Posts in category: Deepmind

We recently caught up with Petar Veličković, a research scientist at DeepMind. Along with his co-authors, Petar is presenting his paper The CLRS Algorithmic Reasoning Benchmark at ICML 2022 in Baltimore, Maryland, USA.Read More

We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal masking. Perceiver AR can directly attend to over a hundred thousand tokens, enabling practical long-context density estimation without the need for hand-crafted sparsity patterns or memory mechanisms.Read More

We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal masking. Perceiver AR can directly attend to over a hundred thousand tokens, enabling practical long-context density estimation without the need for hand-crafted sparsity patterns or memory mechanisms.Read More

Starting this weekend, the thirty-ninth International Conference on Machine Learning (ICML 2022) is meeting from 17-23 July, 2022 at the Baltimore Convention Center in Maryland, USA, and will be running as a hybrid event. Researchers working across artificial intelligence, data science, machine vision, computational biology, speech recognition, and more are presenting and publishing their cutting-edge work in machine learning.Read More

Despite significant effort, current AI systems pale in their understanding of intuitive physics, in comparison to even very young children. In the present work, we address this AI problem, specifically by drawing on the field of developmental psychology.Read More

Despite significant effort, current AI systems pale in their understanding of intuitive physics, in comparison to even very young children. In the present work, we address this AI problem, specifically by drawing on the field of developmental psychology.Read More

In our recent paper, published in Nature Human Behaviour, we provide a proof-of-concept demonstration that deep reinforcement learning (RL) can be used to find economic policies that people will vote for by majority in a simple game. The paper thus addresses a key challenge in AI research – how to train AI systems that align with human values.Read More

In our recent paper, published in Nature Human Behaviour, we provide a proof-of-concept demonstration that deep reinforcement learning (RL) can be used to find economic policies that people will vote for by majority in a simple game. The paper thus addresses a key challenge in AI research – how to train AI systems that align with human values.Read More