The Sequence Chat: Why are Foundation Models so Hard to Explain and What are we Doing About it?
Was this email forwarded to you? Sign up here The Sequence Chat: Why are Foundation Models so Hard to Explain and What are we Doing About it?Addressing some of the interpretability challenges of foundation models and the emerging fields of mechanistic interpretability and behavioral probing.
Large foundation models are like black boxes! We regularly hear this statement associated with the limited interpretability in the current generation of large generative AI models across different modalities. But what really makes these models so difficult to interpret? Is it just size or there are other more intrinsic complexities? The advent of large foundation models has revolutionized the field of artificial intelligence, bringing unprecedented capabilities in natural language processing, image generation, and multi-modal tasks. However, these models present significant challenges in terms of interpretability, far surpassing those encountered in traditional machine learning approaches. This essay explores the landscape of interpretability for large foundation models, examining the limitations of conventional techniques and delving into emerging fields that aim to shed light on the inner workings of these complex systems. When thinking about the interpretability challenges of AI models, an important point to understand is that it wasn’t always like this. Traditional ML Interpretability: A Brief Overview...Subscribe to TheSequence to unlock the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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