Greg Brockman, OpenAI co-founder and former Stripe CTO, writes about his career path, the CTO role, and learning machine learning.
HN commenters point newcomers to his posts on how he became a machine learning practitioner and on the CTO role at Stripe.
So this might not work for you, but I will tell you my path anyway.
1. Was one of the first members of the #ai slack channel inviting some people I had in person conversations about AI with.
2. I posted _a lot_ in there. Stuff about regulatory updates, people using co-pilot, cool github repos, little demo projects I was working on.
3. Now this was pure luck and probably the best thing to push me over the boundary, there was a hackathon. I thought "Hmm if I make a kick ass demo showcasing generative AI here, a lot of high up people will see it" - that 100% happened, CTO reached out to me saying demo was great and that people will be in touch.
4. I started really digging in to how I could provide value to our existing data team - be that code, deploying things, bringing some of my engineering know how to that team. This point the #ai channel really started to grow and the head of data and engineering started talking to me and directing people my way based on what they saw at the hackathon.
5. Did a demo of my hack in the company all hands which the CEO was MC'ing.
6. Started having fortnightly 1 to 1s with head of data at this point
7. Floated idea of team taking a little subset of good and motivated people from other teams for a short time to investigate and implement LLMs in some small way into our apps. That team has now grown to effectively investigate any and all use cases (internal and external) for generative AI.
8. I started reading more theory and also…
> (AGI) — which we define as automated systems that outperform humans at most economically valuable work — [0]
I don't doubt that OpenAI will be doing absolute first class AI research (they are already doing this). It's just that I don't really find this definition of 'GI' compelling, and 'Artificial' really doesn't mean much--just because you didn't find it in a meadow somewhere doesn't mean it doesn't work. So 'A' is a pointless qualification in my opinion.
For me, the important part is what you define 'GI' to be, and I don't like the given definition. What we will have is world class task automation--which is going to be insanely profitable (congrats). But I would prefer not to confuse the idea with HLI(human-level intelligence). See [1] for a good discussion.
They will fail to create AGI--mainly because we have no measurable definition of it. What they care about is how dangerous these systems could potentially be. More than nukes? It doesn't actually matter, who will stop who from using nukes or AGI or a superbug? Only political systems and worldwide cooperation can effectively deal with this...not a startup...not now...not ever. Period.
[0] https://blog.gregbrockman.com/the-openai-mission
[1] https://dl.acm.org/citation.cfm?id=3281635.3271625&coll=portal&dl=ACM
the two most memorable articles I could think of are :
OpenAI co-founder Greg brockman mentioning how he became an ML practitioner, it is surprisingly humble and has many references to other interesting sources https://blog.gregbrockman.com/how-i-became-a-machine-learning-practitioner
the second is very old original paper(1948) for Claude Shannon on information theory, when I blocked some time to read it, it was so jaw droppingly relevant for a really old paper he was going through basics of phone transmissions, and how he constructed bits and gigabytes, and why logarithmic structures matter , it's very dense and good kind of dense, if you're frustrated with the amount of bugs you have to deal with in software, the paper help highlight what have been persistent old problems manifesting in new languages and data structures, it is the kind of paper you would return to many times for reference.
https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf
Greg Brockman's blog[1] has few links on how he picked up ML. Another link at [2] describes the path Michal(blog's author) followed (though it's aligned to "how i got into ..."). Both these blogs walk through how they were able to get into the ML bits of things. They have bunch of links (ex: [3]).
I think it'll help if you can get a job at a company who's main focus is ML, you'll talk to folks who are doing research or solving problems using ML, you'll learn. If not, i hope these links help as folks there (people way smarter than me, a swe) had similar question and documented the steps they took to reduce the gaps in their understanding.
[1] - https://blog.gregbrockman.com/how-i-became-a-machine-learning-practitioner
[2] - https://agentydragon.com/posts/2023-01-11-how-i-got-to-openai.html
[3] - https://github.com/jacobhilton/deep_learning_curriculum
I'm working in the field (and winning contests) without a bachelor. As was said here [0]
"Overall, machine learning systems can be thought of as
a machine learning core — usually an advanced algorithm
which requires a few chapters from Ian’s book to
understand — surrounded by a huge amount of software
engineering."
[0] https://blog.gregbrockman.com/define-cto-openai
So that's what I mostly do - write code, try new ideas :) Surely, I had to refresh my knowledge in some areas like probabilities, and have a general understanding how math works, but anyone can do that.
As far as I can disentangle from [1], OpenAI posits only moderate superhuman performance. The profit would come from a variant where OpenAI subsumes much but not all of the economy and does not bring things to post-scarcity. The nonprofit would take ownership of almost all of the generated wealth, but the investments would still have value since the traditional ownership structure might be left intact.
I don't buy the idea myself, but I could be misinterpreting.
[1] https://blog.gregbrockman.com/the-openai-mission