61 comments
AI is just software, nothing new to see here.
AI safety is primarily a sandboxing problem.
There is no collective action problem, and every company should just slow down if they think they need to slow down.
There is no need for regulation because the existing incentives in the market keep companies from acting badly, which is why no company has ever done anything bad.
If something bad does happen, then we can regulate after the fact.
We will end up creating more jobs than we destroy, so don't worry about it.
Our kids might forget a whole bunch of stuff or never learn it in the first place. But don't worry, they'll come up with new things to learn instead.
The only way to get safety is to move faster because then we will more quickly arrive at safety.
Recursive self-improvement is just what we've always done.
The real danger is alarmism that might scare the public and the young people.
Nothing bad can happen, it can only good happen.
The destroyed jobs and the persons doing them will be different from the new ones created..
>If something bad does happen, then we can regulate after the fact.
How does regulating after the fact undo the harms caused?
> they'll come up with new things to learn instead.
Like they forgot how to communicate face to face, but have learned to communicate via social media?
2008 triggered a glut of cheap experienced workers that slowly reengaged juniors but this time that the entire ladder is going to be more valuable than any grad.
This is an introductory programming course, designed for non-CS students e.g. engineers and scientists.
> Learning outcomes
> 2. Explain key concepts in AI-assisted programming, including Large Language Models (LLMs), prompting, problem decomposition, and top-down design.
> 3. Apply the workflow of AI-assisted programming and prompt-engineering techniques to guide and improve code generated by AI assistants.
This course used to be non-AI (last year), and they rewrote recently to incorporate AI tools, as they realised the writing's on the wall for non-programmers.
It must be quite challenging to write curriculum when the underlying technology (AI) is changing so quickly.
How many of them can read x64 or ARM assembly emitted by their compilers?
How many of them will ever need to?
There's your answer.
It’s how I deeply understand what a RAM lookup vs having it already in a register means for optimization.
These are things you need to know if you want to work on high performance applications or in limited embedded systems.
So, yes, many of us need to and it’s important we keep teaching it to future students.
This is a category error. LLMs are probabilistic. The ones run by an AI company over API, even more so.
Compilers are not.
Jensen Huang has been CEO of NVIDIA for 33 years. That is a role with a very specific type of information environment. He is sort of this weird combination of specialist and - he necessarily has to operate within a certain level of abstraction.
I’m somewhere in the middle, I’m a high achiever but not the highest. I would say I’m above average in my usage of AI at my tech job. The way I’ve learned systems thinking is by being a bit non-specific in what I learn. British history, psychology, software engineering, queuing theory, cooking.
The idea of learning systems but not basic math - the idea of being too discerning in what I’m willing to learn. The entire idea of passing up the ability to learn something like basic math.
So many mental models of the world are developed by engaging with things like basic math. How do you learn systems without learning patterns behind numbers?
If the whole argument is something like it’s now about taste or creativity or being a builder? The way you learn those skills is engagement with all the things. It’s not abandoning all the things to read a book on systems thinking and product management.
It’s not never focus, but if your default position is “maybe I shouldn’t be curious about that”. You’re operating from a deficit.
Jensen gets it!
Related:
https://en.wikipedia.org/wiki/Coupling_(computer_programming...
https://en.wikipedia.org/wiki/Abstraction_layer
https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-a...
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