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No. AI can generate as much synthetic data as we need, on demand. Many SOTA models, in fact, are already being trained with synthetic AI-generated data. See

by cs702 4y ago

No. AI can generate as much synthetic data as we need, on demand. Many SOTA models, in fact, are already being trained with synthetic AI-generated data. See https://en.wikipedia.org/wiki/Betteridge's_law_of_headlines https://en.wikipedia.org/wiki/Betteridge's_law_of_headlines

version_five 4y ago
Lots of reasons this isn't universally true - it only works if you know enough about the data to simulate it, and your stuck within some distribution + human guesses space that's not all encompassing. The easiest counterexample is training LLMs, how are you going to synthesize useful language examples if you want more. Some version of this is true for most applications.
- mirker 4y ago
  Yeah the issue is you can generate data, but it won’t be good data. Training over random strings won’t make you learn language, but it’s technically data.
qeternity 4y ago
You're just sampling from an already sampled distribution. This is not the same thing. There will still be value for fine tuning, but it's no substitute.
- mirekrusin 4y ago
  It's not one way consumer just like humans are not. It can direct long term evolution of reason. For starters it can be used to denoise/dedup/optimise training set to be closer to optimum (to create smaller "copies" of itself). There are instances of things that happened (history, what Paris Hilton did say on 22nd of April etc, big database of mostly irrelevant facts) and truths (math, physics, chemistry etc) where AI can enhance discoveries by helping us to see what we have not yet realised. Both seem endless tbh but personally I'm more interested in latter.
haldujai 4y ago
To my knowledge no SOTA model has been trained on a significant proportion of synthetic data, has this changed? The best examples I know of are instruction tuning sets but that is a minute amount of data compared to the unsupervised training data.
Buttons840 4y ago
> AI can generate as much synthetic data as we need, on demand. I don't think this is right. Can I take an untrained LLM (a neural network with random parameters), and have it start generating garbage, and then train the network to produce more of the same and then have it bootstrap itself to intelligence? Of course not. What if I train it just a little bit first? What if I train it until it produces gibberish, but does occasionally string two words together that are spelled correctly. Can I have it produce petabytes of gibberish and then train on that to reach GTP4's level? You seem to argue that at some point, the AI is able to improve by training on its own output. At what point does that arrive? Because so far we've never seen an AI improve based on its own output. (As far as I know?)
- sanxiyn 4y ago
  AlphaZero in fact improves based on its own output, but I agree it is a special case and probably not generalizable.
  - Buttons840 4y ago
    It's RL though. Its output comes, in part, from interaction with an environment. It also has a well defined objective (win games). GTP doesn't have a clear objective other than "do more of this".
- blackbear_ 4y ago
  > Because so far we've never seen an AI improve based on its own output. Maybe it's because AI is such an overloaded term, but this is pretty commonplace for (semi-)supervised learning algorithms. Pseudo-labeling [1,2] is an example of this that has been around for decades. When done properly it does improve the performance of the original model, up to a certain limit (far from the singularity). Moreover, it is apparently possible to improve a model's performance by augmenting it's training set with synthetic examples generated by a second model [3]. Finally, boosting [4] can also be seen as iteratively leveraging the output of a model to train a slightly better model. In fact, a specific type of boosting often yields state of the art performance on tabular data. [1] https://arxiv.org/abs/2101.06329 https://arxiv.org/abs/2101.06329 [2] https://stats.stackexchange.com/questions/364584/why-does-using-pseudo-labeling-non-trivially-affect-the-results https://stats.stackexchange.com/questions/364584/why-does-us... [3] https://arxiv.org/abs/2304.08466 https://arxiv.org/abs/2304.08466 [4] https://en.m.wikipedia.org/wiki/Boosting_(machine_learning) https://en.m.wikipedia.org/wiki/Boosting_(machine_learning)
hackerlight 4y ago
> AI can generate as much synthetic data as we need, on demand. Doesn't work in majority of domains. You need to know the generating process (e.g. game rules) and build a realistic simulation environment that emulates that, in order to generate data that is useful. Both of these things are out of reach for most applications. I believe the next large step will be multi-modal, where text is contextualized by video so the LLM will be able to concretize what "sitting on a chair" actually means with a single example, without needing to see thousands of textual associations to infer the meaning from the text.

