A few weeks ago my friend and fellow science fiction writer, Ramez Naam, posted a link to an article debunking some myths about bulletproof coffee. Then today I noticed a link on Reddit about a professor at Kansas State University who went on a convenience store diet eating Twinkies to prove that counting calories is what matters most in weight loss, not the nutritional value in food.

I am all for science, and I love to understand exactly how things work and why things have the effect they do. But often I think that in our zeal to get to the truth we overlook the practical question of what actually works.

Let me give you an example. If you ask a dentist whether it’s better to floss before you brush your teeth or after you brush your teeth, they’ll tell you it doesn’t matter. Both are equally effective at preventing dental problems. However, if you look at how many people continue flossing, that is, they stay in compliance with the regimen of flossing, then you find there is a difference. People who floss after they brush their teeth are more likely to continue flossing. (Sorry, I read this a year or two ago, and can’t find a link now.)

Why is this important? Well if you look at diets, the most important factor in weight loss is not how effective the diet is, but in how compliant people are. It’s easy to start a diet, hard to stay on it. Staying on it is the challenge for for most people.

Perhaps we could, in theory, eat exactly 1200 calories of Twinkies every day and lose weight, but in practice how likely are we to continue counting calories meal after meal, week after week?

I think the value that people get out of approaches like bulletproof coffee, low-carb diets, or other structural approaches to dieting (in which the emphasis is on eliminating certain foods rather than counting calories) is that for some people those diets are easier to stick with. This moves us out of the realm of basic chemical/biological science (which is how you might measure effectiveness of a diet), and into the realm of psychology (which is probably where the majority of compliance comes from.)

But even if we evaluate diets for compliance, it doesn’t mean there’s one best solution for all people. Some people might do really well with one diet, and other people do better with a different one. We all have different favorite foods, eating habits, and tolerance for eating the same foods over and over. For other people, a low-carb diet might work really well, others might like to replace breakfast with bulletproof coffee, still others use exercise, and some count calories, and some blend multiple approaches.

So when we see a piece of research that says counting calories is what matters most in weight-loss, we know that it’s wrong. What matters is the combination of compliance (whether people can stick to the diet for whatever period of time is necessary) and effectiveness (how much weight is lost when you’re in compliance.)

Yes, we need science and the understanding of fundamental principles and theory. But what also need to know is how things work in practice, and not just in a general population, but specifically for us.

The way we get there is through personal experimentation. Be willing to try something (within reason, of course) for a period of time and see how it works. If it doesn’t work for you, it doesn’t matter that science says that it works for 80% of people. It only matters that it doesn’t work for you. Learn that it doesn’t work, and then move on to a different trial.

Conversely if something is working for you, then it doesn’t matter if science can’t explain it. It’s working. Don’t mess with it.

When I was a kid, my first computer was a TRS 80 Micro Color Computer 2. It wasn’t the big Trash-80 that most people had. It was a tiny thing, with a chiclet keyboard, and an expansion port on the back to allow you to upgrade from 4 kB of memory to 20 kB. I think it costs $99 and another 20 or 30 for the memory expansion.

When I was 16, I got an Apple II E. This had seven expansion slots which could be used to upgrade memory, add storage, and video capabilities, or add modems. (I had seven modems and was running a chat system, but that’s another story.) My next computer was an Amiga 1000. Although it wasn’t designed for upgradability, I bought an expansion kit which was a daughter board that plugged into the CPU socket and allowed me to upgrade to 1.5 MB of RAM. Later I bought another expansion Kit that was another daughter board that allowed me to replace the 8 MHz CPU with a 16 MHz CPU. I was able to attach three disk drives, and I had an expansion port that would have allowed me to connect a SCSI hard drive if I could’ve afforded one.

After the Amiga 1000, I had a series of IBM PCs and compatibles from 1989 to 2008. What defined the PC’s was a complete ability to build them from scratch and upgrade components as needed. The metal chassis, or box that housed the computer, might need to be upgraded every 10 years or so. The motherboard might be upgraded every four years. The RAM, hard drives, and CPU might be upgraded every two years. This was far more environmentally friendly and cost-effective than buying a new computer every three years.

