Neural networks do achieve impressive things but they also fail to achieve essential things that preclude them from an AGI or Causal NLU ambition, such as the inability to approximate a dumb calculator without significant accuracy loss.
It's a model mismatch, not an inherent impossibility. A calculator needs to have an adaptive number of intermediate steps. Usually our models have fixed depth, but in auto-regressive modelling the tape can become longer as needed by the stepwise algorithm. Recent models show LMs can do arithmetic, symbolic math and common sense chain-of-thought step by step reasoning and reach much higher accuracies.
In other words, we too can't do three digit multiplication in our heads reliably, but can do it much better on paper, step by step. The problem you were mentioning is caused by the bad approach - LMs need intermediate reasoning steps to get from problem to solution, like us. We just need to ask them to produce the whole reasoning chain.
I can’t approximate a dumb calculator without significant accuracy loss. Not without emulating symbolic computation, which current AI is perfectly capable of doing if you ask it the right way.
Whatever makes you think it’s necessary for AGI, when we don’t have it?
NNs fails to do any algorithmy like pathfinding, sorting, etc
The point is not that you have it it's that you can have it by learning and using a pen and paper.
Natural language understanding require both neural network like pattern recognition abilities and advanced algorithmic calculations.
Since neural networks are pathetically bad at algorithmy, we need neuro-symbolic software. However the symbolic part is rigid and program synthesis is exponential.
Therefore the brain is the only technology on earth to be able to dynamically code algorithmic solutions. Neural networks have only solved a subset of the class of automated programs.
