Like most people, I first learned to work with numbers through an Excel spreadsheet. After graduating with an undergraduate philosophy degree, I somehow convinced a medical device marketing firm to give me a job writing Excel reports on the orthopedic biomaterials market. When I first started, I remember not knowing how to anything, but after a few months I became fairly proficient with the tool, and was able to build all sorts of useful models. When you think about it, this is an amazing feature of Excel. Every day, all over the world, people open up a spreadsheet to do some data entry and then, bit by bit, learn to do increasingly complex analytical tasks. Excel is a master at teaching people how to use Excel.

R is not like that. I learned to use R as a side project during law school, and it felt a bit like training with an abusive kung-fu master in the mountains of rural China.

I couldn’t get R to do anything. It wouldn’t read in files, draw a plot or multiply two numbers together. All I could do was generate mystifying errors and get mocked on Stack Overflow for asking redundant questions. This was all made more frustrating by the fact that I could accomplish all of these things in Excel without much difficulty.

This is the basic pain of learning to program. Programming languages are designed to be general in their application and to allow you to accomplish a huge variety of complex tasks with the same basic set of tools. The cost of this generality is a steep learning curve. When you start learning to do basic tasks in R, you are also learning how to do complex things down the road. As you learn more and more, the marginal cost of complex analyses goes down. Excel is the opposite, and is very easy at the beginning, but the marginal cost goes up with the complexity of the problem. If you were to graph this it might look like this:

At the beginning, when you are trying to accomplish simple things like balancing a budget or entering some data by hand, R is definitely harder to learn than Excel. However, as the task gets more complex, it becomes easier to accomplish in R than Excel, because the core structures of Excel are designed for relatively simple use cases and are not the best for more complex problems. This isn’t to say that you can’t solve a lot of complex problems with Excel, it’s just that the tool won’t make it easy for you.

For a lot of us, the pain of learning to program feels like the pain of failure. When the program gives you an incomprehensible error message it feels like it’s telling you that you’re stupid and lack programming aptitude. But after programming for a while, you learn that nobody really understands those errors, and everybody feels like an imposter when their program fails. The pain you feel is not the pain of failure, it’s just the pain of learning.

Four Fundamental Differences Between R and Excel

1) Text-based analysis

Excel is based on the physical spreadsheet, or accountant’s ledger. This was a large piece of paper with rows and columns. Records were stored in the first column on the left, calculations on those records were stored in the boxes to the right, and the sum of those calculations was totaled at the bottom. I would call this a referential model of computation which has a few qualities:

• The data and computation are usually stored in the same place
• Data is identified by its location on the grid. Usually you don’t name a data range in Excel, but instead refer to it by its location, for instance with $A1:C$36
• The calculations are usually the same shape as the data. In other words if you want to multiply 20 numbers stored in cells A1:An by 2, you will need 20 calculations: =A1 * 2, =A2 * 2, ...., =An * 2.

Text based data analysis is different:

• Data and computation are separate. You have one file which stores the data and another file which stores the commands which tell the program how to manipulate that data. This leads to a procedural kind of model in which the raw data is fed through a set of instructions and the output pops out the other side.
• Data is generally referenced by name. Instead of having a dataset which lives in the range of $A1:C$36 you name the data set when you read it in, and refer to it by that name whenever you want to do something with it. You can do this with Excel by naming ranges of cells, but most people don’t do this.

2) Data structures

Excel has only one basic data structure: the cell. Cells are extremely flexible in that they can store numeric, character, logical or formula information. The cost of this flexibility is unpredictability. For instance you can store the character “6” in a cell when you mean to store the number 6.

The basic R data structure is a vector. You can think of a vector like a column in an Excel spreadsheet with the limitation that all the data in that vector must be of the same type. If it is a character vector, every element must be a character; if it is a logical vector, every element must be TRUE or FALSE; if it’s numeric you can trust that every element is a number. There’s no such constraint in Excel: you might have a column which has a bunch of numbers, but then some explanatory test intermingled with the numbers. This isn’t allowed in R.

3) Iteration

Iteration is one of the most powerful features of programming languages and is a big adjustment for Excel users. Iteration is just getting the computer to do the same thing over and over again for some period of time. Maybe you want to draw the same graph based on fifty different data sets, or read and filter a lot of data tables. In a programming language like R you write a script which works for all of the cases which you want to apply it to, and then tell the computer to do the application.

Excel analysts typically do a lot of this iteration themselves. For instance if an Excel analyst wanted to combine ten different .xls files into one big file, they would probably open each one individually, copy the data, and paste it into a master spreadsheet. The analyst is effectively taking the place of a for loop by doing one thing over and over again until a condition is met.

4) Simplification through abstraction

Another major difference is that programming encourages you to simplify your analysis by abstracting common functions from that analysis. In the example above you might find that you have to read in the same type of files over and over again and check that they have the right number of rows. R allows you to write a function which does this:

read_and_check <- function(file){
  out <- read.csv(file)
  if(nrow(out) == 0) {
    stop("There's no data in this file!")
  } else {
    out
  }
}

All this function does is read in a .csv file and then check to see if it has more than zero rows. If it doesn’t, it returns an error. Otherwise it returns the file (which is called “out”). This is a powerful approach because it helps you save time and reduce errors. For instance, if you want to check if the file has more than 23 rows, you only have to change the condition in one place rather than in several spreadsheets.

There’s really no analog for these kinds of functions in an Excel-based workflow, and when most analysts get to this point they just start writing VBA code to do some of this work.

Example: Joining two tables together

I thought I’d illustrate these principles by working through the example of joining two tables together in Excel and R. Let’s say that we had two data tables, one with some information about cars and another with the colour of those cars, and we want to join the two of them together. For the purpose of this exercise, we’re going to assume that the number of cylinders in a car determines its colour.

library(dplyr)
library(knitr)
cars <- mtcars
colours <- data_frame(
  cyl = unique(cars$cyl),
  colour = c("Blue", "Green", "Eggplant")
)

kable(cars[1:10, ]) #kable is just for displaying the table

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2
Valiant	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1
Duster 360	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4
Merc 240D	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2
Merc 230	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2
Merc 280	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4

kable(colours)

cyl	colour
6	Blue
4	Green
8	Eggplant

In Excel you would probably do this using the VLOOKUP() function, which takes a key, and a range, and then looks up the value of that key within that range. I put together an example spreadsheet of this approach here. Notice that in each lookup cell I typed some version of =vlookup(C4,$H$2:$I$5, 2, FALSE). This illustrates a few things. First, the calculation is the same shape as the data, and happens in the same file as the data. We have as many formulas as we have things that we want to lookup, and they are placed right next to the dataset. If you’ve used this approach you can probably remember making mistakes in the process of writing and filling this formula. Second, the data is referred to by its address on the sheet. If we move the lookup table to another sheet, or another place on this sheet, that is going to screw up out lookup. Third, notice that the first entry of the cyl column in the spreadsheet store in C2 is stored as text, which causes error in the lookup function. In R, you would have to store all the calendar values as a numeric or character vector.

To do the same thing in R, we would use this code:

left_join(cars, colours, by = "cyl") %>% 
  filter(row_number() %in% 1:10) %>% # to display only a subset of the data
  kable() 

mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb	colour
21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4	Blue
21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4	Blue
22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1	Green
21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1	Blue
18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2	Eggplant
18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1	Blue
14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4	Eggplant
24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2	Green
22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2	Green
19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4	Blue

Here we refer to the data by its name, use one function to operate on the whole table rather than row by row. Because consistency is enforced for each vector we can’t accidentally store a character entry in a numeric vector.

Iteration

Now let’s say we wanted to get the mean displacement for each colour of car. Most Excel users would probably do this iteration manually, first selecting the table, sorting it by colour and then picking out the ranges that they wanted to average. A more sophisticated analyst would probably use the averageif() function to pick out the criteria they wanted to average on, and so avoid a few errors. Both approaches are implemented in the iteration tab of the spreadsheet.

In R you would do something like this:

left_join(cars, colours, by = "cyl") %>% 
  group_by(colour) %>% 
  summarize(mean_displacement = mean(disp)) %>% 
  kable()

colour	mean_displacement
Blue	183.3143
Eggplant	353.1000
Green	105.1364

What this does is takes the data set, splits it up by the grouping variable, in this case colour, then applies the function in the summarize function to each group. Again, the difference is that we’re always referring to things by name rather than location, there is one line of code which applies the function to the whole dataset, and all of the iterative actions are stored in the script.

Generalizing through functions

Functions are among the more difficult parts of learning to program, and you really can get by for quite a long time without ever learning to use them. I wanted to include them just because they are common and can be quite discouraging for Excel users because they are totally foreign to their workflow. A function is a way of using existing code on new objects. In the case above it might look like this:

join_and_summarize <- function(df, colour_df){
  left_join(df, colour_df, by = "cyl") %>% 
    group_by(colour) %>% 
    summarize(mean_displacement = mean(disp))
}

The things between the function() braces (df and colour_df) are called “arguments”, and when you call the function all it does is take the actual objects you supply to the function and plugs them in to wherever that argument appears between the curly braces. In this case we would plug in cars for the df argument, and colours for the colour_df argument. The function then basically replaces all the dfs with cars and colour_dfs with colours and then evaluates the code.

join_and_summarize(cars, colours) %>% 
  kable() 

colour	mean_displacement
Blue	183.3143
Eggplant	353.1000
Green	105.1364

Conclusion

Excel users have a strong mental model of how data analysis works, and this makes learning to program more difficult. However, learning to program will allow you to do things that you can’t do easily in Excel, and it really is worth the pain of learning the new model.

One of the blessings of the cellphone era: There’s no spam, as there is with email, and no telemarketer calls, as there is with landlines.

Well, there’s not supposedto be. Cellphone spam is illegal.

If you get telemarketing calls, you should sign up for the Do Not Call Registry. You probably already knew about that.

What you probably didn’trealize is that if you get text-message spam, you can forward it to your cellphone carrier for future filtering and blocking. It’s too late for you this time, but you may as well help to spare thousands of your fellow citizens from getting the same cellphone spam.

All you have to do is forward the text message to 7726. (That’s the number spelled out by SPAM on your keypad. Get it?)

If you’re feeling especially patriotic, also report it to the FTC.

And be glad you have at least somechannels to fight back on.