In 2009 or so, I started using Macs. I love OS X, the Mac operating system. And I love most of the applications that run on the Mac. It’s far more stable than Windows, lower maintenance, and often easier to use. Because it’s built on UNIX, I can use all the best programming tools.

However the Macs I’m buying are laptops and laptops are inherently less upgradable. That isn’t to say they’re not upgradable at all. Over the Christmas break I upgraded the older MacBook Pro laptops in our house. In both cases I replaced the magnetic platter hard drive with a much faster SSD, and upgraded the memory: in one case doubling the memory, and in the other case quadrupling the memory.

It was an easy upgrade to do. It took about five minutes to open the case and replace the memory. It may be another five minutes to replace the hard drive. I could have chosen to restore everything from Time Machine, which would’ve been very quick. But in this case I chose to rebuild the operating system and applications from scratch to get a clean install.

By doing this upgrade on these three or four-year-old computers, I just gave them at least another three or four year lifespan. Again, this is environmentally friendly and economically the best approach. It cost about $200 to upgrade one Mac and about $300 to upgrade the other. To buy a comparable machine would have cost between 1000 and $1500.

Now for the bad news. The two most recent laptop purchases in our house were retina MacBook Pros. These are the extra thin models that don’t have a CD drive. They also don’t have upgradable hard drives or memory. This means they’re stuck with whatever you buy. There’s no way to upgrade them, no way to extend their life. Yes, they are beautiful, sleek, lightweight machines. But from an environmental lifecycle and cost they are inferior to their predecessors.

I can somewhat understand cheap electronics, things that costs under $100 or $200, being non-upgradable and simply replaced at the end of their life. But for computers that cost $1000 or more, and embody substantial environmental impact, it is irresponsible and shortsighted to not make them upgradable. I hope that we’ll see a return to upgradable computers in the future.

I saw The Imitation Game with Erin last night. This is the movie based upon the life of Alan Turing, the British mathematician who helped break enigma, and conceived of general purpose computing (à la Turing machines), and is famous for the concept of the Turing test. 

The Turing test, of course, was part of the inspiration for the title The Turing Exception for my new novel.

Although I knew a bit about Alan Turing from past reading and studies I was lucky enough to see George Dyson, author of Turing’s Cathedral, speak at the Defrag conference in November. George Dyson is a science historian and brother of technology analyst Esther Dyson. George gave a great keynote presentation at Defrag and I got to spend an evening chatting with him about Alan Turing, early physicists and mathematicians, the war effort, technology, artificial intelligence, and the singularity. In all, it was a fabulous discussion spanning many topics.

So I was quite excited to see The Imitation Game. From some reviews I glimpsed, it appears the movie isn’t 100% true to the historical record. But having not yet read Turing’s Cathedral, and it having been a while since I studied the details of that time, I was able to enjoy the movie without worrying about technical inaccuracies. I’d call this a must-see for anyone for has an interest in the origin of computers or cryptography.

I can be pretty sensitive to movies, so I ended up pretty emotional and crying at the end of the film. Alan Turing was a brilliant mathematician who we lost at the age of 41 because of his treatment as a homosexual.

Having seen the film, I’m now excited to go read Turing’s Cathedral.

Having related the general outlines of the story (minus the homosexual persecutation) to my kids, they were pretty interested, and wanted to know if we could create an Enigma machine. There’s a great one-page PDF paper enigma machine that allows you to perform the basics of rotor encryption.

Unfortunately, thanks to a business trip in my day job and some bad ergonomics while traveling, I’m struggling with a bout of chronic tendinitis again. So I’ve made an intentional choice to stay away from the keyboard is much as possible.

As part of that practice I bought the new version of Dragon dictate for the Mac. I’m glad to say it’s vastly improved over older versions. I first used Dragon Dictate in 2002 or so, when I first had tendinitis issues related to my day job in computer programming. Back then it was sort of comically wrong. You’d dictate a paragraph of text and maybe 50% would be right.

