It's delegation then.
We can use different words if you like (and I'm not convinced that delegation isn't colloquially a form of abstraction) but you can't control the world by controlling the categories.
We can use different words if you like (and I'm not convinced that delegation isn't colloquially a form of abstraction) but you can't control the world by controlling the categories.
customers dont care about the syntactic sugar/advanced reflection in the codebase of the product that theyre buying. if the end product of the delegator and the expert is the same, employers will go with the faster one every time.
The most harmful myth in all of education is the idea that you need to master some basic building blocks in order to move on to a higher level. That really is just a noticeable exception. At best you can claim that it's difficult for other people to realize that your new way solves the problem, or that people should really learn X because it's generally useful.
I don't see the need for this kind of compulsory education, and it's doing much more harm than good. Bodybuilding doesn't even appear as a codified sport until well after the industrial revolution, it's not until we are free of sustenance labor that human intelligence will peak. Who would be happy with a crummy essay if humans could learn telekinesis?
> Who would be happy with a crummy essay if humans could learn telekinesis?
I'm glad that's not the professional consensus on education, at least for now. And "telekinesis," really?
AI can do better organization than you, it's only inertia and legalities that prevent it from happening. See, without good education, you aren't even able to find a place for yourself.
> The most harmful myth in all of education is the idea that you need to master some basic building blocks in order to move on to a higher level.
That "myth" is supported by abundant empirical evidence, people have tried education without it and it didn't work. My lying eyes kind of confirm it too, I had one hell of time trying to use LLM without getting dumber... it comes so natural to them, skipping steps is seductive but blinding.
> I don't see the need for this kind of compulsory education, and it's doing much more harm than good.
Again, long standing empirical evidence tells as the opposite. I support optional education but we can't even have a double blind study for it - I'm pretty sure those who don't go to school would be home-schooled, too few are dumb enough to let their uneducated children chose their manner and level of education.
Frankly, I'm sure there will be much more studies in this direction. Now this is a university, an independent organization. But, given the amount of money involved, some of future studies will come from the camp vitally interested in people believing that by outsourcing their work to coding agents they are becoming smarter instead of losing achieved skills. Looking forward to reading the first of these.
> Students who repeatedly relied on ChatGPT showed weakened neural connectivity, impaired memory recall, and diminished sense of ownership over their own writing
So we're going to have more bosses, perhaps not in title, who think they're becoming more knowledgeable about a broad range of topics, but are actually in cognitive decline and out of touch with reality on the ground. Great.
Managers tend to hire sub managers to manage their people. You can see this with LLM as well, people see "Oh this prompting is a lot of work, lets make the LLM prompt the LLM".
I guess I'm not 100% sure I agree with my original point though, should a programmer working on JavaScript for a website's frontend be able to implement a browser engine. Probably not, but the original point I was trying to make is I would expect a programmer working on a browser engine to be able to re-implement any abstractions that they're using in their day-to-day work if necessary.
Partially because of all else fails, you'll need to step in and do the thing. Partially because if you can't do it, you can't evaluate whether it's being done properly.
That's not to say you need to be _as good_ at the task as the delegee, but you need to be competent.
For example, this HBR article [1]. Pervasive in all advice about delegation is the assumption that you can do the task being delegated, but that you shouldn't.
> Just that it's not an expectation, e.g., you don't expect a CEO to be able to do the CTO's job.
I think the CEO role is actually the outlier here.
I can only speak to engineering, but my understanding has always been that VPs need to be able to manage individual teams, and engineering managers need to be somewhat competent if there's some dev work that needs to be done.
This only happens as necessary, and it obviously should be rare. But you get in trouble real quickly if you try to delegate things you cannot accomplish yourself.
Do you therefore argue programming languages aren't abstractions?
To address your specific point in the same way: When we're talking about programmers using abstractions, we're usually not talking about the programming language their using, we're talking about the UI framework, networking libraries, etc... they're using. Those are the APIs their calling with their code, and those are all abstractions that are all implemented at (roughly) the same level of abstraction as the programmer's day-to-day work. I'd expect a programmer to be able to re-implement those if necessary.