Adapted fromPogue’s Basics: Tech, by David Pogue, tech columnist for Yahoo Finance. He welcomes nontoxic comments in the Comments below. On the Web, he’s davidpogue.com. On Twitter, he’s @pogue. On email, he’s poguester@yahoo.com. You can read all his articles here, or you can sign up to get his columns by email

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RunKit notebooks are a great way to file bugs on GitHub. Since RunKit automatically packages your code with its entire environment in a Docker container, anyone can clone your bug and start investigating it live in seconds, without any setup or installation. Best of all, the bug will be completely reproducible and avoid the dreaded “works on my machine”. Today we’re excited to be improving this experience even further with a completely revamped UI for filing and analyzing issues on Node.js:

Our initial focus was to make stack traces in Node.js significantly easier to interpret and understand. RunKit interacts directly with the V8 data to provide a much more detailed view than any other implementation we’ve seen. One example is how RunKit dramatically streamlines the experience of navigating the stack trace by inlining the offending code right in the frame, as well as providing documentation for many of Node’s built-in functions:

But we also focused on a lot of little details, like swapping in canonical names for functions whenever we can in order to avoid the more confusing inferred V8 names that sometimes show up. Another thing you’ll notice is that we now show you the version of the package you’re using immediately as you require it. This makes it so that all the dependency information in your notebook is immediately digestable with a glance. It’s a lot of fun when coding, but even more useful when looking at someone’s else’s code.

Together, all these features result in a bug report that surfaces all the pertinent background for a bug with no extra work for the user. Instead of relying on templates to remind people to include all this, RunKit just knows it by default. That’s because with RunKit you’re not just filing a bug, you’re filing an entire container, enabling the coolest feature of all: the ability to run it.

How To Get Started

The easiest way to get started using RunKit to file bugs is to Sign Up and then head on over to a package’s corresponding RunKit page (such as https://runkit.com/npm/lodash). You can then paste the generated link on the package’s bug tracker. If you’re a package maintainer and want a more customized experience, you can use our API to embed RunKit right on your page.

Don’t do big development projects without testing the idea manually first.

Want to build a quest/badging system with unique journeys for different members? Do it manually first (yes, reach out to each member with an email when they reach each stage).

You want all the information before you begin development.

Don’t launch a huge ambassador program, try to find some way of recognizing one person for their expertise and seeing how it works.

Don’t build an automation series without manually testing different ideas first.

Don’t revamp your homepage without testing the unique elements for popularity first.

Making changes post-development is expensive, time-intensive, and costs you a lot of internal capital. You need to test every major development before you do it. These insights will inform and drive what you do.

There aren’t many ideas you can’t test manually first.

Aside, 50% of the time you’ll learn that the big development project has no real impact and you can skip it. You’re welcome!

As I’m sure you know, the current administration banned immigrants from seven countries recently. The New York Times looks at immigrants from these countries who already settled in the United States — their education, salaries, and where they live.

Tags: ban, demographics, immigration

Last year we published a blog post about getting more responsive tapping on iOS. With the release of iOS 10, we’ve made some minor adjustments to the behavior of our fast tapping, and an important change to a very common user interaction: pinch zooming.

Fast Tapping

A common complaint on iOS 9 and earlier was that events triggered by a user tapping the screen were slightly delayed. This was because the browser was waiting to see if the gesture was a double-tap, indicating the user wanted to zoom. Once a small delay had expired without seeing a second tap, the browser would know it was a single tap and dispatch the event. This made some pages that were designed for an instant reaction to tapping feel slightly slow.

As we described in our introductory post, iOS 10 detects situations when a page can support faster taps and dispatches the events instantly, making Web sites feel much more responsive. The feedback has been very positive. However, before iOS 10 shipped we made a few tweaks to the method described in the original article. Here are the current details.

Enabling fast tapping on iOS 10 requires pages to have the following:

1. There must be a meta tag of type viewport
2. The viewport must be defined to have width=device-width
3. The content must be at a scale of 1, which means both:
   a. the user has not manually zoomed off a scale of 1 (e.g. they can have zoomed, but they must have returned to the original scale)
   b. the page content wasn’t so wide that the browser was forced to shrink it to fit

Note: Explaining 3.b, WebKit often sees pages that define width=device-width but then explicitly layout content at very large widths, often greater than 1000px. These sites are usually designed for a large screen, and have added a viewport tag in the hope it makes it a mobile-friendly design. Unfortunately this is not the case. If the browser respected a misleading viewport rule such as this, the user would only see the top left corner of the content—clearly a bad experience. Instead WebKit looks for this scenario and adjusts the zoom factor appropriately. Conceptually, this behaviour is the same as the browser loading the page, then the user pinch zooming out far enough to see all the content, which means the page is no longer at a scale of 1.

Zooming Everywhere

Safari on iOS 10 allows the user to pinch zoom on every page. As a developer, you should be aware of this, and make sure your content works well when zoomed.

What changed? Prior to iOS 10, Safari allowed the content to block the user from zooming on a page by setting user-scalable=no in the viewport, or appropriate min-scale and max-scale values. This unfortunately enabled pages to pick a text size that was unreadable while giving the user no way to zoom. Also, there is now such a wide range of devices with different display dimensions, screen resolutions, pixel densities… it is very difficult to choose an appropriate text size in a design.

Now, we ignore the user-scalable, min-scale and max-scale settings. If you have content that disabled zoom, please test it on iOS 10, and understand that many users will be zooming now.

As users, we’ve all come across content that is too small to comfortably read. We know that a huge number of people appreciate this zooming improvement, even though it might mean some sites that attempt to block zooming are broken until they update.

Zooming in WKWebView Content

You might have an app that mixes a WKWebView with native content, and having the user being able to scale only that content may be inappropriate. In these cases, you can prevent the user from zooming by setting a new property on WKWebViewConfiguration:

There is new API on WKWebViewConfiguration:

var ignoresViewportScaleLimits: Bool

The default value is false, which means that WKWebView content will allow the content to block zooming. This preserves behavior with older versions of iOS.

Meanwhile, Safari and SafariViewController set the value to true. If your app uses a WKWebView in a similar manner, such as showing a large amount of text, we encourage you to change the value to true too.

For feedback, email web-evangelist@apple.com or tweet to @webkit.

Sign into your Hover account today and you’ll have the option to preview a brand new Control Panel to manage your Hover domains and services. We’re launching in a preview mode so early adopters and curious types can start poking around.

You should see a notification bar at the very top that gives you a way to switch to the new Control Panel. Don’t worry though, you can easily switch back. In fact, every time you sign in, we’ll continue to start you in the current Hover dashboard.

This big update has been in the works for quite some time, and we’re finally ready to let all of our customers take a look and start using it if they want. Thanks to those who have been helping us out with user testing and feedback over the last little bit.

All new, but still familiar

The release of a completely new Control Panel is a bit of a scary proposition for us and also probably for you. Over the years, we’ve all gotten used to how to do certain things in Hover, and now we’re making some pretty significant changes.

In designing and building the new Control Panel, we spent a great deal of time making sure that it would still feel fairly familiar if you’ve been a customer for a while. We’ve stuck with many similar patterns within the management tools, and the navigation is very much like the old panel.

There’s a lot that’s brand new. We built this from scratch alongside the existing site so it’s completely new code. With many years more experience under our belts, and armed with lots of advice and feature requests from our customers, we’ve cleaned up and improved a ton of things to make managing your domain names and services even easier.

It’s also true that the way customers manage domains has changed. Gone are the days where all you needed to provide was a few details about the domain name and a way to view and edit some DNS records.

We have automatic setup of domains with Hover Connect, more forwarding options to get your domain pointed at your social media profiles. ICANN rules around trades and verifications are better integrated and we do a far better job making sure you are well-informed about the status of your domain names.

Some highlights:

• The domain list which shows all the domains in your Hover account now has more information (assuming you have more than one domain). You can see at a glance which domains have mailboxes configured, and even who the admin contact is for each domain. The familiar status icons remain so you’ll be able to quickly see any domains that need attention. And no matter how many domains you have, we show them all to you in a single, scrolling list with lots of sorting and filtering options. By default, we sort alphabetically, but domains that are expired, expiring soon, renewing soon or that need verification are brought to the top of the list so they don’t get lost in a long list.
• Each domain also now has what we’re calling a “domain dashboard” — a single page with all the relevant information for that domain. We’ve brought a Hover Connect “module” to the domain dashboard so when you have a new domain, getting it working with your favourite service is quicker and easier than ever. If you buy your domain via one of our partners, then you’ll get specific instructions relevant to setting up your domain with that partner’s service right there.
• We also have new domain dashboard modules for email, and forwards, so you can see exactly what you are doing with your domain at a glance. Tasks like editing a domain’s contacts, or updating name servers have been given a complete refresh to make those tasks much quicker and easier to accomplish.
• You’ll also note we’ve switched to doing most edits in popup windows (called “modals”). This allows you to do edits on things like your domain contacts, name servers and even mailboxes from more places throughout the Control Panel. So if you want to edit a mailbox from the domain dashboard module for email, that works the same as if you do it from the email tab for that domain.

Lots more to come!

We’re still working on a few things, but we think the new Control Panel is ready for most of our users to start trying it out. There are a few rough edges here and there, and some areas of your account aren’t yet done (like account settings, for example), but we’ve been using it internally over the last few months as we built it out and we think its more than ready for everyone.

We’d love to hear what you think. Let us know what works for you, and also what doesn’t work. You’ll see a link to a feedback form at the top of the new Control Panel.

p.s. For those who are less excited by change, we get it! The current Hover dashboard will remain in place for the next few months so you can ease your way in at your own pace.

I was delighted to get to speak with Debbie Millman for her venerable podcast, "Design Matters". If you have an hour to spare, please do check out the conversation — we touched on a ton of topics that are near and dear to my heart.

Spent an hour in conversation with a friend in the US last night discussing the role of dialogue in connecting communities together. My friend has extensive experience working with immigrant, refugee communities and in working with inner city agencies. He’s been personally affected by Trump’s travel edict as his family members are directly targetted by the current travel ban. He’s a man I respect very much.

We were talking about ways to connect and understand the “other side.”  After our conversation I stumbled over this podcast on the “deep story” of what is motivating Trump supporters, and probably both Brexit supporters and other Europeans struggling with how the world is changing and how they perceive their privileges coming apart. We talked about how there is always a thin slice of people that will never sit down with “the other.” We also spoke about the many main street Republicans who feel abandoned by their party and have done since the Tea Party took it over.  It comes down to the fact that arguments on economics and policy cannot overrule the emotional aspects of identity, especially when people feel those identities are under assualt through no fault of their own.

In her new book, Strangers in Their Own Land, sociologist Arlie Hochschild tackles this paradox. She says that while people might vote against their economic needs, they’re actually voting to serve their emotional needs

The image of standing in line to get your rewards and watch people stream past you is compelling. It’s one thing to deconstruct this image with data and facts, but first it’s important to understand it and how people deeply FEEL it.

Deep story is fascinating to me. Here in my home community of Bowen Island, we experience tensions from time to time over our deep story.  We all have ideas about what we think this place is and who we think we are. To some extent that story is an illusion born in our world views and our desires. In a place like Bowen Island, where most of us moved here from somewhere else, our own deep story includes the deep motivation that brought us here.

