In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

首先根据处理形式的不同, 介绍了不同形式数据的特征和各自的典型应用场景以及相应的代表性
处理系统, 总结了大数据处理系统的三大发展趋势; 随后, 对系统支撑下的大数据分析技术和应用 …

知识图谱技术是人工智能技术的重要组成部分, 其建立的具有语义处理能力与开放互联能力的
知识库, 可在智能搜索, 智能问答, 个性化推荐等智能信息服务中产生应用价值 …

Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …

How well can NLP models generalize to a variety of unseen tasks when provided with task
instructions? To address this question, we first introduce Super-NaturalInstructions, a …

Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We
have created various computer-mediated virtual environments including social networks …

We posit that to achieve superhuman agents, future models require superhuman feedback
in order to provide an adequate training signal. Current approaches commonly train reward …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …