The applications of artificial intelligence across multiple sectors are experiencing rapid diversification and acceleration. Using AI to recognise objects, patterns and behaviours can revolutionise multiple industries, including construction, manufacturing, agriculture, finance, transport and Defence. Through the design of bespoke systems that use artificial intelligence to gather, categorise and evaluate key metrics, we’re helping organisations to improve accuracy, data security, market responsiveness and profitability in equal measure.
One of the key barriers that slows down machine learning is the vast quantities of data required, as this can be difficult to access, prohibitively expensive to obtain, or unavailable entirely. Even when the data is available, it needs to be labelled so that the machine learning model can learn from it, which requires a lot of time and can potentially be very costly when carried out manually by people.
On top of this, the accuracy of the data is paramount, as real-world data sets could exclude certain populations or contain errors that will negatively affect the results. Finally, all sensitive data must be used ethically and in compliance with GDPR.
When all of these obstacles are taken into account, the acquisition and analysis of data comes with huge limitations and a hefty price tag.
Zest Consult along with Costain and the University of West England has invested significant time and resources into researching, developing and applying a synthetic data approach to machine learning. The original project focused on construction sites and used synthetic data to train object recognition machine learning models, significantly reducing the time and money required to achieve top results.
In the process, synthetic data brings far greater accuracy, as it is created using AI algorithms that represent data with appropriate balance, distribution and other parameters as opposed to using real-world events where there is less control over such factors. Additionally, synthetic data only mimics the statistical properties of the original data, therefore removing the problem of breaching confidentiality.
The original project was a huge success and enabled the generation of 3D models of workers, components and plant equipment as well as 3D virtual scenes. Together these allowed the recognition, tracking and recording of the presence and proximity of objects and workers from various camera feeds. The potential of synthetic data is enormous and we’re passionate about showing our clients in multiple sectors how it can be applied to their businesses and organisations.
Our research and collaboration has led to huge leaps in machine learning, as subsequent sets of synthetic data can be created even faster and more affordably than the first. Businesses are no longer bound by the restrictions of collecting and using solely real-world data for machine learning, as synthetic data can be generated in significantly less time and at a much lower cost, all while ensuring the development of a more accurate AI model.
As well as designing AI systems ourselves, we can validate those created by other companies. This comprehensive testing service uncovers gaps and constraints, which is followed by in-depth consultancy from our team of artificial intelligence specialists. The return on investment of this service can be outstanding, as a smooth and unrestricted AI system paves the way to ongoing success.
We’re very excited about the endless opportunities offered by synthetic data and the use of artificial intelligence to create a more safe, efficient, ethical and innovative world. To discuss how AI can be applied to your business or for expert advice on how to enhance your current systems, get in touch with our team using our contact form.