Like Sky before it, Netflix is a television company using tech as a crowbar for market entry. The tech has to be good, but it’s still fundamentally a commodity, and all of the questions that matter are TV questions. The same applies to Tesla, and indeed to many other companies using software to enter other industries, especially D2C - what are the questions that matter?
Read MoreAmazon is so new, and so dramatic in its speed and scale and aggression, that we can easily forget how many of the things it’s doing are actually very old.
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 More5bn people have a mobile phone now, and 4bn have a smartphone. Time to stop making charts.
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 MoreApple’s talk about services got specific with a bunch of news subscription services. Most of them are sensible and worthy iteration, but the company still hasn’t explained exactly what it plans with its push into commissioning billions of dollars of premium TV (Spielberg! Oprah!). Maybe all of this is about trust: the old Apple promise was that you don't have to worry if the tech works, and the new promise is you don't have to worry if the tech is scamming you.
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 MoreFacebook’s struggle with abusive behaviour today looks a lot like Microsoft’s struggles with malware 20 years ago: you think open is always better, until people take advantage, and then you have to pivot from default open to default closed.
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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