搜索“DeepMind”

，并且处理器是使用基于这些算法构建的定制执行数据集来训练的。这通常既需要领域专业知识，也需要计算机科学专业知识，因此构成了明显的入门门槛！ 自从我开始从事NAR以来，我一直对降低这一入门门槛抱有浓厚兴趣，同时也希望提供一组在各种任务中都应该有用的“基础处理器”。这促成了为期两年的工程努力，最终开源了CLRS算法推理基准（https://github.com/deepmind/clrs）[44]。 CLRS基准的灵感来自Cormen、Leiserson、Rivest和Stein的经典教材《算法导论》（CLRS）[1]，这是关于经典算法和数据结构最受欢迎的本科教材之一，也是竞赛程序员的“圣经”。尽管它有数千页之多，却只包含约90种不同的算法，而这些算法往往构成了整个软件工程职业生涯的基础！因此，CLRS中的算法可以构成我们所需的坚实的“基础处理器”集合，我们着手让构建和训练NAR处理器来执行CLRS中的算法变得容易。 在其第一个版本中，我们发布了CLRS-30，这是CLRS中三十种算法的数据集和处理器生成器集合，涵盖多种技能：排序、搜索、动态规划、图算法、字符串算法和几何算法。 CLRS-

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tion retrieval. (blog post); Improving language models by retrieving from trillions of tokens (2021): RETRO, for “Retrieval Enhanced TRansfOrmers,” is another approach—this one by DeepMind—to improve LLM accuracy by accessing information not included in their training data. (blog post); LoRA: Low-rank adaptation of large language models (2021): This research out of Microsoft introduced a more efficient alternative to fine-tuning for training LLMs on new data. It’s now become a standard for commu

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estion-answer pairs authored by trivia enthusiasts and independently gathered multiple evidence documents per question; Jeopardy! Questions: contains 200,000+ Jeopardy! questions; DeepMind Q&A Dataset: question/answer pairs from CNN and Daily Mail articles; bAbi: a rich collection of datasets for text understanding by Facebook; FEVER: for fact extraction and verification; SearchQA: question-answer pairs were crawled from from J! Archive, and then augmented with text snippets from Google; Quasar-

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y discourages revisiting the same state within the same episode; Slowly discourages revisiting states that have been visited many times across episodes Later, built on top of NGU, DeepMind proposed “Agent57” (Badia, et al. 2020b), the first deep RL agent that outperforms the standard human benchmark on all 57 Atari games. Two major improvements in Agent57 over NGU are: A population of policies are trained in Agent57, each equipped with a different exploration parameter pair $\{(\beta j, \gamma j

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athcal{L}(B) = \sum {(\mathbf{x} i, \mathbf{x} j, y i, y j)\in B} (r {ij} - \mathbf{1} {y i=y j})^2$ (Note: There is another Relation Network for relational reasoning, proposed by DeepMind. Don’t get confused.) Prototypical Networks Prototypical Networks (Snell, Swersky & Zemel, 2017) use an embedding function $f \theta$ to encode each input into a $M$-dimensional feature vector. A prototype feature vector is defined for every class $c \in \mathcal{C}$, as the mean vector of the embedded support

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rsarial Networks.” arXiv preprint arXiv:1805.08318 (2018). [12] Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel. “A simple neural attentive meta-learner.” ICLR 2018. [13] “WaveNet: A Generative Model for Raw Audio” - Sep 8, 2016 by DeepMind. [14] Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. “Pointer networks.” NIPS 2015. [15] Alex Graves, Greg Wayne, and Ivo Danihelka. “Neural turing machines.” arXiv preprint arXiv:1410.5401 (2014). Architecture; Attention; Transformer; Rnn

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e of Go has been an extremely hard problem in the field of Artificial Intelligence for decades until recent years. AlphaGo and AlphaGo Zero are two programs developed by a team at DeepMind. Both involve deep Convolutional Neural Networks (CNN) and Monte Carlo Tree Search (MCTS) and both have been approved to achieve the level of professional human Go players. Different from AlphaGo that relied on supervised learning from expert human moves, AlphaGo Zero used only reinforcement learning and self-

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