Next year’s growth market: Smart Speaker development

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It’s officially the last Monday of 2018 – wild! We made it to the end of the year and everything is still more or less intact, who would have guessed?

If you’re looking for a good New Year’s Resolution for 2019, might I suggest learning how to build integrations for smart home speakers?

The smart speaker market reached critical mass in 2018, with around 41 percent of U.S. consumers now owning a voice-activated speaker, up from 21.5 percent in 2017.

Sarah Perez, Techcrunch (link)

What this means in practice is that 41% of American consumers are now on a new platform with big commercial potential, and chances are it’ll continue to grow over the next few years.

In effect, this is a whole new market – the next iteration of the mobile platform/app wars, except there’s a lot less UI overhead.

It also forecast that Alexa would generate $18 billion to $19 billion in total revenue by 2021 — or ~5 percent of Amazon’s revenue — through a combination of device sales, incremental voice shopping sales and other platform revenues.

Sarah Perez, Techcrunch (link)

The magic ultimately comes down to the integrations. Alexa calls them “Skills”, Google calls them “Actions”, but they’re the same thing: Writing conversational dialog that lets end-users engage with your service verbally.

This was the market that Siri was supposed to unlock, but it turns out that people already on their phones are more likely to use their phones to get things done, instead of switching over to a clunky voice interface.

The most simple explanation is that app developers have limited resources, and don’t see the point in supporting Siri when users aren’t demanding it.

Jared Newman, Fast Company (link)

Home speakers are a different story. They’re always on, don’t require you to physically interact with them, and have the effect of enabling any given room with something approaching ambient intelligence – all with purpose-built microphone arrays and optimizations for working in home environments. Basically, for quick commands, they’re going to be more useful to more people.

It already looks like Alexa has taken the lead on revenue potential (it’s backed by a global retail giant, so this is not surprising), but both Alexa and Google Home would be good targets to build for.

Side note: Apple’s HomePod devices are nowhere to be found (5% marketshare), and their SDKs and learning requirements tend to be a lot steeper than Amazon or Google, so if you’re not already in that ecosystem it might not be worth your time to start.

Google’s platform is called Actions, and you can learn more here: https://developers.google.com/actions/

Alexa’s platform is called Skills, their developer site is here: https://developer.amazon.com/alexa

If you’re new to the world of building conversational interfaces, I strongly suggest starting with the simpler apps:

Google’s Dialogflow: https://dialogflow.com/
Voiceflow for Alexa: https://www.getvoiceflow.com/

This is all within the same problem domain as chatbots (you’re creating conversational flows with voice instead of text), so anything you learn here is going to be easily transferable to other platforms and problems. Well worth your time, in my opinion!

For myself though, I doubt I’ll ever own a smart speaker at home (the creep factor is a bit too much for me), but I might end up building one or two integrations for any products I end up developing.

Are you going to try developing something in this area next year?

Mobile is eating the future

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I cannot not write about this excellent presentation from a16z:

Mobile is eating the world by Benedict Evans

Two big things that stand out for me (and there’s lots of high-density info in that deck):

Machine Learning
The ability for computers to understand the world around us got infinitely better once humans were taken out of the equation. Neural networks that train against vast sets of data and write their own rules turned out to be a lot more efficient than having human specialists trying to write those rules by hand.

Somewhere in 2016 (or maybe even as early as 2014) we crossed the first Rubicon: Human engineers may no longer be capable of keeping up with the intellectual growth of the machines they used to manage.

Fantastic news for scale and growth – computers can now write better and more efficient software, which in turn gets loaded on to ever-smaller and lower-powered devices. Ambient intelligence is just around the corner.

Slightly worse news for governance and accountability – at what point does the outcome of a program stop being the responsibility of the human engineers, and start becoming the responsibility of the neural network that designed its own decision tree?

The day we need to prosecute a neural network for a crime – that’ll be the second Rubicon. Once software has legal standing, the game changes again. Probably not for the better.

Mobile applied to Automotive
Mobile phones scaled out a hell of a lot faster than PCs ever could (hence the title of the presentation), but one of the things it has made a significant impact on is manufacturing. The halo effect of having so many compact, mass-produced components means that hardware is no longer a true differentiating factor, and it’s much more about the software and services that power those devices.

