ChatGPT and generative AI will change how we work, but how different is this to all the other waves of automation of the last 200 years? What does it mean for employment? Disruption? Coal consumption?
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The wave of enthusiasm around generative networks feels like another Imagenet moment - a step change in what ‘AI’ can do that could generalise far beyond the cool demos. What can it create, and where are the humans in the loop?
Read MoreData is the new oil, we are told. Every country needs a data strategy, and all of us should own our data, and be paid for it. But really, there is no such thing as data, it’s not yours, and it’s not worth anything.
Read MoreIn the last 5-6 years, machine learning has gone from ‘crazy idea from the 1980s’ to ‘software’. That has come with several waves of deployment and several waves of company creation, as we work out what do do with it. It’s the new SQL.
Read MoreWe worry about face recognition just as we worried about databases - we worry what happens if they break, and we worry what happens if they work too well.
Read MoreComputer vision turns imaging into a universal input - it lets computers see. So what kinds of things will become vision problems, and how does that change Google or Instagram?
Read MoreMachine learning is the new centre of tech, and like all big new things there are issues. ‘AI bias’ is much-discussed right now: machine learning finds patterns but sometimes it finds the wrong one, and it can be hard to tell.
Read MoreInternet platforms are mechanical Turks - they can only understand things by finding a way to leverage vast numbers of humans. They’re distributed computers where all of us are the CPUs. How does that affect how we think about abuse, and how might machine learning change this?
Read MoreSmart home today looks a lot like the world of kitchen gadgets a few generations ago - and so does machine learning. We have a bunch of cheap commodity components (DC motors! Cameras! Wifi chips! Voice recognition!) and we’re trying to work out how to bolt them together into things that makes sense. There are lots of experiments - some things will be the toasters or benders of the future, and some will be the electric can-opener.
Read MoreMachine learning means smartphones will (nearly) always take perfect pictures. But it also means they might understand what’s in the picture and why you took it. So what do they do with that? What does the discoverability and communication of AI look like, if you can answer lots of questions but might still be wrong?
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