Unveiling Foundation Models: Opportunities and Risks

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Unveiling Foundation Models: Opportunities and Risks

Table of Contents

  1. Introduction
  2. ViT and Foundation Models
  3. What are Foundation Models?
  4. ViT as a Foundation Model
  5. Understanding and Generalization Power
  6. Incentives and Data Creation
  7. Distribution Shift and Out-of-Distribution Data
  8. Alternatives to Foundation Models
  9. Alignment and Desired Behavior
  10. Conclusion

ViT and Foundation Models

In the world of machine learning, the release of "How to train your ViT?" by the Google Brain team has caused quite a stir. With more than 50,000 models released, the community can now start using them without having to train them again. But what exactly are these models, and do they count as foundation models? In this article, we will explore the concept of foundation models and how ViT fits into this category.

What are Foundation Models?

Foundation models are models that are trained on broad data at Scale and can be adapted to a wide range of downstream tasks. The term was introduced in a paper published by Stanford researchers, where they renamed pre-trained models into foundation models. ViT, or Vision Transformer, is a pre-trained model that has been trained on more than 300 million images. But is this broad enough to be considered a foundation model? The definition of "broad data at scale" is vague, and it is unclear how wide the enumeration of downstream tasks should be.

ViT as a Foundation Model

ViT has been trained on a large dataset, but it is unclear whether this dataset is broad enough to be considered "broad data at scale." The Second condition for a foundation model is that it can be adapted to a wide range of downstream tasks. ViT has been adapted to many downstream tasks, but it is unclear whether this enumeration is wide enough. The definition of foundation models is not very helpful in making terminology more accurate.

Understanding and Generalization Power

One of the main concerns with foundation models is their understanding and generalization power. The paper on foundation models highlights the metaphysical question of what understanding really means. Even if a model would have understanding, we would not be able to say whether it does so because we might not have the right means or even the right measure for understanding. The authors highlight that it's the same with intelligence with the Turing test that has been passed many times already but we still don't call those systems intelligent because we keep moving the goalpost. We even like to leave the question open of whether understanding even matters in these models.

Incentives and Data Creation

The paper on foundation models also discusses incentives and data creation. The authors argue that the tech industry has little incentive to devote significant resources to technologies designed for improving the condition of poor and marginalized people. Universities are seen as a better alternative, but there are concerns about the aversion of academics for actually doing science communication. The paper also emphasizes the need to Create a data hub for the whole community, as these models rarely get any better than the data they are trained on.

Distribution Shift and Out-of-Distribution Data

Another concern with foundation models is distribution shift and out-of-distribution data. The paper argues that pretraining on unlabeled data is an effective, general-purpose way to improve accuracy on out-of-distribution test distributions. However, We Are OOD skeptics in a Sense. We have not seen these models perform well out of their comfort zone, which is OOD. We just make the training data so huge that most of the testing You do is actually still in-training-distribution. In other words, nothing will ever be out of training distribution if the whole world was seen during training.

Alternatives to Foundation Models

If you are looking for real alternatives and real out-of-domain generalization, where your model can really handle new data that is substantially different from what it has seen before, look in other places. Someone in this community proposed Jeff Hawkins' HTM model, which is worth checking out.

Alignment and Desired Behavior

Finally, the paper on foundation models discusses alignment and desired behavior. One challenge is the misalignment between the foundation model's training objective and the desired behavior. This really Speaks from our heart, and if you want our take on this in relation to CLIP, watch our video where we highlight how alignment has already started to be a problem, when CLIP's normal behavior, following its training objective was blamed to have been the victim of an "adversarial attack."

Conclusion

In conclusion, foundation models are a new concept in machine learning that has caused quite a stir. ViT is a pre-trained model that has been trained on a large dataset, but it is unclear whether it is broad enough to be considered a foundation model. The concept of foundation models is not very helpful in making terminology more accurate, and there are concerns about their understanding and generalization power, incentives and data creation, distribution shift and out-of-distribution data, and alignment and desired behavior. There are alternatives to foundation models, and more honesty is needed about where the performance gains of "foundation models" actually come from.

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