But the new version is quite astounding. I’ve dictated several blog post and it’s made zero gross errors. There have been a few small errors, where I have either failed to say what I meant, slurred some words together, or used words or phrases that were very uncommon (like the touring exception or patriot).

If you are familiar with speech recognition about 10 years ago, then you know that between the combination of lower accuracy and problems correcting text, it often became a comedy of errors trying to get what you wanted onto the page. But today, it’s easy enough to just read and then make a few simple corrections at the end.

Just a few years ago I investigated Dragon dictate for the Mac, but at the time the version that was out apparently was very buggy according to reviews. The current version today seems pretty darn solid and fun to use.

If you’re struggling with any kind of repetitive stress injury, give speech recognition a try again even if you had bad results in the past.

It’s been a while since my last post. I spent most of December working toward the final edits on The Turing Exception.

After two rounds of beta reader feedback and edits, I’m feeling pretty good about the way book four ended up.  the manuscript is currently with my copy editor, and I should get it back in a few weeks. Then I’ll make a few more changes and send it for a round of proofreading. Finally, there will be interior layout for the print edition and formatting for the e-book. And hopefully all that will happen by sometime in February, leading to a release by late February if possible.

Also, if you’ve been paying close attention, you’ll notice the title changed slightly. My friend Mike suggested Turing’s Exception as an idea, and that was better than any of the dozens of ideas I’d considered. But then I tested three different variations (Turing’s Exception, The Turing Exception, and Turing Exception), and The Turing Exception was vastly preferred, by about 38 out of 40 people in a poll.

As I’ve mentioned before, Patreon supporters will receive their e-books before the public release, just as soon as I can make them available. Patreon supporters at the five dollar level and above will receive their signed paperback around the time of the public release. This is because the paperback books are just not available any earlier.

You might be wondering why I have a Patreon campaign. The economics of writing are such that I still have to hold a day job in addition to selling books. Except for a few bestsellers, most writers are unable to support themselves solely by writing books.

Have you heard of the Kevin Kelley essay 1000 True Fans? The core idea is that it’s possible for an artist, writer, creator to support themselves if they can create $100 worth of product per year, and have 1000 fans will buy that product. 1000 fans times $100 equals $100,000, and therefore approximately a full-time living.

The challenge is that it’s hard for a writer to create a hundred dollars worth of product per year. I net about $2.50 per book sold, and I can publish about one per year. Even with 10,000 or 20,000 fans, that’s not a full-time income. So the idea with Patreon is to have a closer relationship with a few people, share some more of what I’m creating and create some special rewards just for supporters and hopefully get to the point where writing can support me full-time enabling me to write more than I do today.

I hope that you had a wonderful holiday and happy new year. I wish you the best in 2015.

AvogadroCorpGermanCoverThe German edition of Avogadro Corp is available for preorder from Amazon:

http://www.amazon.de/Avogadro-Corp-Gewalt-k%C3%BCnstlichen-Intelligenz-ebook/dp/B00PN7Z36Q/

It releases in paperback and kindle on December 9th. If you or a friend read German, I hope you’ll check it out.

The success of this translation will be helpful in getting the rest of the series translated to German, and all of my books translated to other languages.

 

With so many discussions happening about the risks and benefits of artificial intelligence (AI), I really want to collect data from a broader population to understand what others think about the possible risks, benefits, and development path.

Take the Survey

I’ve created a nine question survey that takes less than six minutes to complete. Please help contribute to our understand of the perception of artificial intelligence by completing the survey.

Take the survey now

Share the Survey

I hope you’ll also share the survey with others. The more responses we get, the more useful the data becomes.

Share AI survey on Twitter

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Share the link: https://www.surveymonkey.com/s/ai-risks

Thanks to Elon Musk’s fame and his concerns about the risks of AI, it seems like everyone’s talking about it.

