In the whirlwind of technological advancements, artificial intelligence (AI) often becomes the scapegoat for broader societal issues. It’s an easy target, a non-human entity that we can blame for job displacement, privacy concerns, and even ethical dilemmas. However, this perspective is not only simplistic but also misdirected.
The crux of the matter isn’t AI itself, but the economic system under which it operates - capitalism. It’s capitalism that dictates the motives behind AI development and deployment. Under this system, AI is primarily used to maximize profits, often at the expense of the workforce and ethical considerations. This profit-driven motive can lead to job losses as companies seek to cut costs, and it can prioritize corporate interests over privacy and fairness.
So, why should we shift our anger from AI to capitalism? Because AI, as a tool, has immense potential to improve lives, solve complex problems, and create new opportunities. It’s the framework of capitalism, with its inherent drive for profit over people, that often warps these potentials into societal challenges.
By focusing our frustrations on capitalism, we advocate for a change in the system that governs AI’s application. We open up a dialogue about how we can harness AI ethically and equitably, ensuring that its benefits are widely distributed rather than concentrated in the hands of a few. We can push for regulations that protect workers, maintain privacy, and ensure AI is used for the public good.
In conclusion, AI is not the enemy; unchecked capitalism is. It’s time we recognize that our anger should not be at the technology that could pave the way for a better future, but at the economic system that shapes how this technology is used.
LLMs don’t “understand” anything. They’re just very good at making it look like they sort of do
They also tend to have difficulty giving the answer “I don’t know” and will confidently assert something completely incorrect
And this is not generation 0. The field of AI has been around for a long time. It’s just now becoming widespread and used where the avg person can see it
If they’re very good at it, then is there functionally any difference? I think the definition of “understand” people use when railing against AI must include some special pleading that gates off anything that isn’t actually intelligent. When it comes to artificial intelligence, all I care about is if it can accurately fulfill a prompt or answer a question, and in the cases where it does do that accurately I don’t understand why I shouldn’t say that it seems to have “understood the question/prompt.”
I agree that they should be more capable of saying I don’t know, but if you understand the limits of LLMs then they’re still really useful. I can ask it to explain math concepts in simple terms and it makes it a lot easier and faster to learn whatever I want. I can easily verify what it said either with a calculator or with other sources, and it’s never failed me on that front. Or if I’m curious about a religion or what any particular holy text says or doesn’t say, it does a remarkable job giving me relevant results and details that are easily verifiable.
But I’m not going to ask GPT3.5 to play chess with me because I know it’s going to give me blatantly incoherent and illegal moves. Because, while it does understand chess notation, it doesn’t understand how to keep track of the pieces like GPT4 does.
If you can easily validate any of the answers. And you have to to know if they’re actually correct wouldn’t it make more sense to just skip the prompt and do the same thing you would to validate?
I think LLMs have a place. But I don’t think it’s as broad as people seem to think. It makes a lot of sense for boilerplate for example, as it just saves mindless typing. But you still need to have enough knowledge to validate it
If I’m doing something like coding or trying to figure out the math behind some code I want to write, it’s a lot easier to just test what it gave me than it is to go see if anyone on the internet claims it’ll do what I think it does.
And when it comes to finding stuff in texts, a lot of the time that involves me going to the source for context anyways, so it’s hard not to validate what it gave me. And even if it was wrong, the stakes for being wrong about a book is 0, so… It’s not like I’m out here using it to make college presentations, or asking it for medical advice.