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In what technical way are these properties emergent? It seems that stated another way they are simply a natural consequence of more accurate language prediction

by svnt 4y ago

In what technical way are these properties emergent? It seems that stated another way they are simply a natural consequence of more accurate language prediction and therefore not emergent except in how we perceive them.

kerkeslager 4y ago
Because they defined the word "emergent" in a way that is misleading, likely to make their study seem more interesting. To quote the article: > [W]e defined an emergent ability as an ability that is “not present in small models but is present in large models.” It's not a bad study but using that word in that way is extremely poor communication, which has predictably been seized upon by every breathless nontechnical AI enthusiast.
hoseja 4y ago
Isn't that what emergence is?
Nevermark 4y ago
> … they are simply a natural consequence of more … That’s what emergent means. Something scales quantitatively, and after reaching certain levels, without any other special prompting, something else changes qualitatively. Presumably we recognize these things, then learn something after we have investigated and found out what the new mode of operation is, why it needed what it did to emerge, and possibly find new efficiencies or other ways to amplify the emergent effects.
nivenkos 4y ago
Because it's not inherent to the system itself. For example there's nothing inherent to token prediction to make it capable of doing arithmetic and you wouldn't expect a small/briefly trained model to manage it. But if learning a very large model on a huge dataset leads to it "learning" the decimal number system and arithmetic via token prediction then that is an emergent capability. It's just like the Chinese Room thought experiment - https://en.wikipedia.org/wiki/Chinese_room#Chinese_room_thought_experiment https://en.wikipedia.org/wiki/Chinese_room#Chinese_room_thou...
- tlb 4y ago
  Yes, in some sense the recent LLM results show us how big the Chinese room has to be: enough for 10^11 parameters. If the index cards Penrose imagined could hold one column of a matrix, it's around 10^8 cards. It's not surprising that people had poor intuitions for what was possible with that level of complexity.
  - fenomas 4y ago
    > I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about 10^9, to make them play the imitation game so well that.. Turing wrote that in 1950, so I suppose his intuition was within a couple of orders of magnitude...
- deleted 4y ago
  [deleted]
- zarzavat 4y ago
  Perhaps a bad example although I agree with the general argument. There are large datasets of calculations specifically for training large language models. It’s not just picking it up from reading books. And even then these models suck at calculating, half the time they just make an answer up. Calculation appears to be something that is actually not an emergent property of constant-time token prediction. Which we already knew from Turing anyway.
- Traubenfuchs 4y ago
  > For example there's nothing inherent to token prediction to make it capable of doing arithmetic ...aren't we just witnessing proof that this is wrong? Apparently those abilities are inherent to token prediction models with sufficient parameter size.
- cowl 4y ago
  in fact their example of https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/modified_arithmetic https://github.com/google/BIG-bench/tree/main/bigbench/bench... showed 0 emergent behavior in understating modified arithmetic even in large LLMs. the accuracy (and lack of it) in unmodified arithmetic is a simple token replacement heuristic that comes naturally from the transformer model of "attention".

by svnt 4y ago

kerkeslager 4y ago

Because they defined the word "emergent" in a way that is misleading, likely to make their study seem more interesting. To quote the article: > [W]e defined an emergent ability as an ability that is “not present in small models but is present in large models.” It's not a bad study but using that word in that way is extremely poor communication, which has predictably been seized upon by every breathless nontechnical AI enthusiast.

hoseja 4y ago

Isn't that what emergence is?

Nevermark 4y ago

> … they are simply a natural consequence of more … That’s what emergent means. Something scales quantitatively, and after reaching certain levels, without any other special prompting, something else changes qualitatively. Presumably we recognize these things, then learn something after we have investigated and found out what the new mode of operation is, why it needed what it did to emerge, and possibly find new efficiencies or other ways to amplify the emergent effects.

nivenkos 4y ago

Because it's not inherent to the system itself. For example there's nothing inherent to token prediction to make it capable of doing arithmetic and you wouldn't expect a small/briefly trained model to manage it. But if learning a very large model on a huge dataset leads to it "learning" the decimal number system and arithmetic via token prediction then that is an emergent capability. It's just like the Chinese Room thought experiment - https://en.wikipedia.org/wiki/Chinese_room#Chinese_room_thought_experiment https://en.wikipedia.org/wiki/Chinese_room#Chinese_room_thou...

tlb 4y ago
Yes, in some sense the recent LLM results show us how big the Chinese room has to be: enough for 10^11 parameters. If the index cards Penrose imagined could hold one column of a matrix, it's around 10^8 cards. It's not surprising that people had poor intuitions for what was possible with that level of complexity.
- fenomas 4y ago
  > I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about 10^9, to make them play the imitation game so well that.. Turing wrote that in 1950, so I suppose his intuition was within a couple of orders of magnitude...
deleted 4y ago
[deleted]
zarzavat 4y ago
Perhaps a bad example although I agree with the general argument. There are large datasets of calculations specifically for training large language models. It’s not just picking it up from reading books. And even then these models suck at calculating, half the time they just make an answer up. Calculation appears to be something that is actually not an emergent property of constant-time token prediction. Which we already knew from Turing anyway.
Traubenfuchs 4y ago
> For example there's nothing inherent to token prediction to make it capable of doing arithmetic ...aren't we just witnessing proof that this is wrong? Apparently those abilities are inherent to token prediction models with sufficient parameter size.
cowl 4y ago
in fact their example of https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/modified_arithmetic https://github.com/google/BIG-bench/tree/main/bigbench/bench... showed 0 emergent behavior in understating modified arithmetic even in large LLMs. the accuracy (and lack of it) in unmodified arithmetic is a simple token replacement heuristic that comes naturally from the transformer model of "attention".