Our deep learning technology is embodied in the Alchemite™ product range. This artificial intelligence approach originated in work of Dr Gareth Conduit and his collaborators at the University of Cambridge. Alchemite™ comprises a powerful set of algorithms uniquely suited to problems in business, research, product development, and data science to extract value from sparse and noisy data. The Intellegens team further developed the technology and continues to push it forward, based on the requirements of our customers.
Handle sparse data
A self-consistent, iterative algorithm that imputes sparse data.
- Unlike most machine learning approaches, which require a critical mass of complete inputs and outputs for training, Alchemite™ works for datasets with significant gaps in the data.
- It does this through techniques to estimate missing data and then iterate rapidly to improve its model until it converges on a solution.
Reliable non-parametric uncertainty quantification.
- Alchemite™ tells you, clearly and instantly, how much reliance you can place on every estimated data point.
- You can quickly see where more experimental data will improve your model, or identify the most reliable candidate for a new product.
Optimise multiple targets
Simultaneous prediction of multiple properties.
- Unlike traditional methods, which model one output at a time, Alchemite™ predicts all targets and their uncertainties simultaneously.
- Alchemite™ can tackle complex problems that are intractacble for other algorithms; it was originally validated on a project to discover new aerospace alloys that simultaneously satisfied eleven physical criteria.
Learn from large databases
~1000 times less computational resource than alternatives.
- Alchemite™ algorithms are highly efficient compared to other leading machine learning methodologies.
- It can return exceptionally fast results and handle large databases.
Enable a goal-driven approach
A probabilistic, goal-driven methodology.
- Based on your objectives, Alchemite™ mathematical methods enable efficient design of experiments.
- Focus time and expense based on rational assessment of potential performance improvements and the likelihood of success.