4 ms·

how will you earn that money back to the investors?

orangepurple 4y ago
Looks like they are selling a chat bot to Monzo and allegedly Bing uses them for search?!
w1nk 4y ago
So this question can only come from a place where you have no idea what they do in their field. For every news article or arxiv post that you see talking about how this amazing new GPT-N model has broken all sorts of language benchmark scores, you'll notice that basically nobody can reproduce those results. That's mostly due to the barrier of entry with respect to hardware for training the models. Huggingface is releasing APIs and model checkpoints that allow any random internet user to execute (almost) SOTA language models in production. FYI - that's an amazing leap forward and a strong piece of kit for MLEs to have access to. So let me rephrase your question: Is general access to SOTA language models worth 100mm to the software market? I suspect the answer is a resounding yes.
- karpierz 4y ago
  > So let me rephrase your question: Is general access to SOTA language models worth 100mm to the software market? That doesn't answer the original question. The question was: "How will you extract $100M+ from the software market?" Your answer was: "I think that Huggingface will produce $100M worth of value." Which may or may not be true, but just because something produces X amount of value doesn't mean the project will be able to extract that value. See any open source project.
  - w1nk 4y ago
    So if you want someone to answer precisely how they'll extract hundreds of millions of dollars from an emerging market, I have to imagine this isn't the correct forum to expect such answers. Enabling the general software community access to SOTA language models will absolutely unlock an order of magnitude more money (than 100mm) over time. At least for now their obvious strategy for capturing this value is providing these APIs to enable it, and I suspect they'll gladly host such versions for the orgs that don't have the capacity to fine tune / host their own LLMs.
    - karpierz 4y ago
      Let me try to make the point clearer: 1. Investors expect Huggingface to extract more than $100M from the market. Otherwise they'd be called 'donors'. 2. If they openly publish models, then their APIs will be undercut by other providers who can take the published model and host it for cheaper. It would be cheaper for other companies because: they don't need to pay the cost of training the model, and they can specialize in simply hosting models. 3. Because of 2), Huggingface would need to avoid allowing other companies to host models, including internal APIs (because then providers would simply spin up to making hosting those internal APIs easy). 4) Because of 3), their policy of publishing trained models openly has to change. So the question that the original poster was asking is: what Huggingface policies will change, given the need to make returns on this investment? The original poster is likely thinking of OpenAI, which went down a similar route (starting training open models, took in a bunch of money, realized that openly publishing them wasn't sustainable, kept the models secret and created locked down APIs for accessing them). > So if you want someone to answer precisely how they'll extract hundreds of millions of dollars from an emerging market, I have to imagine this isn't the correct forum to expect such answers. This market isn't new; Google, AWS, OpenAI, etc. all have APIs they charge for. They also have services to host trained models for you. How will Huggingface make money without resorting to hiding its models?
      - skdotdan 4y ago
