Explainablity and transparency are key ideals-straight from Plato's realm of the forms-that developers must hold themselves to in order to facilitate proper ethical and sociological inspection of AI models. At digiLab, this is our bread and butter. This is why we have been registered onto the Ethical AI Database (EAIDB).
The Ethical AI Database
Tech Nation Rising Stars 5.0 - delighted national winners!
After four rounds of competition from city to national, digiLab's approach has landed the company a winning place in Tech Nation’s UK-wide Rising Stars competition. The competition, which attracted over 400 entrants, recognises and celebrates the UK's most exciting and innovative tech scale-ups.
Winners of Prestigious Tech South West Ones to Watch Award
We're delighted to be recognised by Tech South West in their Ones to Watch awards. We're in great company with other businesses across the South West, and are looking forward to working with Tech South West and our peers over the next year, taking advantage of the business benefits on offer through the awards programme.
Understanding Uncertainty Quantification: Propagation of Uncertainty
Are you looking to get the most accurate information from your models, and deploy them with confidence? Uncertainty quantification (UQ) is a set of essential computational tools that can help make sure your models are both accurate and reliable. And while UQ can be a complex subject, it doesn't have to be overwhelming! In this blog post, we will break down how Uncertainty Quantification works by discussing the concept of propagation of uncertainty — as well as looking at some practical applications "in the wild!" using two simple uncertainty propagation methods: Monte Carlo Simulation and Polynomial Chaos.
Understanding Uncertainty Quantification: The Different Types
"All models are wrong, but some are useful" is a famous quote attributed to the British Statistician George Box. In this series, we explore the field of Uncertainty Quantification (aka UQ), a field which seeks to quantify how wrong a model might be. Uncertainty Quantification is an increasingly important field as engineering and science seek to rely more heavily on simulations and machine learning algorithms to make critical decisions. In this series, we introduce the key concepts, give examples of the principle algorithms and describe the academic challenges at the frontiers of Uncertainty Quantification.
Announcing our new Chair of the digilab board
We’re delighted to announce that Alan Prior is taking over as Chair of the digiLab board.