And deeper beneath the personal deep story we bring is the emergent and slowly changing story of the island’s identity.  Over the last couple of years, as a member of our local Economic Development Committee, I have worked with friends and colleagues to understand our deep story. Once you can see it, it reveals the deep yes’ and deep no’s that make things happen or hold things back.  People are often surprised by things that go on in our little community, but understanding the deep story helps to explain where these things come from.

When you understand the deep story, you can find deep places to connect together and important places of engagement and curiosity. Dialogue gets more interesting as we set out to learn about each other, what we care about, what we assume is true, and what is essential to our identity. Strategy that does not take the power of identity into consideration creates implementation plans that will inevitably endure oblique assaults on its efficacy.  Understanding the deep story and identity of a place or a person is essential to resilience, collaboration and peacemaking across difference.  A healthy community can hold different stories in all their complexity, even when those stories conflict with each other.  An unhealthy community pits one story against another, and cynical leaders do the same.

We have a choice as citizens.  This podcast helps us become resourceful in making that choice.

Yes, I know, I’ve already reviewed Apple’s new laptop line, the MacBook Pro.

But that was when they first came out. When I had a review unit supplied by Apple (AAPL). Before I spent $3,000 on my own, suped-up, top-of-the-line 13-inch MacBook Pro.

I know: That’s an obscene amount of money. But this is my main machine, my livelihood. If you add up the hours, I spend more time with my laptop than I do with my bed, car, home, or family. I figured it would be worth the splurge.

As it turns out, that’s a big Yes and a big No.

Life with this thing has been a roller coaster: one emotional whiplash after another. “Cool!” “Oh, NO!” “Cool!” “Oh, NO!”

Looking good on paper

Four things attracted me to this new laptop. First, you can get it with a 1-terabyte “hard drive” (actually a giant flash drive). I’m a big photos-and-videos guy. I’ve spent the last five years struggling against the storage limitations of MacBook Airs. I’m ready.

Second, the size. This thing is at least an inch smaller than the MacBook Air all the way around. Apple shaved away most of the margin around the screen. I’ve wrestled with my laptop on an airplane tray for the last time.

Third, the screen. I’m finally ready for Retina resolution on my main machine—and having enough brightness to light up a runway doesn’t hurt, either.

Fourth, being able to log in with a touch, thanks to the fingerprint reader. (You can read about the Touch Bar here. It’s super handy to be able to adjust the volume or brightness with one quick swipe, and navigating a video is super quick—but otherwise, I don’t use it much.)

I knew I was also getting far better speakers; a much bigger trackpad; the Touch Bar above the keyboard; and four USB-C jacks instead of the usual USB, video, and power jacks. I didn’t think any of those things would affect me.

At the time.

Up: The power thing

I know everybody bellyaches about the loss of the standard jacks. But USB-C is awesome, man. You can’t plug this cable upside-down. There’s no right end or wrong end. A single cable carries audio, video, power, and data.

The whole industry is going to USB-C—phones, tablets, laptops, desktops—so get used to it.

Right off the bat, I love that you can plug the MacBook Pro’s power cord into either side, since any of its four USB-C jacks can accommodate it. Useful more often than you’d think.

Yeah, it’s sad that we’ve lost Apple’s MagSafe magnetic power-cord connector. But the fact that we don’t have to buy Apple’s power cords anymore easily makes up for it.

For example, I like to have a spare charger next to my bed, and another one in my laptop bag for travel. But I don’t like paying $70 or $80 to Apple for spare cords.

Now, I don’t have to. Any old USB-C charging cord will work. You can get power from the wall, from your car’s cigarette jack, from one of those backup batteries, or even from another USB-C laptop! Like mouth-to-mouth resuscitation for laptops.

I bought a $27 Dell charger and a Udoli one for $35. The Dell one lights up to show that it’s getting power, which the new Apple cord doesn’t. The Udoli one has a regular USB jack on the side for charging your phone or Fitbit (FIT), which the Apple cord also doesn’t.

Now, in theory, any USB-C device can charge from any USB-C charger; the voltage and whatnot is adjusted automatically. You can even charge the MacBook Pro from a phone charger. It’ll take forever, but it’ll charge.

My cheapo Dell charger supplies 27 watts; the Udoli offes 45 watts. Neither, in other words, charges as fast as the Apple charger (61 watts). And the Dell charger does a weird thing when the laptop is closed: It chimes every couple of minutes, as though the power is being unplugged and replugged.

I don’t care. I got away with price murder on these spare chargers, and they’re smaller and handier than Apple’s.

Oh! And I also bought, for $15, a charger that plugs into my car cigarette lighter. It juices up the laptop great (I’m frequently the passenger on long drives to the airport), and has an extra USB jack for charging something else.

Down: The Trackpad

Apple made its new trackpad huge, and I can’t figure out why. What does that get you?

What it gets me is accidental clicks, caused by my left thumb as it hovers while I type. My cursor or insertion point suddenly pops into the wrong place or the wrong window.

Apparently, this problem is much worse if you’ve turned on Tap to Click (which requires only touching the trackpad, not actually clicking down on it, to register a click).

I’ve solved the problem by taping a piece of cardboard to the trackpad, in essence shrinking it. Real classy.

Up: The Keyboard

The key travel has gotten dissed for its shallowness, but I can really fly typing on this keyboard. It’s crisp and firm. Loud as hell, but crisp and firm.

Neutral: The adapter thing

Of course, switching into MacBook Pro Land means getting adapters for everything that normally plugs into USB. Sometimes, that just takes the form of a $6 replacement cable; sometimes, you need a $3 plug adapter.

Everybody talks about how many dongles they’ll need, but that’s not been my experience. I still have one Fitibit cord, one MiFi cord, one digital camera charging cord, and so on; it’s just that some of them have an adapter plug on the end.

I did have to buy a new Lightning-to-USB-C cable for my iPhone. Apple’s version is $19, which is absurd. I found this awesome one on Amazon (AMZN) for $9. It has a sturdy, non-tangling nylon fabric outer shell, available in three metallic colors to match the three MacBook Pro colors.

I’ve read here and there that cheapo Chinese USB-C adapters and cables can be glitchy. But all the ones I’ve picked up on Amazon have worked like a champ, except for the Dell dinging-power-cord thing.

Down: No card slot

I deeply, deeply miss a memory-card slot. Used to be, transferring photos from a camera was as easy as popping out its memory card and slamming it into my MacBook Air. Now I have to go looking for my $8 USB-C card reader, or hook up the camera with a cable.

I no longer have any video-output jack, either—like VGA, HDMI, or Mini-DVI. So for $45, I bought a multi-jack dongle that offers both VGA and HDMI—and Ethernet and a regular USB jack. So I’m covered there.

Up: One-cable docking station

I spend a lot of time doing book layouts, so I sprang $524 for the LG UltraFine 4K 21-inch monitor. What’s amazing about it is not just the gorgeous image; it’s that one USB-C cable connects it to the MacBook. That single cable charges the laptop, carries audio and video to the monitor’s screen and speakers, and conducts data both directions (there are four USB-C jacks on the back of the monitor).

I used to hook up my MacBook Air to a docking contraption every time I came home from the road. Now, I plug in one cable, and my entire desktop system is ready to go. It’s fairly awesome.

Down: Shorter battery life

Apple says this smaller laptop has a smaller battery than the MacBook Air—and yet that it still gets the same 10 hours of work time.

That’s baloney.

There’s been a lot of confusion and analysis about why the MacBook Pro does or does not get the battery life it’s supposed to. But this much I can say for sure: You get better battery life if you install the latest Mac OS version, 10.12.3. And if you keep the screen dimmer than full brightness. And if you don’t do heavy-lifting work like Photoshop, video editing, and games.

This much I can also say for sure: No matter what you do, you won’t get as much life out of this battery as you would doing exactly the same work on the MacBook Air. It’s a 33% smaller battery; it’s not going to have the same capacity. I usually get six or seven hours from it.

I wasn’t ready for that, and it’s a real drag. Thank goodness I’ve got chargers all around me.

Whiplash

If you’re a Mac person and can’t afford to switch to Windows, then the new MacBook Pro is it. Apple doesn’t intend to update the MacBook Air or the older MacBook Pros anymore. The future is this or nothing.

But you know what? This really isn’t a MacBook at all.

I mean, it doesn’t have the same anything. Screen, jacks, power cord, keyboard, battery, trackpad…it has almost nothing in common with previous Apple laptops.

It’s much better in some ways, and much worse in others. You’ve been warned; keep hands and feet inside the tram at all times.

David Pogue, tech columnist for Yahoo Finance, welcomes non-toxic comments in the Comments below. On the web, he’s davidpogue.com. On Twitter, he’s @pogue. On email, he’s poguester@yahoo.com. You can read all his articles here, or you can sign up to get his columns by email.

Over the past 10 years thinking and writing about reproducible research, I’ve come to the conclusion that much of the discussion is incomplete. While I think we as a scientific community have come a long way in changing people’s thinking about data and code and making them available to others, there are some key sticking points that keep coming up that are preventing further progress in the area.

When I used to write about reproducibility, I felt that the primary challenge/roadblock was a lack of tooling. Much has changed in just the last five years though, and many new tools have been developed to make life a lot easier. Packages like knitr (for R), markdown, and iPython notebooks, have made writing reproducible data analysis documents a lot easier. Web sites like GitHub and many others have made distributing analyses a lot simpler because now everyone effectively has a free web site (this was NOT true in 2005).

Even still, our basic definition of reproducibility is incomplete. Most people would say that a data analysis is reproducible if the analytic data and metadata are available and the code that did the analysis is available. Furthermore, it would be preferable to have some documentation to go along with both. But there are some key issues that need to be resolved to complete this general definition.

Reproducible for Whom?

In discussions about reproducibility with others, the topic of who should be able to reproduce the analysis only occasionally comes up. There’s a general sense, especially amongst academics, that anyone should be able to reproduce any analysis if they wanted to.

There is an analogy with free software here in the sense that free software can be free for some people and not for others. This made more sense in the days before the Internet when distribution was much more costly. The idea here was that I could write software for a client and give them the source code for that software (as they would surely demand). The software is free for them but not for anyone else. But free software ultimately only matters when it comes to distribution. Once I distribute a piece of software, that’s when all the restrictions come into play. However, if I only distribute it to a few people, I only need to guarantee that those few people have those freedoms.

Richard Stallman once said that something like 90% of software was free software because almost all software being written was custom software for individual clients (I have no idea where he got this number). Even if the number is wrong, the point still stands that if I write software for a single person, it can be free for that person even if no one in the world has access to the software.

Of course, now with the Internet, everything pretty much gets distributed to everyone because there’s nothing stopping someone from taking a piece of free software and posting it on a web site. But the idea still holds: Free software only needs to be free for the people who receive it.

