Can you write laws, or lay down ethical principles, for a technology that will be used in entirely different ways, for different purposes, in different industries? What does that mean if it’s changing entirely every 18 months?
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ChatGPT and LLMs can do anything (or look like they can), so what can you do with them? How do you know? Do we move to chat bots as a magical general-purpose interface, or do we unbundle them back into single-purpose software? What are the products?
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If you put all the world’s knowledge into an AI model and use it to make something new, who owns that and who gets paid? This is a completely new problem that we’ve been arguing about for 500 years.
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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?
Read MoreThe 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.
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