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Ed Webb

Lacktribution: Be Like Everyone Else - CogDogBlog - 0 views

  • What exactly are the issues about attributing? Why is it good to not have to attribute? Is it a severe challenge to attribute? Does it hurt? Does it call for technical or academic skills beyond reach? Does it consume great amounts of time, resources? Why, among professional designers and technologists is it such a good thing to be free of this odious chore? I can translate this typical reason to use public domain content, “I prefer to be lazy.”
  • There is a larger implication when you reuse content and choose not to attribute. Out in the flow of all other information, it more or less says to readers, “all images are free to pilfer. Just google and take them all. Be like me.”
  • It’s not about the rules of the license, it’s about maybe, maybe, operating in this mechanized place as a human, rather than a copy cat.
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  • Google search results gives more weight to pxhere.com where the image has a mighty 4 views (some of which are me) over the original image, with almost 5000 views.
  • What kind of algorithm is that? It’s one that does not favor the individual. Image search results will favor sites like Needpix, Pixsels, Pixnio, Peakpx, Nicepic, and they still favor the really slimy maxpixel which is a direct rip off of pixabay.
  • did you know that the liberating world of “use any photo you want w/o the hassle of attribution” is such a bucket of questionable slime? And that Google, with all of their algorithmic prowess, gives more favorable results to sites that lift photos than to the ones where the originals exist?
  • So yes, just reuse photos without taking all of the severe effort to give credit to the source, because “you don’t have to.” Be a copycat. Show your flag of Lacktribution. Like everyone else. I will not. I adhere to Thanktribution.
Ed Webb

Dark Social: We Have the Whole History of the Web Wrong - Alexis C. Madrigal - The Atla... - 0 views

  • this vast trove of social traffic is essentially invisible to most analytics programs. I call it DARK SOCIAL. It shows up variously in programs as "direct" or "typed/bookmarked" traffic, which implies to many site owners that you actually have a bookmark or typed in www.theatlantic.com into your browser. But that's not actually what's happening a lot of the time. Most of the time, someone Gchatted someone a link, or it came in on a big email distribution list, or your dad sent it to you
  • the idea that "social networks" and "social media" sites created a social web is pervasive. Everyone behaves as if the traffic your stories receive from the social networks (Facebook, Reddit, Twitter, StumbleUpon) is the same as all of your social traffic
  • if you think optimizing your Facebook page and Tweets is "optimizing for social," you're only halfway (or maybe 30 percent) correct. The only real way to optimize for social spread is in the nature of the content itself. There's no way to game email or people's instant messages. There's no power users you can contact. There's no algorithms to understand. This is pure social, uncut
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  • Almost 69 percent of social referrals were dark! Facebook came in second at 20 percent. Twitter was down at 6 percent
  • direct socia
  • the social sites that arrived in the 2000s did not create the social web, but they did structure it. This is really, really significant. In large part, they made sharing on the Internet an act of publishing (!), with all the attendant changes that come with that switch. Publishing social interactions makes them more visible, searchable, and adds a lot of metadata to your simple link or photo post. There are some great things about this, but social networks also give a novel, permanent identity to your online persona. Your taste can be monetized, by you or (much more likely) the service itself
  • the tradeoffs we make on social networks is not the one that we're told we're making. We're not giving our personal data in exchange for the ability to share links with friends. Massive numbers of people -- a larger set than exists on any social network -- already do that outside the social networks. Rather, we're exchanging our personal data in exchange for the ability to publish and archive a record of our sharing. That may be a transaction you want to make, but it might not be the one you've been told you made. 
  • "Only about four percent of total traffic is on mobile at all, so, at least as a percentage of total referrals, app referrals must be a tiny percentage,"
  • only 0.3 percent of total traffic has the Facebook mobile site as a referrer and less than 0.1 percent has the Facebook mobile app
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    Heh. Social is really social, not 'social' - who knew?
Ed Webb

Social Media is Killing the LMS Star - A Bootleg of Bryan Alexander's Lost Presentation... - 0 views