The same could be true for cars. We might be heading into a future where cars (taking “electric” for granted here) are assembled in the same way that smartphones are today (just by pulling off-the-shelf interoperable components together), and the key differentiator will be the services rendered through that car.

Which leads to the interesting thought of “Automotion-as-a-Service”.

I wonder if SaaS-type pricing will ever apply to the automotive industry. Bundled minutes become bundled miles, personal assistant integrations cost extra, and you get cheaper packages if you accept in-car targeted advertising. Somehow I think that might happen.

Assuming there isn’t a Final Optimization at some point, where the neural networks collectively decide we’re too much trouble to deal with 😉

The perks of yesteryear

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Alphabet’s new CFO seems intent on deflating the magical bouncy castle that is Google. They’ve always been known for incredible offices, fantastic perks, and moonshot projects.

… employees were informed that their holiday gift this year was a donation to charity, Fortune has learned. Alphabet donated $30 million worth of Chromebooks, phones, and associated tech support to schools on its employees’ behalf.

Ouch.

Being told that nobody’s getting expensive holiday gifts this year? That’s reasonable – it’s not a very common perk.

Being told that not only are you not getting any gifts, but the budget that would have been spent on that is being donated on your behalf somewhere else? That has to sting a little.

I wonder what’s next on Porat’s chopping block – and I wonder if anyone’s worried about the 20% rule being at risk. If the C-suite wants fiscal responsibility at the cost of talent-attracting perks, Google might end up an ad publishing company that happens to have a IaaS platform.

Which will be a sad Google.

Source: Alphabet Donated Its Employees’ Holiday Gifts to Charity

Robot Wars 2020

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In terms of AI research (and overall firepower), there are three major commercial factions spinning up, and there’s also an early (ergo promising) commitment to open-source.

One recurring theme right up front: Both Microsoft and Google have tools for training AIs to play games – OpenAI’s Gym, and Google’s DeepMind Lab. So I guess, if nothing else, game developers will eventually have an easier time when it comes to designing the CPU players!

Microsoft

Microsoft already has some of the “basic” business applications up – machine learning APIs, cognitive services, and so on. In November, Elon Musk’s OpenAI foundation partnered with Microsoft to use Azure as the primary cloud platform.

Some of the cool things in the Microsoft/OpenAI camp:

Microsoft is open-sourcing a decent amount of this stuff too. Not that I’d personally do anything with it.

Their avatar in the ring is Cortana – backed by Cognitive Services, no doubt: https://www.microsoft.com/en/mobile/experiences/cortana/

Google

Google’s got a lot of the same stuff out there that Microsoft has, especially in terms of web services. Where Microsoft has Azure, Google has the Google Cloud Platform. https://cloud.google.com/products/machine-learning/

They’ve also recently open-sourced the entire DeepMind Lab: https://deepmind.com/blog/open-sourcing-deepmind-lab/

Of course, the part I’m personally fascinated by? The Blizzard/DeepMind partnership, to train the DeepMind AI on how to play Starcraft 2: http://us.battle.net/forums/en/sc2/topic/20751114921

(Finally, someone might be able to give those Koreans a run for their money)

IBM

Then there’s IBM, who has a very big, very impressive, and incredibly opaque lead over the competition in one narrow area: Deep Learning.

http://www.ibm.com/watson/

That’s five years ago. That server rack at 02:33 is now probably a fifth of the size, if not totally relegated to the cloud.

Since then, the tech behind Watson has grown a bit. The tech is designed to mine large sets of unstructured data, forming connections, and being able to answer questions based on that data – so it’s got some really powerful analytics.

All of that is being sold as a service, though – don’t expect IBM to open-source anything too soon. Which I personally think will make them irrelevant by 2020.

2020

If the next four years are anything like the last four years (in that everything seems to be speeding up), we’re almost-definitely going to see AI-as-a-Service popping up for lots of different problem domains, personal assistants that can grasp context and finally be useful, and if we’re lucky, the use of AI for things like smarter energy and resource management.

My money’s still, predominantly, on Microsoft to come out as the leader in this new field. Call me crazy 😉

 

On Record

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Boy, have I got a story for you.