One difficulty that I’ve noticed is agreement on exactly what risk we’re talking about. I’ve had several discussions in just the last few days, both at the Defrag conference in Colorado and online.

One thing I’ve noticed is that the risk naysayers tend to say “I don’t believe there is risk due to AI”. But when you probe them further, what they are often saying is “I don’t believe there is existential risk from a skynet scenario due to a super-intelligence created from existing technology.” The second statement is far narrower, so let’s dig into the components of it.

Existential risk is defined by Nick Bostrum as a risk “where an adverse outcome would either annihilate Earth-originating intelligent life or permanently and drastically curtail its potential.” Essentially, we’re talking about either the extinction of humankind, or something close to it. However, most of us would agree that there are very bad outcomes that are nowhere near an existential risk. For example, about 4% of the global population died in WWII. That’s not an existential risk, but it’s still horrific by anybody’s standards.

Runaway AI, accelerating super-intelligence, or hard takeoff are all terms that refer to the idea that once an artificial intelligence is created, it will recursively improve its own intelligence, becoming vastly smarter and more powerful in a matter of hours, days, or months. We have no idea if this will happen (I don’t think it’s likely), but simply because we don’t have a hard takeoff doesn’t mean that an AI would be stagnant or lack power compared to people. There are many different ways even a modest AI with the creativity, motivation, and drive equivalent to that of a human could affect a great deal more than a human could:

  • Humans can type 50 words a minute. AI could communicate with tens of thousands of computers simultaneously.
  • Humans can drive one car at a time. AI could fly all the world’s airplanes simultaneously.
  • Humans can trip one circuit breaker. AI could trip all the world’s circuit breakers.
  • Humans can reproduce a handful of times over the course of a lifetime. AI could reproduce millions of times over the course of a day.
  • Humans evolve over the course of tens of thousands of years or more. Computers become 50% more powerful each year.

So for many reasons, even if we don’t have a hard takeoff, we can still have AI actions and improvement that occur far faster, and with far wider effect than we humans are adapted to handling.

Skynet scenario, terminator scenario, or killer robots are terms that refer to the idea that AI could choose to wage open warfare on humans using robots. This is just one type of risk, of many different possibilities. Other ways that AI could harm us include deliberate mechanisms, like trying to manipulate us by controlling the information we see, or by killing off particular people that pose threats, or by extorting us to deliver services they want. This idea of manipulation is important, because while death is terrible, the loss of free will is pretty bad too.

Frankly, most of those seem silly or unlikely compared to unintentional harm that AI could cause: the electrical grid could go down, transportation could stop working, our home climate control could stop functioning, or a virus could crash all computers. If these don’t seem very threatening, consider…

  • What if one winter, for whatever reason, homes wouldn’t heat? How many people would freeze to death?
  • Consider that Google’s self-driving car doesn’t have any manual controls. It’s the AI or it’s no-go. More vehicles will move in this direction, especially all forms of bulk delivery. If all transportation stopped, how would people in cities get food when their 3-day supply runs out?
  • How long can those city dwellers last without fresh water if pumping stations are under computer control and they stop?

Existing technology: Some will argue that because we don’t have strong AI (e.g. human level intelligence or better) now, there’s no point in even talking about risk. However, this sounds like “Let’s not build any asteroid defenses until we clearly see an asteroid headed for Earth”. It’s far too late by then. Similarly, once the AI is here, it’s too late to talk about precautions.

In conclusion, if you have a conversation about AI risks, be clear what you’re talking about. Frankly, all of humanity being killed by robots under the control of a super-intelligence AI doesn’t even seem worth talking about compared to all of the more likely risks. A better conversation might start with a question like this:

Are we at risk of death, manipulation, or other harm from future AI, whether deliberate or accidental, and if so, what can we do to decrease those risks?

This presentation by Sarah Bird was one of the highlights of #DefragCon. I really loved what she said and all the data she shared.

