While many AI companies rely exclusively on public datasets and external academic partnerships to train computational models, running an in-house laboratory allows researchers to perform high-throughput experiments, synthesise proprietary biological data, and validate machine learning outputs in real time.
While many AI companies rely exclusively on public datasets and external academic partnerships to train computational models, running an in-house laboratory allows researchers to perform high-throughput...
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While many AI companies rely exclusively on public datasets and external academic partnerships to train computational models, running an in-house laboratory allows researchers to perform high-throughput experiments, synthesise proprietary biological data, and validate machine learning outputs in real time.
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