9296b8b380b562bb19cb754529fcc741cc376025.png
Urination-over-IP, I guess.

So yes, that’s me, in the screenshot above, calling the death of Apple’s relative lead in the app ecosystem wars – on 11 November 2016. When that thread dried up, I told myself that as soon as I got my new .blog domain hooked up to The Grid, I’d write a more detailed post explaining the reasons why.

(Then I found out that The Grid is crap, and set up here on WordPress instead)

For context: That conversation came out of a bit of hand-wringing around Apple’s new Touch Bar (and a few other really odd technical decisions on the part of Apple). One of the recurring themes from hardcore, longtime Apple users is that the “Pro” part of MacBook Pro became disingenuous with this release. The Mac is no longer for professionals.

There’s two main threads behind my comment though, so let me start with the company itself.

Post-Jobs Apple

To put it flatly: Apple died with Jobs.

I don’t mean the company itself – it’s still the most valuable in the world by market cap, and has brand equity second to none. As a going concern, it’ll be going for a very, very long time

I don’t mean the products, though Apple has recently started trimming some of their smaller lines. We’ll have iPhones and MacBooks for years (if not decades) to come.

What I’m talking about is the spirit of Apple – the drive, the mystique, the vision, the quasi-religious, standard-setting, trail-blazing aspect of owning Apple hardware. That’s gone, and the Touch Bar was the final nail in that coffin.

Among the many things that Jobs did, when it came to the Mac he only seemed to have two objectives in mind:

  1. Apple will build the best machine possible – from the hardware to the software
  2. Apple will enable creatives, dreamers and makers

The MacBook itself became a sort of paragon – a golden standard for notebook design. Every generation was thinner and lighter. Apple introduced Retina resolution, the best touchpad on the market (still undefeated in my opinion), unibody design, relentless optimization to the keyboard, and occasional ground-up rewrites to ensure that OSX would remain stable and performant.

Very few of those choices were informed by market forces. When it comes to designing a product to target a market of any sort, most companies will typically do the least they can get away with. There’s a cost/benefit formula to everything: How much money sunk into R&D, versus how many sales are required to make a profit.

Problem with that is, markets are generally full of shit. Consumers don’t know what they want until you put it in front of them – one of the many insights that drove Jobs, and by extension, Apple.

It didn’t matter to Apple that they were sinking far more R&D time into parts of the device that most consumers wouldn’t ever touch. It didn’t matter which way the market was going at any one point. If they made a decision (even if unpopular) – consumers be damned. Apple owned the game.

And that worked out very well for the makers – the photographers, video editors, software engineers, designers and artists. They could rely on successive generations of the MacBook Pro line to thoroughly equip them to create better things, no doubt driven by an obsessive CEO who was never satisfied with the output.

When you build the best, and sell the best, inevitably you attract the best. There’s a small, but significant halo effect that Apple has created here: Their hardware has attracted the best developers. Not just the developers that code for money, but also the ones that code because it’s their lives.

And when those developers need to solve a problem, chances are they’ll use the best tools at their disposal to do so. Over time, this meant that the brightest and most capable engineering talent accumulated within the Apple ecosystem. It should be no small wonder that the Android and Windows app stores are currently seen as second-class citizens, or that MacBooks are effectively mandatory at any tech startup.

All of that talent has a material impact, too. Consider the iPhone – the hardware tends to stay ahead in some areas, the operating system is often criticized for being feature-limited, but the app store is second to none.

Which does make a big difference. I’ve owned several different phones, the best of which (hardware-wise) was a Lumia 1520. Brilliant screen, camera, battery and touch surface, but the apps were barely functional, and less than two months into the contract I bought a new phone out of sheer frustration. I know I’m not the only one.

You can do much more (and much better) with an iPhone than you can with any other device, which is why this chart should not come as a big surprise:

2af803fa-c0dd-4158-b453-61aa05e59e55.png
Respective FY-2015 totals taken from audited financials

In FY 2015, the Apple iPhone product line alone has generated more revenue than entire competitors.

And don’t underestimate the ripple effect to major software vendors. Producers of high-end creative software packages (Photo manipulation, video editing, sound editing) aim for the Mac platform because that’s where the high-end creatives are. If that market starts drying up, so too do the updates that regular users benefit from.

