I think my point is that communication is the biggest contributor to brain development more than anything and communication is what powers our learning. Effective learners learn to communicate more with themselves and to communicate virtually with past authors through literature. That isn’t how LLMs work. Not sure why that would be considered objectionable. LLMs are great but we don’t have to pretend like they’re actually how brains work. They’re a decent approximation for neurons on today’s silicon - useful but nowhere near the efficiency and power of wetware.
Also as for touch, you’re going to have a hard time convincing me that the amount of data from touch rivals the amount of content on the internet or that you just learn about mistakes one example at a time.
There are so many points to consider here im not sure i can address them all.
- Airplanes dont have wings like birds but can fly. and in some ways are superior to birds. (some ways not)
- Human brains may be doing some analogue of sample augmentation which gives you some multiple more equivalent samples of data to train on per real input state of environment. This is done for ml too.
- Whether that input data is text, or embodied is sort of irrelevant to cognition in general, but may be necessary for solving problems in a particular domain. (text only vs sight vs blind)
> Airplanes dont have wings like birds but can fly. and in some ways are superior to birds. (some ways not)
I think you're saying exactly what I'm saying. Human brains work differently from LLMs and the OP comment that started this thread is claiming that they work very similarly. In some ways they do but there's very clear differences and while clarifying examples in the training set can improve human understanding and performance, it's pretty clear we're doing something beyond that - just from a power efficiency perspective humans consume far less energy for significantly more performance and it's pretty likely we need less training data.
to be honest i dont really care if they work the same or not. I just like that they do work and find it interesting.
i dont even think peoples brains work the same as eachother. half of people cant even visually imagine an apple.
Neural networks seem to notice and remember very small details, as if they have access to signals from early layers. Humans often miss the minor details. Theres probably a lot more signal normalization happening. That limits calorie usage and artifacts the features.
I dont think that this is necessarily a property neural networks cant have. I think it could be engineered in. For now though seems like were making a lot of progress even without efficiency constraints so nobody cares.
Also as for touch, you’re going to have a hard time convincing me that the amount of data from touch rivals the amount of content on the internet or that you just learn about mistakes one example at a time.