Task Agnostic Architecture for Algorithm Induction via Implicit Composition

For many years, different fields in applied machine learning such as computer vision, speech or natural language processing have been building domainspecialised solutions, mainly fueled by the need for hand-crafted features to then be used with classical machine learning methods. Currently, we are witnessing an opposing trend towards developing more generalist architectures, driven by Large Language Models and multi-modal foundational models. These architectures are designed to tackle a variety of tasks, including those that were previously unseen and using inputs across multiple modalities. Taking this trend of generalization to the extreme suggests the possibility of a single deep network architecture capable of solving all tasks, rather than just one or a limited subset as currently observed. This position paper aims to explore the idea that developing such a unified architecture may not be as difficult as one may think and propose a theoretical framework of how it could be constructed. Our proposal is based on the following assumptions. Firstly, the solution to any given task can be expressed through a sequence of instructions, as most tasks necessitate implementation in code on conventional computing hardware, which operates inherently in a sequential manner. Second, recent Generative AI, especially Transformer-based models, demonstrate not only a leap in performance but also a potential as an architecture capable of constructing algorithms for a wide range of domains. For example, GPT-4 shows exceptional capability at in-context learning of novel tasks which is hard to explain in any other way than the ability to compose novel solutions from fragments on previously learnt algorithms. Third, the observation that the main missing component in developing a truly generalised network is an efficient approach for self-consistent input of previously learnt sub-steps of an algorithm and their (implicit) composition during the network's internal forward pass.