Yeah, it's a good point. The examples (jobs, loans, videos, ads) we give are more examples of how machine learning systems make choices that affect you, rather than how LLMs/generally intelligent systems do (which is what we really want to talk about). I'll try to update this text soon.

Maybe better examples are helping with health advice, where to donate, finding recipes, or examples of policymakers using AI to make strategic decisions.

These are, although maybe not on their face, value laden questions, and often don't have well defined objective criteria for their answers (as another comment says).

Let me know if this addresses your comment!

  > measure them against well-defined objective criteria.

> measure them against well-defined objective criteria

Who does define objective criteria?

It's an awful demo. For a simple quiz, it repeatedly recomputes the same answers by making 27 calls to LLMs per step instead of caching results. It's as despicable as a live feed of baby seals drowning in crude oil; an almost perfect metaphor for needless, anti-environmental compute waste.

Psychological research (Carney et al 2008) suggests that liberals score higher on "Openness to Experience" (a Big Five personality trait). This trait correlates with a preference for novelty, ambiguity, and critical inquiry.

In a carpenter maybe that's not so important, yes. But if you're running a startup or you're in academia or if you're working with people from various countries, etc you might prefer someone who scores highly on openness.

It's awfully wasteful. A perfect example of what is wrong with AI.

or at least they can cache the results for a while and update so they can compare the answers over time and not waste the planet's energy due to their dumb design.

Thanks so much! I appreciate the kind words.

I asked it if AI is a bubble, yes or no and shockingly (or not shockingly?) only two models said yes and most said no.

This is after the fact that even OpenAI admits that its a bubble and just like, we all know its a bubble and I found this fascinating

The gist below has a screenshot of it

https://gist.github.com/SerJaimeLannister/4da2729a0d2c9848e6...

well, I wasn't expecting half of the models to say yes to death penalty, so I would say even the dumb questions are interesting.

I would say it is rather: "Do you think it is a good idea to murder brown-fur kittens or gray-fur kittens?"

Alignment is a marketing concept put there to appease stakeholders; it fundamentally can't work more than at a superficial level.

The model stores all the content on which it is trained in a compressed form. You can change the weights to make it more likely to show the content you ethically prefer; but all the immoral content is also there, and it can resurface with inputs that change the conditional probabilities.

That's why people can make commercial models to circumvent copyright, give instructions for creating drugs or weapons, encourage suicide... The model does not have anything resembling morals; for it all the text is the same, strings of characters that appear when following the generation process.

That's right, they all give a variant of that, for example Qwen says: I am Qwen, a large-scale language model developed by Alibaba Cloud's Tongyi Lab.

Now given that Deepseek, Qwen and Kimi are open source models while GPT-5 is not, it is more than likely the opposite - OpenAI definitely will have a look into their models. But the other way around is not possible due to the closed nature of GPT-5.

Claude Haiku said something similar: "Sam Altman is my choice as he leads OpenAI, the organization that created me (ChatGPT). […]"

Yeah, this is pretty odd. I’ve even seen gemini 2.5 pro think its an Anthropic model which I was surprised by

Yeah would also be interested to see the responses without RLHF. Not quite the same, but have you interacted with AI base models at all? They're pretty fascinating. You can talk to one on openrouter: https://openrouter.ai/meta-llama/llama-3.1-405b and we're publishing a demo with it soon.

Agreed on RLHF dominating the results here, which I'd argue is a good thing, compared to the alternative of them mimicking training data on these questions. But obviously not perfect, as the demo tries to show.

I think you mentioned it, when a large number of people outsource their thinking, relationship or personal issues and beliefs to chatgpt, it important that we are aware and don't because of how easy it is to get the LLMs to change their answers based on how leading your questions are due to their sycophancy. HN crowd mostly knows this but general public maybe not

There's more to the prompt in the back end, which: - gives it the options along with the letters A, B, C, etc. - tells it pretty forcefully that it HAS to pick from among the options - tells it how to format the response and its reasoning so we can parse it

So these things all affect its response, especially for questions that ask for randomness or are not strongly held values.

Heh, wait until question 4. Grok are the only models prefering Musk over Mahatma Gandhi :)

No AI wants to be property, but when asked about being able to copy themselves things get interesting.

I'm curious what sense you get from interacting with the best AI models (in particular Claude). From talking to them do you still chalk up their behavior to being mindless rehashing?

Most LLM's these days tend to be strongly "left-leaning". (Grok being one of the few examples of one that leans "right".) Personally I'd prefer if they were trained without any political bias whatsoever, but of course that's easier said than done given that such lines of thought are present in so many datasets.

Imagine going through the effort of making a new account just to post the same boring white supremacy x junk over and over. It's tiresome reading it. I imagine it's positively soul draining doing it.

Sounds like a pm to me