  • Note that this isn’t just a technological alternate history. It also describes a different set of social and cultural practices.
  • CMSes lumber along like radio, still playing into the air as they continue to gradually shift ever farther away on the margins. In comparison, Web 2.0 is like movies and tv combined, plus printed books and magazines. That’s where the sheer scale, creative ferment, and wife-ranging influence reside. This is the necessary background for discussing how to integrate learning and the digital world.
  • These virtual classes are like musical practice rooms, small chambers where one may try out the instrument in silent isolation. It is not connectivism but disconnectivism.
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  • CMSes shift from being merely retrograde to being actively regressive if we consider the broader, subtler changes in the digital teaching landscape. Web 2.0 has rapidly grown an enormous amount of content through what Yochai Benkler calls “peer-based commons production.” One effect of this has been to grow a large area for informal learning, which students (and staff) access without our benign interference. Students (and staff) also contribute to this peering world; more on this later. For now, we can observe that as teachers we grapple with this mechanism of change through many means, but the CMS in its silo’d isolation is not a useful tool.
  • those curious about teaching with social media have easy access to a growing, accessible community of experienced staff by means of those very media. A meta-community of Web 2.0 academic practitioners is now too vast to catalogue. Academics in every discipline blog about their work. Wikis record their efforts and thoughts, as do podcasts. The reverse is true of the CMS, the very architecture of which forbids such peer-to-peer information sharing. For example, the Resource Center for Cyberculture Studies (RCCS) has for many years maintained a descriptive listing of courses about digital culture across the disciplines. During the 1990s that number grew with each semester. But after the explosive growth of CMSes that number dwindled. Not the number of classes taught, but the number of classes which could even be described. According to the RCCS’ founder, David Silver (University of San Francisco), this is due to the isolation of class content in CMS containers.
  • unless we consider the CMS environment to be a sort of corporate intranet simulation, the CMS set of community skills is unusual, rarely applicable to post-graduation examples. In other words, while a CMS might help privacy concerns, it is at best a partial, not sufficient solution, and can even be inappropriate for already online students.
  • That experiential, teachable moment of selecting one’s copyright stance is eliminated by the CMS.
  • Another argument in favor of CMSes over Web 2.0 concerns the latter’s open nature. It is too open, goes the thought, constituting a “Wild West” experience of unfettered information flow and unpleasant forms of access. Campuses should run CMSes to create shielded environments, iPhone-style walled gardens that protect the learning process from the Lovecraftian chaos without.
  • social sifting, information literacy, using the wisdom of crowds, and others. Such strategies are widely discussed, easily accessed, and continually revised and honed.
  • at present, radio CMS is the Clear Channel of online learning.
  • For now, the CMS landsape is a multi-institutional dark Web, an invisible, unsearchable, un-mash-up-able archipelago of hidden learning content.
  • Can the practice of using a CMS prepare either teacher or student to think critically about this new shape for information literacy? Moreover, can we use the traditional CMS to share thoughts and practices about this topic?
  • The internet of things refers to a vastly more challenging concept, the association of digital information with the physical world. It covers such diverse instances as RFID chips attached to books or shipping pallets, connecting a product’s scanned UPC code to a Web-based database, assigning unique digital identifiers to physical locations, and the broader enterprise of augmented reality. It includes problems as varied as building search that covers both the World Wide Web and one’s mobile device, revising copyright to include digital content associated with private locations, and trying to salvage what’s left of privacy. How does this connect with our topic? Consider a recent article by Tim O’Reilly and John Battle, where they argue that the internet of things is actually growing knowledge about itself. The combination of people, networks, and objects is building descriptions about objects, largely in folksonomic form. That is, people are tagging the world, and sharing those tags. It’s worth quoting a passage in full: “It’s also possible to give structure to what appears to be unstructured data by teaching an application how to recognize the connection between the two. For example, You R Here, an iPhone app, neatly combines these two approaches. You use your iPhone camera to take a photo of a map that contains details not found on generic mapping applications such as Google maps – say a trailhead map in a park, or another hiking map. Use the phone’s GPS to set your current location on the map. Walk a distance away, and set a second point. Now your iPhone can track your position on that custom map image as easily as it can on Google maps.” (http://www.web2summit.com/web2009/public/schedule/detail/10194) What world is better placed to connect academia productively with such projects, the open social Web or the CMS?
  • imagine the CMS function of every class much like class email, a necessary feature, but not by any means the broadest technological element. Similarly the e-reserves function is of immense practical value. There may be no better way to share copyrighted academic materials with a class, at this point. These logistical functions could well play on.
Ed Webb

