The Digitization of Everything or the Coming of the Internet of Flying Things

My post on Inside Tech Talk:

The only bit of news—and a lot of buzz—that came out of Jeff Bezos’ interview with Charlie Rose on CBS’ 60 Minutes last Sunday was the unveiling of Prime Air. It’s an Amazon “R&D project” that is investigating the delivery of packages (up to five pounds) by aerial drones, getting them to the customer’s door in 30 minutes or less.

On its website, Amazon says that “We hope the FAA’s rules will be in place as early as sometime in 2015. We will be ready at that time.” Indeed, the FAA promises to be ready by 2015 according to its “roadmap for integration of civil unmanned aircraft systems.” But in the interview, Bezos refused to commit to a specific date, talking about the work they still need to do to ensure reliability and redundancy.

Reliability is a great challenge today for octocopters (the type of drones Amazon will use) as can be seen in this YouTube video. And even if Amazon finds out how to reduce the risk to a minimum, it will always be there, to say nothing about the opposition from privacy advocates, noise complainers, and others. So why is Bezos investing in the future (or fiction) of delivery?

Some explain it as a gimmick aimed to distract investors’ attention from Amazon’s latest financial report, as if the lack of profits is anything new to Amazon’s investors. Others may see it as Google-envy, one-upping its rival by adding “flying” to “autonomous vehicles,” as if Bezos has suddenly metamorphosed into a Brin.

I see it as a logical extension of the focus on the speed of delivery that has been driving Bezos and Amazon for eighteen years. As Bezos explained to Wired’s Steven Levy when he was asked about the link between retail and Amazon’s new thrust into consumer electronics: “…we’ve always focused on reducing the time between order and delivery. In hardware, it’s the same principle. An example is the time between when we take delivery on a processor to when it’s being used in a device by a customer.”

Bezos is bothered by waste and inefficiency the same way Steve Jobs was bothered by lack of imagination or bad aesthetics.

Product design was the core of Steve Jobs’ strategy to ride the digitization of everything. The speed of delivery is the core of Bezos’. With drone delivery, he is trying to eliminate the wasteful irritation of the last mile. No matter how close to where his customers live he is going to build Amazon’s warehouses (36 and counting), the most he can promise is same-day delivery. So he is going to circumvent and disrupt UPS and FedEx, the delivery partners that have been helped so much by Amazon’s success over the last decade. In the future, he may even disrupt his own drone delivery, by completely digitizing the last mile, through 3D printing in the customer’s house.

In the 60 Minutes interview, Bezos explained everything he does by fear of disruption. He believes that Amazon, like so many other companies in the past, will be disrupted one day and will simply disappear. He just hopes that will happen after he dies. In the meantime, he rides the digitization of everything with a maniacal focus on the speed of delivery.

Update: See here for Fred Smith of FedEx talking in 2009 (!) about their desire to use drones. HT @daviottenheimer

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DataKind’s Jack Porway on Data Science

[youtube=http://www.youtube.com/watch?v=Mm1RplOU0cQ&w=560&h=315]

“If you leave an excited data scientist on his own to solve a problem, he’s going to solve his own problem – which is usually parking his car, or finding a bar to drink at. The trick that we worked on was actually less about data and more about translation, about finding a way for data scientists to speak the language of the people who were trying to solve the big problems… the biggest [challenge] is actually the framing of the problem: really finding the question. As any good data scientist will tell you, it’s not so much about the data, it’s the question you start with”–Jack Porway, DataKind

More here

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5 Minutes on the Myth of the Data Science Platypus (Video)

[youtube=http://www.youtube.com/watch?v=9f-XXR9j6m8&w=420&h=315]

“Data science is in danger of being a fad. Data scientists need to build a reputation for providing actual value”–Kim Stedman

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A Practical Introduction to Data Science Skills (Video)

Google’s Michael Manoochehri at DataEDGE 2013 presenting an introduction to  data analysis and suggestions for how to become a data scientist (his notes for the presentation are here).
[youtube=http://www.youtube.com/watch?v=rpwZ_i-9U0o&w=560&h=315]

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Big Data, Small World: Kirk Borne at TEDxGeorgeMasonU (Video)

Kirk Borne is Professor of Astrophysics and Computational Science in the George Mason University School of Physics, Astronomy, and Computational Sciences (SPACS). Turns out he is the father of the term “unknown unknowns” – things we do not know we don’t know – popularized by former secretary of defense Donald Rumsfeld and later by Avinash Kaushik as “the unique space in which big data analysts should actually play.”

 

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Big Data Friday

“I can’t express how infuriated I am that my credit history, phone activity, and online browsing habits are being systematically collected and archived without my knowledge by undisclosed organizations that aren’t trying to sell me products”–Area Man

“PRISM 1.0 was a little glitchy, and now that we’ve smoothed out the bugs, well, your privacy, especially inside your own home, will be a thing of the past. The technology is so good that it will basically be as if a member of the NSA is standing right behind you at all times”– NSA director General Keith B. Alexander announcing PRISM 2.0

“NSA email me with job offer. Offer say ‘To accept, nod once. To decline, nod twice’”–BigDataBorat

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Big Data Friday: Borasky’s Law

  • Murphy’s Law: Anything that can go wrong, will go wrong.
  • O’Toole’s Corollary: Murphy was an optimist.
  • Sturgeon’s Law: 95 percent of everything is crap.
  • Mencken’s Law: Nobody ever went broke underestimating the intelligence of the American public.

Borasky’s Law: Sturgeon and Mencken were optimists, too.

Source: What Hath Von Neumann Wrought?

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LinkedIn’s Daniel Tunkelang on How to Interview a Data Scientist

Tunkelang: The O’Reilly Strata Conference brings together an incredible community of people working on big data. This year, I decided to do something different for my presentation. Rather than talk about science or technology, I addressed the practical problem of interviewing the candidates to build teams of data scientists.

[slideshare id=16798687&w=427&h=356&sc=no]

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Vincent Granville’s 66 job interview questions for data scientists

 

  1. What is the biggest data set that you processed, and how did you process it, what were the results?
  2. Tell me two success stories about your analytic or computer science projects? How was lift (or success) measured?
  3. What is: lift, KPI, robustness, model fitting, design of experiments, 80/20 rule?
  4. What is: collaborative filtering, n-grams, map reduce, cosine distance?
  5. How to optimize a web crawler to run much faster, extract better information, and better summarize data to produce cleaner databases?
  6. How would you come up with a solution to identify plagiarism?
  7. How to detect individual paid accounts shared by multiple users?
  8. Should click data be handled in real time? Why? In which contexts?
  9. What is better: good data or good models? And how do you define “good”? Is there a universal good model? Are there any models that are definitely not so good?
  10. What is probabilistic merging (AKA fuzzy merging)? Is it easier to handle with SQL or other languages? Which languages would you choose for semi-structured text data reconciliation?

To see the other 56 questions assessing “the technical horizontal knowledge of a senior candidate at a high level” go here 

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Data Science at Netflix with Elastic MapReduce

[youtube http://www.youtube.com/watch?v=oGcZ7WVx6EI]

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