The end of mobile


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I’ve been making charts of internet use, mobile phones and smartphones since the early 2000s. At one point, they were confounding and exciting – could it really be growing that fast? How many people would have these things? Now, we know the answer: everyone. Everyone would have one.

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There are about 5.3bn people on earth aged over 15. Of these, around 5bn have a mobile phone. This is an estimate: I’m going with the GSMA’s but most others are in the same range. The data challenge is that mobile operators collectively know how many people have a SIM card, but a lot of people have more than one. Meanwhile, ownership starts at aged 10 or so in developed markets, whereas in some developing markets half of the population is under 15, which means that a penetration number given as a share of the total population masks a much higher

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Notes on AI Bias


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Machine learning is one of the most important fundamental trends in tech today, and it’s one of the main ways that tech will change things in the broader world in the next decade. As part of this, there are aspects to machine learning that cause concern – its potential impact on employment, for example, and its use for purposes that we might consider unethical, such as new capabilities it might give to oppressive governments. Another, and the topic of this post, is the problem of AI bias.

It’s not simple. 

  1. What is AI Bias?

  2. What could go wrong (and right)?

  3. What do we do about it?

What is ’AI Bias’?

“Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked, with care.”

-Geoffrey Bowker

Until about 2013, If you wanted to make a software system that could, say, recognise a cat Continue reading “Notes on AI Bias”

Finding the point of human leverage


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One of the paradoxes of today’s internet platforms is they are vastly automated, and have no human control or interaction over what any given person sees, and yet they are also totally dependent on human behavior, because what they’re really doing is observing, extracting and inferring things from what hundreds of millions or billions of people do.

The genesis of this was PageRank. Instead of relying on hand-crafted rules to understand what each page might be about, or indexing the raw text, PageRank looks at what people have done or have said about that page. Who linked to it, what text did they use, and who linked to the people who linked to it? And at the other end of the pipe, Google gets every user to curate every set of search results by hand: it gives you 10 blue links and you tell Google which one was right. The

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Apple Plus – brand versus subscription


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  • We expected a TV event and got a subscription event – news & magazines, games, a credit card and Oprah. There are plenty of product questions one could ask (especially on TV, where we still don’t know the scale of the ambition – is Apple taking on Netflix and spending $10bn+, or spending $1bn for ‘marketing’?) But mostly this feels like solid incremental execution: product managers doing product management

  • The obvious view: these subscription services are about cutting churn and driving incremental revenue – $10-$20-$30 per user for hundreds of millions of iPhones, plus more reasons (especially the Card) why it’s harder to switch from iPhone to Android.

  • More interesting: Apple’s evolving brand promise. The old Apple promise was that you don’t have to worry if the tech works. The new promise is you don’t have to worry if the tech is scamming you. Everything Apple showed was about curation, safety Continue reading “Apple Plus – brand versus subscription”

Smart home, machine learning and discovery


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  • ‘Smart home’ today is in the same place as electric things in the home a generation or two ago: everyone will have some of these things, but we’re working out which makes sense. Everyone got a toaster or a blender, but no-one got an electric can opener, and smart home look the same. We’re in discovery mode.

  • Machine learning has a lot of the same questions: how do we combine these commodity components into products that make sense – both in the home and on platforms and smartphones?


My grandparents could have told you how many electric motors they owned. There was one in the car, one in the fridge, one in the vacuum cleaner, and they probably owned a dozen in total. Today we have no idea and it’s not a meaningful question, but we probably do know how many devices we own with a network connection. Again, our Continue reading “Smart home, machine learning and discovery”

Microsoft, Facebook, trust and privacy


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  • There are strong parallels between organised abuse of Facebook and FB’s attempts to respond, in the last 24 months, and malware on Windows and Office and Microsoft’s attempts to respond, 20 years ago.  

  • Initial responses in both cases have taken two paths: tactical changes to development and API practices to try to make the existing model more secure, and attempts to scan for known bad actors and bad behavior (virus scanners then and human moderators now) 

  • For Microsoft’s malware problem, however, this was not the long-term answer: instead the industry changed what security looked like by moving to SaaS and the cloud and then to fundamentally different operating system models (ChromeOS, iOS) that make the malware threat close to irrelevant.

  • Facebook’s pivot towards messaging and end-to-end encryption is (partly) an attempt to do the same: changing the model so that the threat is irrelevant. But where the move to Continue reading “Microsoft, Facebook, trust and privacy”

Cameras that understand: portrait mode and Google Lens


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I’ve talked quite a lot about the impact of machine learning and computer vision in general on everything from e-commerce recommendation to social to all kinds of cool industrial applications, but it’s also interesting just to look at the effect that machine learning is having on actual cameras. 

