The problem is that once it will be normal to request IDs for porn, the same will be extended to everything else with excuses like “let’s do it for the kids” or “if you don’t have anything to hide…”.
VPNs… yeah sure, until there will be a crackdown on those too.
Are hiring managers actually less likely to hire women if they ask for market-rate pay, as opposed to men when they do the same?
If instead of giving passive aggressive replies you would spend a moment to reflect on what I wrote you would understand that ChatGPT reflect the reality, including any bias. In short the answer is yes with high probability.
LLMs do not give the correct answer, just the most probable sequence of words based on the training.
That kind of studies (because there are hundreds) highlight two things:
1- LLMs could be incorrect, biased, or give fake information (the so called hallucinations). 2- the previous point stems from the training material proving the existence of bias in the society.
In other words, having an LLM recommending lower salaries for women is a proof that there is a gender gap.
You sort of described RAG. It can improve alignment, but the training is hard to overcome.
See Grok that bounces from “woke” results to “full nazi” without hitting the mid point desired by Musk.
The study wanted to highlight the bias, not to recommend ChatGTP’s advice
More because of US regression (note: it was a joke)
The problem is to use LLMs for the wrong things expecting correct answers.
I do not believe that LLMs will ever be able to replace humans in tasks designed for humans. The reason is that human tasks require tacit knowledge (=job experience) and that stuff is not written down in training material.
However, we will start to have tasks for LLMs pretty soon. It was already observed that LLMs work better on stuff produced by other LLMs.
It’s neither. LLMs are statistical models: if the training material contains bias (women get lower salaries) the output will reflect that bias.
By the end of Trump’s term, China may also have better civil rights than the US.
Quite frankly I disagree on almost everything. While the — or repeating the question may be legitimate red flags, the rest is not. There is a reason for that.
LLMs are trained on old material in which — was more common. Since they imitate what they know, here’s why we get all those —s. Repeating the question is a technique to reduce hallucinations, that’s why it is also quite common. Everything else is just how many people write. The average style may sound more academic and less “natural” simply because the training on academic papers usually has more weight than the training on blog posts. The rule of three is common in AI because it’s common human. Emojis were a thing in corporate messages way before AIs. The word choice highly depends on the writers’ culture, including whether they are native speakers or not. And so on.
Besides that, one can tweak the style easily. This is generated with AI using a simple prompt
This is the same prompt enhanced with some tricks.
Nice video, but the truth is that false positives and false negatives are so common that AI-detection techniques alone cannot be trusted without more context.