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  "documentTitle": "2020 Air Street Capital The State of AI Report 2020",
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      "text": "Algorithm: An unambiguous specification of how to solve a particular problem.\nModel: Once a ML algorithm has been trained on data, the output of the process is known as the model. This can then be used to make predictions.\nSupervised learning: A model attempts to learn to transform one kind of data into another kind of data using labelled examples. This is the most common kind of ML algorithm today.\nUnsupervised learning: A model attempts to learn a dataset's structure, often seeking to identify latent groupings in the data without any explicit labels. The output of unsupervised learning often makes for good inputs to a supervised learning algorithm at a later point.\nTransfer learning: An approach to modelling that uses knowledge gained in one problem to bootstrap a different or related problem, thereby reducing the need for significant additional training data and compute.\nNatural language processing (NLP): Enabling machines to analyse, understand and manipulate language.\nComputer vision: Enabling machines to analyse, understand and manipulate images and video.",
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