That said, the analogy is not perfect. Software and research are not the same thing. They key difference is that you can’t call something research unless is generally available and disseminated. If Pfizer comes up with the cure for cancer and never tells anyone about it, it’s not research. If I discover that there’s a 9th planet and only tell my neighbor about it, it’s not research. Many companies might call those activities research (particularly from an tax/accounting point of view) but since society doesn’t get to learn about them, it’s not research.

If research is by definition disseminated to all, then it should therefore be reproducible by all. However, there are at least two circumstances in which we do not even pretend to believe this is possible.

1. Imbalance of resources: If I conduct a data analysis that requires the world’s largest supercomputer, I can make all the code and data available that I want–few people will be able to actually reproduce it. That’s an extreme case, but even if I were to make use of a dramatically smaller computing cluster it’s unlikely that anyone would be able to recreate those resources. So I can distribute something that’s reproducible in theory but not in reality by most people.
2. Protected data: Numerous analyses in the biomedical sciences make use of protected health information that cannot easily be disseminated. Privacy is an important issue, in part, because in many cases it allows us to collect the data in the first place. However, most would agree we cannot simply post that data for all to see in the name of reproducibility. First, it is against the law, and second it would likely deter anyone from agreeing to participate in any study in the future.

We can pretend that we can make data analyses reproducible for all, but in reality it’s not possible. So perhaps it would make sense for us to consider whether a limiting principle should be applied. The danger of not considering it is that one may take things to the extreme—if it can’t be made reproducible for all, then why bother trying? A partial solution is needed here.

For How Long?

Another question that needs to be resolved for reproducibility to be a widely implemented and sustainable phenomenon is for how long should something be reproducible? Ultimately, this is a question about time and resources because ensuring that data and code can be made available and can run on current platforms in perpetuity requires substantial time and money. In the academic community, where projects are often funded off of grants or contracts with finite lifespans, often the money is long gone even though the data and code must be maintained. The question then is who pays for the maintainence and the upkeep of the data and code?

I’ve never heard a satisfactory answer to this question. If the answer is that data analyses should be reproducible forever, then we need to consider a different funding model. This position would require a perpetual funds model, essentially an endowment, for each project that is disseminated and claims to be reproducible. The endowment would pay for things like servers for hosting the code and data and perhaps engineers to adapt and adjust the code as the surrounding environment changes. While there are a number of repositories that have developed scalable operating models, it’s not clear to me that the funding model is completely sustainable.

If we look at how scientific publications are sustained, we see that it’s largely private enterprise that shoulders the burden. Journals house most of the publications out there and they charge a fee for access (some for profit, some not for profit). Whether the reader pays or the author pays is not relevant, the point is that a decision has been made about who pays.

The author-pays model is interesting though. Here, an author pays a publication charge of ~$2,000, and the reader never pays anything for access (in perpetuity, presumably). The $2,000 payment by the author is like a one-time capital expense for maintaining that one publication forever (a mini-endowment, in a sense). It works for authors because grant/contract supported research often budget for one-time publication charges. There’s no need for continued payments after a grant/contract has expired.

The publication system is quite a bit simpler because almost all publications are the same size and require the same resources for access—basically a web site that can serve up PDF files and people to maintain it. For data analyses, one could see things potentially getting out of control. For a large analysis with terabytes of data, what would the one-time up-front fee be to house the data and pay for anyone to access it for free forever?

Using Amazon’s monthly cost estimator we can get a rough sense of what the pure data storage might cost. Suppose we have a 10GB dataset that we want to store and we anticipate that it might be downloaded 10 times per month. This would cost about $7.65 per month, or $91.80 per year. If we assume Amazon raises their prices about 3% per year and a discount rate of 5%, the total cost for the storage is $4,590. If we tack on 20% for other costs, that brings us to $5,508. This is perhaps not unreasonable, and the scenario would certainly include most people. For comparison a 1 TB dataset downloaded once a year, using the same formula gives us a one-time cost of about $40,000. This is real money when it comes to fixed research budgets and would likely require some discussion of trade-offs.

Summary

Reproducibility is a necessity in science, but it’s high time that we start considering the practical implications of actually doing the job. There are still holdouts when it comes to the basic idea of reproducibiltiy, but they are fewer and farther between. If we do not seriously consider the details of how to implement reproducibility, perhaps by introducing some limiting principles, we may never be able to achieve any sort of widespread adoption.

Time is weird. The alleged dimension of time has been under investigation by the physics police on charges of relativity weirdness and quantum weirdness. The math is hard, but you can see it in the ominous glint in the eyes of physicists who have had a couple of drinks.

But subjective time is even more suspicious. Each observer possesses detailed and privileged access to a single entity’s experience of time (his own); however, this does not guarantee the ability to perceive one’s perceptions of time accurately, so as to report about it to the self or others. Access to the time perception of others is mediated by language and clever experimental designs. Unfortunately, the language of time is a zone of overload and squirrelly equivocation. Vyvyan Evans (2004) counts eight distinct meanings of the English noun “time,” each with different grammatical properties. Time can be a countable noun (“it happened three times”) or a mass noun (“some time ago”); agentic time (“time heals all wounds”) behaves like a proper noun, refusing definite and indefinite articles.

Perhaps we will get some purchase with chronesthesia, since Greek classical compounds are well-known for injecting rigor into the wayward vernacular. Chronesthesia is the sense of time – specifically, the ability to mentally project oneself into the future and the past, as in memory, planning, and fantasy (Tulving, 2002). It is sometimes called mental time travel. But already there is weirdness: why should the “time sense” be concerned with the imaginary, rather than the perception of time as it is actually experienced (duration, sequentiality, causality)?

Linear temporality (time as a sequential series of experiences) and chronesthesia (time as many simulations of past and future) are not conflicting models. Rather, they are deeply interlocking models that constantly construct each other. They are both illusions, though the way in which they are illusions is different. However, they are both highly functional, and the ways in which they are functional are complementary.

The Fabula of Linear Temporality

In folklore, the fabula is a stripped-down version of the events of a story in chronological order – a sort of minimal timeline of just the facts. This is in contrast to the way that the story is told (syuzhet), which may be nonlinear and told from the perspective of many characters, including unreliable narrators. Fabula corresponds to linear, sequential time; syuzhet corresponds to the chronesthetic experience.

Consider the fabula of the grocery store. You walk into the store and take a basket. Then you pick up items around the store and put them into the basket. Then you walk to the cashier, wait in line, and transfer your items to the checkout counter. The items are bagged; you pay for them, and carry them away.

This is a perfectly useful conception of grocery shopping. It functions as a script to help us use the grocery store, and it is articulable to others, in case we have some kind of grocery-store-related problem that we need to seek help with (e.g., is haggling permitted?).

The hidden side of the grocery store is that it is a zone of private fantasy and mental time travel. Perhaps there is a particular dish that you want to make. You imagine making the dish and the ingredients that go into it, informed by memories of past cooking experiences and recipe texts. You try to match what is desired to what is available. Products themselves may trigger memories and desires. Cupcakes? Raw kale? You may reach for fresh Brussels sprouts motivated by a fantasy of your future self eating roasted Brussels sprouts; you may draw your hand back, remembering that you let the last batch go bad; you may buy them anyway, thinking, “this time.” If they go bad anyway, then in a sense, your purchase was not of Brussels sprouts as food, but of Brussels sprouts as a scaffolding for a particular self-fantasy. Weird time threatens the thingness of things.

Now. Here you are at the cashier. You may rehearse the interaction, wonder if you will have to bag your own groceries, remember the times when cashiers made the joke of pretending to charge you for the cold bags you carry with you. Should you prepare a polite laugh? And then it’s over, and in a month you might not remember it at all. The experience will be folded into the grocery store script in long-term memory, if any trace of it remains.

I think it’s interesting how much mental time travel is involved in crushingly mundane activities. As I became a better cook, I noticed that when I got a food idea (a new dish or way of cooking), I would spend a great deal of time mentally simulating the process of slicing, sautéing, whisking, sprinkling, baking. The future simulations “reach back” into memory, collating scraps of memories of ingredient, flavor, and technique into a new whole. Mental simulations are rarely smooth: they hit obstacles that must be worked around, and particular segments must be re-simulated repeatedly. The fabula of a “recipe” reflects only a small portion of the reality of cooking. But it is a very useful condensation, providing a scaffolding for chronesthetic experience. And it is very easy to communicate.

Chronesthetic time

Linear timelines or scripts, along with memories in a richer sense, provide the basis for mental time travel. But linear timelines must themselves be abstracted (or extracted) from actual chronesthetic experience. Linear timelines are not simply available to perception; they must be constructed, with effort, out of the raw chronesthetic experience. The consensus social experience of time and the private experience of time mutually build each other.

Deeply Interlocking Time

“Deep Interlock and Ambiguity” is one of Christopher Alexander’s (2002) fundamental properties. Multiple elements “hook into” or grip each other, meeting in a zone of ambiguity that doesn’t clearly belong to either element. For example, a building surrounded by an arcade or gallery (in the architectural sense) deeply interlocks interior and exterior, meeting in a zone of ambiguity that is neither outdoors nor indoors. The shapes created by the columns of the arcade and the shapes created out of the space enclosed by the arcade seem to grip each other. The building becomes less separate from its surroundings.

Deep interlock between outdoors & indoors at the Alhambra

Deep interlock can occur in ornaments, as in this detail of tile-work and brick, from the Tabriz Mosque (Alexander, 2002, at p. 198). The apricot-colored brick boundary has hook-shaped extensions that interlock with the botanical designs within and without, so that the black interior is deeply gripped. The hooks form spade shapes in each corner, in addition to having their own strong shape. All elements support each other; there is no separation, despite the fact that there is a strong boundary.

Detail in the 16th-century Tabriz Mosque

Time is deeply interlocking in this way: fingers reach into the past and the future, uniting in the zone of ambiguity formed by the chronesthetic being. Present experience takes its shape from flights into simulated future and past. The future takes its shape in part from the contents of simulated futures.

Past and future meet in a zone of ambiguity

Interestingly, there is evidence that remembering the past and imagining the future are not opposites, but expressions of a unified underlying capacity. Imagining past and future events seem to light up the same brain areas, and people with deficits in imagining the past (amnesia) tend to also have deficits in imagining and planning for the future (Schacter et al., 2008). Thus we can talk about constructing the past and “remembering” the future.

Iteration

Mental time travel to the future, or simulation, can be modeled as iterations on game theory problems, as in the Keynesian beauty contest. In the “guess 2/3 of the average” game, participants each choose a number between 0 and 100, inclusive; the object is to choose a number that is 2/3 of the average of the guesses of all participants.

A naive player might choose at random. Or he might observe that the maximum correct answer is 66 (if everyone chose 100). So, he thinks, everyone will guess below 66. If they do so at random, the correct answer would be around 34. So, iterating again, he thinks, everyone else knows this, so everyone will guess 34. In that case, the correct answer is about 21. The iteration continues down to the Nash equilibrium of 0. Extremely simplified simulations of the future, repeated – and, I should include, projecting those simulations onto the minds of other players – reveal a dominant strategy.