How to Build a B2B Software Company Without a Sales Team
Sarah Bird, CEO Moz — @SarahBird

  • Moz
    • $30M/year revenue
    • growing from 2007 to current day
    • Moz makers software that helps marketing professional
  • Requirements for selling B2B software without a sales team
    • A nearly frictionless funnel
      • i hate asking for money
      • we made a company company that rarely asks you for money
      • People find our community through our Google and social shares.
        • they enjoy our free content: helpful, beautiful.
        • Q&A section.
        • mozinars: webinars to learn about SEO, etc.
      • eventually, you may sign up for a free trial. 85% of sign up for a free trial.
      • customers visit us 8 times before signing up for a free trial.
      • moz subscription: $99/month is most popular (and cheapest) plan
    • Large, Passionate Community
      • We had a community for 10 years.,
      • We were a community first. Started as a blog about SEO
      • Content is co-created and curated by the community.
      • Practice what we preach.
      • 800k marketers joined moz community.
      • Come for the content, stay for the software.
      • No sales people, but really good community manager.
        • their jobs is to foster inclusive and generous environment to learn about marketing.
    • Big Market
      • if you’re going after a small market, just hire someone to go talk to those people.
    • Low CAC & COGs business model
      • Cost of Customer Acquisition
      • Avg customer lifetime value: $980
      • average customer lifetime: 9 months
      • fully-loaded CAC: $137
      • approximate cost of providing service: $21/month
      • payback period: month 2
      • Customer Lifetime Value is on the low-end
        • moz: $980
        • constant contact: $1500
        • but we have the highest CLTV/cost ratio
        • cost
          • moz: $137
          • constant contact: $650
    • Rethink Retention
      • Churn is very high in the first 3 months: 25% / 15% / 8%
      • But by month 4, churn stabilizes. Now you are a qualified customers.
      • Looking at first 3 months. composed of:
        • People I’m going to lose no matter what i do. they are not target customer.
        • people i should be keeping, but i’m not.
        • people who i will keep even if i don’t spend effort on them. they “got it” right away.
      • Don’t worry about the first group. they are not the target customr. let them go.
      • second group: keeps me up at night.
      • you must know how to tell these groups apart, especially with respect to their feedback. feedback of the first group should be ignored!
    • Heart-Centered, Authentic, Customer Success
      • Need awesome customer support team. we don’t have salespeople up front. Instead, we treat them really well once they are paying us.
      • We don’t try to use robots to save money.
      • We talk to the customers, visit their websites, suggest improvements.
      • We don’t have a storefront or physical presence. so how do we make the relationships longer, stronger? we sent out happy packets of moz fun stuff.
  • Benefits
    • Your community is a flywheel.
      • it takes time to get up to speed.
      • once the flywheel starts spinning, the community starts to create itself.
      • now moz is just the stewards of the community.
      • it’s like hosting a really great house-party of respectful guests.
      • it’s an incredible barrier to entry for competitors.
        • there’s no shortcut, no way to buy into this.
    • Low Burn rate helps when the economy goes in the shitter.
      • no sales team means less burn.
      • less capital required.
      • easier to self-funded.
      • no community to calculate.
    • the strategy generates lots of predictable recurring revenue: 96% of revenue is recurring.
    • risk is distributed across a broad customer base. even if the best customer leaves, it’s no big deal.
    • we can pour more dollars into R&D
      • third group: don’t worry about them either.
  • Caveats
    • No magic growth lever: can’t just scale from 5 salespeople to 10 salespeople.
    • Will public markets and VCs continue to prize growth rate over burn rate?
  • Future of B2B Sales
    • Every business is a publisher.
    • Every business has a community.
    • Are you managing it?
    • Increased transparency around quality and pricing.
      • should lead to more corporate accountability.
    • Multi-channel, customer driven contact
    • customers want shorter contract cycles. Nobody wants to be locked into anything anymore.
    • Software sales begin with the people who use the software. They advocate to the C-suite.

These are my session notes from Defrag 2014 (#Defragcon).

I normally break my notes out and add some context to them, but I’m short of time, so I’m simply posting raw notes below.

