I was just watching a tiktok with a black girl going over how race is a social construct. This felt wrong to me so I decided to back check her facts.
(she was right, BTW)
Now I've been using Microsoft's Copilot which is baked into Bing right now. It's fairly robust and sure it has it's quirks but by and large it cuts out the middle man of having to find facts on your own and gives a breakdown of whatever your looking for followed by a list of sources it got it's information from.
So I asked it a simple straightforward question:
"I need a breakdown on the theory behind human race classifications"
And it started to do so. quite well in fact. it started listing historical context behind the question and was just bringing up Johann Friedrich Blumenbach, who was a German physician, naturalist, physiologist, and anthropologist. He is considered to be a main founder of zoology and anthropology as comparative, scientific disciplines. He has been called the "founder of racial classifications."
But right in the middle of the breakdown on him all the previous information disappeared and said, I'm sorry I can't provide you with this information at this time.
I pointed out that it was doing so and quite well.
It said that no it did not provide any information on said subject and we should perhaps look at another subject.
Now nothing i did could have fallen under some sort of racist context. i was looking for historical scientific information. But Bing in it's infinite wisdom felt the subject was too touchy and will not even broach the subject.
When other's, be it corporations or people start to decide which information a person can and cannot access, is a damn slippery slope we better level out before AI starts to roll out en masse.
PS. Google had no trouble giving me the information when i requested it. i just had to look up his name on my own.
One of the key thing that LLMs lack is a knowledge layer. In many ways, modern LLMs are hyper advanced predictive text. Don't get me wrong, what they produce is awesome and can be extremely useful, but it's still fundamentally limited.
Ultimately, a useful AI will need some level of understanding. It will need to be able to switch between casual chatter, and information delivery. It will need to be able to crosscheck its own conclusions before delivering them. There are groups working on this, but they are quite a bit behind LLMs. When they catch up, and the 2 can be linked/combined then things will get VERY interesting!
Totally agree, there's a big hole in the current crop of applications. I think there's not enough focus on the application side; they want to do everything within the model itself, but LLMs are not the most efficient way to store and retrieve large amounts of information.
They're great at taking a small to medium amount of information and formatting it in sensible ways. But that information should ideally come from an external, reliable source.
RAG serves as a knowledge layer.
What they really lack right now is effective introspection and executive function.
Too many people are trying to build a single model to do things correctly rather than layering models to do things correctly, which more closely approximates how the brain works.
We are shocked when AI chooses to nuke people in a wargame, but conveniently gloss over the fact that nearly every human put in front of a giant red button saying "Launch nukes" is going to have an intrusive thought to push the button. This is part of how we have an exploratory search around choices and consequences and rely on a functioning prefrontal cortex to inhibit those thoughts after working through the consequences. We need to be layering generative models behind additional post-processing layers that take similar approaches of reflection and refinement. It's just more expensive to do things that way, so cheap low effort things like chatbots still suck.