Online learning provider Udacity has laid off nearly a quarter of its staff members. Sebastian Thrun, the company's co-founder, said in a written statement that Udacity made the “painful decision to part ways with 125 employees in the U.S. and Brazil” in order to “realign” the company’s focus.

Digital privacy has come a long way since June 2013. In the five years since documents provided by Edward Snowden became the basis for a series of revelations that tore away a veil of secrecy around broad surveillance programs run by the National Security Agency, there have been shifts in both technology and policy that have changed the center of gravity for personal electronic privacy in the United States and around the world. Sadly, not all of the changes have been positive. And Snowden's true legacy is a lot more complicated than his admirers (or his critics) will admit.

Open source is an amoral, depoliticized substitute for the free-software movement. It was explicitly started with that intent. It was a reaction campaign, set up in 1998 by Eric Raymond—he’d written ‘The Cathedral and the Bazaar’—and others, to counter the support we were getting for software freedom. When it started, Eric Raymond called me to tell me about this new term and asked if I wanted to use it. I said, I’ll have to think about it. By the next day I had realized it would be a disaster for us. It meant disconnecting free software from the idea that users deserve freedom. So I rejected it.

Emily Zhong, 20, a computer science major, piped up. “Surprisingly, a lot of my friends now are like, ‘I don’t really want to work for Facebook,’” she said, citing “privacy stuff, fake news, personal data, all of it.” “Before it was this glorious, magical thing to work there,” said Jazz Singh, 18, also studying computer science. “Now it’s like, just because it does what you want doesn’t mean it’s doing good.”

Facts come from the realm of the humanities, numbers represent the social sciences, and data the natural sciences. When people talk about the decline of the humanities, they are actually talking about the rise and fall of the fact, as well as other factors. When people try to re-establish the prestige of the humanities with the digital humanities and large data sets, that is no longer the humanities. What humanists do comes from a different epistemological scale of a unit of knowledge.

A February 2014 iteration of Facebook’s software upgraded the number of options for gender identification from 2 to 58. Drawing on critical theoretical approaches to technology, queer theory, and insights from science and technology studies, this iteration is situated within a 10-year history of software and user modifications that pivot around gender. I argue that the gender binary has regulated Facebook’s design strategy while the co-existence of binary and non-binary affordances has enabled the company to serve both users and advertising clients simultaneously. Three findings are revealed: (1) an original programming decision to store three values for gender in Facebook’s database became an important fissure for non-binary possibilities, (2) gender became increasingly valuable over time, and (3) in the deep level of the database, non-binary users are reconfigured into a binary system. This analysis also exposes Facebook’s focus on authenticity as an insincere yet highly marketable regulatory regime.

"Lombroso’s theory was, like a great deal currently coming out of the world of tech, an engineering 'solution' in search of a problem."

"The business school researchers don’t get a mention because their data is often covered by a confidentiality agreement. The machine learning crowd are just embarrassing."

“What started out as being the most fun job imaginable quickly slid into something that felt deeply bleak and lonely”

"Kempelen’s Mechanical Turk was a forerunner of today’s systems of artificial intelligence, not because it managed to play a game well, as with IBM’s Deep Blue or Google’s AlphaGo, but because many AI systems are, in large part, also technical illusions designed to fool the public. Whether it’s content moderation for social media or image recognition for police surveillance, claims abound about the effectiveness of AI-powered analytics, when, in reality, the cognitive labor comes from an office building full of (low-waged) workers. We can call this way of building and presenting such systems — whether analog automatons or digital software — Potemkin AI. "

"The humanities are ablaze."

In a precedent-setting case, an Ontario arbitrator has directed Ryerson University to ensure that student evaluations of teaching, or SETs, “are not used to measure teaching effectiveness for promotion or tenure.”

The absurdity of this process is mind-boggling, and it has absolutely no added value of any kind: neither for authors, nor for reviewers, nor for publishing, nor for readers. My only explanation is that it is, in fact, a purely symbolic exercise, a tribal ritual, if you want, to demonstrate the “digitality” of the “digital humanities.” As I have pointed out in an earlier post (The Digital Age, in German; The Digital Age (English remix)), “digital” here is merely a synonym for “modern.”

While history, English, and the rest have faded, only one set of humanities fields without a foot in the sciences has clearly held its own: the much newer (and smaller) disciplines the statistical agency joins together as ethnic, gender, and cultural studies.

Program comprehension is an important, but hard to measure cognitive process. This makes it difficult to provide suitable programming languages, tools, or coding conventions to support developers in their everyday work. Here, we explore whether functional magnetic resonance imaging (fMRI) is feasible for soundly measuring program comprehension. To this end, we observed 17 participants inside an fMRI scanner while they were comprehending source code. The results show a clear, distinct activation of five brain regions, which are related to working memory, attention, and language processing, which all fit well to our understanding of program comprehension. Furthermore, we found reduced activity in the default mode network, indicating the cognitive effort necessary for program comprehension. We also observed that familiarity with Java as underlying programming language reduced cognitive effort during program comprehension. To gain confidence in the results and the method, we replicated the study with 11 new participants and largely confirmed our findings. Our results encourage us and, hopefully, others to use fMRI to observe programmers and, in the long run, answer questions, such as: How should we train programmers? Can we train someone to become an excellent programmer? How effective are new languages and tools for program comprehension?