Followup

  • Slack — superior chat, with channels and per channel notifications. Lots of integrations. Seems better than both Campfire and Hip Chat.
Chris Anderson
3D Robotics
  • Use drones for farmers to spot irrigation, pest problems, soil differences.
  • Can’t see the patterns from the ground
  • Visual and near-infrared.
  • Push button operation: One button to “do your thing”
  • What it enables:
    • better farming.
    • produce more with less resources.
    • don’t overwater.
    • don’t underwater and lose crops.
    • don’t apply pesticides everywhere, just where the problem is.
    • tailor to the soil.
  • it’s big data for farmers.
    • it turns an open-loop system into a closed-loop system
George Dyson
author Turing’s Cathedral
From Analog to Digital
  • Alan Turing: 1912-1954
  • Turing “being digital was more important than being electronic”
  • It is possible to invent a single machine which can compute any computable program.
  • Movie: The Imitation Game — true movie about Alan Turing
  • Insisted on hardware random number generated because software algorithms to generate random numbers cannot be trusted, nor can the authorities (whom he worked for)
  • John von Neumann: continued Alan Turing’s work, always gave him credit.
  • Where Turing was hated by his government, von Neumann got everything from his government: funding of millions of dollars.
  • Baumberger: made his riches in retail, decide to found an institution of learning
  • “The usefulness of useless information” — just hire great minds and let them work on whatever they want, and good things will come.
  • Thanks to German-Nazi situation in the 1930s, it was “cheap” to get jewish intellectuals.
  • The second professor hired: Albert Einstein.
  • In Britain, they took the brightest people to work on encryption. In the US, we took them to Los Alamos to build the atomic bomb.
  • ….lots of interesting history…
  • By the end of Turing’s life, he had moved past determinism. He believes it was important for machines to be able to make mistakes in order to have intuition and ingenuity.
  • What’s next?
    • Three-dimensional computation.
      • Turing gave us 1-dimension.
      • von Neumann gave us 2-d.
    • Template-based addressing
      • In biology, we use template-based addressing. “I want a copy of this protein that matches this pattern.” No need to specify a particular address of a particular protein.
    • Pulse-frequency computing
    • Analog computing
Amber Case
Esri
Designing Calm Technology
  • 50 billion devices will be online by 2020 — Cisco
  • Smart watch: how many of the notifications you get are really useful, and how many are bothering you?
  • Imagine the dystopian kitchen of the future: all the appliances clamoring for your attention, all needing firmware updates before you can cook, and having connectivity problems.
  • Calm Technology
    • Mark Weiser and John Seely Brown describe calm technology as “that which informs but doesn’t demand our focus or attention.” [1]
  • “Technology shouldn’t require all of our attention, just some of it, and only when necessary.”
  • The coming age of calm technology…
  • If the cloud goes down, I should be able to still turn down my thermostat.
  • Calm technology makes things to the peripherally of our attention. Placing things in the peripherally allow us to pay less attention to many more things.
  • A tea kettle: calm technology. You set it, you forget about it, it whistles when it’s ready. No unnecessary alerts.
  • A little tech goes a long way…
  • We’re addicted to adding features: consumers want it, we like to build it. But that adds cost to manufacturing and to service and support.
  • Toilet occupied sign: doesn’t need to be translated, easily understand, even if color-blind.
  • Light-status systems: Hue Lightbulb connected to a weather report.
  • Light-LEDs attached to Beeminder report: green, yellow, red. Do you need to pay attention? Instead of checking app 10 times a day, nervous about missing goals.
  • We live in a somewhat dystopic world: not perfect, but we deal with it.
  • Two principles
    • a technology should inform and encalm
    • make use of periphery attention
  • Design for people first
    • machines shouldn’t act like humans
    • humans shouldn’t act like machines
    • Amplify the best part of each
  • Technology can communicate, but doesn’t need to speak.
  • Roomba: happy tone when done, unhappy tone when stuck.
  • Create ambient awareness through different senses