The problem with this analogy is obvious when you imagine an assembler generating machine code that doesn't work half of the time and a human trying to correct that.
However, the specific discussion here is about delegating the work of writing to an LLM, vs abstracting the work of writing via deterministic systems like libraries, frameworks, modules, etc. It is specifically not about abstracting the work of compiling, constructing, or smelting.
They are probabilistic. Running them on even different hardware yields different results. And the deltas compound the longer your context and the more tokens you're using (like when writing code).
But more importantly, always selecting the most likely token traps the LLM in loops, reduces overall quality, and is infeasible at scale.
There are reasons that literally no LLM that you use runs deterministically.
Only when you turn the temperature up they become probabilistic for a given input in that case. If you take shortcuts in implementing the inference, then sure, rounding errors may accumulate and prevent that, but that is not an issue with the models but with your choice of how to implement the inference.
Yes, and no. They’re abstractions in the sense of hiding the implementation details of the underlying assembly. Similarly, assembly hides the implementation details of the cpu, memory, and other hw components.
However, except with programming languages you don’t need to know the details of the underlying layers except for very rare cases. The abstraction that programming languages provide is simple, deterministic, and well documented. So, in 99.999% of cases, you can reason based on the guarantees of the language, regardless of how those guarantees are provided. With LLMs, the relation between input and output is much more loose. The output is non-deterministic, and tiny changes to the input can create enormous changes in the output seemingly without reason. It’s much shakier ground to build on.
The behaviour of the = operator in Python is certainly deterministic and well-documented, but depending on context it can result in either a copy (2x memory consumption) or a pointer (+64bit memory consumption). Values that were previously pointers can also suddenly become copies following later permutation. Do you think this through every time you use =? The consequences of this can be significant (e.g. operating on a large file in memory); I have seen SWEs make errors in FastAPI multipart upload pipelines that have increased memory consumption by 2x, 3x, in this manner.
Meanwhile I can ask an LLM to generate me Rust code, and it is clearly obvious what impact the generated code has on memory consumption. If it is a reassignment (b = a) it will be a move, and future attempts to access the value of a would refuse to compile and be highlighted immediately in an IDE linter. If the LLM does b = &a, it is clearly borrowing, which has the size of a pointer (+64bits). If the LLM did b = a.clone(), I would clearly be able to see that we are duplicating this data structure in memory (2x consumption).
The LLM code certainly is non-deterministic; it will be different depending on the questions I asked (unlike a compiler). However, in this particular example, the chosen output format/language (Rust) directly exposes me to the underlying behaviour in a way that is both lower-level than Python (what I might choose to write quick code myself) yet also much, much more interpretable as a human than, say, a binary that GCC produces. I think this has significant value.
I guess that could be a problematic behavior if you want reproducibility ala (relatively) reproducible abstraction like compilers. With LLMs, there are too many uncontrollable variables to precisely reproduce a result from the same input.
There is another form of delegation where the work needed to be done is imposed onto another, in order to exploit and extract value. We are trying to do this with LLMs now, but we also did this during the Industrial Revolution, and before that, humanity enslaved each other to get the labor to extract value out of the land. This value extraction leads to degeneration, something that happens when living systems dies.
While the Industrial Revolution afforded humanity a middle-class, and appeared to distribute the wealth that came about — resulting in better standards of living — it came along with numerous ills that as a society, we still have not really figured out.
I think that, collectively, we figure that the LLMs can do the things no one wants to do, and so _everyone_ can enjoy a better standard of living. I think doing it this way, though, leads to a life without purpose or meaning. I am not at all convinced that LLMs are going to give us back that time … not unless we figure out how to develop AIs that help grow humans instead of replacing them.
The following article is an example of what I mean by designing an AI that helps develop people instead of replacing them: https://hazelweakly.me/blog/stop-building-ai-tools-backwards...
I don't think you can get the kinds of robots they want without also inventing the artificial equivalent of soul. So their whole moral sidestep to reimplement slavery won't even work. Enslaving sapient beings is evil whether they are made of meat or metal.