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Neural Network Inference with Leo | Aleo Blog

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Throughout this blog, we will be referencing snippets of code. You can copy these snippets by clicking the top right of the box. The complete source code for this article can be found on GitHub here.‍ Artificial intelligence (AI) can solve many tasks that previously required human intelligence, enhancing the capabilities of software systems. Most AI systems nowadays are built upon neural networks that have proven to be capable of performing complex tasks and have paved the way for many of the recent AI breakthroughs. The typical AI workflow consists of two phases: training and inference. Along this workflow, the data-intensive nature of modern AI systems has raised privacy concerns. During training, especially the training data should often be protected. Afterward, the AI model parameters, as well as the model input and output may be protected. The comparison of both phases and the data to protect is highlighted in the following table. AI engineering stage Model training Inference Goal

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