    • haptics vs auditory alerts
    • light status vs. full display
  • Calm Technology and Privacy
    • privacy is the ability not to be surprised. “i didn’t expect that. now i don’t trust it.”
  • Feature phones
    • limited features, text and voice calls, few apps, became widespread over time
  • Smartphone cameras
    • not well known, not everybody had it.
    • social norm created that it was okay to have a phone in your pocket. we’re not terrified that people are taking pictures: because we know what it looks like when something is taking a picture.
  • Google Glass Launch
    • Reduced play, confusion, speculation, fear.
    • Had the features come out slowly, maybe less fear.
    • but the feature came out all at once.
    • are you recording me? are you recording everything? what are you tracking? what are you seeing? what are you doing?
    • poorly understood.
  • Great design allows people to accomplish their goals in the least amount of movies
  • Calm technology allows people to accomplish the same goals with the least amount of mental cost
  • A person’s primary task should not be computing, but being human.
Helen Greiner
CyPhy Works
Robots Take Flight
  • commercial grade aerial robots that provide actionable insights for data driven decision making
  • PARC tethered aerial robot
    • persistent real-time imagery and other sensing
    • on-going real-time and cloud based analytic service
    • 500-feet with microfilament power.
    • stays up indefinitely.
  • 2014: Entertaining/Recording
  • 2015/16: Protecting/Inspecting: Military, public safety, wildlife, cell towers, agriculture
  • 2017/18: Evaluating/Managing: Situation awareness, operations management, asset tracking, modeling/mapping.
  • 2019: Packaging/Delivery
  • “If you can order something and get it delivered within 30 minutes, that’s the last barrier to ordering online.” because i only buy something in a store if I need it right away.
  • Concept delivery drone: like an osprey, vertical takeoff but horizontal flight.
  • Tethered drone can handle 20mph winds with 30mph gusts.
    • built to military competition spec.
  • how do you handle tangling, especially in interior conditions?
    • externally: spooler is monitoring tension.
    • internally: spooler is on the helicopter, so it avoids ever putting tension on the line. disposable filament.
Lorinda Brandon
@lindybrandon
Monkey selfies and other conundrums
Who owns your data?
  • Your data footprint
    • explicit data
    • implicit data
  • trendy to think about environmental footprint.
  • explicit: what you intentionally put online: a blog post, photo, or social media update.
  • implicit data
    • derived information
    • not provided intentionally
    • may not be available or visible to the person who provided the data
  • The Biggest Lie on the internet: I’ve read the terms of use.
  • But even if you read the terms of use, that’s not where implicit data comes in. That’s usually in the privacy policy.
  • Before the connected age:
    • helicopters flew over roads to figure out the traffic conditions.
  • Now, no helicopters.
    • your phone is contributing that data.
    • anonymously.
    • and it benefits you with better routing.
  • Samsung Privacy Policy
    • collective brain syndrome: i watched two footballs out of many playing over the weekend. On the following morning, my samsung phone showed me the final scores of just the two games I watched.
    • Very cool, but sorta creepy.
    • I read the policy in detail: it took a couple of hours.
  • Things Samsungs collect:
    • device info
    • time and duration of your use of the service
    • search query terms you enter
    • location information
    • voice information: such as recording of your voice
    • other information: the apps you use, the websites you visit, and how you interact with content offered through a service.
  • Who they share it with.
    • They don’t share it for 3rd party marketing. but they do share for the purpose of their businesses
    • Affiliates
    • business partners
    • Service providers
    • other parties in connection with corporate transactions
    • other parties when required by law
    • other parties with your consent (this is the only one you opt-in to)
  • Smart Meter – Data and privacy concerns
    • power company claims they own it, and they can share/sell it to whom they like.
    • What they collect:
      • individual appliances used in the household
      • power usage data is easily available