"Van Noy and Ruder also found that showing students their earning potential lowered their expectations for how much they would make, especially among students in STEM and business fields."

I shared in the optimism. [...] But a shift was already in the air.

"Contemporary art and tech projects, we argue, are in danger of succumbing to the same techno-utopianism as their 1960s iterations, and the same military-industrial allegiances that tainted the earlier projects continue to underpin twenty-first-century collaborations."

These posters are a probe for Our Weird Times. What happens when it takes the same effort to make 200,000 posters that each sell once as it does to manufacture one poster that sells 200,000 times? Perhaps actual people have always had an infinity of different desires, but it was never possible to satisfy each and every one. Now, though, under certain circumstances, Walmart.com or Amazon can host hundreds of thousands of products, each designed to ensnare ... well ... maybe just one customer. And some company in a faceless office in Los Angeles can make some money creating things that will almost certainly never sell more than a single copy.

By accident more than design, coders now comprise a Fifth Estate and as 21st-century citizens we need to be able to interrogate them as deeply as we interrogate politicians, marketers, the players of Wall Street and the media.

Prolog was the language to be associated with, if you were on the 1980s AI bandwagon (and the Japanese were going to take over the world unless we did something about it, e.g., spend money); this time around, it’s machine learning.

It could be argued that computers have been instrumentalized as assault weapons in a series of philosophical crusades against hermeneutics, from Bense to Moretti as well as in the media theory of Friedrich Kittler. [...] By this virtue of failure, computer science for the first time makes it possible to state more precisely the limits of formal analysis and the necessities of hermeneutic interpretation, as opposed to vague or stereotyped romanticist criteria for assessing the literary or poetic quality of a text.

[N]ewer research shows that stylometry can also apply to artificial language samples, like code. Software developers, it turns out, leave behind a fingerprint as well.

Rachel Greenstadt, an associate professor of computer science at Drexel University, and Aylin Caliskan, Greenstadt's former PhD student and now an assistant professor at George Washington University, have found that code, like other forms of stylistic expression, are not anonymous.

[T]he world of programming languages could be stabilizing a bit after a decade of change.

“The Amazon recruiter on the other end responded with apparent astonishment: ‘Wow I honestly had no idea’.”

Unfortunately, this anti-pattern has been followed by many organisations in an attempt to increase efficiency.

This highly stimulating book is in the first place a new and innovative take on the well-known two cultures debate. Yet rather than stressing the added value of bridging the gap between the two major strands – arts and sciences, alpha and beta – that came out from Western modernization and secularization since the Renaissance, Lennon argues that in some cases – that of philology, the humanist study of language, and that of cryptology, the mathematical and technical science of codes and ciphers –, the two cultures gap is much smaller than we often think.

DevOps is one the offshoots of software abstraction, where this time it was not the hardware, processes or any code complexities that were abstracted — but a way to abstract the IT personnel out (the “Ops” part).

"We may wonder, then, whether the mutations under way in the information economy do not resemble identifiable trends in US private universities, again with the prospect of a turn towards social oligarchy"

The flow of developers starting out with R will slow down, casual R users will have nothing to lose from trying out another language when the right project comes along (another language on the CV looks good and Python is a bigger market). There will be groups where everybody uses R and will continue to use R because that is what everybody else in the group uses. Ten-Twenty years from now R, developers could be working in a ghost town.

“...one staffer told me that their jobs used to be about wrangling cat videos and now they had degenerated into a daily hell of ethics debates about the fate of humanity.”

I occasionally worry that talk of the "humanities crisis" could be a self-fulfilling prophecy. Fortunately or unfortunately, though, the shift seems to have more to do with content than with labels. The social science fields that most closely resemble humanistic ones--sociology, anthropology, international relations, and political science--have also seen serious drops. [...] The big winners in recent years have been health professions, including nursing; computer science and engineering; biological science and to a lesser degree, physical sciences; and what I oddly call "leisure," which includes things like sports management and exercise studies.

Computational capital is thus also racial capitalism, the longue durée digitization of racialization and, not in any way incidentally, of regimes of gender and sexuality.

Over the last few decades the tech industry has built systems so fundamentally insecure, so rotten to their core, that we now have no real choice but to trust its largest and most powerful companies to protect us. I’m all too aware of the grim irony.

On "three interrelated challenges to the post-World War II information order: twenty years of digital technological disruption, the 2008 economic crisis, and politically and economically motivated challenges to the industrial news media."

"The cosy relationship between industry and academia—including the corporate takeover of entire university laboratories, as with Uber and Carnegie Mellon—is seldom viewed with the same scrutiny as, say, the pharmaceutical industry’s interference in medical research."

"Mr. Stamos also said Information Warfare was a new working group at Stanford with about 14 faculty members across academic disciplines."

