We – as a society – had the opportunity to prevent SARS-CoV-2 becoming endemic. We largely wasted it, by not locking down early enough or for long enough to remove it from the population. And we didn’t use the lockdown period to set up effective data collection, testing, tracking and analytic tools to enable rapid and fine-grained response to predicted changes in incidence (by the time you’re working with actual data, you’re already behind in your response). Continue reading Time for a different approach?
In developing our daily-predictive AI for Covid-19 infections , we’ve come across some, ah, interesting quirks in the official UK data: previously, we’d been using the government’s daily download data set for England, hoovering it into udu and thence driving the internal and R-based analytic and learning models. We’ve done the same for Scotland, Wales and Northern Ireland, from their respective data gateways, and merged the outcome to create a consistent baseline for analysis. Overall then, a bit clumsy, but perfectly workable. Continue reading Damned (Official) Statistics…
The project’s goal was to demonstrate the feasibility of a service to provide a single point of advice to support anyone planning activities that affect the natural environment, to help them understand the environmental impact of their proposal, to advise them on what they could do to mitigate any impact and to outline what consents and processes they’d then need to follow. It will also be possible, over time, to build a dynamic picture of the impact of human activity on the natural environment by a wide range of measures, including climate impacts. Continue reading Two Worlds completes R&D Project with SNH
Most AI practitioners will argue that the risk to humanity from AI doesn’t (and won’t) come from an AI waking up one day, deciding that the best way to solve the world’s problems is to wipe out humanity and then serendipitously finding that it’s in control of the world’s nuclear weapons. On the principle that cock-up trumps conspiracy, pretty much every time, we’re far more likely to take a range of hits from the misapplication of an AI that’s either too stupid1 to do the job that’s been asked of it or where those deploying it are incapable of understanding its limitations (or indeed don’t care, as long as they’ve cashed out before it all falls apart). Broadly speaking, machine systems fail for one or more of these reasons: Continue reading Wye AI, Man!
I spend much of my time working on various Smart City programmes: anything from modelling need and opportunity to designing architectures for the fusion of large and diverse data sets with live sensor and device data (IoT) and the analytics needed to make the results coherent, timely and relevant. I also live in a very small community, where I was founder of a community company whose efforts have led to our little corner of the Scottish Highlands being in the top 1% of global broadband connectivity. We’re now starting to use that infrastructure to create opportunities for new services and means of service delivery, applying the principles of Smart City programmes to the needs of rural and remote communities, based on the tripod of providing the tools (in the form of the infrastructure), helping people acquire appropriate skills and then nurturing the ideas that then emerge.
I spend quite a lot of my time doing due diligence on innovation funding applications. I’ve been doing this for rather longer than is comfortable to contemplate so, over the years, I’ve seen progressive tides of hype wash in, fill a few rock pools, and then wash out again, only to re-emerge a few years later – assuming it had any merit in the first place – in a form that actually works as part of the overall problem-solving ecosystem. That innovation-development-hype-disappointment cycle may actually happen several times before the rest of the innovations needed for an idea to gain market traction catch up. That’s certainly been the case with VR and AR, with IoT and, most of all, with ArtificiaI Intelligence (AI). Continue reading Then a Miracle Occurs: The Hype of AI Pitches
AI (that’s Artificial Intelligence – I have to be clear here as I live in a farming community and conversations have been known to take a strange turn) is a flavour of the moment and is riding high on the arm-waving curve of the hype cycle. We’ve been here before though – as a notion, AI has been through more loops of the hype cycle than most technologies, with successive waves of mutually reinforcing innovation and fiction conspiring to promise more than contemporary understanding could deliver.