Simplified chronesthesia

Unfortunately, when games like this are played in real life (including more complex forms, such as poker), it is not the case that everyone plays the dominant strategy. 0 is usually incorrect in groups of real humans; they are more likely to average closer to 21. This is because real humans don’t iterate perfectly – and because humans know that other humans don’t iterate perfectly. Only if the answer to the game were common knowledge among the group would choosing zero be the correct answer.

The process of life – even simple life – reproducing itself in the course of evolution is analogous to game theory iteration, with similar results. The times at which migratory birds lay their eggs is a function of the history of thousands of generations of successful clutches. Organisms are a “best guess” at what will survive, reproduce, and flow into the future. Chronesthetic beings are a best guess at how to make best guesses.

There is some debate as to whether humans are the only species that imagines itself backwards and forwards in time. Nonlinguistic animals cannot report their experiences; however, scientists working tirelessly to annoy corvids and rats (among others) have produced some evidence of mental time travel in animals (Schacter et al., 2008). Corvids, such as scrub jays, cache food of varying perishability for months-long storage. Their ability to cache and relocate food speaks to a time sense – and they are apparently savvy enough to re-cache food in secret if another jay catches them caching the first time. The rat method is more invasive. Rats run mazes with electrodes sticking out of their brains, connected to particular neurons associated with places in the maze. These neurons seem to fire in the correct order during rat dreams, as if the rats were rehearsing in a sort of dream training camp. When running a familiar maze while awake, the place neurons fire before the rats arrive at the associated place, as if the rat were imagining the future course of events.

We just don’t know. But it’s premature to say that time is only deeply interlocked in human minds. It may be that simulation is a very old tool.

After Temporality

Phylogenetically, we find ourselves after temporality in the sequential sense; we are past or beyond the experience of time as a sequential series of moments and sense impressions.

Simulations, however, seem to have a strong relationship to events that actually occur on the consensus timeline. Some simulations seem to be about planning (as in simulating the interaction with a cashier at a grocery store). Other simulations seem to be a form of pleasurable escape, as in sexual fantasy or self-aggrandizing imaginings. People experiencing severe mental pain (as in depression) seem to fall out of time or get stuck in time; they demonstrate a reduced capacity to vividly imagine future (or even past) scenarios (Schacter et al., 2008). Time itself becomes poisoned by affect; the pleasure of reaching out and deeply interlocking with future and past is lost.

In the case of planning-type simulations judged to be positive, we are after temporality in that we seek after making these simulations come true on the consensus timeline. Relaxing notions of agency, we might say that the fantasies themselves are after temporality, auditioning to become real. It is not clear how intentional mental time travel is; a good portion of it can be classified as mind-wandering (Stawarczyk et al., 2011). The future and past can spring up to us, seemingly unbidden. Of course, there is no guarantee that a pleasing mental simulation will translate into a pleasing timeline reality.

The signs and symbols of language form a scaffolding for collective mental time travel, as in political/religious narratives of transformation and salvation. Common knowledge is powerful, as we have seen. Signs and symbols especially seem to be after temporality, in the sense of seeking to become real in the consensus timeline. The tactic of semiocide – “a situation in which signs and stories that are significant for someone are destroyed because of someone else’s malevolence or carelessness, thereby stealing a part of the former’s identity (Puura 2013)” – can shape both simulated and temporal futures. Fantasy colonized reality long ago. The war for the future plays out in the realm of fantasy and sign, as well as brick and blood.

References

Alexander, C. 2002. The phenomenon of life: The nature of order, book 1. Berkeley: Center for Environmental Structure.

Evans, V. 2004. How we conceptualise time: Language, meaning and temporal cognition. Essays in Arts and Sciences 33:13-44.

Puura, I., 2013. Nature in our memory. Sign Systems Studies 41:1:150-153.

Schacter, D.L., Addis, D.R. and Buckner, R.L., 2008. Episodic simulation of future events. Annals of the New York Academy of Sciences, 1124:1:39-60.

Stawarczyk, D., Majerus, S., Maj, M., Van der Linden, M. and D’Argembeau, A., 2011. Mind-wandering: phenomenology and function as assessed with a novel experience sampling method. Acta psychologica, 136:3:370-381.

Tulving, E. 2002. Chronesthesia: Conscious awareness of subjective time. In D.T. Stuss & R.C. Knight (Eds.), Principles of frontal lobe function (pp. 311–325). New York: Oxford University Press.

This week on News 1130 radio in Vancouver, I spoke about these tech topics for Wired Wednesday with Ben Wilson:

• Inirv React smartknob answers the question – “Did I leave the stove on?” (source)
• Gita: Vespa’s new personal cargo carrying robot (source)
• Ford Smartlink adds modern conveniences to older vehicles (source)

The post Wired Wednesday: Inirv React, Gita robot & Ford Smartlink appeared first on johnbiehler.com.

One of my items on my 2015 list of favourite gear was my Swvre pants. I found them to be warm, and ideal for winter riding. I bought them during a visit to Calgary. When I went to the Swvre website a couple of months ago, I saw that they had midweight regular fit downtown pants and I figured that if they were the same as the first pair, but a little more dressy, that would be a warmer alternative to my three season pants, my Outlier SD’s.

It should be noted that Swvre now produces the bulk of their stuff offshore, although their black label line is still sewn in LA. For these pants, there is about a $40 difference between the domestic and imported versions. Furthermore, the imported versions have waist sizes in 1″ increments, whereas black label only comes in even sizes.

Here are a few pictures that I shot when I got the new pants.

The downtown pants are the black pair to the left. You can see a zipper on one pocket, and the lack of reflective belt loops.

the little dart on the knee of my other pants is missing for a cleaner look.

There are slash pockets that look dressier, as well as a small coin pocket.

So far, so good. However, before I ordered them I tried to get some info from Swvre to verify that they were in fact the same fabric as my other pants, but I got no response. The new ones seemed much lighter than the old. Sure enough, when I put them on a scale I got: (left to right) Swvre downtown: 337g (31W, 32L), Outlier SD’s: 412 g, and older Swvres: 438 g. So clearly then are not the same pant as my older pair. To be precise, I’m not sure what the model of the older pair was.; they could have been a “three season pant”, but they are clearly lighter than their current winter weight pant.

Riding with the new pants, I got the impression that they were colder than the older pair, but slight more wind and water resistant than the SD’s.  So far so good.

However, after cold water wash and hang to dry, the new pants shrunk in both length and width. The length was particularly bad. Below is a picture of the old and the new pants, after the new pants have been washed twice. You can see that the new pair has shrunk in inseam length about 3.5 cm.

In summary, I’m afraid that I can’t recommend the Swevre midweight downtown pant. It has all the good features such as 4 way stretch fabric and a gusseted crotch that is ideal for biking. However, the shrinkage is not satisfactory. I’ll stick with the older pair of Swvres, and my SD’s for warmer weather.

This talk was delivered at the University of Richmond. The full slide deck can be found here.

Thank you very much for inviting me to speak here at the University of Richmond – particularly to Ryan Brazell for recognizing my work and the urgency of the conversations that hopefully my visit here will stimulate.

Hopefully. Funny word that – “hope.” Funny, those four letters used so iconically to describe a Presidential campaign from a young Illinois Senator, a campaign that seems now lifetimes ago. Hope.

My talks – and I guess I’ll warn you in advance if you aren’t familiar with my work – are not known for being full of hope. Or rather I’ve never believed the hype that we should put all our faith in, rest all our hope on technology. But I’ve never been hopeless. I’ve never believed humans are powerless. I’ve never believed we could not act or we could not do better.

There were a couple of days, following our decision about the title and topic of this keynote – “Ed-Tech in a Time of Trump,” when I wondered if we’d even see a Trump presidency. Would some revelation about his business dealings, his relationship with Russia, his disdain for the Constitution prevent his inauguration? Should we have been so lucky, I suppose. Hope.

The thing is, I’d still be giving the much the same talk, just with a different title. “A Time of Trump” could be “A Time of Neoliberalism” or “A Time of Libertarianism” or “A Time of Algorithmic Discrimination” or “A Time of Economic Precarity.” All of this is – from President Trump to the so-called “new economy” – has been fueled to some extent by digital technologies; and that fuel, despite what I think many who work in and around education technology have long believed – have long hoped – is not necessarily (heck, even remotely) progressive.

I’ve had a sinking feeling in my stomach about the future of education technology long before Americans – 26% of them, at least – selected Donald Trump as our next President. I am, after all, “ed-tech’s Cassandra.” But President Trump has brought to the forefront many of the concerns I’ve tried to share about the politics and the practices of digital technologies. I want to state here at the outset of this talk: we should be thinking about these things no matter who is in the White House, no matter who runs the Department of Education (no matter whether we have a federal department of education or not). We should be thinking about these things no matter who heads our university. We should be asking – always and again and again: just what sort of future is this technological future of education that we are told we must embrace?

Of course, the future of education is always tied to its past, to the history of education. The future of technology is inexorably tied to its own history as well. This means that despite all the rhetoric about “disruption” and “innovation,” what we find in technology is a layering onto older ideas and practices and models and systems. The networks of canals, for example, were built along rivers. Railroads followed the canals. The telegraph followed the railroad. The telephone, the telegraph. The Internet, the telephone and the television. The Internet is largely built upon a technological infrastructure first mapped and built for freight. It’s no surprise the Internet views us as objects, as products, our personal data as a commodity.

When I use the word “technology,” I draw from the work of physicist Ursula Franklin who spoke of technology as a practice: “Technology is not the sum of the artifacts, of the wheels and gears, of the rails and electronic transmitters,” she wrote. “Technology is a system. It entails far more than its individual material components. Technology involves organization, procedures, symbols, new words, equations, and, most of all, a mindset.” “Technology also needs to be examined as an agent of power and control,” Franklin insisted, and her work highlighted “how much modern technology drew from the prepared soil of the structures of traditional institutions, such as the church and the military.”

I’m going to largely sidestep a discussion of the church today, although I think there’s plenty we could say about faith and ritual and obeisance and technological evangelism. That’s a topic for another keynote perhaps. And I won’t dwell too much on the military either – how military industrial complexes point us towards technological industrial complexes (and to ed-tech industrial complexes in turn). But computing technologies undeniably carry with them the legacy of their military origins. Command. Control. Communication. Intelligence.

As Donna Haraway argues in her famous “Cyborg Manifesto,” “Feminist cyborg stories have the task of recoding communication and intelligence to subvert command and control.” I want those of us working in and with education technologies to ask if that is the task we’ve actually undertaken. Are our technologies or our stories about technologies feminist? If so, when? If so, how? Do our technologies or our stories work in the interest of justice and equity? Or, rather, have we adopted technologies for teaching and learning that are much more aligned with that military mission of command and control? The mission of the military. The mission of the church. The mission of the university.