"In example after example, the toxic combination of filter bubbles, simplistic ranking mechanisms and algorithmic recommendation is seen as clearly in elite networks as in more accessible but supposedly less self-aware groups – but, we are told, the effects really aren’t as bad as we’re being told they are"

Those who can’t be bribed are blackmailed. Thus it is no longer just that the myth of the machine is based on the idea that the megamachine is “absolutely irresistible” and “ultimately beneficial” but that it now includes the idea that to push back is “unforgivably detrimental.”

"I definitely feel like this was a silly chapter in my life. I’m very glad I got to pay off my student loans. But I don’t feel good about the work. In fact I feel pretty embarrassed about it."

"The maker movement is one prominent translation of Digital Proudhonism into a challenge to the contemporary organization of production, with allegedly radical effects on politics and economics."

With the recent growth and popularity of DevOps in industry, practitioner outlets, and publications within the areas of computer science and software engineering, the aim of this paper is to address the question, "Is Information Systems late to the party?" In hopes of shedding light on this question, the authors conducted a thorough and systematic review of the publications contained in the Association for Information Systems eLibrary to examine the current state of DevOps research within the area of Information Systems. By following guidelines established for conducting reviews of literature in Information Systems, the authors identified 42 papers containing the keyword DevOps or Devops. Further analysis of each paper revealed only six papers that directly addressed the topic of DevOps. The authors conclude that Information Systems needs to fully "join the party" and that DevOps represents a fertile ground for increased Information Systems research.

"In its most-developed markets, Facebook is simply running out of people: there are not enough Americans or Europeans alive for the company to double in size again. And on a global scale, Facebook is just as limited by access to the net itself."

“Is there, was there, will there be, a digital turn?”

Tech has a fractal irresponsibility problem: Each small worry reflects in miniature the shape of the industry’s big problems.

The Royal Public Health Society is behind Scroll Free September, which is targeting users of Facebook, Instagram, Twitter and Snapchat.

It believes logging off could improve sleep, relationships and wellbeing.

NHS England said it was right to highlight social media's role in a rise in young people's mental health issues.

It owes that resilience in part thanks to social media burnout. Stankov says search traffic to Inoreader has nearly doubled since 2015, all organically. "RSS readers have not only survived in the era of social media, but are driving more and more attention back to themselves, as people are realizing the pitfalls" of relying too much on Facebook and others, Stankov says.

Things are going unexpectedly wrong. I lean toward this explanation, with sprinkles of the first three. I tend to believe Facebook was caught off guard because of the simultaneous deterioration of user growth, particularly in the U.S. and Canada, which generate most of its revenue; in its revenue growth rate and expectations for a further slowdown; and in its profit-margin forecast.

It had become an article of investor faith on Wall Street and in Silicon Valley: Quarter after quarter, year after year, the world’s biggest technology companies would keep raking in new users and ever-higher revenue. And with that, their share prices would continue to march upward, sloughing off any stumbles.

This week, that myth was shattered.

The ice of AI’s first winter only fully retreated at the beginning of this decade after a new generation of researchers started publishing papers about successful applications of a technique called “deep learning”.

While this was fundamentally a decades-old statistical technique similar to Rosenblatt’s perceptron, increases in computational power and availability of huge data sets meant that deep learning was becoming practical for tasks such as speech recognition, image recognition and language translation. As reports of deep learning’s “unreasonable effectiveness” circulated among researchers, enrollments at universities in machine-learning classes surged, corporations started to invest billions of dollars to find talent familiar with the newest techniques, and countless startups attempting to apply AI to transport or medicine or finance were founded.

As this resurgence got under way, AI hype in the media resumed after a long hiatus.

"They knew armed guards would be required to protect their compounds from the angry mobs. But how would they pay the guards once money was worthless?"

Because the compiler was poisoned with other source code that we didn't have. And that source code, that now existed only in the executable compiler, put those changes back into the compiler source before it compiled it. But this time it didn't modify the /usr/src copy, it copied it to a hidden directory, modified the compiler source, compiled itself from there, and deleted the hidden directory. It took an AT&T tech to find this. The ex-grad student had poisoned the compiler to poison itself when it was recompiled.

The future evolution of C++, Java and Python is being driven by very different interested parties, and it’s going to be interesting watching events unfold over the next 5-10 years.

Not all IT leaders like the term DevOps: Some prefer to just call it the agile way of working. But however you describe it, this style of working – which prizes speed, experimentation, and collaboration, all happening on nimble, cross-functional teams – has taken the enterprise by storm. It has demanded new IT leadership strategies. Above all, it has demanded culture change, as teams ditch old processes, rip down rules between groups, and accept “failures” as quick lessons on how to iterate their way to better products and services.

Ruby has seen the steepest decline in this timeframe, going from the 2nd most popular language in 2011 with over 18% of users to the 11th most popular today with 3.2% of users [...] Ruby has more than 3x the number of active users using the language than in 2011. It just hasn't grown nearly as fast as other languages.