  Accurate, real-time measurements of price index changes using electronic
records are essential for tracking inflation and productivity in today's
economic environment. We develop empirical hedonic models that can process
large amounts of unstructured product data (text, images, prices, quantities)
and output accurate hedonic price estimates and derived indices. To accomplish
this, we generate abstract product attributes, or ``features,'' from text
descriptions and images using deep neural networks, and then use these
attributes to estimate the hedonic price function. Specifically, we convert
textual information about the product to numeric features using large language
models based on transformers, trained or fine-tuned using product descriptions,
and convert the product image to numeric features using a residual network
model. To produce the estimated hedonic price function, we again use a
multi-task neural network trained to predict a product's price in all time
periods simultaneously. To demonstrate the performance of this approach, we
apply the models to Amazon's data for first-party apparel sales and estimate
hedonic prices. The resulting models have high predictive accuracy, with $R^2$
ranging from $80\%$ to $90\%$. Finally, we construct the AI-based hedonic
Fisher price index, chained at the year-over-year frequency. We contrast the
index with the CPI and other electronic indices.
