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> Global usage data > Local usage data Okay, I'm gonna stop right here: Firstly, this would require re-training and tuning the model CONSTANTLY. Which is com

> Global usage data > Local usage data Okay, I'm gonna stop right here: Firstly, this would require re-training and tuning the model CONSTANTLY. Which is computationally expensive, on top of the already expensive running of the trained model. So this isn't happening, least of all in a local context. Secondly, regarding global data, that would also require IMMENSE data cleanup, otherwise it would run the risk of training the model on less than desirable input. https://mashable.com/article/meta-facebook-ai-chatbot-racism-donald-trump https://mashable.com/article/meta-facebook-ai-chatbot-racism... Thirdly, we haven't even talked about the legal implications of using global usage data for training the next generation of models. I would love seeing corporations trying to explain that to, say, the EU regulators, with regards to the GDPR. > Alas, OSS devs tend to get bored on 'non-sexy' subjects. I have already given an example for an OSS software product in the generative AI space with superb UX. I can produce countless other examples across all realms of software. Take a look at Krita. The Dolphin file browser. The entire KDE deskop environment. Libreoffice. Firefox. Blender. And btw. there are also countless commercial products with horrible UX. > SD is way more in tune, subject is way more popular with devs I guess. SD benefits from having a base model that already meets or exceeds the performance of closed source models. I see no reason why devs wouldn't be equally motivated when a sufficiently advanced LLM base model comes along. > Deploying an app requires more friction. Why? App stores exist.

yyyk 3y ago
>expensive >IMMENSE data cleanup There are definitely challenges, but your own link shows they're already trying, and that's following the more famous Tay failure. The incentives are obvious, while I doubt the challenge - at least regarding global user data - is insurmountable. It's rather well suited to BigCorp capabilities (and more difficult for OSS). BigCorps are perfectly willing and able to deploy an army of moderators if required. Not too different than what OpenAI used to jumpstart its GPT. The reward is a market valued in billions, the moderators get minimum or 3rd world wage. If I were a BigCorp I'd jump on it. [EDIT: we can see from the front page MEZO article that tuning does not have to be computationally expensive] >I would love seeing corporations trying to explain that to, say, the EU regulators, with regards to the GDPR. I don't think it's a big problem: For once, BigCorp is truly not interested in PII for training the model. Compared to what they're already doing in other fields, no reason they shouldn't be able to pass retraining easily. >SD benefits from having a base model that already meets or exceeds the performance of closed source models. I wanted to avoid saying it, but there are obvious SD usecases which the typical commercial interests would rather avoid. There are very motivated existing communities, which are far more likely to have a GPU. Adobe is weaker overall. The model is more accessible compared to still non-trivial LLM initial training. IMHO, these are more likely reasons than the raw technical comparison which I don't think the regular user or even regular dev bothers with. >I have already given an example for an OSS software product in the generative AI space with superb UX Which is why I bother writing these comments. Because OSS can compete by being good enough. But I see BigCorp strategies which give the incumbents a good chance to keep a stranglehold given the way it's going currently. Right now the ecosystem tends towards overconfidence (dumb dumb Google memo), and I think highlighting the challenges may help correction in time. >there are also countless commercial products with horrible UX. True. Which shows moats have more reason than technical comparisons. >Why? App stores exist. You still need visibility to get users to install your model. There's the (surmountable) technical challenge of deployment across varied configurations. Did I mention the biggest App stores are run by the closed competition? [Insert million HN threads about App store policies] It should be obvious that the companies who get to install their model API by default without asking the user have an advantage, and the devs having to submit their models to be approved by the these companies are at a disadvantage.
- usrbinbash 3y ago
  > I wanted to avoid saying it, but there are obvious SD usecases which the typical commercial interests would rather avoid. There are also many more use cases that don't fall into these categories. My point still stands: SD is a prime example for what happens when a desirable OSS technology becomes competitive in quality and is then tinkered with by a near limitless amount of creative and talented developers. > to keep a stranglehold given the way it's going currently. > Which shows moats have more reason than technical comparisons. This isn't office software, there is no "we always used X" factor since the technology is still in its early phase, and my thoughts about interactions with "user data" in the LLM space, have been outlined above. Again: Strangleholds, moats, whatever we want to call it, only work if there is a competitive advantage that the competition cannot reach itself. So far, that's better model performance and ease of use. The former gap is shrinking with every week, the latter will resolve itself the same way it did for SD once the performance is good enough. When that happens, OSS solutions are the ones with advantages that cannot be easily imitated: They run on premises, only cost utilities, can work offline, and can be endlessly tinkered with and improved upon by a near limitless talent pool. But, as has been said before, much remains to be seen, and there are many unknown factors that will influence the outcome. Therefore I thank you for the discussion. I look forward to seeing the next developments in this tech, and I'm confident that we're going to see OSS being as successful in the LLM space as it is in most areas of computing.