The Wired Campus - At One English College, Facebook Serves as a Retention Tool - The Ch... - 0 views

  • According to Gloucestershire College, in England, Facebook and other social-networking Web sites can do more than provide a platform for vacation photos, favorite quotes, and status updates; they can help reduce dropout rates, the BBC reports.The media-curriculum manager at the college, Perry Perrott, says that with the advent of social media, students have been better at keeping in touch with faculty members, which has lead to a “significant improvement in retention.”After seeing how popular social-networking sites were with students, Mr. Perry says the college decided to embrace the technology as a cost-free way to further engage the campus.
Ed Webb

9 Ways Online Teaching Should be Different from Face-to-Face | Cult of Pedagogy - 0 views

  • Resist the temptation to dive right into curriculum at the start of the school year. Things will go more smoothly if you devote the early weeks to building community so students feel connected. Social emotional skills can be woven in during this time. On top of that, students need practice with whatever digital tools you’ll be using. So focus your lessons on those things, intertwining the two when possible. 
  • Online instruction is made up largely of asynchronous instruction, which students can access at any time. This is ideal, because requiring attendance for synchronous instruction puts some students at an immediate disadvantage if they don’t have the same access to technology, reliable internet, or a flexible home schedule. 
  • you’re likely to offer “face-to-face” or synchronous opportunities at some point, and one way to make them happen more easily is to have students meet in small groups. While it’s nearly impossible to arrange for 30 students to attend a meeting at once, assigning four students to meet is much more manageable.
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  • What works best, Kitchen says, is to keep direct instruction—things like brief video lectures and readings—in asynchronous form, using checks for understanding like embedded questions or exit slips.  You can then use synchronous meetings for more interactive, engaging work. “If we want students showing up, if we want them to know that this is worth their time,” Kitchen explains, “it really needs to be something active and engaging for them. Any time they can work with the material, categorize it, organize it, share further thoughts on it, have a discussion, all of those are great things to do in small groups.” 
  • The Jigsaw method, where students form expert groups on a particular chunk of content, then teach that content to other students. Discussion strategies adapted for virtual settingsUsing best practices for cooperative learning Visible Thinking routinesGamestorming and other business related protocols adapted for education, where students take on the role of customers/stakeholders
  • What really holds leverage for the students? What has endurance? What knowledge is essential?What knowledge and skills do students need to have before they move to the next grade level or the next class?What practices can be emphasized that transfer across many content areas?  Skills like analyzing, constructing arguments, building a strong knowledge base through texts, and speaking can all be taught through many different subjects. What tools can serve multiple purposes? Teaching students to use something like Padlet gives them opportunities to use audio, drawing, writing, and video. Non-digital tools can also work: Students can use things they find around the house, like toilet paper rolls, to fulfill other assignments, and then submit their work with a photo.
  • Online instruction is not conducive to covering large amounts of content, so you have to choose wisely, teaching the most important things at a slower pace.
  • Provide instructions in a consistent location and at a consistent time. This advice was already given for parents, but it’s worth repeating here through the lens of instructional design: Set up lessons so that students know where to find instructions every time. Make instructions explicit. Read and re-read to make sure these are as clear as possible. Make dogfooding your lessons a regular practice to root out problem areas.Offer multimodal instructions. If possible, provide both written and video instructions for assignments, so students can choose the format that works best for them. You might also offer a synchronous weekly or daily meeting; what’s great about doing these online is that even if you teach several sections of the same class per day, students are no longer restricted to class times and can attend whatever meeting works best for them.
  • put the emphasis on formative feedback as students work through assignments and tasks, rather than simply grading them at the end. 
  • In online learning, Kitchen says, “There are so many ways that students can cheat, so if we’re giving them just the traditional quiz or test, it’s really easy for them to be able to just look up that information.” A great solution to this problem is to have students create things.
  • For assessment, use a detailed rubric that highlights the learning goals the end product will demonstrate. A single-point rubric works well for this.To help students discover tools to work with, this list of tools is organized by the type of product each one creates. Another great source of ideas is the Teacher’s Guide to Tech.When developing the assignment, rather than focusing on the end product, start by getting clear on what you want students to DO with that product.
  • Clear and consistent communicationCreating explicit and consistent rituals and routinesUsing research-based instructional strategiesDetermining whether to use digital or non-digital tools for an assignment A focus on authentic learning, where authentic products are created and students have voice and choice in assignments
Ed Webb

