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I don't understand those objections. Yes, one of the key insights of modern DL-based ML is that size matters, neural networks start producing interesting gener

I don't understand those objections. Yes, one of the key insights of modern DL-based ML is that size matters, neural networks start producing interesting generalisation only once you have large parameter spaces. Indeed, one might wonder if size is all that matters? I don't see what is wrong with search indices, every computer is a gigantic search index, because main memory is a map from an index (the memory address) to 2^n bit integers, the value stored at the index. So anything that can be computed at all, can be computed by "a search index". The important thing that, prima facie, distinguish NNs from eg SQL databases, is the use real numbers as latent space: because real numbers are a metric space, so you have a notion of distance which allows you do use efficient local search like gradient descent. Moreover, when you ask this 'mere' search index (NNs) about something that it was not trained on, it can (and probably does) return something that is close in this metric space to something it learned. That did not work at all in the 1990s. Today it works spectacularly well in some domains. So an interesting question now is, to use your language: is human intelligence anything more than "building a search index" and "looking up examples in the search index and combining"? So this is a variation of Searle's Chinese room, but unlike him in 1980, we have working Chinese rooms!

mjburgess 4y ago
It is important to note that the search space is just the training data. That space does not span enough dimensions to actually solve the problems in question. For example, we act in the world so as to set the state of possible causal variables -- and thereby can distinguis causes. Above, graph algorithms operate on a discrete graph space and hence on the graph. GPT here isn't doing that. Seeing NNs as distance-based searches in a compressed 'training space' demystifies what solutions can be found. Here the text space defined by the training set does not permit a graph traversal, so eg., the NN 'uses' edges that don't exist We are not searches across such spaces, which are radically impoverished and not parametrised by time or action
- YorkshireSeason 4y ago
  It is not the case that "the search space is just the training data", the search space in NNs are some variants of real multivariate functions. The training data can be all manner of things, in LLMs, they are text in natural language. Graphs or natural language text are mapped from a discrete space to a continuous metric space. It is true that LLMs cannot currently solve complex mathematics problems reliably. Whether that's an intrinsic shortcoming of transformers, or not is an interesting open question, that is being investigated as we speak. A lot of well-known DL research teams bet on transformers beating humans in mathematics in the next few years, see e.g. https://imo-grand-challenge.github.io/
jmfldn 4y ago
"is human intelligence anything more than "building a search index" and "looking up examples in the search index and combining"?" Yes, it is more than this. Even if we can simulate things that appear like thinking using these methods, what we do bears no comparison. That's not a criticism of the computer here but the brain doesn't work like this. Ultimately, maybe the question "can computers think" is meaningless as Turing himself said. If the output appears like it is the product of thought, then what difference does it make?

mjburgess 4y ago

It is important to note that the search space is just the training data. That space does not span enough dimensions to actually solve the problems in question. For example, we act in the world so as to set the state of possible causal variables -- and thereby can distinguis causes. Above, graph algorithms operate on a discrete graph space and hence on the graph. GPT here isn't doing that. Seeing NNs as distance-based searches in a compressed 'training space' demystifies what solutions can be found. Here the text space defined by the training set does not permit a graph traversal, so eg., the NN 'uses' edges that don't exist We are not searches across such spaces, which are radically impoverished and not parametrised by time or action

YorkshireSeason 4y ago
It is not the case that "the search space is just the training data", the search space in NNs are some variants of real multivariate functions. The training data can be all manner of things, in LLMs, they are text in natural language. Graphs or natural language text are mapped from a discrete space to a continuous metric space. It is true that LLMs cannot currently solve complex mathematics problems reliably. Whether that's an intrinsic shortcoming of transformers, or not is an interesting open question, that is being investigated as we speak. A lot of well-known DL research teams bet on transformers beating humans in mathematics in the next few years, see e.g. https://imo-grand-challenge.github.io/

jmfldn 4y ago

"is human intelligence anything more than "building a search index" and "looking up examples in the search index and combining"?" Yes, it is more than this. Even if we can simulate things that appear like thinking using these methods, what we do bears no comparison. That's not a criticism of the computer here but the brain doesn't work like this. Ultimately, maybe the question "can computers think" is meaningless as Turing himself said. If the output appears like it is the product of thought, then what difference does it make?