4 ms·

My opinion is entirely opposite - we need conversational AI for exploring the multitude of possibilities and identify what works, what doesn't work, what is/isn

My opinion is entirely opposite - we need conversational AI for exploring the multitude of possibilities and identify what works, what doesn't work, what is/isn't useful given the obvious limitations on accuracy, truthfulness and interpretability; but once we can identify a specific narrow use case then we can fine-tune a ML system for that isn't conversational but is able to provide better results with whatever domain-specific structure is required (linking to specific sources, including external structured data, providing certainty metrics, filtering results according to domain-specific criteria instead of the conversational political correctness filters which fail some domains, treatment info which was correct but has become outdated, etc, etc) that can be done better in non-conversational systems.

panarky 4y ago
The P in GPT is for "pretrained". The large pretrained model is a prereq for domain-specific models.
- PeterisP 4y ago
  Not necessarily, you don't need to make a domain-specific model from a general model, you can definitely make a large pretrained domain-specific model from scratch by training it only on domain-specific data, which can result in a smaller and more efficient model. Furthermore, when making task-specific models, an 'encoder' architecture (similar to BERT) often works better than a 'decoder' architecture (similar to GPTx), so you might want to use a similar-but-different architecture than the general model intended to be conversational/generative.
  - panarky 4y ago
    If you want to build a domain-specific classifier that determines whether an image is a dog or a cat, and you have 50 labeled images of dogs and cats, it's much better to start with a large model pretrained on millions of images, and then specialize it by training on 50 images of dogs and cats. Try to start with a NN and 50 images of dogs and cats, and it won't work very well.
    - PeterisP 4y ago
      Sure, that's correct, but that's absolutely unrelated to what we were talking about; your example is about the general concept of transfer learning to task-specific annotated data, not about domain-specific pretrained models. For example, if you want a domain-specific model for the legal domain, then you can pre-train a large self-supervised model on every single legal-related document in the world you can get your hands on, instead of a general mix of news and fiction and blogs and everything else - and that might be a more efficient starting point for however many(few) annotated examples you have for your task-specific classifier than the general model.
      - panarky 4y ago
        Legal-related documents are a minuscule fraction of the corpus the large model is trained on. The resulting model won't have the conceptual fluency that the large model has. It's like training a human baby with legal briefs and expecting her to be a good lawyer.

panarky 4y ago

The P in GPT is for "pretrained". The large pretrained model is a prereq for domain-specific models.

PeterisP 4y ago
Not necessarily, you don't need to make a domain-specific model from a general model, you can definitely make a large pretrained domain-specific model from scratch by training it only on domain-specific data, which can result in a smaller and more efficient model. Furthermore, when making task-specific models, an 'encoder' architecture (similar to BERT) often works better than a 'decoder' architecture (similar to GPTx), so you might want to use a similar-but-different architecture than the general model intended to be conversational/generative.
- panarky 4y ago
  If you want to build a domain-specific classifier that determines whether an image is a dog or a cat, and you have 50 labeled images of dogs and cats, it's much better to start with a large model pretrained on millions of images, and then specialize it by training on 50 images of dogs and cats. Try to start with a NN and 50 images of dogs and cats, and it won't work very well.
  - PeterisP 4y ago
    Sure, that's correct, but that's absolutely unrelated to what we were talking about; your example is about the general concept of transfer learning to task-specific annotated data, not about domain-specific pretrained models. For example, if you want a domain-specific model for the legal domain, then you can pre-train a large self-supervised model on every single legal-related document in the world you can get your hands on, instead of a general mix of news and fiction and blogs and everything else - and that might be a more efficient starting point for however many(few) annotated examples you have for your task-specific classifier than the general model.
    - panarky 4y ago
      Legal-related documents are a minuscule fraction of the corpus the large model is trained on. The resulting model won't have the conceptual fluency that the large model has. It's like training a human baby with legal briefs and expecting her to be a good lawyer.