I do think that some might hear Haraway’s framing – a call to “recode communication and intelligence” – and insist that that’s exactly what education technologies do and they do so in a progressive reshaping of traditional education institutions and practices. Education technologies facilitate communication, expanding learning networks beyond the classroom. And they boost intelligence – namely, how knowledge is created and shared.

Perhaps they do.

But do our ed-tech practices ever actually recode or subvert command and control? Do (or how do) our digital communication practices differ from those designed by the military? And most importantly, I’d say, does (or how does) our notion of intelligence?

“Intelligence” – this is the one to watch and listen for. (Yes, that’s ironic that “ed-tech in a time of Trump” will be all about intelligence, but hear me out.)

“Intelligence” means understanding, intellectual, mental faculty. Testing intelligence, as Stephen Jay Gould and others have argued, has a long history of ranking and racism. The word “intelligence” is also used, of course, to describe the gathering and assessment of tactical information – information, often confidential information, with political or military value. The history of computing emerges from cryptography, tracking and cracking state secrets. And the word “intelligence” is now used – oh so casually – to describe so-called “thinking machines”: algorithms, robots, AI.

It’s probably obvious – particularly when we think of the latter – that our notions of “intelligence” are deeply intertwined with technologies. “Computers will make us smarter” – you know those assertions. But we’ve long used machines to measure and assess “intelligence” and to monitor and surveil for the sake of “intelligence.” And again, let’s recall Franklin’s definition of technologies includes not just hardware or software, but ideas, practices, models, and systems.

One of the “hot new trends” in education technology is “learning analytics” – this idea that if you collect enough data about students that you can analyze it and in turn algorithmically direct students towards more efficient and productive behaviors, institutions towards more efficient and productive outcomes. Command. Control. Intelligence.

And I confess, it’s that phrase “collect enough data about students” that has me gravely concerned about “ed-tech in a time of Trump.” I’m concerned, in no small part, because students are often unaware of the amount of data that schools and the software companies they contract with know about them. I’m concerned because students are compelled to use software in educational settings. You can’t opt out of the learning management system. You can’t opt out of the student information system. You can’t opt out of required digital textbooks or digital assignments or digital assessments. You can’t opt out of the billing system or the financial aid system. You can’t opt of having your cafeteria purchases, Internet usage, dorm room access, fitness center habits tracked. Your data as a student is scattered across multiple applications and multiple databases, most of which I’d wager are not owned or managed by the school itself but rather outsourced to a third-party provider.

School software (and I’m including K–12 software here alongside higher ed) knows your name, your birth date, your mailing address, your home address, your race or ethnicity, your gender (I should note here that many education technologies still require “male” or “female” and do not allow for alternate gender expressions). It knows your marital status. It knows your student identification number (it might know your Social Security Number). It has a photo of you, so it knows your face. It knows the town and state in which you were born. Your immigration status. Your first language and whether or not that first language is English. It knows your parents’ language at home. It knows your income status – that is, at the K–12 level, if you quality for a free or reduced lunch and at the higher ed level, if you qualify for a Pell Grant. It knows if you are the member of a military family. It knows if you have any special education needs. It knows if you were identified as “gifted and talented.” It knows if you graduated high school or passed a high school equivalency exam. It knows your attendance history – how often you miss class as well as which schools you’ve previously attended. It knows your behavioral history. It knows your criminal history. It knows your participation in sports or other extracurricular activities. It knows your grade level. It knows your major. It knows the courses you’ve taken and the grades you’ve earned. It knows your standardized test scores.

Obviously it’s not a new practice to track much of that data, and as such these practices are not dependent entirely on new technologies. There are various legal and policy mandates that have demanded for some time now that schools collect this information. Now we put it in “the cloud” rather than in a manila folder in a locked file cabinet. Now we outsource this to software vendors, many of whom promise that because of the era of “big data” that we should collect even more information about students – all their clicks and their time spent “on task,” perhaps even their biometric data and their location in real time – so as to glean more and better insights. Insights that the vendors will then sell back to the school.

Big data.

Command. Control. Intelligence.

This is the part of the talk, I reckon, when someone who speaks about the dangers and drawbacks of “big data” turns the focus to information security and privacy. No doubt schools are incredibly vulnerable on the former front. Since 2005, US universities have been the victim of almost 550 data breaches involving nearly 13 million known records. We typically think of these hacks as going after Social Security Numbers or credit card information or something that’s of value on the black market.

The risk isn’t only hacking. It’s also the rather thoughtless practices of information collection, information sharing, and information storage. Many software companies claim that the data that’s in their systems is their data. It’s questionable if much of this data – particularly metadata – is covered by FERPA. As such, student data can be sold and shared, particularly when the contracts signed with a school do not prevent a software company from doing so. Moreover, these contracts often do not specify how long student data can be kept.

In this current political climate – ed-tech in a time of Trump – I think universities need to realize that there’s a lot more at stake than just financially motivated cybercrime. Think Wikileaks’ role in the Presidential election, for example. Now think about what would happen if the contents of your email account was released to the public. President Trump has made it a little bit easier, perhaps, to come up with “worse case scenarios” when it comes to politically-targeted hacks, and we might be able to imagine these in light of all the data that higher ed institutions have about students (and faculty).

Again, the risk isn’t only hacking. It’s amassing data in the first place. It’s profiling. It’s tracking. It’s surveilling. It’s identifying “students at risk” and students who are “risks.”

Several years ago – actually, it’s been five or six or seven now – when I was first working as a freelance tech journalist, I interviewed an author about a book he’d written on big data and privacy. He made one of those casual remarks that you hear quite often from people who work in computing technologies: privacy is dead. He’d given up on the idea that privacy was possible or perhaps even desirable; what he wanted instead was transparency – that is, to know who has your data, what data, what they do with it, who they share it with, how long they keep it, and so on. You can’t really protect your data from being “out there,” he argued, but you should be able to keep an eye on where “out there” it exists.

This particular author reminded me that we’ve counted and tracked and profiled people for decades and decades and decades and decades. In some ways, that’s the project of the Census – first conducted in the United States in 1790. It’s certainly the project of much of the data collection that happens at school. And we’ve undertaken these practices since well before there was “big data” or computers to collect and crunch it. Then he made a comment that, even at the time, I found deeply upsetting. “Just as long as we don’t see a return of Nazism,” he joked, “we’ll be okay. Because it’s pretty easy to know if you’re a Jew. You don’t have to tell Facebook. Facebook knows.”

We can substitute other identities there. It’s easy to know if you’re Muslim. It’s easy to know if you’re queer. It’s easy to know if you’re pregnant. It’s easy to know if you’re Black or Latino or if your parents are Syrian or French. It’s easy to know your political affinities. And you needn’t have given over that data, you needn’t have “checked those boxes” in your student information system in order for the software to develop a fairly sophisticated profile about you.

This is a punch card, a paper-based method of proto-programming, one of the earliest ways in which machines could be automated. It’s a relic, a piece of “old tech,” if you will, but it’s also a political symbol. Think draft cards. Think the slogan “Do not fold, spindle or mutilate.” Think Mario Savio on the steps of Sproul Hall at UC Berkeley in 1964, insisting angrily that students not be viewed as raw materials in the university machine.

The first punch cards were developed to control the loom, industrializing the craft of weaving women around 1725. The earliest design – a paper tape with holes punched in it – was improved upon until the turn of the 19th century, when Joseph Marie Jacquard first demonstrated a mechanism to automate loom operation.

Jacquard’s invention inspired Charles Babbage, often credited with originating the idea of a programmable computer. A mathematician, Babbage believed that “number cards,” “pierced with certain holes,” could operate the Analytical Engine, his plans for a computational device. “We may say most aptly that the Analytical Engine weaves algebraical patterns just as the Jacquard-loom weaves flowers and leaves,” Ada Lovelace, Babbage’s translator and the first computer programmer, wrote.

But it was Herman Hollerith who invented the recording of data on this medium so that it could then be read by a machine. Earlier punch cards – like those designed by Jacquard – were used to control the machine. They weren’t used to store data. But Hollerith did just that. The first Hollerith card had 12 rows and 9 columns, and data was recorded by the presence or absence of a hole at a specific location on a card.

Hollerith founded The Tabulating Machine Company in 1896, one of four companies consolidated to form Computing-Tabulating-Recording Company, later renamed the International Business Machines Corporation. IBM.

Hollerith’s punch card technology was first used in the US Census in 1890 to record individual’s traits – their gender, race, nationality, occupation, age, marital status. These cards could then be efficiently sorted to quantify the nation. The Census was thrilled as it had taken almost a decade to tabulate the results of the 1880 census, and by using the new technology, the agency saved $5 million.

Hollerith’s machines were also used by Nicholas II, the czar of Russia for the first (and only) census of the Russian Imperial Empire in 1897. And they were adopted by Hitler’s regime in Germany. As Edwin Black chronicles in his book IBM and the Holocaust,

When Hitler came to power, a central Nazi goal was to identify and destroy Germany’s 600,000-member Jewish community. To Nazis, Jews were not just those who practiced Judaism, but those of Jewish blood, regardless of their assimilation, intermarriage, religious activity, or even conversion to Christianity. Only after Jews were identified could they be targeted for asset confiscation, ghettoization, deportation, and ultimately extermination. To search generations of communal, church, and governmental records all across Germany – and later throughout Europe – was a cross-indexing task so monumental, it called for a computer. But in 1933, no computer existed.

What did exist at the time was the punch card and the IBM machine, sold to the Nazi government by the company’s German subsidiary, Dehomag.

Hitler’s regime made it clear from the outset that it was not interested in merely identifying those Jews who claimed religious affiliation, who said that they were Jewish. It wanted to be able to find those who had Jewish ancestry, Jewish “blood,” those who were not Aryan.

Hitler called for a census in 1933, and Germans filled out the census on pen and paper – one form per household. There was a census again in 1939, and as the Third Reich expanded, so did the Nazi compulsion for data collection. Census forms were coded and punched by hand and then sorted and counted by machine. IBM punch cards and IBM machines. During its relationship with the Nazi regime – one lasting throughout Hitler’s rule, throughout World War II – IBM derived about a third of its profits from selling punch cards.

Column 22 on the punch card was for religion – punched at hole 1 to indicate Protestant, hole 2 for Catholic, hole 3 for Jew. The Jewish cards were processed separately. The cards were sorted and indexed and filtered by profession, national origin, address, and other traits. The information was correlated with other data – community lists, land registers, medical information – in order to create a database, “a profession-by-profession, city-by-city, and indeed a block-by-block revelation of the Jewish presence.”

