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No, a human isn't born with a set of knowledge like a freshly trained LLM, keeping the model fixed and responding to input. The analog to the model changes base
by not2b 3y ago
No, a human isn't born with a set of knowledge like a freshly trained LLM, keeping the model fixed and responding to input. The analog to the model changes based on the human's experience. Just making bigger and bigger LLMs won't give you this.
- Waterluvian 3y agoSo a human is different because it keeps training its neural network?
- eurekin 3y agoWhoa, imagine you get a good base LLM model and save all conversations with it. Run a batch process every night to fine tune a LORA on convo dataset. If I ever came across such a chat bot I'd probably freak out as to why it remembers things outside of the context window, without summarisation
- MichaelZuo 3y agoThat's a pretty neat idea, I would be surprised if no one is already working on that.
- PaulDavisThe1st 3y agoone reason why a human is different: just based on word count alone, most LLM's are trained on 3-5 orders of magnitude more input. could be a difference that makes no difference, or ...
- dTal 3y agoBit of an unfair comparison when humans also have a bunch of senses that LLMs don't have. They might be trained on orders of magnitude more words, but more data? Doubtful.
- not2b 3y agoThat's the key. I'm reminded of the Helen Keller story. She was completely blind and deaf. Her teacher spent a very long time signing into her hand. It took a very long time before she realized that the sign for "water" designated the thing she could feel flowing onto her hand; before that breakthrough the signs were meaningless to her. An LLM only knows the structure of language. It doesn't know that there is an external physical world that the language refers to. It only can predict what words follow which other words, and which output is preferred. Without any senses (and the huge bandwidth of information provided by them) an LLM is very crippled.
- dTal 3y agoCrippled, yes, but I would disagree that it is fundamentally limited, or that an input stream of human language is inadequate to bootstrap "meaning", or in some way philosophically inferior to native biological senses. It's very interesting you bring up Helen Keller because she's generally regarded as possessing the same level of sentience - and indeed intelligence - as anyone else, despite the extreme narrowness of her sensory input. It took her much longer to get going, but it's not as if she only understood concepts that directly related to touch. The experience with "water" taught her the concept of a symbol, and from there she could bootstrap everything else. LLMs already work with symbols - that is their sense. In fact we're all a bit like Helen Keller, in the sense that if sensory input is the basis for our entire world model, then it is a very small foundation supporting an incredibly vast and intricate edifice. There is a considerable abstraction gap between concepts like "capitalism" and any direct sensory input. We all of us, all the time, manipulate concepts without thinking through what they "mean" all the way to something we can see and touch.
- tysam_and 3y agoAh, but I believe you forget the implicit biases of genetic programming. Instincts in my experience are the skeleton, and in a sense the default basis functions for the structure of how we live, see, do, and learn.
- not2b 3y agoNo, I don't forget that. There's obviously a starting point, behaviors and abilities that newborns already have. The point is that the model is not static.
- mlboss 3y agoHumans are born with millions of years of evolutionary training embedded in their dna and brain. We are not born with nothing.
- KirillPanov 3y agoYeah but chimpanzees and cats and mice have all the same million-year-old stuff that we do. The million-year-old stuff is not what makes humans interesting.
- regularfry 3y agoIt's what makes humans possible. "Necessary but not sufficient" is the phrase that pays.
- mlboss 3y agoI have been thinking about this lately. Things that are recently developed like language skills are easiest to replicate by machines. But things that too took a time to develop like walking and grabbing are still something that machines struggle with.