Emil's Story as a Self-Taught AI Researcher (2020)
floydhub.ghost.io
floydhub.ghost.io
I end-up doing part-time work for Google at the interaction of Art/Culture and ML doing project like this (https://artsandculture.google.com/story/the-klimt-color-enig...), I saved up enough to build an ML rig (https://www.emilwallner.com/p/ml-rig), since I worked 2-3 days a week, I could spend the rest of my time doing research. I spent 1-2 years working on reasoning, trying different adaptive compute mechanisms and RL on code and mathematics (similar to R1/o1), however, I realised it was hard to compete with the established labs, and if I published my work it was hard to monetize it to have enough time to stop doing consulting work and fund my compute needs.
Instead, I started researching AI colorization, and launched it as a side-project (https://www.reddit.com/r/InternetIsBeautiful/comments/xe6avh...), I ended up having a few hundred thousand users in a few weeks and realized it had enough legs to bootstrap into a company. So I left my consulting gig at Google to go full-time on the colorization project (Palette: https://palette.fm/).
Fast forward to today, Palette is still running with a healthy margin, I’ve outsourced most of the things and I can spend most of my time doing AI research. I’d love to publish and open-source more, but since it becomes too easy to copy, it makes it hard to fund myself and my compute needs.
Happy to answer any questions.
> Most of technology in rural Ghana was considered magic, like phones and TVs I guess that's no longer the case. :)
> after invoking the spirit of their dead chief
"Hey, Frank used to say stuff like that! Boy do I miss Frank."
OP isn't exaggerating his experience, and the interviewer doesn't find this unusual enough to unpack.
*the exception here would be if someone throws so much money at you it would be unwise to say no; however, this is rare enough that I think most people of the self-taught audodidact variety can assume it won't happen.
This article was posted in 2020, would you change anything regarding the ideal curriculum in becoming a self-taught software engineer now? Assume zero prior programming experience (can see my previous HN submission question).
The AI autodidact guideline you gave in 2019, would you change anything now?
Would appreciate any advice or roadmap to follow, your story is inspiring.
I made a free e-book here (https://emilwallner.gumroad.com/l/no-ml-degree), and I believe most of the key points are still valid, however, when I learned software engineering and ML, ML was a rather small field and tools like chatGPT and claude didn’t exist.
Imo, asking about a curriculum is the wrong framing, for me, it was more about how to find resources to focus full-time and being in an environment that increases my motivation.
I started learning software engineering at home taking courses, but I procrastinated too much to be effective, maybe I did around 10 hours of effective learning per week. For me, studying C at 42 (https://www.42network.org/42-schools/), a free peer-to-peer school was crucial, and I recommend something similar. It enabled me to focus 70-90 hours a week, and after 6 months I was good enough to get competitive startup job offers.
During my time, the FastAI course (https://www.fast.ai/) was the best practical AI course. I'd probably spend a week looking for ambitious projects made by recent autodidacts, and ask them which course they think is best now. And spend max 1-2 months taking the course.
As for picking projects and building a portfolio, the advice in my e-book is still valid. An ambitious but realistic timeframe for landing a FAANG job is 3-5 years. Once you have a solid portfolio, I’d recommend joining say a YC-startup or similar with ex-FAANG employees to get up to speed and references. My first gig was at the YC-startup FloydHub with ex-FAANG employees.
If you are self-taught it’s often easier to get on the FAANG radar by making highly domain specific portfolio projects that are core to their business, or making open-source contributions to their projects. The other route is applying for jobs, however, most people without an ivy-level degree don’t pass the screening stage. If you choose this path, plan for at least 6 month to learn the first part of Ian Goodfellow’s book (https://www.deeplearningbook.org/) using say ChatGPT as your tutor, also grasp the key content in Chip Huyen’s books (https://huyenchip.com/), learn cracking the coding interview, and get good at solving leetcode hard problems.
IMO the confident Tweets about how to become a good researcher and hire good researchers look pretty weird next to the lack of any apparent research papers (or even visible research products) five years later.
He was a weird dude. I think he grew up in a household where outrageous boasting was common and had been doing it so long that it was his authentic self. He desperately wanted people to be impressed with him, and he had mastered a strategy that had a consistent yield, but the people it didn't work on looked down on him pretty severely, and he didn't have any way of earning their respect. He had to trust in the fact that over time he would tend to find himself surrounded by people he could manipulate.
So the most logical way was to bootstrap an AI start-up in the area I'm interested, so that's what I'm doing. Unfortunately, it's hard to publish or contribute to open-source, since it becomes too easy to copy, which cuts my margins and ability to fund my research and compute.
Now I spend most of my days doing AI research, and outsource most other parts, really enjoying it :)
Wouldn't colorization count as research? In vision domain there are a lot of papers like this. Just arranging and rearranging known blocks and getting SOTA result on some datasets. ;)