It was a database of inference, relying heavily on statistics alongside those IBM machines. This wasn’t just about those who’d “ticked the box” that they were Jewish. Nazi “race science” believed it could identify Jews by collecting and analyzing as much data as possible about the population. “The solution is that every interesting feature of a statistical nature … can be summarized … by one basic factor,” the Reich Statistical Office boasted. “This basic factor is the Hollerith punch card.”

Command. Control. Intelligence.

The punch card and the mechanized processing of its data were used to identify Jews, as well as Roma and other “undesirables” so they could be imprisoned, so their businesses and homes could be confiscated, so their possessions could be inventoried and sold. The punch card and the mechanized processing of its data was used to determine which “undesirables” should be sterilized, to track the shipment of prisoners to the death camps, and to keep tabs on those imprisoned and sentenced to die therein. All of this recorded on IBM punch cards. IBM machines.

The CEO of IBM at this time, by the way: Thomas Watson. Yes, this is who IBM has named their “artificial intelligence” product Watson after. IBM Watson, which has partnered with Pearson and with Sesame Street, to “personalize learning” through data collection and data analytics.

Now a quick aside, since I’ve mentioned Nazis.

Back in 1990, in the early days of the commercialized Internet, those heady days of Usenet newsgroup discussion boards, attorney Mike Godwin “set out on a project in memetic engineering.” Godwin felt as though comparisons to Nazis occurred too frequently in online discussions. He believed that accusations that someone or some idea was “Hitler-like” were thrown about too carelessly. “Godwin’s Law,” as it came to be known, says that “As an online discussion grows longer, the probability of a comparison involving Hitler approaches 1.” Godwin’s Law has since been invoked to decree that once someone mentions Hitler or Nazis, that person has lost the debate altogether. Pointing out Nazism online is off-limits.

Perhaps we can start to see now how dangerous, how damaging to critical discourse this even rather casual edict has been.

Let us remember the words of Supreme Court Justice Robert Jackson in his opening statement for the prosecution at the Nuremburg Trials:

What makes this inquest significant is that these prisoners represent sinister influences that will lurk in the world long after their bodies have returned to dust. We will show them to be living symbols of racial hatreds, of terrorism and violence, and of the arrogance and cruelty of power. … Civilization can afford no compromise with the social forces which would gain renewed strength if we deal ambiguously or indecisively with the men in whom those forces now precariously survive.

We need to identify and we need to confront the ideas and the practices that are the lingering legacies of Nazism and fascism. We need to identify and we need to confront them in our technologies. Yes, in our education technologies. Remember: our technologies are ideas; they are practices. Now is the time for an ed-tech antifa, and I cannot believe I have to say that out loud to you.

And so you hear a lot of folks in recent months say “read Hannah Arendt.” And I don’t disagree. Read Arendt. Read The Origins of Totalitarianism. Read her reporting from the Nuremberg Trials.

But also read James Baldwin. Also realize that this politics and practice of surveillance and genocide isn’t just something we can pin on Nazi Germany. It’s actually deeply embedded in the American experience. It is part of this country as a technology.

Let’s think about that first US census, back in 1790, when federal marshals asked for the name of each head of household as well as the numbers of household members who were free white males over age 16, free white males under 16, free white females, other free persons, and slaves. In 1820, the categories were free white males, free white female, free colored males and females, and slaves. In 1850, the categories were white, Black, Mulatto, Black slaves, Mulatto slaves. In 1860, white, Black, Mulatto, Black slaves, Mulatto slaves, Indian. In 1870, white, Black, Mulatto, Indian, Chinese. In 1890, white, Black, Mulatto, Quadroon, Octoroon, Indian, Chinese, Japanese. In 1930, white, Negro, Indian, Chinese, Japanese, Filipino, Korean, Hindu, Mexican.

You might see in these changing categories a changing demographic; or you might see this as the construction and institutionalization of categories of race – particularly race set apart from a whiteness of unspecified national origin, particularly race that the governing ideology and governing system wants identified and wants managed. The construction of Blackness. “Census enumeration is a means through which a state manages its residents by way of formalized categories that fix individuals within a certain time and a particular space,” as Simone Browne writes in her book Dark Matters: On the Surveillance of Blackness, “making the census a technology that renders a population legible in racializing as well as gendering ways.” It is “a technology of disciplinary power that classifies, examines, and quantifies populations.”

Command. Control. Intelligence.

Does the data collection and data analysis undertaken by schools work in a similar way? How does the data collection and data analysis undertaken by schools work? What bodies and beliefs are constituted therein? Is whiteness and maleness always there as “the norm” against which all others are compared? Are we then constructing and even naturalizing certain bodies and certain minds as “undesirable” bodies and “undesirable” minds in the classroom, in our institutions by our obsession with data, by our obsession with counting, tracking, and profiling?

Who are the “undesirables” of ed-tech software and education institutions? Those students who are identified as “cheats,” perhaps. When we turn the cameras on, for example with proctoring software, those students whose faces and gestures are viewed – visually, biometrically, algorithmically – as “suspicious.” Those students who are identified as “out of place.” Not in the right major. Not in the right class. Not in the right school. Not in the right country. Those students who are identified – through surveillance and through algorithms – as “at risk.” At risk of failure. At risk of dropping out. At risk of not repaying their student loans. At risk of becoming “radicalized.” At risk of radicalizing others. What about those educators at risk of radicalizing others. Let’s be honest with ourselves, ed-tech in a time of Trump will undermine educators as well as students; it will undermine academic freedom. It’s already happening. Trump’s tweets this morning about Berkeley.

What do schools do with the capabilities of ed-tech as surveillance technology now in the time of a Trump? The proctoring software and learning analytics software and “student success” platforms all market themselves to schools claiming that they can truly “see” what students are up to, that they can predict what students will become. (“How will this student affect our averages?”) These technologies claim they can identify a “problem” student, and the implication, I think, is that then someone at the institution “fixes” her or him. Helps the student graduate. Convinces the student to leave.

But these technologies do not see students. And sadly, we do not see students. This is cultural. This is institutional. We do not see who is struggling. And let’s ask why we think, as the New York Times argued today, we need big data to make sure students graduate. Universities have not developed or maintained practices of compassion. Practices are technologies; technologies are practices. We’ve chosen computers instead of care. (When I say “we” here I mean institutions not individuals within institutions. But I mean some individuals too.) Education has chosen “command, control, intelligence.” Education gathers data about students. It quantifies students. It has adopted a racialized and gendered surveillance system – one that committed to disciplining minds and bodies – through our education technologies, through our education practices.

All along the way, or perhaps somewhere along the way, we have confused surveillance for care.

And that’s my takeaway for folks here today: when you work for a company or an institution that collects or trades data, you’re making it easy to surveil people and the stakes are high. They’re always high for the most vulnerable. By collecting so much data, you’re making it easy to discipline people. You’re making it easy to control people. You’re putting people at risk. You’re putting students at risk.

You can delete the data. You can limit its collection. You can restrict who sees it. You can inform students. You can encourage students to resist. Students have always resisted school surveillance.

But I hope that you also think about the culture of school. What sort of institutions will we have in a time of Trump? Ones that value open inquiry and academic freedom? I swear to you this: more data will not protect you. Not in this world of “alternate facts,” to be sure. Our relationships to one another, however, just might. We must rebuild institutions that value humans’ minds and lives and integrity and safety. And that means, in its current incarnation at least, in this current climate, ed-tech has very very little to offer us.

The big risk in leading is looking behind you and seeing no-one following.

You would look pretty silly if you were the only person at the airport holding a protest sign.

Very few people lead (or show initiative) in any community for precisely this reason, they might look a bit silly.

The overwhelming majority join in once everyone else is already there. That’s when it’s safe.

We’ve all seen the dancing guy. Somebody has to be first or nothing happens.

This is why those that show leadership and take the initiative are so valuable. They are the people that are willing to put their ego on the line to make something happen.

These people are precious. You’re going to need several of these people to survive. Do whatever you can to support them.

Don’t knock their initiatives because they’re off-brand or not part of the roadmap. Support them. Give them ideas and promote what they’re doing.

Too many reject new ideas for the community by default. I suggest supporting and promoting it by default.

Or to put it simply, make it easier to be a leader in your community.

Christian Laesser takes an abstract look at how different languages represent Vincent van Gogh through various Wikipedia pages.

The visualization explores how different languages present Van Gogh’s work and life by images. Inspired by Geolinguistic Contrasts in Wikipedia. The viz tries to show different narative strategies by showing the image type, origin date and authorship. You can reveal the connections between languages by hovering the images.

I’m not quite convinced this helps with understanding, but I appreciate the experimentation.

Tags: art, Vincent van Gogh, Wikipedia

The name Devo comes “from their concept of ‘de-evolution‘ — the idea that instead of continuing to evolve, mankind has actually begun to regress, as evidenced by the dysfunction and herd mentality of American society.”^[9]  –Devo’s Wikipedia article

Devo’s theories of evolution have never seemed more relevant, so the latest Reclaim Hosting server is named in honor of the early 70s video/music pioneers who brought an entrenched, surreal social satire to their work. One of their unique contributions was their elaborate and trippy music videos with recurring characters such as Booji Boy and General Boy, music video narratives that prefigured MTV. Interestingly, Devo was formed in response to the Kent State shootings in 1970-where several of the band members went to school-and were conceptualized as a satirical attack on the militaristic, consumer-driven logic of contemporary U.S. culture. With the mainstream success of “Whip It” (1980), they also became representative of pop New Wave for a entire generation of kids heading into the 80s (myself included).

Yet, despite their early critiques of consumer culture, Devo was not beyond shilling for Pioneer’s Laserdisc technology in the early 80s. Their craziest work shows up in the 1984 video compilation We’re All Devo, featuring their music videos from 1976 – 1983, much of which is re-released ten years later in another compilation of their videos from 1976-1990: The Complete Truth about De-Evolution (1993). Both came out on VHS and Laserdisc, the latter work using their Pioneer promotional clips as an organizing principal. While effectively goofing on their own willingness to shill, the blurry line between a sustained critique on pop culture and indulging it always made their later work oddly uncomfortable.*

That said, Devo’s concept art-as-entertainment approach to their music and videos (I own the Laserdiscs and they are a prized possession) puts them in that interesting category of musicians who are equal parts performance/concept artists. Sharing as much with bands like The Residents as Flock of Seagulls But unlike most of the New Wave decadents, the Akron, Ohio spud boys introduced a brave new philosophy of a changing world order premised on de-evolution. A theory we might do well to consider in some depth presently.

*I would be lying if I didn’t acknowledge it was hard to stomach the hypocrisy of a band constantly complaining about corporate music shilling for Disney during the mid oughts. But sadly it seems if just about any band stays around long enough they will eventually cannibalize their catalog for profit—it’s Devo in action

Most read reviews this week: The Best Wi-Fi Router (for Most People), The Best Wi-Fi Mesh Networking Kits for Most People, and The Best Indoor HDTV Antenna. [See all 10 most popular reviews this week.]

via Photo Challenge: Solitude

There are quite a few photos like this on my Flickr stream. Unlike crows or starlings, the Great Blue Heron prefer solitude – at least when (s)he is going fishing.

