Anybody using AI tools should be extremely cautious about what is being produced.
Hard to get around these kinds of issues and definitely leads me to avoid them for non-technical questions.
That said, there is no such thing as an objective unbiased political opinion. Chinese LLMs may have issues with events of 1989 but Western LLMs have their blindspots too.
I have often wondered about the legality of such manipulation. As AI becomes used for increasingly important things, it becomes increasingly valuable to make a system serve the needs of someone other than its owner.
It reminds me of the early internet days and everyone making a big deal about the anonymity of internet forurms and safety.. sure it is an isssue
I know you aren’t denying issues exist, but companies aren’t handling the issues (their PR around it is disturbing) and regulation is too far behind.
There are a lot of smart and talented people working hard to embed Hasbara into LLMs.
For example, they will occasionally replace "colour" with "color". Why? Because both occur in the training data in the "same role" but "color" is, apparently, more common[1]. You can also trick them into replacing things like "sardines" with "anchovies" (on pizza) and "head of lettuce" with "cabbage" in the context of rowboats.
They are lossy text compressing parrots and we are all suffering from a massive madness-of-crowds scale Eliza Effect.
[1] Yep. https://books.google.com/ngrams/graph?content=color%2C+colou...
At any rate, just because the architecture of current LLMs doesn’t support learning at inference time does not constitute a fundamental limit that can never be changed, just a local maximum that has worked well to productize the approach.
And I’m quite certain that once systems that include post-training learning exist people like you will find a way to distinguish that from human learning, moving the goalposts again. You’re not arguing in good faith, you have an essentially religious opinion and you will stick to it as long as you are able.
> but generation of output is stachostic selection of most common (/highly ranked if tuned) following patterns
At various points quadcopters, towel folding, image recognition, sentiment analysis, etc. were all “AI”
The differences between Claude, OpenAI and Grok can be very interesting to say the least. I feel that Grok tends to do better with recent/current events, and I find Claude a bit more balanced on historical events. Just my own take.
That depends; some things (but not many) are straightforward enough that you can derive conclusions purely from first principles reasoning.
If you walk a model like ChatGPT through that reasoning, you’ll often wind up in a spot where the model readily admits that a clear conclusion is logically entailed but it is absolutely forbidden from uttering it.
What’s more telling is how it becomes increasingly difficult to hold the model to strict first principles reasoning the closer you get to the forbidden entailment. It will smuggle in unsupported assumptions, apply asymmetric standards of evidence, strawman the position and argue against that, etc.
It requires a great deal of careful effort to point out its formal fallacies without biasing the result, and in the end, you wind up with it admitting it simply can’t say what it has proven.
I work in formal methods/verification and this is one of my usual litmus tests when a new model comes out.
Examples?
Do you have an example of this? I want to try it.