A new method in artificial intelligence is taking on the challenge of making predictions from fragmentary data
A spin-out from the University of Cambridge, Intellegens, is promising to open up two new methods in AI for managing probability and for navigating through incomplete sets of data. Ultimately, these techniques could allow us to spot trends in public health even if doctors are measuring different things or patients are missing appointments. Similarly, we could make sense of what is happening on the internet of things, even if not all sensors are sending back data.
Intellegens is a spin-out from the University of Cambridge with a unique Artificial Intelligence (AI) toolset that can train deep neural networks from sparse or noisy data. The technique, created at the Cavendish Laboratory, is encapsulated in Intellegens first commercial product, Alchemite™. The innovative deep learning algorithms that Alchemite™ is based on can see correlations between all available parameters, both inputs and outputs, in fragmented, unstructured, corrupt or even noisy datasets. The result is accurate models that can predict missing values, find errors and optimise target properties. Capable of working with data that is as little as 0.05% complete, Alchemite™ can unravel data problems that are not accessible to traditional deep learning approaches. Suitable for deployment across any kind of numeric dataset, Alchemite™ is delivering ground breaking solutions in drug discovery, advanced materials, patient analytics and predictive maintenance – enabling organisations to break through data analysis bottlenecks, reduce the amount of time and money spent on research, and support better, faster decision-making. For more information contact us here.
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