For both Apple and Google, most of the advances in smartphone cameras now happen in software. The marketing term for this is ‘computational photography’, which really just means that as well as trying to make a better lens and sensor, which are subject to the rules of physics and the size of the phone, we use software (now, mostly, machine learning or ‘AI’) to try to get a better picture out of the raw data coming from the hardware. Hence, Apple launched ‘portrait mode’ on a phone with a dual-lens system but uses software to assemble that data into a single

Yes, this is ‘Khrushchev Remembers’
Yes, this is a Taccia lamp
No, this is not a bath

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Is Alexa working?


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Amazon’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 (including third party devices with Alexa embedded) – not all of these will equate to active users, but if even 50m are in use this is still a significant proportion of  Amazon accounts. After the very limited success of the Fire tablet and the failure of the Fire Phone, Amazon has a big success. 

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?

Answering this Continue reading “Is Alexa working?”

5G: if you build it, we will fill it.


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In early 2000, right at the top of the dotcom bubble, the mobile bubble and the broadband bubble, European mobile operators spent €110bn on licenses for 3G spectrum. Now, almost 20 years later, I’ve just got back from CES, and 5G is a Topic. Many of my friends at big companies tell me that ‘what is 5G?’ floats around a lot of corporate headquarters almost as much as ‘what is machine learning?’ does. 

There are a bunch of different ways to answer this. If I was still a telecoms analyst, I would be spending a lot of time thinking about spectrum, deployment schedules and capex – mobile operators around the world spend several hundred billion dollars a year on network capex, and 5G will become a big part of that. I’d talk about network efficiencies, refarming, vendors, Huawei, chipsets, and maybe NFV. But I’m not a telecoms Continue reading “5G: if you build it, we will fill it.”

Does AI make strong tech companies stronger?


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Machine 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. 

First, what does it mean to say that machine learning is about data? Due to the academic culture that ML comes from, pretty much all of the primary science is published as soon as it’s created – almost everything new is a paper that you can read and build with. But what do you build? Well, in the past, if a software engineer wanted to create a system to recognise something, they Continue reading “Does AI make strong tech companies stronger?”

The end of the beginning


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I gave this presentation at a16z’s annual tech conference last week.

Close to three quarters of all the adults on earth now have a smartphone, and most of the rest will get one in the next few years. However, the use of this connectivity is still only just beginning. Ecommerce is still only a small fraction of retail spending, and many other areas that will be transformed by software and the internet in the next decade or two have barely been touched. Global retail is perhaps $25 trillion dollars, after all.

Meanwhile, as companies address more and more of this with software and the internet, they do it in new ways. We began with models that presumed low internet penetration, low speeds, little consumer readiness and little capital. Now all of those are inverted. So, we used to do apartment listings and now Opendoor will buy your home; we used Continue reading “The end of the beginning”

Tesla, software and disruption


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“We’ve learned and struggled for a few years here figuring out how to make a decent phone. PC guys are not going to just figure this out. They’re not going to just walk in.” – Ed Colligan, CEO of Palm, 2006, on rumours of an Apple phone

“They laughed at Columbus and they laughed at the Wright brothers. But they also laughed at Bozo the Clown.” – Carl Sagan

When Nokia people looked at the first iPhone, they saw a not-great phone with some cool features that they were going to build too, being produced at a small fraction of the volumes they were selling. They shrugged. “No 3G, and just look at the camera!” 

When many car company people look at a Tesla, they see a not-great car with some cool features that they’re going to build too, being produced at a small fraction of the

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 In this chart, grey is 2017, orange is 2023 and yellow is 2028
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Ways to think about machine learning


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We’re now four or five years into the current explosion of machine learning, and pretty much everyone has heard of it. It’s not just that startups are forming every day or that the big tech platform companies are rebuilding themselves around it – everyone outside tech has read the Economist or BusinessWeek cover story, and many big companies have some projects underway. We know this is a Next Big Thing.

Going a step further, we mostly understand what neural networks might be, in theory, and we get that this might be about patterns and data. Machine learning lets us find patterns or structures in data that are implicit and probabilistic (hence ‘inferred’) rather than explicit, that previously only people and not computers could find. They address a class of questions that were previously ‘hard for computers and easy for people’, or, perhaps more usefully, ‘hard for people to describe to computers’.

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The death of the newsfeed


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When I got married, my future wife and I were both quite sure that we would have a nice small, quiet wedding – none of these massive, extravagant parties with hundreds of people for us! We’d just invite close family and friends. Then, we actually made a list of ‘close family and friends’… and realized why people have 100 or 200 people at a wedding. You know a lot more people than you think.

