Occasional training tool and/or the future of high end entertainment — just possibly. But, hey, no one likes to feel sick at the movies. Feeling like you want to barf is only a good use of your time and energy if you’ve eaten something that might otherwise kill you.
The wider question in the gaming space, which excels at entertainment and escapism, remains how big VR might get when the hardware isn’t so pricey and clunky, and when investing time and energy in building compelling content can start to look like less of a sinkhole prospect for developers who can now be seen pulling their horns in.
These same types of content makers may well be having their heads turned by the potential of AR gaming. Which can, as noted above, already point to the existence of essential content generating wild excitement among millions of consumers.
Rather like the solar eclipse, AR has been shown turning masses of heads and even gathering huge crowds of like-minded folks together in public.
For now there is simply no argument: AR > VR.
Group items tagged
This VR cycle is dead | TechCrunch - 0 views
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As TechCrunch’s Lucas Matney noted: “Over the past several months it’s become clear that the war is no longer HTC and Oculus trying to discover who is Betamax and who is VHS, now they’re just trying to ensure that high-end VR doesn’t turn out to be LaserDisc. Though few of the big players are keen to readily admit it, many investors and analysts have been less than thrilled with the pace of headset sales over the past year.” The bald fact that neither HTC nor Facebook/Oculus has released sales figures for their respective VR headsets speaks volumes. (Analyst estimates aren’t generous, suggesting <500k units apiece.) Sony did put out some sales figures for its Playstation VR headset in February which caused some initial excitement — with the company claiming 915,000 units sold since October 2016. But by June that figure had merely drifted past 1M.
Is It Time To Ban Computers From Classrooms? : 13.7: Cosmos And Culture : NPR - 0 views
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A new paper delivers a clear verdict on computers in the classroom - but a variety of important questions remain open, like how they interfere with student learning, says psychologist Tania Lombrozo.
Boyer- Enlarging the Perspective.pdf - 2 views
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Boyer outlines four types of scholarship, with implications for DP&S' work. Boyer notes that scholarship is often equated with research, and argues here for a more flexible, open-minded approach to definitions of scholarship, reminding us that "scholarship in earlier times referred to a variety of creative work carried on in a variety of places, and its integrity was measured by the ability to think, communicate, and learn." The crux of his argument is what he sees as the relationship between theory and practice, which enable and reinforce one another. Outlining four types of scholarship-discovery, integration, application, and teaching-Boyer makes it clear that scholarship is more than siloed, disciplinary research. It also includes those activities which make the findings of scholarship comprehensible to others, and the application of those findings to specific problems beyond the Ivory Tower. In Boyer's four-part model, discovery is essentially research, i.e., the creation of new knowledge. Integration involves drawing connections between existing knowledges, and making those knowledges intelligible to audiences outside the academy. Application is the use of scholarly understanding to answer questions and solve problems. It overlaps to some extent with the notion of service, though not all service is scholarship: "To be considered scholarship, service activities must be tied directly to one's special field of knowledge and relate to, and flow directly out of, this professional activity. Such service is serious, demanding work, requiting the rigor-and the accountability-traditionally associated with research activities." Teaching, the final component of Boyer's model, is self-evident, and for him "teaching, at its best, means not only transmitting knowledge, but transforming and extending it as well."
The "Digital" Scholarship Disconnect | EDUCAUSE - 0 views
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Digital scholarship is an incredibly awkward term that people have come up with to describe a complex group of developments. The phrase is really, at some basic level, nonsensical. After all, scholarship is scholarship. Doing science is doing science. We don't find the Department of Digital Physics arguing with the Department of Non–Digital Physics about who's doing "real" physics.
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Soon, people wanted to start talking more broadly about newly technology-enabled scholarly work, not just in science; in part this was because of some very dramatic and high-visibility developments in using digital technology in various humanistic investigations. To do so, they came up with the neologisms we enjoy today—awful phrases like e-scholarship and digital scholarship.Having said that, I do view the term digital scholarship basically as shorthand for the entire body of changing scholarly practice, a reminder and recognition of the fact that most areas of scholarly work today have been transformed, to a lesser or greater extent, by a series of information technologies: High-performance computing, which allows us to build simulation models and to conduct very-large-scale data analysis Visualization technologies, including interactive visualizations Technologies for creating, curating, and sharing large databases and large collections of data High-performance networking, which allows us to share resources across the network and to gain access to experimental or observational equipment and which allows geographically dispersed individuals to communicate and collaborate; implicit here are ideas such as the rise of lightweight challenge-focused virtual organizations
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We now have enormous curated databases serving various disciplines: GenBank for gene sequences; the Worldwide Protein Data Bank for protein structures; and the Sloan Digital Sky Survey and planned successors for (synoptic) astronomical observations. All of these are relied upon by large numbers of working scientists. Yet the people who compiled these databases are often not regarded by their colleagues as "real" scientists but, rather, as "once-scientists" who got off-track and started doing resource-building for the community. And it's true: many resource-builders don't have the time to be actively doing science (i.e., analysis and discovery); instead, they are building and enabling the tools that will advance the collective scientific enterprise in other, less traditional ways. The academic and research community faces a fundamental challenge in developing norms and practices that recognize and reward these essential contributions.This idea—of people not doing "real" research, even though they are building up resources that can enable others to do research—has played out as well in the humanities. The humanists have often tried to make a careful distinction between the work of building a base of evidence and the work of interpreting that evidence to support some particular analysis, thesis, and/or set of conclusions; this is a little easier in the humanities because the scale of collaboration surrounding emerging digital resources and their exploitation for scholarship is smaller (contrast this to the literal "cast of thousands" at CERN) and it's common here to see the leading participants play both roles: resource-builder and "working" scholar.
