AI Technology & Industry Review
In a new paper Learning Universal Predictors, a Google DeepMind research team proposes the utilization of Universal Turing Machines (UTMs) for generating training data, thereby enhancing meta-learning and enabling trained neural networks capable of mastering universal prediction strategies.
In a new paper Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding, the team introduces meta-prompting. This innovative scaffolding approach proves to be highly effective, surpassing standard prompting by 17.1%, expert (dynamic) prompting by 17.3%, and multi-persona prompting by 15.2%.
In the new paper Meta-Learned Models of Cognition, a team from the Max Planck Institute for Biological Cybernetics (Max-Planck-Gesellschaft, MPG) and DeepMind proposes the establishment of a research program focused on meta-learned models of cognition. The team cites machine learning papers demonstrating how meta-learning can be used to construct Bayes-optimal learning algorithms and suggests it can significantly expand the scope of the rational analysis of cognition.
In the new paper Toolformer: Language Models Can Teach Themselves to Use Tools, a team from Meta AI Research and the Universitat Pompeu Fabra proposes Toolformer, a model that self-learns how to choose and use external tools such as search engines, calculators, and translation systems to boost performance on downstream tasks.
In the new paper Mnemosyne: Learning to Train Transformers with Transformers, a research team from Google and Columbia University presents Mnemosyne Optimizer, a learning-to-learn system for training entire neural network architectures without any task-specific optimizer tuning.
In the new paper Optimistic Meta-Gradients, a DeepMind research team explores the connection between gradient-based meta-learning and convex optimization, demonstrating that optimism in meta-learning is achievable via the Bootstrapped Meta-Gradients approach.
In the new paper Beyond Bayes-Optimality: Meta-Learning What You Know You Don’t Know, researchers from DeepMind and Stanford University use modified meta-training algorithms to build agents with risk- and ambiguity-sensitivity.
In the new paper p-Meta: Towards On-device Deep Model Adaptation, a research team from ETH Zurich, Singapore Management University and Beihang University proposes p-Meta, a novel meta-learning method for data- and memory-efficient on-device adaption of deep neural networks for IoT applications.
In the new paper Meta-Learning Sparse Compression Networks, a DeepMind research team proposes steps for scaling implicit neural representations (INRs). The resulting meta-learning sparse compression networks can represent diverse data modalities such as images, manifolds, signed distance functions, 3D shapes, and scenes, achieving state-of-the-art results on some of them.
A research team from Mila, Québec Artificial Intelligence Institute, Université de Montréal, CIFAR and IVADO Labs challenges the assumption that task diversity will improve model performance in meta-learning, finding instead that repeating the same tasks over the training phase can achieve performance similar to models trained on uniform sampling.
A research team from the University of Washington, Facebook AI Research and the Allen Institute for AI introduces Meta-training for InContext Learning (MetaICL), a new meta-training framework for few-shot learning where an LM is meta-trained to learn in-context — conditioning on training examples to recover the task and make predictions.
In the paper Introducing Symmetries to Black Box Meta Reinforcement Learning, a research team from DeepMind and The Swiss AI Lab IDSIA explores the role of symmetries in meta generalization and shows that introducing more symmetries to black-box meta-learners can improve their ability to generalize to unseen action and observation spaces, tasks, and environments.