by cs702 4y ago

version_five 4y ago

Lots of reasons this isn't universally true - it only works if you know enough about the data to simulate it, and your stuck within some distribution + human guesses space that's not all encompassing. The easiest counterexample is training LLMs, how are you going to synthesize useful language examples if you want more. Some version of this is true for most applications.

mirker 4y ago
Yeah the issue is you can generate data, but it won’t be good data. Training over random strings won’t make you learn language, but it’s technically data.

qeternity 4y ago

You're just sampling from an already sampled distribution. This is not the same thing. There will still be value for fine tuning, but it's no substitute.

mirekrusin 4y ago
It's not one way consumer just like humans are not. It can direct long term evolution of reason. For starters it can be used to denoise/dedup/optimise training set to be closer to optimum (to create smaller "copies" of itself). There are instances of things that happened (history, what Paris Hilton did say on 22nd of April etc, big database of mostly irrelevant facts) and truths (math, physics, chemistry etc) where AI can enhance discoveries by helping us to see what we have not yet realised. Both seem endless tbh but personally I'm more interested in latter.

haldujai 4y ago

To my knowledge no SOTA model has been trained on a significant proportion of synthetic data, has this changed? The best examples I know of are instruction tuning sets but that is a minute amount of data compared to the unsupervised training data.

Buttons840 4y ago

> AI can generate as much synthetic data as we need, on demand. I don't think this is right. Can I take an untrained LLM (a neural network with random parameters), and have it start generating garbage, and then train the network to produce more of the same and then have it bootstrap itself to intelligence? Of course not. What if I train it just a little bit first? What if I train it until it produces gibberish, but does occasionally string two words together that are spelled correctly. Can I have it produce petabytes of gibberish and then train on that to reach GTP4's level? You seem to argue that at some point, the AI is able to improve by training on its own output. At what point does that arrive? Because so far we've never seen an AI improve based on its own output. (As far as I know?)

sanxiyn 4y ago
AlphaZero in fact improves based on its own output, but I agree it is a special case and probably not generalizable.
- Buttons840 4y ago
  It's RL though. Its output comes, in part, from interaction with an environment. It also has a well defined objective (win games). GTP doesn't have a clear objective other than "do more of this".
blackbear_ 4y ago
> Because so far we've never seen an AI improve based on its own output. Maybe it's because AI is such an overloaded term, but this is pretty commonplace for (semi-)supervised learning algorithms. Pseudo-labeling [1,2] is an example of this that has been around for decades. When done properly it does improve the performance of the original model, up to a certain limit (far from the singularity). Moreover, it is apparently possible to improve a model's performance by augmenting it's training set with synthetic examples generated by a second model [3]. Finally, boosting [4] can also be seen as iteratively leveraging the output of a model to train a slightly better model. In fact, a specific type of boosting often yields state of the art performance on tabular data. [1] https://arxiv.org/abs/2101.06329 https://arxiv.org/abs/2101.06329 [2] https://stats.stackexchange.com/questions/364584/why-does-using-pseudo-labeling-non-trivially-affect-the-results https://stats.stackexchange.com/questions/364584/why-does-us... [3] https://arxiv.org/abs/2304.08466 https://arxiv.org/abs/2304.08466 [4] https://en.m.wikipedia.org/wiki/Boosting_(machine_learning) https://en.m.wikipedia.org/wiki/Boosting_(machine_learning)

hackerlight 4y ago

> AI can generate as much synthetic data as we need, on demand. Doesn't work in majority of domains. You need to know the generating process (e.g. game rules) and build a realistic simulation environment that emulates that, in order to generate data that is useful. Both of these things are out of reach for most applications. I believe the next large step will be multi-modal, where text is contextualized by video so the LLM will be able to concretize what "sitting on a chair" actually means with a single example, without needing to see thousands of textual associations to infer the meaning from the text.