Professor of Digital Computation, Imperial College London
Sarah O’Connor ’s We Are Not Machines: The Fight for the Future of Work accompanied me on my summer holiday, but it has continued to unsettle how I think about work. It is an engaging, accessible book, and precisely the kind that prompts a recalibration of what you want your own work to be about. In the age of AI, it offers a useful lens for deciding what we should automate, what we should…
This is the third post in a sequence relating to the geometry of block number formats as angle preservers. In my previous post, I argued that block number formats remain direction preservers even when their block scales are quantised all the way down to powers of two, as is common in some number representations like Continue reading Precision Matters in Block Scales
In the UK, the House of Commons Select Committee on Science, Innovation and Technology is currently holding an inquiry into Low-Energy Computing . The remit of the inquiry can be found below. AI model sizes and data volumes are growing significantly. At the same time, areas like quantum computing and protein synthesis also require increasing amounts Continue reading Parliamentary Inquiry on…
In a couple of recent posts [1,2], I have been trying to reason through the geometric properties of block number formats. The basic idea is that when a group of numbers shares a scale factor, the small low-precision numbers inside the block are no longer meaningfully trying to approximate scalar magnitudes, as the scale has Continue reading Directionality in Low Precision
In the UK, the Parliamentary Select Committee on Education is currently holding an inquiry into The use of Artificial Intelligence and EdTech in Education . I am reproducing my submission to this inquiry below, in case it is of value to others. The text can also be found alongside all other submissions made at Continue reading AI in Education
In my previous post, I argued that block number formats can be understood geometrically as direction preservers. That argument relied on an idealization: once a block direction had been chosen, its scale could be set optimally as an arbitrary real number. Real hardware formats do not usually work that way. In many practical schemes, block Continue reading Block Number Formats are (Still!)…
I ve recently returned from the SIAM PP 2026 conference and as always, conferences help provide time for research reflection. One thing I ve been reflecting on during my journey back is the various explanations people give for why the machine learning world is so keen on block number formats (MX, NVFP, etc.) see my earlier Continue reading Block Number Formats are Direction Preservers
This month I took a trip to California for the FPGA 2026 conference, together with my two new PhD students Ben Zhang and Bardia Zadeh, which I combined with a number of visits in the San Francisco Bay Area in the days preceding the conference. This post provides a brief summary of my visit. First Continue reading California / FPGA 2026
I ve always struggled with the concept of doing your best , especially with regards to avoiding harm. This morning from my sick bed I ve been playing around with how I could think about formalising this question (I always find formalisation helps understanding). I have not got very far with the formalisation itself, but here are some Continue reading Do Your Best: A Social Question?
This blog post sets up and analyses a simple mathematical model of Marx s controversial Law of the Tendency of the Rate of Profit to Fall (Capital III §13-15). We will work under a simple normalisation of value that directly corresponds to the assumption of a fixed labour pool combined with the labour theory of value Continue reading Productivity and Rate of Profit