Face sits at an intersection between hosted third party LLMs and building from scratch. Having some knowledge with the HF ecosystem, the transformers library, and the models available will be valuable.

There's a lot wrapped into those items. I entirely expect these to evolve as I get into more details, but in the early going these are the learning objectives I'm setting for myself. When I feel confident in these areas could be measured on the order of years... and that's okay. Slow consistent work. A lot is changing in the AI ecosystem so I'm being careful at the beginning to not invest heavily in any single aspect. By starting with these larger categories I can learn and reserve the right to narrow a focus later.

I'm looking forward to this. Having a structure has kept me slowly learning and becoming more confident. I'll continue posting my weekly learning and will revisit these objectives time-to-time to see at a macro level how I am progressing.