Deep learning models can be trained to understand language and the context of how words are used in sentences. Instead of providing an algorithm with rules, they can be taught using examples that they can subsequently generalise and learn from. It is this emphasis on pattern recognition that enables them to be applied to situations where pre-defined rules don’t exist. However, the accuracy of such models is highly dependent on the quality of the training data used to build them.
SciBite AI combines the flexibility of deep learning pattern recognition with the reliability of SciBite’s semantic technologies. The use cases in this document highlight the power of SciBite AI, which provides a framework to incorporate different Machine Learning approaches, ensuring that it can be applied to a wide range of problems.
To learn more, download the full use case.
Large language models (LLMs) have limitations when applied to search due to their inability to distinguish between fact and fiction, potential privacy concerns, and provenance issues. LLMs can, however, support search when used in conjunction with FAIR data and could even support the democratisation of data, if used correctly…Read
In our previous blog, we explained why FAIR data is important not only for biotech and pharmaceutical companies but also for their partners. Here we describe how ontologies are the key to having the richly described metadata that is at the heart of making data FAIR. Let’s explore how ontologies help with each aspect of the FAIR data principles…Read
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