For deep learning, size is power. Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These large pre-trained models or Jacks of All Trades (JATs), when fine-tuned for downstream tasks, are gaining importance in driving deep learning advancements. However, environments with tight resource constraints, changing objectives and intentions, or varied task requirements, could limit the real-world utility of a singular JAT. Hence, in tandem with current trends towards building increasingly large JATs, this paper conducts an initial exploration into concepts underlying the creation of a diverse set of compact machine learning model sets. Composed of many smaller and specialized models, the Set of Sets is formulated to simultaneously fulfil many task settings and environmental conditions. A means to arrive at such a set tractably in one pass of a neuroevolutionary multitasking algorithm is presented for the first time, bringing us closer to models that are collectively Masters of All Trades.

翻译：对于深度学习而言，规模即力量。在广泛数据上训练、面向多种任务的大规模神经网络正处于人工智能的前沿。这些大型预训练模型或称“通才模型”，经过下游任务微调后，正日益成为推动深度学习进步的关键力量。然而，在资源严格受限、目标意图动态变化或任务需求多样化的环境中，单一通才模型的实际效用可能受限。因此，在当今趋向构建日益庞大通才模型的潮流中，本文首次对构建多样化紧凑机器学习模型集的基础概念进行了初步探索。该模型集由众多小型化、专业化的模型组成，其设计旨在同时满足多种任务场景与环境条件。本文首次提出了一种通过神经进化多任务算法单次运行即可高效获得此类模型集的方法，使我们更接近实现“集体专才”的模型目标。