I really like that it talks about the ontological systems that are completely and utterly disregarded by the models. But then the article whiffed and forgot all about how those systems could inform models only to talk about how it constrains them. The reality is the models do NOT consider any ontological basis beyond what is encoded in the language used to train them. What needs to be done is to allow the LLMs to somehow tap into ontological models as part of the process for generating responses. Then you could plug in different ontologies to make specialized systems.

Wow, AI researchers are not only adopting philosophy jargon, but they're starting to cover some familiar territory. That is the difference between signifier (language) and signified (reality).

The problem is that spoken language is vague, colloquial, and subjective. Therefore spoken language can never produce something specific, universal, or objective.

When the Generative Agents system was evaluated for how “believably human” the agents acted, researchers found the AI versions scored higher than actual human actors.

That's a neat finding. I feel like there's a lot to unpack there around how our expectations are formed.

AI is getting a much more widespread use than people with a technical background. So its application, namely in education but in all other non-CS disciplines will be through people with limited understanding of the biases. It is importing them to make them explicit, to underline that an LLM will produce the same biases it deduced from testing data and its loss function. But lots functions and test data are not public knowledge, studies need to be performed to understand how the coders’ own biases influenced the LLM scheme itself.

A photo has less bias because we know what it is representing: a photo only shows what can be seen. But the same understanding is not clear AI. Why showing a photo-realistic tree versus a biological diagram? Choices have been made, of which a broader audience needs to be aware of.

Thus, a user receives an answer that has already undergone a filtering of sorts.

Wouldn't this be an expected trait of a system predicting next most likely token based on lossy compression of specific datasets and other lossy optimization?

A photo has less bias because we know what it is representing: a photo only shows what can be seen.

i agree with you on ai but the above statement is ignoring what photography is and biases intrinsic to it.

You see, that understanding you expect to be developed for ai is not there for you for photography.

In theory something similar could be done with enough training. Guess what that would cost. Does enough clean water and energy exist to train it? Probably best not to find out, but techbros will try.

I don't think a logical system like an ontology is really capable of being represented in neural networks with any real fidelity.

Depends. For an expert, that is self evident (even if it might not be clear which biases have been incorporated). But that is not how it has been marketed. Chatgpt and similar are perceived as answering “the truth” at all times, and that skews the user’s understanding of the answers. Researching how deeply the answers are affected by the coders’ bias is the focus of their research and a worthwhile undertaking to avoid overlooking something important

For an expert, that is self evident

I am far from an expert, but it seemed obvious to ne.