yyyk 3y ago

>expensive >IMMENSE data cleanup There are definitely challenges, but your own link shows they're already trying, and that's following the more famous Tay failure. The incentives are obvious, while I doubt the challenge - at least regarding global user data - is insurmountable. It's rather well suited to BigCorp capabilities (and more difficult for OSS). BigCorps are perfectly willing and able to deploy an army of moderators if required. Not too different than what OpenAI used to jumpstart its GPT. The reward is a market valued in billions, the moderators get minimum or 3rd world wage. If I were a BigCorp I'd jump on it. [EDIT: we can see from the front page MEZO article that tuning does not have to be computationally expensive] >I would love seeing corporations trying to explain that to, say, the EU regulators, with regards to the GDPR. I don't think it's a big problem: For once, BigCorp is truly not interested in PII for training the model. Compared to what they're already doing in other fields, no reason they shouldn't be able to pass retraining easily. >SD benefits from having a base model that already meets or exceeds the performance of closed source models. I wanted to avoid saying it, but there are obvious SD usecases which the typical commercial interests would rather avoid. There are very motivated existing communities, which are far more likely to have a GPU. Adobe is weaker overall. The model is more accessible compared to still non-trivial LLM initial training. IMHO, these are more likely reasons than the raw technical comparison which I don't think the regular user or even regular dev bothers with. >I have already given an example for an OSS software product in the generative AI space with superb UX Which is why I bother writing these comments. Because OSS can compete by being good enough. But I see BigCorp strategies which give the incumbents a good chance to keep a stranglehold given the way it's going currently. Right now the ecosystem tends towards overconfidence (dumb dumb Google memo), and I think highlighting the challenges may help correction in time. >there are also countless commercial products with horrible UX. True. Which shows moats have more reason than technical comparisons. >Why? App stores exist. You still need visibility to get users to install your model. There's the (surmountable) technical challenge of deployment across varied configurations. Did I mention the biggest App stores are run by the closed competition? [Insert million HN threads about App store policies] It should be obvious that the companies who get to install their model API by default without asking the user have an advantage, and the devs having to submit their models to be approved by the these companies are at a disadvantage.

usrbinbash 3y ago
> I wanted to avoid saying it, but there are obvious SD usecases which the typical commercial interests would rather avoid. There are also many more use cases that don't fall into these categories. My point still stands: SD is a prime example for what happens when a desirable OSS technology becomes competitive in quality and is then tinkered with by a near limitless amount of creative and talented developers. > to keep a stranglehold given the way it's going currently. > Which shows moats have more reason than technical comparisons. This isn't office software, there is no "we always used X" factor since the technology is still in its early phase, and my thoughts about interactions with "user data" in the LLM space, have been outlined above. Again: Strangleholds, moats, whatever we want to call it, only work if there is a competitive advantage that the competition cannot reach itself. So far, that's better model performance and ease of use. The former gap is shrinking with every week, the latter will resolve itself the same way it did for SD once the performance is good enough. When that happens, OSS solutions are the ones with advantages that cannot be easily imitated: They run on premises, only cost utilities, can work offline, and can be endlessly tinkered with and improved upon by a near limitless talent pool. But, as has been said before, much remains to be seen, and there are many unknown factors that will influence the outcome. Therefore I thank you for the discussion. I look forward to seeing the next developments in this tech, and I'm confident that we're going to see OSS being as successful in the LLM space as it is in most areas of computing.