University of Tokyo create a new integrated three-dimensional circuit architecture for artificial intelligence applications with spiraling stacks of memory modules. Researchers at the University of Tokyo Institute of Industrial Science in Japan stacked resistive random-access memory modules for artificial intelligence (AI) applications in a novel three-dimensional spiral. The modules feature oxide semiconductor access transistors, which boost the efficiency of the machine learning training process. The team further enhanced energy efficiency via a system of binarized neural networks, which restricts the parameters to be either 1 or -1, rather than any number, to compress the volume of data to be stored. In having the device interpret a database of handwritten digits, the researchers learned that increasing the size of each circuit layer could improve algorithmic accuracy to approximately 90%.

Another problem is that the more granular the forecast – SKU at store level by week, for example – the higher the forecast error tends to be. "For sure, the greater degree of error in the store-level forecast, the greater the impact on the lost sale calculation," Fenwick said. "However, even if we hit a 70% accuracy measure, we're still capturing 70% of the potential lost demand in the store due to stock outs. Which, from a forecasting perspective, is a lot better than capturing zero lost demand. As the saying goes, 'if you only forecast to sales, you'll only ever stock to … what you sold.'"

Machine learning can be used to improve forecasts. The basic idea is that a demand forecast is made, a machine learning engine ingests data on how accurate that forecast was, and then the machine autonomously applies better math to improve the next forecast. This is explained in more depth in a previous article.