      • data transmitted inside and outside the grid
    • In Ohio, law enforcement using it to locate grow houses.
  • Your device != your data
  • Monkey selfies
    • Case where photographer was setting up for photo shoot.
    • Monkey stole camera, took selfies.
    • Photographer got camera back.
    • Who owns the copyright on the photos?
    • Not the photographer, who didn’t take them.
    • Not the monkey, because the monkey didn’t have intent.
    • So it’s in the public domain.
  • Options
    • DoNotTrack.us – sends signal that indicates opt-out preference.
    • Disconnect.me – movement to get vendors to identify what data and data sharing is happening.
    • Opennotice.org – minimal viable consent receipt, which creates a repository of your consent.
    • ClearButton.net – MIT project to express desire to know who has your data, work with manufacturers.
  • Innovate Responsibly
    • If you are a creator, be sensitive to people’s needs.
    • Even if you are doing altruist stuff, you’ve still got to be transparent and responsible.
How to Distribute and Make Money from your API
Orlando Kalossakas, Mash-ape
@orliesaurus
  • API management
  • API marketplace: find APIs to use
  • Devices connect to the internet
    • 2013: 8.7B
    • 2015: 15B
    • 2020: 50B
  • App stores
    • 1.4M: Google play
    • 1.2M: Apple
    • 300k: Microsoft
    • 140K: Amazon
  • Jeff Bezos:
    • “turn everything into APIs, or I fire you.”
    • A couple of years later
  • Mashape.com: hosts over 10,000 private and public API
  • Google / Twitter / Facebook: Billions of API calls per day
  • Mashape pricing
    • 92% free
    • 5.6% freemium
    • 1.4% paid
  • Consumers of mash ape APIs more than doubling every year.
  • API forms:
    • As a product: the customer uses the API directly add capabilities
    • As an extension of a product: the API is used in conjunction with the product to add value.
    • As promotion: The API is used as a mechanism to promote the product.
  • paid or freemium flavors
    • pay as you go, with or without tiers
    • monthly recurring
    • unit price
    • rev share
    • transaction fee
  • depending on business model, you might end up paying developers to use your API
    • if you are expedia or amazon, you’re paying the developers to integrate with you.
  • Things to consider…
    • is your audience right?
    • Do your competitors have APIs?
    • Could they copy your model easily?
    • How does the API fit into your roadmap?
  • Preparing…
    • discovery
    • security
    • monitoring / qa
    • testing
    • support
    • documentation*
    • monetization*
    • *most important
  • How will you publish your API?
    • onboarding and documentation are the face of your API?
    • Mashape: if you have interactive documentation, consumers are more likely to use it.
  • Achieving great developer experience
    • Track endpoint analytics
    • track documentation/s web analytics
    • get involved in physical hackathons
    • keep api documentation up to date
    • don’t break things.
Blend Web IDEs, Open Source and PaaS to Create and Deploy APIs
Jerome Louvel , Restlet
  • New API landscape:
    • web of data (semantic)
    • cloud computing & hybrid architectures
    • cross-channel user experiences
    • mobile and contextual access to services
    • Multiplicity of HCI modes (human computer interaction)
    • always-on and instantaneous service
  • Impacts on API Dev
    • New types of APIs
      • Internal and external APIs
      • composite and micro APIs
      • experience and open APIs
    • Number of APIs increases
      • channels growth
      • history of versions
      • micro services pattern
      • quality of service
    • industrialization needed
      • new development workflows
  • API-driven approach benefits
    • a pivot API descriptor
    • server skeletons & mock generations
    • up-to-date client SDKs and docs
    • rapid API crafting and implementation
  • Code-first or API-first approaches
    • can be combined using code introspect ors to extract, and code generators to resync.
  • Crafting an API
    • swagger, apiary, raml, miredot, restlet studio
    • new generation of tools:
      • IDE-type
      • web-based
    • example: swagger editor is GUI app
    • RESTlet visual studio
Connecting All Things (drone, sphero, raspberry pi, phillips hue) to Build a Rube Goldberg Machine
Kirsten Hunter
  • API evanglist at Akamai
  • cylon-sphero
  • node.js
  • cylon library makes it easy to control robotics