Internet 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?
Read MoreAmazon’s Alexa has been a huge, impressive and unexpected achievement. Amazon created a category from scratch and left both the AI leader Google and the device leader Apple scrambling in its wake. It’s now sold 100m units. So far, though, this success is pretty contingent - we do still have to ask what Amazon actually gains from this. What do consumers do with these devices that helps Amazon? What fundamental strategic benefit does it get? Amazon has put an end-point into tens of millions of homes - what does it do with it?
Read MoreMachine learning is probably the most important fundamental trend in technology today. Since the foundation of machine learning is data - lots and lots of data - it’s quite common to hear that the concern that companies that already have lots of data will get even stronger. There is some truth to this, but in fairly narrow ways, and meanwhile ML is also seeing much diffusion of capability - there may be as much decentralization as centralization.
Read MoreEveryone has heard of machine learning now, and every big company is working on projects around ‘AI’. We know this is a Next Big Thing. But we don’t yet have a settled sense of quite what machine learning means - what it will mean for tech companies or for companies in the broader economy, how to think structurally about what new things it could enable, and what important problems it might actually be able to solve.
Read MoreThe trap that some voice UIs fall into is that you pretend the users are talking to HAL 9000 when actually, you've just built a better IVR, and have no idea how to get from the IVR to HAL. How can we find the mental models for this to work - to bring less rather than more friction?
Read MoreMachine learning means every image ever taken can be searched or analyzed and insight extracted, at massive scale. Every glossy magazine archive is now structured data, and so is every video feed. How does this change retail?
Read MoreWith Amazon's Echo, Snapchat Spectacles or the Apple Watch, we're unbundling not just components but apps, and especially pieces of apps. We take an input or an output from an app on a phone and move it to a new context. We remove friction, but we also remove choices.
Read MoreWe’re going from computers with cameras, that take photos, to computers with eyes, that can see.
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