论文标题
Kitbit：一种用于解决智能测试和数值系列的新型AI模型
KitBit: A New AI Model for Solving Intelligence Tests and Numerical Series
论文作者
论文摘要
智能测试的分辨率，特别是数值序列，对AI系统的评估引起了极大的兴趣。我们提出了一种称为Kitbit的新计算模型，该模型使用简化的算法及其组合来构建一个预测模型，该模型在数值序列中找到了基础模式，例如IQ测试中包含的模型以及更复杂的其他模型。我们介绍了该模型的基本原理及其在不同情况下的应用。首先，对从各种来源收集的智商测试中使用的一组数字系列进行了测试。接下来，我们的模型成功应用于用于评估文献中报道的模型的序列。在这两种情况下，系统都可以使用标准计算能力在不到一秒钟的时间内解决这些类型的问题。最后，Kitbit的算法首次应用于著名的OEI数据库的整个序列的完整集。我们以算法列表的形式找到了一个模式，并在迄今为止最大的系列数量中预测了以下术语。这些结果证明了kitbit解决可以用数值表示的复杂问题的潜力。
The resolution of intelligence tests, in particular numerical sequences, has been of great interest in the evaluation of AI systems. We present a new computational model called KitBit that uses a reduced set of algorithms and their combinations to build a predictive model that finds the underlying pattern in numerical sequences, such as those included in IQ tests and others of much greater complexity. We present the fundamentals of the model and its application in different cases. First, the system is tested on a set of number series used in IQ tests collected from various sources. Next, our model is successfully applied on the sequences used to evaluate the models reported in the literature. In both cases, the system is capable of solving these types of problems in less than a second using standard computing power. Finally, KitBit's algorithms have been applied for the first time to the complete set of entire sequences of the well-known OEIS database. We find a pattern in the form of a list of algorithms and predict the following terms in the largest number of series to date. These results demonstrate the potential of KitBit to solve complex problems that could be represented numerically.
