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  "documentTitle": "2019 Air Street Capital The State of AI Report 2019",
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  "authorName": "Nathan Benaich and Ian Hogarth",
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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: This is the most common kind of (commercial) ML algorithm today where the system is presented with labelled examples to explicitly learn from.\nUnsupervised learning: In contrast to supervised learning, the ML algorithm has to infer the inherent structure of the data that is not annotated with labels.\nTransfer learning: This is an area of research in ML that focuses on storing knowledge gained in one problem and applying it to a different or related problem, thereby reducing the need for additional training data and compute.\nNatural language processing (NLP): Enables machines to analyse, understand and manipulate textual data.\nComputer vision: Enabling machines to analyse, understand and manipulate images and video.",
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