None of this is relevant to the subject at hand.

There's actually only one reason.

They can't get credit for anything else.

African nations are exceptionally capital poor. Basically all projects are funded by foreign investment banks.

Last decade and a half was significant reduction on any power plant infrastructure loans that were for anything other than solar and wind, of which time after 2015 (Paris Agreement) was almost a total ban. This hit even the one exception in Africa: SA, and is one of the reasons for their constant blackouts. Though as is the case with this nonsense in most of Africa, it's far from being the only cause.

So last decade and a half, Africans were screaming at Westerners to please let them have loans so they could have reliable sources of energy so they could have stable grids like Westerners do. None were given. Meanwhile Chinese basically farmed them as a location to dump their massive solar oversupply for last decade or so.

It's all about access to the capital. Africans got whatever got funded. That's it.

In case you're wondering why everyone who wants electricity has these small diesel generators in Africa, this is why. Intermittents ensure that grid cannot be stable, while omnipresent copper thieves put massive nails in stability's coffin.

>As for the comparison to AI, the problem is, AI *must* be told what to do. It won't magically grow into a "mature developer." That's not a natural progression. It always assumes that the prompt accurately describes what it should do. It has no way to know that the prompt was wrong or incomplete in the first place.

This is wrong. You seem to be unaware that current sycophancy in mainline models is a specific choice made in AI model weights to maximize people returning to the model.

It's highly likely that one of the solutions that will be used in specialist fields where rejection of the input if it's insufficient in some critical way is reduction in pro-sycophancy model weighing. I.e. model will actually have a much greater ability to tell you "I can't do that Dave" and then explain why it can't do it.

Some narrow specialist models already do this through ControlNet style "AI that corrects and guides human AI prompts for optimal outcomes", where it will tell you in case of some of the common prompting errors before passing the input to the worker model.

So you do understand the problem then.

Would it then be fair in your view to reframe the specific problem you have into the two following components?

1. This is the worst AI will ever be at being manageable by people. It will continue to improve until it's better, just like what happened with everything where AI is already better.
2. You can manage AI current gen AI with similar methods you'd need in managing your average "yes saar, of course saar, I'll go do what you say right away saar" Indian developer stereotype.

Notably, once you accept the second one, you quickly realize that you can use ControlNet style methodology of "just use a specialized AI to curate your inputs into your preferred task specific model". And for even better results, you can add model alloying into this specialized AI, so it can utilize the best way to handle the sycophantic worker. "Have a different worker check entirety of his work to see where the failures lie and fix them".

I can tell you never had to do managerial work, as you're unaware that one of the most common stereotypes of a worker. The guy who will say "yes boss" no matter what is asked of him, and you'll find out you asked too much of him only when he fails to do the task correctly and this failure is reported on. Often by someone else.

This is even worse with people that come from Indian culture, where "yes boss" is the expected answer regardless of how impossible the ask is.

What you're describing is fundamentally a managerial skill set.

A lot of software developers struggle with those, and quite a few are borderline incapable of it. That's going to be increasingly a problem, unless we manage to get AI trained on individual preferences, and correcting their responses into proper AI prompts. I.e. narrow model that AI incapable worker can interact with you generate a prompt for the major model that will do the actual work.

Ironically to get your computer to do what you want to, you need to train on how to use it correctly.

Prompting correctly is a part of using it correctly.

Is it worker's fault when manager fails to explain the task correctly?

This is the part that most of the "I can't make AI work" crowd miss. When you give AI instructions, it's like giving instructions to a human worker.

You need to understand its strengths and its weaknesses at least to a reasonable degree, and you need to be careful delineating what task entails, what it doesn't entail, what's a priority and what is of low relevance.

A lot of very good experts at their specific field make for horrible managers because they don't know how to explain aforementioned things. They only know how to do it themselves.

This is the most common point of failure both when leading people and when prompting AI in my experience. Essentially all those leadership skills? They matter a lot now, even for mere subject matter experts, because prompting AI is leadership just as ordering a person to perform a set of tasks with a specific goal is leadership.

Before most subject matter experts didn't need any meaningful leadership skills. They just needed to do the things related to subject they're expert in, and leadership was handled by people with a different skill set (less deep and more broad expertise coupled with at least some leadership skills).

This is a training problem.

Not of AI mind you, but of human doing the prompting. When AI fails this badly, it means prompt has poor instructions.

This is fixed by training people to prompt better. It's similar to teaching people how to be good managers. You need knowledge in subject matter, but your primary expertise should in be identifying, segmenting and communicating each individual part of the task with high precision.

This is not easy, and it's not the same skill set as that of typical coder.

It's hatred horse breeders and horse messengers had for cars and their drivers.

DVRs were the starting point. The namesake for what you're talking about, tivoization, is Tivo, the DVR that existed way back when TV was still analog and being displayed on CRTs.

It's why the GPLv3 was made: to add clauses to forbid tivoization. Instead, a lot of the open source community moved in the opposite direction, moving to licenses that allowed companies even more freedom to lock up their code.

Good luck. This is not a new fight by any means. You could argue that the FSF has been fighting it for almost half a century. People by and large do not care.

I'd be amazed if this wasn't already the case. We've already been through this with Tivo, it was one of the reasons behind the creation of the GPLv3. Tivo based their DVRs on Linux, and provided downloads of the Linux code. But their DVRs used hardware DRM to ensure that only code signed by Tivo would run, making it so that even with the open source code, you couldn't run changes on the hardware.

From what I can tell, Vizio is doing the same thing, but isn't providing downloads to the kernel code they're using. It's possible that there's some proprietary hardware drivers that they don't want to release code to, but Nvidia has already show how to work around that.

I expect the end result to be like Tivo: a bunch of archives of the open source software used in the TV, but none of the code required to make it useful and no signing key necessary to allow any changes to run on the TV itself.

Notably, this is the exact reason why they had to immediately start having exceptions in the law.

In this case they started with farmers, and you'll likely have problems across insurance industry, which will require way more exceptions added on later.

You can't count on voting records to mean anything, thanks to the "designated villains:" the politicians whose job it is to tank a law that a party wants to be on record as having voted for, but don't want to pass. We're watching this happen right now with votes on the Iran war. Democrats don't want them to pass. What they want is to be on the record as being against it and want Republicans to be on the record as supporting it, even though there is no chance they'll do anything to stop it if they get the power to do so.

Both sides play games like this, with the end result being that only laws that have the support of large donors having any real chance of passing. Who votes for and who votes against is always carefully calculated to let vulnerable politicians give the appearance of supporting things constituents support, while never needing to support those things in actual fact.

Wow, Apple, screwing over a partner? Who ever could have seen this coming?

I don't understand why anyone would ever partner on Apple on anything. They are notorious for screwing over their partners at this point. There's even a term for it, "Sherlocking." People seem to have forgotten that Apple's "privacy" stance originated as Steve Jobs not wanting to share any of the data "Apple owned" with anyone else.