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Interesting short piece on challenges of digital scholarship
Academe's Extinction Event: Failure, Whiskey, and Professional Collapse at the MLA - Th... - 0 views
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At some point, though, a presenter began reading a paper that caused me to look up at once from the wiki. This was Anna Kornbluh, of the University of Illinois at Chicago. Her paper was written in the same language as the others, but scythelike; she plied the language with weird skill, as if slicing a path toward some promontory of insight — and I found, to my surprise and unease, that I wanted to follow her there and stand and look out. Advertisement Her thesis was unsparing. “We have rhapsodized demolition as liberation while literally laying ruin to the university,” she argued, “a horror to be beheld by future historians — in the unlikely event there are any.” Literary theorists, by prizing an ethos of destruction in the name of freedom, had ironically aligned themselves with the external forces — political, administrative — that had for years conspired to obliterate the institution in which they work. “Human beings,” though, “are essentially builders,” she noted, channeling Marx — “architects of ideas” as well as topplers of norms. Both gestures, affirmation and dissent, are “life-sustaining”; ideally they coexist, equipoised, twin components of a fulfilled life. A reconstructed university — and wider world — would depend on recovering the constructive and visionary impulse, which the profession had too long devalued in favor of critique. “Get building,” she enjoined the room.
Evaluating Digital Humanities Beyond the Tenure Track Part 2: For Employers | Notes fro... - 0 views
Evaluating Digital Humanities Beyond the Tenure Track Part 1: For Employees | Notes fro... - 0 views
New World Notes: Palmer Luckey's Support for Pro-Trump Group Reminds Me of His Support ... - 0 views
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Palmer Luckey is the founder of Oculus.
Build a Better Monster: Morality, Machine Learning, and Mass Surveillance - 0 views
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Unfortunately, the enemy is complacency. Tech workers trust their founders, find labor organizing distasteful, and are happy to leave larger ethical questions to management. A workplace free of 'politics' is just one of the many perks the tech industry offers its pampered employees. So our one chance to enact meaningful change is slipping away. Unless something happens to mobilize the tech workforce, or unless the advertising bubble finally bursts, we can expect the weird, topsy-turvy status quo of 2017 to solidify into the new reality. But even though we're likely to fail, all we can do is try. Good intentions are not going to make these structural problems go away. Talking about them is not going to fix them. We have to do something.
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Can we fix it? Institutions can be destroyed quickly; they take a long time to build. A lot of what we call ‘disruption’ in the tech industry has just been killing flawed but established institutions, and mining them for parts. When we do this, we make a dangerous assumption about our ability to undo our own bad decisions, or the time span required to build institutions that match the needs of new realities. Right now, a small caste of programmers is in charge of the surveillance economy, and has broad latitude to change it. But this situation will not last for long. The kinds of black-box machine learning that have been so successful in the age of mass surveillance are going to become commoditized and will no longer require skilled artisans to deploy. Moreover, powerful people have noted and benefited from the special power of social media in the political arena. They will not sit by and let programmers dismantle useful tools for influence and social control. It doesn’t matter that the tech industry considers itself apolitical and rationalist. Powerful people did not get to be that way by voluntarily ceding power. The window of time in which the tech industry can still act is brief: while tech workers retain relatively high influence in their companies, and before powerful political interests have put down roots in the tech industry.
The Digital-Humanities Bust - The Chronicle of Higher Education - 0 views
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To ask about the field is really to ask how or what DH knows, and what it allows us to know. The answer, it turns out, is not much. Let’s begin with the tension between promise and product. Any neophyte to digital-humanities literature notices its extravagant rhetoric of exuberance. The field may be "transforming long-established disciplines like history or literary criticism," according to a Stanford Literary Lab email likely unread or disregarded by a majority in those disciplines. Laura Mandell, director of the Initiative for Digital Humanities, Media, and Culture at Texas A&M University, promises to break "the book format" without explaining why one might want to — even as books, against all predictions, doggedly persist, filling the airplane-hanger-sized warehouses of Amazon.com.
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A similar shortfall is evident when digital humanists turn to straight literary criticism. "Distant reading," a method of studying novels without reading them, uses computer scanning to search for "units that are much smaller or much larger than the text" (in Franco Moretti’s words) — tropes, at one end, genres or systems, at the other. One of the most intelligent examples of the technique is Richard Jean So and Andrew Piper’s 2016 Atlantic article, "How Has the MFA Changed the American Novel?" (based on their research for articles published in academic journals). The authors set out to quantify "how similar authors were across a range of literary aspects, including diction, style, theme, setting." But they never cite exactly what the computers were asked to quantify. In the real world of novels, after all, style, theme, and character are often achieved relationally — that is, without leaving a trace in words or phrases recognizable as patterns by a program.
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Perhaps toward that end, So, an assistant professor of English at the University of Chicago, wrote an elaborate article in Critical Inquiry with Hoyt Long (also of Chicago) on the uses of machine learning and "literary pattern recognition" in the study of modernist haiku poetry. Here they actually do specify what they instructed programmers to look for, and what computers actually counted. But the explanation introduces new problems that somehow escape the authors. By their own admission, some of their interpretations derive from what they knew "in advance"; hence the findings do not need the data and, as a result, are somewhat pointless. After 30 pages of highly technical discussion, the payoff is to tell us that haikus have formal features different from other short poems. We already knew that.
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