I started taking pictures of these birds when I heard about the Citizen Science project.

By the way, I notice that – as usual – my picture breaks the “rule of thirds” for composition. That is because of the way that autofocus used to work on my previous cameras. This is taken with an iPhone, but I seem to to have broken the habit of centering the subject – which in this case seems to have nicely separated the bird from the background. But there is no bokeh as this was taken midday on a sunny day in August.

By the way: this post is in response to the Weekly Photo Challenge. You will note that there is also a link at the very top of the post which gets automatically inserted by WordPress when you use the button on the challenge page. But the pingback there is malformatted – as if you click on it, you go to a different page than the one where the challenge is posted. If you are doing photo challenges as a way to see how others respond that link is useless. Also note that it does not tell you – as the correct pingback does – that the weekly challenge is being moved to Wednesdays!

I’ve suddenly discovered that I’ve become dependent on reading lips.

I’ve worn hearing aids for years; I have what basically amounts to arthritis of the ears, and believe me, it’s better than arthritis in your knees or carpal tunnel or whatever. But I don’t hear terrifically well.

So last Saturday I was in Logan Airport’s International Terminal, talking to a reporter, and she wanted to do a standup with her iPhone. She asked if I wanted to use my name, and was surprised when I said “sure”.

So, it’s a big noisy room with lots of echoes – a really challenging environment, but I was coping fine until she starts with the video. To get a good angle, naturally, she holds the camera in front of her face. Suddenly, I can’t hear a word she’s saying. Literally: I cannot make anything out.

Now, when the pitcher and the catcher are talking on the mound, I don’t have a clue. When Tom Brady is talking on the field, I have no idea what he’s saying, unless (of course) he’s saying “Fuck!” which he says a lot, but everyone knows that one. But it seems that, day to day, I’ve been learning.

Kind of alarming.

Virtue and Character

Ethical Rules

Categorical Imperative

Utilitarianism

Egoism

Social Contract

MetaEthics

Does Might Make Right?

Naturalism

Moral Sentiment

Relativism

Here’s my fully-optimized technique for referencing sources, developed over many years of authoring. (Yes, I am a huge book nerd.) You get to read it thanks to today’s reader question: Dear Dr. Wobs, What is the best way to cite sources or give notes in a blog or book? What format do you use to cite sources/notes … Continued

The post The best way to create a footnote or citation in print or online (Ask Dr. Wobs) appeared first on without bullshit.

I’ve received hundreds of votes for the Bullshitty Awards. I suspect I know who’s going to win. But there’s still time to vote. Voting closes on Friday, February 3 at midnight, Eastern time. Vote here. And then tune in to find out: Whose apology was least sincere, Donald Trump or Ryan Lochte? Who was the most … Continued

The post One more day to vote for bullshitter of the year (tune in Feb 16 to hear who won) appeared first on without bullshit.

Dennis Thomas, Learning Solutions Magazine, Feb 05, 2017

This is a view of knowledge and learning that I think is wrong (and would argue has been disproven in application) but which is nonetheless believed - either implicitly or explicitly - by many. The idea is that all knowledge can be understood conceptually a nd semantically, and that it all fits a giant puzzle explaining the universe, which can be understood using "time-tested a priori knowledge."

Sharing the work we do around web and information security is an important role of Mozilla Security. We often get questions on specific security technologies, both from our engineers who work on Mozilla products and services, and from the community interested in using these technologies in their own environments.

Today, we introduce a new podcast, called Mozilla Security Bytes, where we discuss these security technologies in details.

In this first episode, we talk about Content Security Policy, or CSP, with Christoph Kerschbaumer, Frederik Braun and Dylan Hardison. We cover both the history and future of CSP, and various issues we have learned while implementing it on our own sites and services.

We hope you find the discussion interesting, and feel free to share your feedback with us at security-bytes@mozilla.com.

Download link, Podcast subscription URL

 Links mentioned in the episode

• Analysis of the Alexa Top 1M sites (October 2016)
• Reflections on Trusting Trust
• X-Frame-Options: All About Clickjacking?
• Google’s CSP Evaluator
• H5SC Minichallenge 3: “Sh*t, it’s CSP!”
• Injecting CSP for Fun and Security

The post Mozilla Security Bytes, ep. 1: Content Security Policy appeared first on Mozilla Security Blog.

While it seems the last few months have seen Instagram preoccupied with replicating Snapchat, the company appears set on finally adding new photo-sharing functionality to its popular app.

The latest version of Instagram’s Android app, 10.7.0, allows some users to share 10 photos inside of a single post. Effectively, the company is finally adding albums. If you’ve seen the multi-image posts some brands have used on their feeds, then you know what regular users can expect to see.

In the feature’s current iteration, some users are reporting being able to apply a single filter to all 10 photos at the same time, or a separate filter to each image. Some reports indicate that users who are able to access the ability to post multiple photos can’t actually post the images to their timeline. It’s likely that Instagram is just testing the feature currently and has plans to roll it out to more users in the near future.

Viewers can move through a multi-photo post by swiping to the right, with each image recording likes and comments separately.

Users who currently have access to the feature have not been able to share their albums, suggesting Instagram may have pushed the test out earlier than intended.

Image courtesy of Droid Life.

Source: Droid Life

The post Instagram is testing multi-photo posts with its Android app appeared first on MobileSyrup.com.

Following YouTube’s latest iOS update, 12.03, iPhone owners can now use their Apple-made mobile device as a remote control.

The update allows users to control a variety of Chromecast devices, as well as smart TVs and game consoles running the YouTube app, from their iOS’s lock screen.

While it’s a relatively small change, this will save heavy users of Google video platform the hassle of unlocking their device each time they want to alter playback on their Chromecast.

The update also adds playback controls to the Apple Watch.

Download the update from the iOS App Store.

Source: iTunes App Store Via: Engadget

The post YouTube for iOS adds lock screen controls appeared first on MobileSyrup.com.

Nintendo says it has sold 1.5 million NES Classic consoles worldwide, according to data revealed during a recent corporate strategy meeting.

The NES Classic has been difficult to buy across North America and most of the world following its November release. Recently, Nintendo of America took to Twitter to emphasize that it’s working towards keeping up with the massive demand for the retro console and that it’s “increasing production.”

NES Classic Edition global sell-through 1.5 million; increasing production.

— Takashi Mochizuki (@mochi_wsj) February 1, 2017

While the console is a cool collectible, mainly because it looks like a ultra-detailed miniature version of the original Nintendo Entertainment System, it functions nearly identically to many of the emulators currently available on a variety of other consoles and devices.

Also, the 2.5ft controller cable included with the NES Classic’s gamepad makes very little sense. Playing games directly in front of the television on the floor just isn’t much fun as an adult.

The information was originally translated by Wall Street Journal reported Takashi Mochizuki.

The post Nintendo says it has sold 1.5 million NES Classics worldwide appeared first on MobileSyrup.com.

As I enter Hugo McCloud’s studio through a garage door, I wonder if I’ve accidentally ventured somewhere I’m not supposed to be. Stepping just inside the door, I'm surrounded by rugged brown woven sacks hanging from the ceiling, adjacent to a punching bag and wooden gymnastic rings.

McCloud’s studio seems to be a natural extension of his still-industrial Brooklyn neighborhood. The tough-looking space fits the artist well: The former industrial designer carries his background with him into the art world. Unlike Mark Bradford and Rudolf Stingel—two of his artistic inspirations—he ditches canvas in favor of roofing paper. Defying the idea that there’s no room for innovation in painting, McCloud uses such heavy-duty instruments such as torches and hammers.

The Bushwick studio looks like an abandoned warehouse, but with white walls and bright sunlight. It does not feel industrially detached: there’s a motorcycle parked in the very back, and an adorable painting by 8-year-old McCloud made hangs above his desk—it depicts a gentle bird, which is decidedly unlike his current work.

Paint splatters blanket everything here. Even McCloud’s MacBook is covered in colorful paint splatters. He recently riffed on that charm while collaborating with Brother Vellies and Ryan Roche on a collection of suffragette-inspired sweaters that benefitted the Women’s March—the artist wore the knits around his studio, letting paint rub off them. 

The contrasts between the loud, intense process of his work, the buzzing, humming, and clanking, and the resultant pieces, is surprising. His all-white and all-gold works evoke luxe Baroque prints, transporting the viewer far from the artist’s utilitarian space and into their grandiose and dramatic patterns.

McCloud’s mysterious “veiled” pieces, presented at Sean Kelly gallery this month, are both poetic and pragmatic, one-half painted, the other half wrapped in aluminium foil. To McCloud, the appeal of veiling is partially in embracing the experiment. He always paints the entire canvas before covering one half in aluminium foil, building a sense of intrigue around the hidden portion.

During exhibitions, these veiled works in particular entice gallery visitors. At one of McCloud’s book signing events, I observed people first approaching the paintings to see what’s hiding behind the foil, then, catching their own reflections, stepping back to gaze into that incidental mirror.

McCloud says that it’s important to him to embrace the mistakes and imperfections in his process. During his first experiments with foil, he was enchanted by how glassy the unblemished foil surface was. But in the veiling process, the perfect smoothness was destroyed, and leaving texture in its wake.

“In these moments, you have to stop and reevaluate,” McCloud says. “Sometimes you have to realize that you intended to do something impossible. You have to shift your attention and focus on what’s actually possible.”

He presses his hand against the foil to show me that damage is just one touch away. McCloud then eliminates the lingering and distracting marks by embracing the subtly crinkled effect throughout the work—the paint underneath allows the artist to “make a few more mistakes.”  

McCloud is continually experimenting. He dislikes repeating the same process over and over, so each piece changes as he works on it. This is one of the reasons why he doesn’t use assistants in his creative process—it’s impossible to direct someone’s improvisation.

“As an artist, you should never settle with just one thing,” McCloud says as we walk toward his more recent work, which will be exhibited at the Armory Show in March. “I constantly ask myself: How can I keep pushing? What’s the next thing?” In his new work, he clearly used a paintbrush, and the veil has taken a new form—projecting the cutout shape over the foil, McCloud hand-cut a serpentine pattern to eclipse the painting.

Like other creatives, McCloud is afflicted by wanderlust. He is known to spend many weeks traveling and, in fact, left New York shortly after my studio visit. He does not, however, bring back cheesy memorabilia. Instead, after a previous trip, he brought back the hanging woven sacks he'd later use in his work by the hundreds.

As we wrap up the tour, we talk about the everyday struggles of being creative. “There are battles and hurdles within each artist,” he says. “I am interested in the balance between perfection and imperfection. I want to let irregularities coexist with refinement."

Click here to see more works from Hugo McCloud. 

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