Roadmap to catch-up with AI, LLMs, etc.
A simple approach and some references to get you started
A friend recently asked me how I would craft a roadmap to catch-up with AI, LLMs, etc.
I’ve linked the response to this prompt from perplexity, and pi. Below is my take based on what I’ve done so far. In case the links above expire, here’s a screenshot of the prompt and you can search on pi.ai or perplexity.ai yourself.
My guiding principles were:
Take an iterative approach as this is a fast moving space.
Shortlist a couple of projects to build depth within this vast space
Use the tools regularly (ideally daily) and take notes to reinforce the learning.
To make it more concrete, the resources I would recommend are:
Subscribe to content that will introduce key terminologies and players in this space. My favourite sources are:
Reading his work has helped expand my awareness of the technology and players in the space. I think he is an essential reading for anyone in leadership, entrepreneurship, etc.
His writings are balanced, and generally rooted in research. He links to both AI skeptics and AI optimists, which I haven’t come across anywhere else.
He is quite prolific, and that has helped me keep up-to-date.
His activity in LinkedIn are more engineer friendly, but would be useful even if you are a bit rusty.
Most content is skewed towards technologists. But there are definitely episodes that would be useful for those in Product, Policy, Leadership, etc.
Once you develop an intuition for the space, deep dive into your areas of interests
This 30 min video helps visualise how GPTs work
Andrew Ng’s DeepLearning platform, provides structured courses starting at 101 level.
Use the tools every day
I use perplexity instead of google to find information, and pi for brainstorming ideas/approach/plan.
Once you have shortlisted a personal project, use focussed courses like these from deeplearning
This is the time to get started
This report from Reuters Institute and Oxford University from May 2024 indicates that awareness of the technology is still in early stages even in developed economies. This aligns with my view that most professionals are in various stages of catching-up.
While there have been a lot of developments in AI over the last year, it’s never late to get started and certainly not hard to develop useful skills. I hope my notes were useful to fellow travellers. I would love to learn how you have been approaching this challenge.