Blog Articles

For decades, robotics has promised us machines that can lift heavier, move faster, and compute more precisely than any human ever could. And yet, despite knowing that the human body is objectively not an optimal engineering design for most tasks, we remain strangely fixated on building robots that look and behave like us.

ts one of those phrases that has become almost unquestioned in education - warning: incoming rant post: If we want to be accessible, were told, we should provide materials in an editable format so students can adjust them to meet their needs: change fonts, tweak colours, increase spacing. On the surface, that feels like the right thing to do. But it also raises an important question: why are we expecting students to fix our documents in the first place?

We are approaching a moment that feels increasingly familiar in the history of innovation. A powerful new technology emerges, it is rapidly adopted, its misuse becomes visible, and public enthusiasm turns into anxiety. At that point, the same underlying question resurfaces: do we restrict the tool itself, or do we take responsibility for how it is used?

I had almost forgotten about Augmented Reality (AR) - it feels like a technology that we were all desperatly waiting for but now is a distant memory, however Ive recently found myself playing with it again. It started as a quick experiment to see how far modern AR frameworks get you with almost no effort, and quickly became a reminder of how much effort I once invested in something that now barely registers as remarkable.

Being a leader in computer science is a bit like training for a marathon while wearing an XR headset: youre running toward the future, the scenery keeps glitching, and occasionally you walk straight into a virtual wall you swear wasnt there a second ago. Between writing research proposals, testing XR soundscapes without tripping over the dog, debugging cognitive-load experiments, and still signing up for the Manchester Marathon again, Ive realised that academic life isnt governed by logic. Its governed by laws: unofficial, unscientific, and irritatingly accurate ones.

At the stroke of midnight on Friday night, I found myself standing beside the stone cross in Meriden, the historic centre of England, waiting for what felt suspiciously like a prison break. Over the years it has quietly become an annual tradition for me: Escape from Meriden just happens to coincide perfectly with the opportunity to disappear into the darkness and hide on my birthday under the entirely reasonable justification of “it’s a race.” The rules couldn’t be simpler: start at midnight and run as far from the centre of England as you can in 24 hours. No fixed route, no checkpoints, just pure directionless freedom.

An RFC — Request for Comments — is a formal document from the Internet Engineering Task Force (IETF) or related bodies that specifies methods, behaviors, research, or innovations relevant to the functioning of the internet.

The Dilbert Principle has long been my go-to management textbook. Yes, I know it’s a comic strip. But as anyone who’s worked in large organisations will tell you, Dilbert often reads more like a documentary than satire.

At home, I live in a digital jungle. There are 8 Amazon Echo devices spread across the house, 12 smart bulbs that illuminate on command, 12 smart sockets that silently manage everything from the fish tank lights to festive decorations, a smart thermostat and 4 wireless temperature sensors monitoring room comfort, 3 Wi-Fi mesh nodes keeping everything connected, a smart fridge, a smart oven, 5 smart TVs, 2 smart car chargers and everyone in the house has a phone and laptop.

What the energy cost of Generative AI really means — and whether sustainability-minded institutions should use it anyway. As conversations around climate responsibility intensify, a growing concern has emerged in Higher Education (HE): should institutions committed to sustainability be using energy-hungry tools like Generative AI?