Google and Meta moved cautiously on AI. Then came OpenAI's ChatGPT. - The Washington Post - 0 views

  • The surge of attention around ChatGPT is prompting pressure inside tech giants including Meta and Google to move faster, potentially sweeping safety concerns aside
  • Tech giants have been skittish since public debacles like Microsoft’s Tay, which it took down in less than a day in 2016 after trolls prompted the bot to call for a race war, suggest Hitler was right and tweet “Jews did 9/11.”
  • Some AI ethicists fear that Big Tech’s rush to market could expose billions of people to potential harms — such as sharing inaccurate information, generating fake photos or giving students the ability to cheat on school tests — before trust and safety experts have been able to study the risks. Others in the field share OpenAI’s philosophy that releasing the tools to the public, often nominally in a “beta” phase after mitigating some predictable risks, is the only way to assess real world harms.
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  • Silicon Valley’s sudden willingness to consider taking more reputational risk arrives as tech stocks are tumbling
  • A chatbot that pointed to one answer directly from Google could increase its liability if the response was found to be harmful or plagiarized.
  • AI has been through several hype cycles over the past decade, but the furor over DALL-E and ChatGPT has reached new heights.
  • Soon after OpenAI released ChatGPT, tech influencers on Twitter began to predict that generative AI would spell the demise of Google search. ChatGPT delivered simple answers in an accessible way and didn’t ask users to rifle through blue links. Besides, after a quarter of a century, Google’s search interface had grown bloated with ads and marketers trying to game the system.
  • Inside big tech companies, the system of checks and balances for vetting the ethical implications of cutting-edge AI isn’t as established as privacy or data security. Typically teams of AI researchers and engineers publish papers on their findings, incorporate their technology into the company’s existing infrastructure or develop new products, a process that can sometimes clash with other teams working on responsible AI over pressure to see innovation reach the public sooner.
  • Chatbots like OpenAI routinely make factual errors and often switch their answers depending on how a question is asked
  • To Timnit Gebru, executive director of the nonprofit Distributed AI Research Institute, the prospect of Google sidelining its responsible AI team doesn’t necessarily signal a shift in power or safety concerns, because those warning of the potential harms were never empowered to begin with. “If we were lucky, we’d get invited to a meeting,” said Gebru, who helped lead Google’s Ethical AI team until she was fired for a paper criticizing large language models.
  • Rumman Chowdhury, who led Twitter’s machine-learning ethics team until Elon Musk disbanded it in November, said she expects companies like Google to increasingly sideline internal critics and ethicists as they scramble to catch up with OpenAI.“We thought it was going to be China pushing the U.S., but looks like it’s start-ups,” she said.
Ed Webb

'There is no standard': investigation finds AI algorithms objectify women's bodies | Ar... - 0 views