I was reminded of this recently by the fact that, according to Facebook, its average user is eligible to see at least 1,500 items per day in their newsfeed. Rather like the wedding with 200 people, this seems absurd. But then, it turns out, that over the course of a few years you do ‘friend’ 200 or 300 people. And if you’ve friended 300 people, and each of them post a couple of pictures, tap like Continue reading “The death of the newsfeed”

Steps to autonomy


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The standard way to talk about autonomous cars, shown in this diagram, is to talk about levels. L1 is the cruise control in your father’s car. L2 adds some sensors, so it will try to slow down if the car in front does, and stay within the lane markings, but you still need to have your hands on or near the wheel. L3 will drive for you but you need to be ready to take over, Level 4 will drive for you in some situations but not others, and Level 5 doesn’t need a human driver ‘ever’ and doesn’t have a steering wheel.

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This seems pretty straightforward, until you start thinking about how you might actually deploy this – and about the fact that some places are easier to drive in than others. 

As we can already see with the early tests being done with prototype autonomous cars (with Continue reading “Steps to autonomy”

Steps to autonomy


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The standard way to talk about autonomous cars, shown in this diagram, is to talk about levels. L1 is the cruise control in your father’s car. L2 adds some sensors, so it will try to slow down if the car in front does, and stay within the lane markings, but you still need to have your hands on or near the wheel. L3 will drive for you but you need to be ready to take over, Level 4 will drive for you in some situations but not others, and Level 5 doesn’t need a human driver ‘ever’ and doesn’t have a steering wheel.

IMG_0274.JPG

This seems pretty straightforward, until you start thinking about how you might actually deploy this – and about the fact that some places are easier to drive in than others. 

As we can already see with the early tests being done with prototype autonomous cars (with Continue reading “Steps to autonomy”

Bridges and LIDAR


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In 1999, when WAP was the future of mobile, the industry group behind SIM cards worked out a way to use the programmable space on a SIM to build a complete WAP browser. This meant that instead of having to wait for consumers to buy new phones with WAP built-in, mobile operators could push a WAP browser onto every phone already in use over the air and get people to start using these services straight away. 

This looked like genius – if you worked for the SIM industry group. The problem was that any phone that hadn’t shipped with a WAP browser also, ipso facto, had no kind of dedicated data network access (GPRS at the time) and so would be accessing these services over dial-up at something under 9.6 Kbits/second (and paying per minute for call time), and also almost certainly only had a one or two Continue reading “Bridges and LIDAR”

Bridges and LIDAR


This post is by Benedict Evans from Benedict Evans


Click here to view on the original site: Original Post




In 1999, when WAP was the future of mobile, the industry group behind SIM cards worked out a way to use the programmable space on a SIM to build a complete WAP browser. This meant that instead of having to wait for consumers to buy new phones with WAP built-in, mobile operators could push a WAP browser onto every phone already in use over the air and get people to start using these services straight away. 

This looked like genius – if you worked for the SIM industry group. The problem was that any phone that hadn’t shipped with a WAP browser also, ipso facto, had no kind of dedicated data network access (GPRS at the time) and so would be accessing these services over dial-up at something under 9.6 Kbits/second (and paying per minute for call time), and also almost certainly only had a one or two Continue reading “Bridges and LIDAR”

Bridges and LIDAR


This post is by Benedict Evans from Benedict Evans


Click here to view on the original site: Original Post




In 1999, when WAP was the future of mobile, the industry group behind SIM cards worked out a way to use the programmable space on a SIM to build a complete WAP browser. This meant that instead of having to wait for consumers to buy new phones with WAP built-in, mobile operators could push a WAP browser onto every phone already in use over the air and get people to start using these services straight away. 

This looked like genius – if you worked for the SIM industry group. The problem was that any phone that hadn’t shipped with a WAP browser also, ipso facto, had no kind of dedicated data network access (GPRS at the time) and so would be accessing these services over dial-up at something under 9.6 Kbits/second (and paying per minute for call time), and also almost certainly only had a one or two Continue reading “Bridges and LIDAR”

Smart homes and vegetable peelers


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A few weeks ago I spent several days marching around CES in Las Vegas (along with close to 200,000 other people), and as in previous years I saw ‘smart’ versions of just about anything you can imagine and many you can’t. I also heard just about any thesis you can imagine, from ‘this is all nonsense’ to ‘this is the next platform and voice-based AI will transform our homes and replace the smartphone.’

I’m not quite sure what my grand unified thesis on ‘smart home’ is, but I think there are some building blocks to try to get closer to one:

  1. Will people buy ‘smart’ anything at all? Will people buy a whole lot of smart things, or just one or two (for example, a door lock, a thermostat and nothing else). Why?
  2. If they do buy more than a handful of things, will they all be connected into Continue reading “Smart homes and vegetable peelers”