No matter how fast and accurately your AI apps can spit out code (or PowerPoints, or excel spreadsheets, or business plans, etc) you will still need humans to understand how stuff works. If it’s truly business critical software, you can’t get around the fact that humans need to deeply understand how and why it works, in case something goes wrong and they need to explain to the CEO what happened.
Even in a world where the software is 100% written by AI in 1 millisecond by a country of geniuses in a data center, humans still need to have their hands firmly on the wheel if they won’t want to risk their businesses well being. That means taking the time to understand what the AI put together. That will be the bottleneck regardless of how fast and smart AI is. Because unless the CEO wants to be held accountable for what the AI builds and deploys, humans will need to be there to take the responsibility for its output.
The humans will still own the business (unless you are proposing some alternative version of AI ownership), so in effect there will be always a human who is concerned about their business’s well being.
I doubt that we would get into a world where a company would be allowed to run without human involvement (AI directors and AI management) as you will have nobody to hold accountable.
What is so enjoyable about this is that it'll be all musk's fault when we look back and realize Tesla just died off over time. Once other companies started actually competing with him, he wasn't smart enough to hold the lead. Lucid, byd, even hyundai, all made better cars within a few years, so Elon just basically gave up. I love that
That’s insane. He’s the David Goggins of coyotes. Also, that water is cold as hell this time of year. I couldn’t do that. Give him a metal and enter him in the Escape from Alcatraz triathlon (I assume he can ride a bicycle?)
Well if you did, I’d say congrats, the arctic is now more secure, but your once loyal allies now dislike you. Was that worth the cost? In a hot war with a near peer you would want them on your side. The odds they would be willing to do so are now far lower.
Also, if you find yourself saying “Trump is doing just what I would do in this situation!” that is not a good sign. Unless you have tons of experience and expertise in geopolitics and international relations, you probably wouldn’t make the smartest moves in this scenario if you were president.
I agree and disagree. In my day job as an AI engineer I rarely if ever need to use any “classic” deep learning to get things done. However, I’m a firm believer that understanding the internals of a LLM can set you apart as an gen AI engineer, if you’re interested in becoming the top 1% in your field. There can and will be situations where your intuition about the constraints of your model is superior compared to peers who consider the LLM a black box. I had this advice given directly to me years ago, in person, by Clem Delangue of Hugging Face - I took it seriously and really doubled down on understanding the guts of LLMs. I think it’s served me well.
I’d give similar advice to any coding bootcamp grad: yes you can get far by just knowing python and React, but to reach the absolute peak of your potential and join the ranks of the very best in the world in your field, you’ll eventually want to dive deep into computer architecture and lower level languages. Knowing these deeply will help you apply your higher level code more effectively than your coding bootcamp classmates over the course of a career.
I suppose I actually agree with you, and I would give the same advice to junior engineers too. I've spent my career going further down the stack than I really needed to for my job and it has paid off: everything from assembly language to database internals to details of unix syscalls to distributed consensus algorithms to how garbage collection works inside CPython. It's only useful occasionally, but when it is useful, it's for the most difficult performance problems or nasty bugs that other engineers have had trouble solving. If you're the best technical troubleshooter at your company, people do notice. And going deeper helps with system design too: distributed systems have all kinds of subtleties.
I mostly do it because it's interesting and I don't like mysteries, and that's why I'm relearning transformers, but I hope knowing LLM internals will be useful one day too.
Wouldn't you say that people who pursue deep architectural knowledge should just go down the AI Researcher career track? I feel like that's where that sort of knowledge actualy matters.
I’d go further: it’s not enough to be able to prove that your code works. It’s required that you also understand why it works.
Otherwise you’ll end up in situations where it passes all test cases yet fails for something unexpected in the real world, and you don’t know why, because you don’t even know what’s going on under the hood.
reply