  • AI tags photos of women in everyday situations as sexually suggestive. They also rate pictures of women as more “racy” or sexually suggestive than comparable pictures of men.
  • suppressed the reach of countless images featuring women’s bodies, and hurt female-led businesses – further amplifying societal disparities.
  • “Objectification of women seems deeply embedded in the system.”
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  • Shadowbanning has been documented for years, but the Guardian journalists may have found a missing link to understand the phenomenon: biased AI algorithms. Social media platforms seem to leverage these algorithms to rate images and limit the reach of content that they consider too racy. The problem seems to be that these AI algorithms have built-in gender bias, rating women more racy than images containing men.
  • “You are looking at decontextualized information where a bra is being seen as inherently racy rather than a thing that many women wear every day as a basic item of clothing,”
  • “You cannot have one single uncontested definition of raciness.”
  • these algorithms were probably labeled by straight men, who may associate men working out with fitness, but may consider an image of a woman working out as racy. It’s also possible that these ratings seem gender biased in the US and in Europe because the labelers may have been from a place with a more conservative culture
  • “There’s no standard of quality here,”
  • “I will censor as artistically as possible any nipples. I find this so offensive to art, but also to women,” she said. “I almost feel like I’m part of perpetuating that ridiculous cycle that I don’t want to have any part of.”
  • many people, including chronically ill and disabled folks, rely on making money through social media and shadowbanning harms their business
Ed Webb

ChatGPT Is a Blurry JPEG of the Web | The New Yorker - 0 views

  • Think of ChatGPT as a blurry JPEG of all the text on the Web. It retains much of the information on the Web, in the same way that a JPEG retains much of the information of a higher-resolution image, but, if you’re looking for an exact sequence of bits, you won’t find it; all you will ever get is an approximation. But, because the approximation is presented in the form of grammatical text, which ChatGPT excels at creating, it’s usually acceptable. You’re still looking at a blurry JPEG, but the blurriness occurs in a way that doesn’t make the picture as a whole look less sharp.
  • a way to understand the “hallucinations,” or nonsensical answers to factual questions, to which large-language models such as ChatGPT are all too prone. These hallucinations are compression artifacts, but—like the incorrect labels generated by the Xerox photocopier—they are plausible enough that identifying them requires comparing them against the originals, which in this case means either the Web or our own knowledge of the world. When we think about them this way, such hallucinations are anything but surprising; if a compression algorithm is designed to reconstruct text after ninety-nine per cent of the original has been discarded, we should expect that significant portions of what it generates will be entirely fabricated.
  • ChatGPT is so good at this form of interpolation that people find it entertaining: they’ve discovered a “blur” tool for paragraphs instead of photos, and are having a blast playing with it.
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  • large-language models like ChatGPT are often extolled as the cutting edge of artificial intelligence, it may sound dismissive—or at least deflating—to describe them as lossy text-compression algorithms. I do think that this perspective offers a useful corrective to the tendency to anthropomorphize large-language models
  • Even though large-language models often hallucinate, when they’re lucid they sound like they actually understand subjects like economic theory
  • The fact that ChatGPT rephrases material from the Web instead of quoting it word for word makes it seem like a student expressing ideas in her own words, rather than simply regurgitating what she’s read; it creates the illusion that ChatGPT understands the material. In human students, rote memorization isn’t an indicator of genuine learning, so ChatGPT’s inability to produce exact quotes from Web pages is precisely what makes us think that it has learned something. When we’re dealing with sequences of words, lossy compression looks smarter than lossless compression.
  • Even if it is possible to restrict large-language models from engaging in fabrication, should we use them to generate Web content? This would make sense only if our goal is to repackage information that’s already available on the Web. Some companies exist to do just that—we usually call them content mills. Perhaps the blurriness of large-language models will be useful to them, as a way of avoiding copyright infringement. Generally speaking, though, I’d say that anything that’s good for content mills is not good for people searching for information.
  • If and when we start seeing models producing output that’s as good as their input, then the analogy of lossy compression will no longer be applicable.
  • starting with a blurry copy of unoriginal work isn’t a good way to create original work
  • Having students write essays isn’t merely a way to test their grasp of the material; it gives them experience in articulating their thoughts. If students never have to write essays that we have all read before, they will never gain the skills needed to write something that we have never read.
  • Sometimes it’s only in the process of writing that you discover your original ideas. Some might say that the output of large-language models doesn’t look all that different from a human writer’s first draft, but, again, I think this is a superficial resemblance. Your first draft isn’t an unoriginal idea expressed clearly; it’s an original idea expressed poorly, and it is accompanied by your amorphous dissatisfaction, your awareness of the distance between what it says and what you want it to say. That’s what directs you during rewriting, and that’s one of the things lacking when you start with text generated by an A.I.
  • What use is there in having something that rephrases the Web?
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