Stating you hate LLMs because you care for humanity directly supposes that those who do not hate LLMs, do not care for humanity.

You accused me of tribalism. I responded that the only tribal thing about my stance is that I care about the “tribe” of humanity. How you got what you got out of it, I truly don’t know. You were the one throwing out ad hominems, dude.

So your issue is specifically with interacting with AI using natural human language?

At this point I have no idea what you’re on about. The concerns regarding LLMs are widely documented. Some examples:

• They must be trained on stolen data to be vaguely useful. Yes, training on the Common Crawl still counts as stealing. No, there’s not enough royalty free data to train on which would create a useful LLM.
• “Useful” in this context is extremely debatable. The architecture itself makes hallucinations a mathematical certainty. The chatbot must use the internet to have general up-to-date knowledge and that opens it up to prompt injection.
• Security is an unsolvable problem for LLMs. Even if you don’t give it access to the internet, it can still be prompt injected via an external document. Prompt injection cannot be fixed because all inputs go through the same place - the prompt. ‘Agentic’ use cases are hilariously insecure and are, again, insecurable. A system prompt “guard rail” is not a security measure.
• Context rot is another probably unsolvable problem which renders large context windows useless.
• the amount of electricity and water needed to train the LLM and then infer outputs is untenable (there is no proof inference is profitable).
• The amount of hardware necessary to power LLMs is untenable (no, local quantized models are not “almost as good” and were not trained for free). I can’t buy a new PC and that’s insane.
• Because the LLMs are just fancy autocomplete (yes, they are, and no amount of backpropagation and attention heads will change that), they just produce most likely text, not actual answers. As such, the answers are often incorrect but *sound *like they are. Therefore, no LLM can be trusted to produce accurate information at any point in time.
• The entire “AI” industry is unprofitable and propped up on debt and circular financing by an industry that’s out of hypergrowth ideas, while gaslighting normal people that it’s the future.
• There is no future where this ends well. Either the bubble bursts and the economy collapses, or “AI” takes our jobs and we’re left to starve.
• LLMs are a fun toy, but no one has found a valuable use case for it that couldn’t have been achieved using other means. Oh, people are vibe coding their own software with it? Then where are all the world-changing startups that completely transform our lives? Where’s a single “AI” success story that people are excited to use, besides some people falling for the sycophancy of chatbots?
• The existence od LLMs has objectively made the world a worse place, due to AI slop and misinformation. It was also used as an excuse when companies lay people off. LLMs are, terrifyingly, used in medicine, where they hallucinate patient notes saying the wrong breast has cancer, or that the patient is a drug addict (when they’re not). They’re being used to avoid accountability when bombing schools. CEOs and managers uncritically enforce LLM adoption despite the known and very obvious risks and limitations.

I could go on. There’s so much more. No, I’m not against machine learning. I’m not against deep learning. I’m against LLMs - a technology which takes human input and outputs text and needs enormous amounts of human text, electricity and water to produce a fancy autocomplete which has extremely narrow use cases at best and is used to enshittify the entire world while stealing our resources.

But the original comment was about how you can’t trust AI (in this context LLM) output. You still can’t. You still shouldn’t.

As far as we know, no AI generated output can ever be trusted without careful verification. Only deterministic algorithms can be given that level of trust.