        I don't think OpenAI is a valid comparison. Huggingface's mission, unlike in the case of OpenAI, is not training models, but being the standard service for sharing them. The vast majority of models and datasets available at the Huggingface Hub have been provided by third-party companies or researchers. They aim to be the Github of ML models and data, not an AI-building startup.
        k8si 4y ago
        Yep - "Huggingface Enterprise" just like there's "Github Enterprise" seems like a straightforward way to make money, at least to me? Does Microsoft make good money from Github Enterprise?
      - 4y ago
  - k8si 4y ago
    It's expensive to hire NLP labor right now, and has been for awhile. Seems like one strategy could be: HF provides a cheaper & more scalable alternative to having to hire an in-house NLP team. Basically NLP becomes synonymous with HF. And they amortize the cost of hiring their own NLP engineers by developing a few models/model-based services that lots of businesses would be willing to pay for. E.g. 'foundation models' for different verticals like healthcare etc. Then it'll also be a lot easier to either fully automate or at least scale up work that's specific to each paying customer (because fine-tuning should go much more quickly, just essentially be a hyperparameter tuning cycle in as many cases as they can get away with).
    - visarga 4y ago
      NLP engineers spend 10% of their time training models, and 90% preparing the dataset and learning about the specifics of the task. I don't think this scales like selling software.
- linspace 4y ago
  It's a completely different thing to produce something of value and to get paid for it. There are plenty of examples. Immediately come to mind operating systems or compilers. Or a lot of completely underfunded but fundamental open source tools that everybody uses but nobody pays.
- jollybean 4y ago
  So this answer can only come from a place where you have no idea how business works. There's a gaping difference between 'value creation' and 'value capture'. Some products create incredible value for many parties, but don't have an easy way to capture value. Some products create negative value for the system, but are oriented towards capturing a lot of money. Wikipedia, Web Browsers a lot of Open Source libs. - examples of the kinds of things that can be invaluable, but whereupon it's difficult to capture value.
Areibman 4y ago
Since everyone seems to be avoiding direct answers, I have a few ideas: 1. Open source the free, lower quality models (I.e. fewer epochs trained) but sell perpetual licenses to higher quality ones. 2. API access to high quality pre-trained models. Similar to OpenAI, but code would be open source. 3. Licensed access to a Databricks/Collab-esque style development environment. Jupyter is great, but once they establish a big enough community and enough killer features, they could adopt paying users. 4. ML Ops infrastructure as a service for enterprise 5. Consulting
- option 4y ago
  4 alone is obviously a 1B+ business in the next 5 years
  - prepend 4y ago
    I think that anyone will have a hard time competing with AWS and Azure in this space (although it will be a $1B+ business).
    - jerpint 4y ago
      You can run HF on sagemaker, they're not necessarily competing
- joshcryer 4y ago
  One of the draws to use Hugging Face is because the open source community is giving those trained models away. What they are doing is permitting people to use those models affordably (something like a million words would be $10 if I recall correctly, you need to spend $10k minimum to run GPT-Neo-x in your bedroom just to print out one prompt). I'm not saying these are bad ideas but they need to more focus on their friendly front end and clean API access. There will be a DALL-E 2-Neo-x in a few months. People are going to want to run it without being limited by OpenAI's terrible interface.

