Neither artificial intelligence designed to play Turing's imitation game, nor
augmented intelligence built to maximize the human manipulation of information
are tuned to accelerate innovation and improve humanity's collective advance
against its greatest challenges. We reconceptualize and pilot beneficial AI to
radically augment human understanding by complementing rather than competing
with human cognitive capacity. Our approach to complementary intelligence
builds on insights underlying the wisdom of crowds, which hinges on the
independence and diversity of crowd members' information and approach. By
programmatically incorporating information on the evolving distribution of
scientific expertise from research papers, our approach follows the
distribution of content in the literature while avoiding the scientific crowd
and the hypotheses cognitively available to it. We use this approach to
generate valuable predictions for what materials possess valuable
energy-related properties (e.g., thermoelectricity), and what compounds possess
valuable medical properties (e.g., asthma) that complement the human scientific
crowd. We demonstrate that our complementary predictions, if identified by
human scientists and inventors at all, are only discovered years further into
the future. When we evaluate the promise of our predictions with
first-principles equations, we demonstrate that increased complementarity of
our predictions does not decrease and in some cases increases the probability
that the predictions possess the targeted properties. In summary, by tuning AI
to avoid the crowd, we can generate hypotheses unlikely to be imagined or
pursued until the distant future and promise to punctuate scientific advance.
By identifying and correcting for collective human bias, these models also
suggest opportunities to improve human prediction by reformulating science
education for discovery.

通过利用程序化的科学专业知识，在避免科学群体和其认知偏差的前提下，利用互补智能生成的有价值的预测不能在人类科学家和发明家的时代被想象或追求，并且通过检测和纠正人类认知偏差，为完善科学教育提供了机会。

增强人类探索的互补人工智能

Complementary artificial intelligence designed to augment human discovery

Gender prediction has typically focused on lexical and social network
features, yielding good performance, but making systems highly language-,
topic-, and platform-dependent. Cross-lingual embeddings circumvent some of
these limitations, but capture gender-specific style less. We propose an
alternative: bleaching text, i.e., transforming lexical strings into more
abstract features. This study provides evidence that such features allow for
better transfer across languages. Moreover, we present a first study on the
ability of humans to perform cross-lingual gender prediction. We find that
human predictive power proves similar to that of our bleached models, and both
perform better than lexical models.

通过使用抽象特征代替传统的词汇文本，可以提高性别预测在不同语言之间的迁移性能，而这种基于抽象特征的预测方法人类的预测能力不比传统方法差，并且优于词汇模型。