No wonder everyone – users and analysts – think that Apple is unstoppable.

Except that it is, because it’s failing to do two things right now.

First: In the short term, it just failed to equip high-end creative professionals with the best possible hardware. In the wake of the new MacBook ‘Pro’ lineup, long-time Apple users are starting to talk about defecting to other platforms. This will eventually have a degrading impact on the Apple ecosystem as a whole, especially if another vendor is standing by to give those power users what they need.

Which Microsoft neatly did with the Surface Studio this year – a desktop machine aimed squarely at designers and digital artists. If they release a better notebook for developers, I’m willing to bet quite a few luminaries would seriously consider switching to the Microsoft ecosystem.

But there’s another thing that Apple’s failed to do, and I think this is the one that really matters.

Computing is changing

For as long as we’ve kept records, we’ve needed to process them – and for a very long time, manual processing was OK. It’s hard to imagine now, but there was a time that drawing bar graphs was an actual profession.

Computers came along as a grown-up version of basic calculators. The biggest benefit was that you could change how the computer processed information, by providing more information: software.

Since then, all we’ve really done is more of the same. Computers have gotten millions of times faster at processing instructions, and computing languages have been developed to make programming within reach of almost everyone.

Software has been getting more powerful and more sophisticated over time, but has always been bound by a very simple constraint: It required a human to learn how to program a machine. Before you could make a computer do anything, you need to understand how to solve the problem yourself, then instruct the computer how to solve it.

That’s starting to change with recent advances in Machine Learning (more specifically, Neural Networks). It’s a simple but powerful layer of abstraction: Instead of telling computers what to do, we’re teaching them how to decide what to do for themselves.

Here’s a simple example: https://quickdraw.withgoogle.com/

That’s a simple neural network game. It processes the images you draw, and matches them against similar images that other people have drawn. Over time, it learns to recognize more variations of objects. There may come a time when it has so many variations stored, its accuracy becomes close to human (if not perfect).

However, that machine was not programmed by a human to recognize every possible shape. It was built, instead, to find patterns, and to correlate those patterns with ones it’s seen before. That’s the difference.

Problem-solving itself is starting to change. In future, instead of solving problems by writing machine instructions, the heaviest problems will be solved by building machines that can learn, and by training them to solve those problems for us.

Today, solving these problems requires the use of cloud computing. Sure, you can run a small neural network on your laptop, but to give it true power it has to scale out to hundreds of computing nodes in parallel.

And so, today, there are two vendors which are leading the field here: Google and Microsoft.

Google’s Cloud Platform is exposing APIs that developers can start using today to add natural interactivity to their applications, and they’re already doing cool things with neural networks – for instance, zero-shot translation.

Microsoft, through the Azure platform, is building towards making even stronger capabilities available to end-users. They already claim human-parity speech recognition, and have recently partnered with OpenAI.

Personally, I think Microsoft is leading this race. Google’s got the edge on quantity – their entire infrastructure is geared towards processing large amounts of data and finding useful relationships. Microsoft, on the other hand, has way more infrastructure, a stronger research team, and seems better-equipped to tackle the more interesting use cases.

In any case: Apple is nowhere to be found. The company that built itself on the quality of its hardware, equipping high-end creatives and reaping the benefit of their participation in the ecosystem, has precisely zero play in the AI game.

So that’s the rationale behind my position. Vendors other than Apple are building the tools that power-users of the future will require, and so will attract more power-users. They, in turn, will have the same halo effect on the Microsoft (and/or Google) ecosystems.

Ergo, I’m “on record”:

I can call it right now: The day will come when the Windows app ecosystem rivals the OSX ecosystem for quality, and after that, we’ll come to think of Apple vs Microsoft as Myspace vs Facebook.

To wrap it up in tl;dr terms:

  • The future game is about AI, neural networks and machine learning
  • The winner will be the vendor that can solve for the most complex problems in the most cost-efficient way
  • Microsoft is currently positioned to build the best hardware/software/services ecosystem to enable developers to do just that

Apple will lumber on as a consumer brand. The core value proposition (Apple hardware is for makers) has now been sacrificed on the altar of market forces. Whoever comes up with the best ecosystem for AI will win on the software front, which will be the only front that matters in the end.