Looks like they are selling a chat bot to Monzo and allegedly Bing uses them for search?!

w1nk 4y ago

So this question can only come from a place where you have no idea what they do in their field. For every news article or arxiv post that you see talking about how this amazing new GPT-N model has broken all sorts of language benchmark scores, you'll notice that basically nobody can reproduce those results. That's mostly due to the barrier of entry with respect to hardware for training the models. Huggingface is releasing APIs and model checkpoints that allow any random internet user to execute (almost) SOTA language models in production. FYI - that's an amazing leap forward and a strong piece of kit for MLEs to have access to. So let me rephrase your question: Is general access to SOTA language models worth 100mm to the software market? I suspect the answer is a resounding yes.

karpierz 4y ago
> So let me rephrase your question: Is general access to SOTA language models worth 100mm to the software market? That doesn't answer the original question. The question was: "How will you extract $100M+ from the software market?" Your answer was: "I think that Huggingface will produce $100M worth of value." Which may or may not be true, but just because something produces X amount of value doesn't mean the project will be able to extract that value. See any open source project.
- w1nk 4y ago
  So if you want someone to answer precisely how they'll extract hundreds of millions of dollars from an emerging market, I have to imagine this isn't the correct forum to expect such answers. Enabling the general software community access to SOTA language models will absolutely unlock an order of magnitude more money (than 100mm) over time. At least for now their obvious strategy for capturing this value is providing these APIs to enable it, and I suspect they'll gladly host such versions for the orgs that don't have the capacity to fine tune / host their own LLMs.
  - karpierz 4y ago
    Let me try to make the point clearer: 1. Investors expect Huggingface to extract more than $100M from the market. Otherwise they'd be called 'donors'. 2. If they openly publish models, then their APIs will be undercut by other providers who can take the published model and host it for cheaper. It would be cheaper for other companies because: they don't need to pay the cost of training the model, and they can specialize in simply hosting models. 3. Because of 2), Huggingface would need to avoid allowing other companies to host models, including internal APIs (because then providers would simply spin up to making hosting those internal APIs easy). 4) Because of 3), their policy of publishing trained models openly has to change. So the question that the original poster was asking is: what Huggingface policies will change, given the need to make returns on this investment? The original poster is likely thinking of OpenAI, which went down a similar route (starting training open models, took in a bunch of money, realized that openly publishing them wasn't sustainable, kept the models secret and created locked down APIs for accessing them). > So if you want someone to answer precisely how they'll extract hundreds of millions of dollars from an emerging market, I have to imagine this isn't the correct forum to expect such answers. This market isn't new; Google, AWS, OpenAI, etc. all have APIs they charge for. They also have services to host trained models for you. How will Huggingface make money without resorting to hiding its models?
    - skdotdan 4y ago
      I don't think OpenAI is a valid comparison. Huggingface's mission, unlike in the case of OpenAI, is not training models, but being the standard service for sharing them. The vast majority of models and datasets available at the Huggingface Hub have been provided by third-party companies or researchers. They aim to be the Github of ML models and data, not an AI-building startup.
      - k8si 4y ago
        Yep - "Huggingface Enterprise" just like there's "Github Enterprise" seems like a straightforward way to make money, at least to me? Does Microsoft make good money from Github Enterprise?
    - 4y ago
- k8si 4y ago
  It's expensive to hire NLP labor right now, and has been for awhile. Seems like one strategy could be: HF provides a cheaper & more scalable alternative to having to hire an in-house NLP team. Basically NLP becomes synonymous with HF. And they amortize the cost of hiring their own NLP engineers by developing a few models/model-based services that lots of businesses would be willing to pay for. E.g. 'foundation models' for different verticals like healthcare etc. Then it'll also be a lot easier to either fully automate or at least scale up work that's specific to each paying customer (because fine-tuning should go much more quickly, just essentially be a hyperparameter tuning cycle in as many cases as they can get away with).
  - visarga 4y ago
    NLP engineers spend 10% of their time training models, and 90% preparing the dataset and learning about the specifics of the task. I don't think this scales like selling software.
linspace 4y ago
It's a completely different thing to produce something of value and to get paid for it. There are plenty of examples. Immediately come to mind operating systems or compilers. Or a lot of completely underfunded but fundamental open source tools that everybody uses but nobody pays.
jollybean 4y ago
So this answer can only come from a place where you have no idea how business works. There's a gaping difference between 'value creation' and 'value capture'. Some products create incredible value for many parties, but don't have an easy way to capture value. Some products create negative value for the system, but are oriented towards capturing a lot of money. Wikipedia, Web Browsers a lot of Open Source libs. - examples of the kinds of things that can be invaluable, but whereupon it's difficult to capture value.

Areibman 4y ago

Since everyone seems to be avoiding direct answers, I have a few ideas: 1. Open source the free, lower quality models (I.e. fewer epochs trained) but sell perpetual licenses to higher quality ones. 2. API access to high quality pre-trained models. Similar to OpenAI, but code would be open source. 3. Licensed access to a Databricks/Collab-esque style development environment. Jupyter is great, but once they establish a big enough community and enough killer features, they could adopt paying users. 4. ML Ops infrastructure as a service for enterprise 5. Consulting

option 4y ago
4 alone is obviously a 1B+ business in the next 5 years
- prepend 4y ago
  I think that anyone will have a hard time competing with AWS and Azure in this space (although it will be a $1B+ business).
  - jerpint 4y ago
    You can run HF on sagemaker, they're not necessarily competing
joshcryer 4y ago
One of the draws to use Hugging Face is because the open source community is giving those trained models away. What they are doing is permitting people to use those models affordably (something like a million words would be $10 if I recall correctly, you need to spend $10k minimum to run GPT-Neo-x in your bedroom just to print out one prompt). I'm not saying these are bad ideas but they need to more focus on their friendly front end and clean API access. There will be a DALL-E 2-Neo-x in a few months. People are going to want to run it without being limited by OpenAI's terrible interface.