Using GitHub In Your Classroom

I continue to work with GitHub in my classes.  What I was working on when I ran across this article, Why version control is required for Comp 20 at Tufts University, is the outcome and justification.  It was a bit more for me to work with while I organize and plan for Continuous Improvement in my own execution.

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What to cover in class: Apps or PWA?

I found this article describing a portion of one side of the argument, app or not, which may well suggest an answer the question of where to spend out time in CS here.  Read the full article and the comments on Medium.  The author is not without an unbiased view point, consider what this might mean in relationship to our last review of the NYTimes 7 Minute Workout PWA, or click the author’s examples

I recently wrote an article called “Native Apps are Doomed.” I was surprised at how many people were defending native apps. In all honesty, the user experience story for native apps has never been impressive. The numbers paint a bleak picture for native app success rates that teams need to be aware of when they make important decisions about how to build a new app.

Native apps face two gigantic hurdles trying to compete with Progressive Web Apps (PWAs):

  • Instead of writing 3 different apps, one for Android, one for iOS, and one for the web, PWA app makers only need to build one app that works for all 3.
  • App install friction is suffocating native apps.

App store friction is a major obstacle. It takes about 6 clicks to install a native app, and with each click, you lose about 20% of your users. Deciding to install an app is a lot harder than deciding to use a web app. You have to click install, wait for the app to download, worry about how much space it will take, and worry about the scary permissions it will require. Native apps lose a lot of their potential users before they even click install.

With a progressive web app, you visit a URL and immediately get to try the app. If you continue to use it, you get prompted to install it to your home screen with one click. From that point on, it behaves like a native app. It can work offline, take photos, use WebGL for 3D games, access the GPU for hardware accelerated processing, record audio, etc… The web platform has grown up. It’s time to take it seriously. See “10 Must See Web Apps & Games”for examples of what the web can do.

……

Did a Human or a Computer Write This?

If an Algorithm Wrote This, How Would You Even Know?

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LET me hazard a guess that you think a real person has written what you’re reading. Maybe you’re right. Maybe not. Perhaps you should ask me to confirm it the way your computer does when it demands that you type those letters and numbers crammed like abstract art into that annoying little box.

Because, these days, a shocking amount of what we’re reading is created not by humans, but by computer algorithms. We probably should have suspected that the information assaulting us 24/7 couldn’t all have been created by people bent over their laptops.

It’s understandable. The multitude of digital avenues now available to us demand content with an appetite that human effort can no longer satisfy. This demand, paired with ever more sophisticated technology, is spawning an industry of “automated narrative generation.”

Companies in this business aim to relieve humans from the burden of the writing process by using algorithms and natural language generators to create written content. Feed their platforms some data — financial earnings statistics, let’s say — and poof! In seconds, out comes a narrative that tells whatever story needs to be told.

A shocking amount of what we’re reading is created not by humans, but by computer algorithms. Can you tell the difference? Take the quiz.

OPEN INTERACTIVE FEATURE

These robo-writers don’t just regurgitate data, either; they create human-sounding stories in whatever voice — from staid to sassy — befits the intended audience. Or different audiences. They’re that smart. And when you read the output, you’d never guess the writer doesn’t have a heartbeat.

Consider the opening sentences of these two sports pieces:

“Things looked bleak for the Angels when they trailed by two runs in the ninth inning, but Los Angeles recovered thanks to a key single from Vladimir Guerrero to pull out a 7-6 victory over the Boston Red Sox at Fenway Park on Sunday.”

“The University of Michigan baseball team used a four-run fifth inning to salvage the final game in its three-game weekend series with Iowa, winning 7-5 on Saturday afternoon (April 24) at the Wilpon Baseball Complex, home of historic Ray Fisher Stadium.”

If you can’t tell which was written by a human, you’re not alone. According to a study conducted by Christer Clerwall of Karlstad University in Sweden and published in Journalism Practice, when presented with sports stories not unlike these, study respondents couldn’t tell the difference. (Machine first, human second, in our example, by the way.)

Algorithms and natural language generators have been around for a while, but they’re getting better and faster as the demand for them spurs investment and innovation. The sheer volume and complexity of the Big Data we generate, too much for mere mortals to tackle, calls for artificial rather than human intelligence to derive meaning from it all.

Set loose on the mother lode — especially stats-rich domains like finance, sports and merchandising — the new software platforms apply advanced metrics to identify patterns, trends and data anomalies. They then rapidly craft the explanatory narrative, stepping in as robo-journalists to replace humans.

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The Associated Press uses Automated Insights’ Wordsmith platform to create more than 3,000 financial reports per quarter. It published a story on Apple’s latest record-busting earnings within minutes of their release.Forbes uses Narrative Science’s Quill platform for similar efforts and refers to the firm as a partner.

Then we have Quakebot, the algorithm The Los Angeles Times uses to analyze geological data. It was the “author” of the first news report of the 4.7 magnitude earthquake that hit Southern California last year, published on the newspaper’s website just moments after the event. The newspaper also uses algorithms to enhance its homicide reporting.

But we should be forgiven a sense of unease. These software processes, which are, after all, a black box to us, might skew to some predicated norm, or contain biases that we can’t possibly discern. Not to mention that we may be missing out on the insights a curious and fertile human mind could impart when considering the same information.

The mantra around all of this carries the usual liberation theme: Robo-journalism will free humans to do more reporting and less data processing.

That would be nice, but Kristian Hammond, Narrative Science’s co-founder, estimates that 90 percent of news could be algorithmically generated by the mid-2020s, much of it without human intervention. If this projection is anywhere near accurate, we’re on a slippery slope.

Yes, but can a machine convincing tell us that it was at an event when it wasn’t, the way Brian Williams and Bill O’Reilly can?I don’t think…

It’s mainly robo-journalism now, but it doesn’t stop there. As software stealthily replaces us as communicators, algorithmic content is rapidly permeating the nooks and crannies of our culture, from government affairs to fantasy football to reviews of your next pair of shoes.

Automated Insights states that its software created one billion stories last year, many with no human intervention; its home page, as well as Narrative Science’s, displays logos of customers all of us would recognize: Samsung, Comcast, The A.P., Edmunds.com and Yahoo. What are the chances that you haven’t consumed such content without realizing it?

Books are robo-written, too. Consider the works of Philip M. Parker, a management science professor at the French business school Insead: Hispatented algorithmic system has generated more than a million books,more than 100,000 of which are available on Amazon. Give him a technical or arcane subject and his system will mine data and write a book or report, mimicking the thought process, he says, of a person who might write on the topic. Et voilà, “The Official Patient’s Sourcebook on Acne Rosacea.”

Narrative Science claims it can create “a narrative that is indistinguishable from a human-written one,” and Automated Insights says it specializes in writing “just like a human would,” but that’s precisely what gives me pause. The phrase is becoming a de facto parenthetical — not just for content creation, but where most technology is concerned.

Our phones can speak to us (just as a human would). Our home appliances can take commands (just as a human would). Our cars will be able to drive themselves (just as a human would). What does “human” even mean?

With technology, the next evolutionary step always seems logical. That’s the danger. As it seduces us again and again, we relinquish a little part of ourselves. We rarely step back to reflect on whether, ultimately, we’re giving up more than we’re getting.

Then again, who has time to think about that when there’s so much information to absorb every day? After all, we’re only human.

Related: Interactive Quiz: Did a Human or a Computer Write This? A shocking amount of what we’re reading is created not by humans, but by computer algorithms. Can you tell the difference? Take the quiz.