The problem with all these generative models is that they never know when they're unsure of something. This includes audio transcription generative models like Whisper. If you say something incomprehensible it will often make something up that it "thinks" you said. Humans do it to to some degree when they misheard something. But people will often say "this made no sense, or I don't know what I heard, or can you spell that for me and explain what this term is" while AI will just roll with a best guess no matter how wildly incorrect. Kids often do stuff like this, and certainly many adults as well. But not all, and what makes some people question things while others just accept things at face value isn't fully explained by science. Could it be an IQ thing? Could it be past life experiences and lessons? Why are some people so naive and make stuff up while others aren't?
Right now their entire bet is on one thing - bigger AI (more parameters), and feed it more data. They're hoping that by giving it a bigger brain and exposing it to more data, some critical threshold will be reached and things like wisdom and common sense will suddenly show up. And that idea isn't entirely faulty because it is supported by something called "emergent" abilities - that by scaling up the models, the data, and the compute, they found that new abilities that weren't explicitly trained into the models and didn't exist in smaller models suddenly appeared. There are some research papers on this topic:
Emergent Abilities of Large Language Models
Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks. This paper instead discusses an unpredictable phenomenon that we refer to as emergent abilities of large language models. We consider an ability to be emergent...
arxiv.org
Are Emergent Abilities in Large Language Models just In-Context Learning?
Large language models, comprising billions of parameters and pre-trained on extensive web-scale corpora, have been claimed to acquire certain capabilities without having been specifically trained on them. These capabilities, referred to as "emergent abilities," have been a driving force in...
arxiv.org
Are Emergent Abilities of Large Language Models a Mirage?
Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to...
arxiv.org
And supposedly you can't predict when (at what models scale etc) any given ability will emerge, and the degree of that ability when it does emerge. But not everyone agrees that this is a real thing either - some suppose that the abilities only *appear* to emerge due to the limitation of testing for those abilities not being sensitive to pick up progress towards the ability - kinda like in a test when a teacher doesn't ever give partial credit even if the answer is 99% correct, and only scores it as correct when it's 100%. Then the test result won't differentiate between students who didn't get any part of the answer correct and those who got it almost right.
But can anything and everything "emerge" in such a way? Probably not, but it will be interesting to see where all this goes.
