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Ed Webb

Ian Bogost - Beyond Blogs - 0 views

www.bogost.com/...beyond_blogs.shtml

blogging blogs bogost meta digital humanities

shared by Ed Webb on 23 Mar 11 - No Cached
  • I wish these were the sorts of questions so-called digital humanists considered, rather than figuring out how to pay homage to the latest received web app or to build new tools to do the same old work. But as I recently argued, a real digital humanism isn't one that's digital, but one that's concerned with the present and the future. A part of that concern involves considering the way we want to interact with one another and the world as scholars, and to intervene in that process by making it happen. Such a question is far more interesting and productive than debating the relative merits of blogs or online journals, acts that amount to celebrations of how little has really changed.
  • Perhaps a blog isn't a great tool for (philosophical; videogame) discussion or even for knowledge retention, etc... but a whole *blogosphere*...? If individuals (and individual memory in particular) are included within the scope of "the blogosphere" then surely someone remembers the "important" posts, like you seemed to be asking for...?
Ed Webb

Wired Campus: U. of Richmond Creates a Wikipedia for Undergraduate Scholars -... - 0 views

chronicle.com/...dia-for-undergraduate-scholars

history data resource research

shared by Ed Webb on 08 Apr 09 - Cached
  • The current model for teaching and learning is based on a relative scarcity of research and writing, not an excess. With that in mind, Mr. Torget and several others have created a Web site called History Engine to help students around the country work together on a shared tool to make sense of history documents online. Students generate brief essays on American history, and the History Engine aggregates the essays and makes them navigable by tags. Call it Wikipedia for students. Except better. First of all, its content is moderated by professors. Second, while Wikipedia still presents information two-dimensionally, History Engine employs mapping technology to organize scholarship by time period, geographic location, and themes.
  • “The challenge of a digital age is that that writing assignment hasn’t changed since the age of the typewriter,” Mr. Torget said. “The digital medium requires us to rethink how we make those assignments.”
Ed Webb

State of learning management systems in higher education - elearnspace - 0 views

www.elearnspace.org/...nt-systems-in-higher-education

elearnspace siemens LMS Blackboard Moodle

shared by Ed Webb on 26 Aug 09 - Cached
  • The presentation includes the best diagram I’ve seen on LMS development, market share and current state:
Ed Webb

The Myth Of AI | Edge.org - 0 views

www.edge.org/...the-myth-of-ai

AI opinion analysis

shared by Ed Webb on 25 Jan 23 - No Cached
  • The distinction between a corporation and an algorithm is fading. Does that make an algorithm a person? Here we have this interesting confluence between two totally different worlds. We have the world of money and politics and the so-called conservative Supreme Court, with this other world of what we can call artificial intelligence, which is a movement within the technical culture to find an equivalence between computers and people. In both cases, there's an intellectual tradition that goes back many decades. Previously they'd been separated; they'd been worlds apart. Now, suddenly they've been intertwined.
  • Since our economy has shifted to what I call a surveillance economy, but let's say an economy where algorithms guide people a lot, we have this very odd situation where you have these algorithms that rely on big data in order to figure out who you should date, who you should sleep with, what music you should listen to, what books you should read, and on and on and on. And people often accept that because there's no empirical alternative to compare it to, there's no baseline. It's bad personal science. It's bad self-understanding.
  • there's no way to tell where the border is between measurement and manipulation in these systems
  • ...8 more annotations...
  • It's not so much a rise of evil as a rise of nonsense. It's a mass incompetence, as opposed to Skynet from the Terminator movies. That's what this type of AI turns into.
  • What's happened here is that translators haven't been made obsolete. What's happened instead is that the structure through which we receive the efforts of real people in order to make translations happen has been optimized, but those people are still needed.
  • In order to create this illusion of a freestanding autonomous artificial intelligent creature, we have to ignore the contributions from all the people whose data we're grabbing in order to make it work. That has a negative economic consequence.
  • If you talk to translators, they're facing a predicament, which is very similar to some of the other early victim populations, due to the particular way we digitize things. It's similar to what's happened with recording musicians, or investigative journalists—which is the one that bothers me the most—or photographers. What they're seeing is a severe decline in how much they're paid, what opportunities they have, their long-term prospects.
  • because of the mythology about AI, the services are presented as though they are these mystical, magical personas. IBM makes a dramatic case that they've created this entity that they call different things at different times—Deep Blue and so forth. The consumer tech companies, we tend to put a face in front of them, like a Cortana or a Siri
  • If you talk about AI as a set of techniques, as a field of study in mathematics or engineering, it brings benefits. If we talk about AI as a mythology of creating a post-human species, it creates a series of problems that I've just gone over, which include acceptance of bad user interfaces, where you can't tell if you're being manipulated or not, and everything is ambiguous. It creates incompetence, because you don't know whether recommendations are coming from anything real or just self-fulfilling prophecies from a manipulative system that spun off on its own, and economic negativity, because you're gradually pulling formal economic benefits away from the people who supply the data that makes the scheme work.
  • This idea that some lab somewhere is making these autonomous algorithms that can take over the world is a way of avoiding the profoundly uncomfortable political problem, which is that if there's some actuator that can do harm, we have to figure out some way that people don't do harm with it. There are about to be a whole bunch of those. And that'll involve some kind of new societal structure that isn't perfect anarchy. Nobody in the tech world wants to face that, so we lose ourselves in these fantasies of AI. But if you could somehow prevent AI from ever happening, it would have nothing to do with the actual problem that we fear, and that's the sad thing, the difficult thing we have to face.
  • To reject your own ignorance just casts you into a silly state where you're a lesser scientist. I don't see that so much in the neuroscience field, but it comes from the computer world so much, and the computer world is so influential because it has so much money and influence that it does start to bleed over into all kinds of other things.
Ed Webb

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

www.newyorker.com/...pt-is-a-blurry-jpeg-of-the-web

AI internet analysis opinion LLMs writing education pedagogy

shared by Ed Webb on 10 Feb 23 - Cached
  • 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.
  • ...9 more annotations...
  • 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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