Artificial and Augmented Intelligence for Automated Scientific Discovery

人工智能是一个被广泛使用的术语，它让人联想到科幻小说中的许多计算机。它代表了思想、算法、计算模型和知识系统的集合。最近特定类型的机器学习（例如深度神经网络）的成功再次激发了科学界对真实世界复杂性的洞察力的兴趣。这种类型的方法补充了以前使用的知识工程系统，但是它们需要大量的数据进行训练。以化学和材料科学为例，我们可以看到，传统的科学发现方法使用相对较少的、往往不确定的数据，这些数据是通过人类的洞察力提炼出来的，以产生预测和可测试的理论，这些理论可能随着新数据的出现而发展。在这些科学领域，越来越多的数据正在变得可用，“大数据”的影响与现在几乎所有科学都依赖于计算辅助的现实相对应。尽管如此，训练新的人工智能系统所需的高质量数据的数量是无法直接获得的，即使最近在自动化方面取得了进展。作为网络的基础，我们建议使用“模拟放大”作为自动化实验、模拟、人工智能学习、预测、比较、设计、进一步实验周期的关键要素，以创造一个环境，在这个环境中，领先的人工智能发展可以应用于化学和材料的发现。

AI is a widely used term that conjurers up many of the computers from science fiction. Its stands for a whole collection of ideas, algorithms, computational models and knowledge systems. Recent success of particular types of machine learning (e.g. deep neutral nets) have again excited the interest of the scientific community in delivering insight into the complexity of the real world. This type of approach compliments the knowledge engineering systems that have previously been used, however they require massive amounts of data to be trained. Taking the chemical and materials sciences as exemplar areas we can see that the traditional approaches to scientific discovery work with relatively small amounts of often uncertain data which is distilled by human insight to yield predictions and testable theories which may evolve as new data becomes available. In these areas of science more data is becoming available and the impact of 'larger data' parallels the reality that almost all science now depends on computational assistance. Never-the-less the quantity of quality data needed to train the new AI systems is simply not directly available even with recent advances in automation. As a basis for the network we propose to use 'amplification by simulation' as a key element of the cycle of automated experiments, simulation, AI learning, prediction, comparison, design, further experiments, to create the environment in which leading AI developments can be applied to the chemical and materials discovery.