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    <title>Pinboard (rybesh)</title>
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    <description>recent bookmarks from rybesh</description>
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  </channel><item rdf:about="https://llmstxt.org/">
    <title>The /llms.txt file – llms-txt</title>
    <dc:date>2026-03-13T19:01:39+00:00</dc:date>
    <link>https://llmstxt.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[We propose adding a /llms.txt markdown file to websites to provide LLM-friendly content. This file offers brief background information, guidance, and links to detailed markdown files.]]></description>
<dc:subject>ai documentation standards</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:5767b51d06b0/</dc:identifier>
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<item rdf:about="https://www.belfercenter.org/publication/ai-and-trust">
    <title>AI and Trust | Belfer Center for Science and International Affairs</title>
    <dc:date>2023-12-06T21:20:58+00:00</dc:date>
    <link>https://www.belfercenter.org/publication/ai-and-trust</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In this talk, I am going to make several arguments. One, that there are two different kinds of trust—interpersonal trust and social trust—and that we regularly confuse them. Two, that the confusion will increase with artificial intelligence. We will make a fundamental category error. We will think of AIs as friends when they're really just services. Three, that the corporations controlling AI systems will take advantage of our confusion to take advantage of us. They will not be trustworthy. And four, that it is the role of government to create trust in society. And therefore, it is their role to create an environment for trustworthy AI. And that means regulation. Not regulating AI, but regulating the organizations that control and use AI.

]]></description>
<dc:subject>ai trust security politics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:8a878dd92de4/</dc:identifier>
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<item rdf:about="https://knivesandpaintbrushes.org/projects/why-oatmeal-is-cheap/">
    <title>📝Why Oatmeal is Cheap</title>
    <dc:date>2023-04-28T18:38:21+00:00</dc:date>
    <link>https://knivesandpaintbrushes.org/projects/why-oatmeal-is-cheap/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Although procedural generation is popular among game developers, academic research on the topic has primarily focused on new applications, with some research into empirical analysis. In this paper we relate theoretical work in information theory to the generation of content for games. We prove that there is a relationship between the Kolomogorov complexity of the most complex artifact a generator can produce, and the size of that generator’s possibility space. In doing so, we identify the limiting relationship between the knowledge encoded in a generator, the density of its output space, and the intricacy of the artifacts it produces. We relate our result to the experience of expert procedural generator designers, and illustrate it with some examples.
]]></description>
<dc:subject>ai games information theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:37e65a585856/</dc:identifier>
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<item rdf:about="https://mkremins.github.io/syllabi/csts-2023-01/">
    <title>Creativity Support Tools (Winter 2023)</title>
    <dc:date>2023-04-19T20:39:40+00:00</dc:date>
    <link>https://mkremins.github.io/syllabi/csts-2023-01/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Design, development, and evaluation of software systems intended to support human creativity. Students will read, write responses to, and discuss research papers on creativity support tools (CSTs), human-AI interaction, and related topics; work in small groups to create a software tool that supports artists, writers, designers, musicians, or other creative practitioners; and write a final project report that could serve as the seed for a future peer-reviewed conference or journal publication. HCI-focused complement to COEN 291 Computational Creativity, which approaches creativity from an AI-focused perspective.

]]></description>
<dc:subject>syllabus ai creativity tools hci</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:d7423a8eb1bb/</dc:identifier>
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<item rdf:about="https://www.nybooks.com/online/2023/03/22/deepl-edizioni/">
    <title>DeepL Edizioni | Tim Parks | The New York Review of Books</title>
    <dc:date>2023-03-22T13:21:14+00:00</dc:date>
    <link>https://www.nybooks.com/online/2023/03/22/deepl-edizioni/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[“… the difference between academic copy and a tourist brochure, art catalogue, or political speech is greater still. The software cannot recognize this context; it has not been trained to reframe a text in a particular style, genre, or format. Nor is it in the brief of the post-editor to start reorganizing all the syntax as professional translators often do; if it were, the process might well take even longer than old-fashioned manual translation. Thus the widespread use of machine translation will very likely fill the world with texts that may be grammatically correct and even semantically accurate, yet nevertheless alien to the spirit of the language they were written in.“]]></description>
<dc:subject>translation information genre ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:39542a57256b/</dc:identifier>
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<item rdf:about="https://langchain.readthedocs.io/en/latest/">
    <title>Welcome to LangChain — 🦜🔗 LangChain 0.0.116</title>
    <dc:date>2023-03-20T00:57:54+00:00</dc:date>
    <link>https://langchain.readthedocs.io/en/latest/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you are able to combine them with other sources of computation or knowledge.

This library is aimed at assisting in the development of those types of applications.]]></description>
<dc:subject>llm api nlp query tools ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:53165156a945/</dc:identifier>
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<item rdf:about="https://www.vice.com/en/article/epzyva/ai-chatgpt-tokens-words-break-reddit">
    <title>ChatGPT Can Be Broken by Entering These Strange Words, And Nobody Is Sure Why</title>
    <dc:date>2023-02-12T22:10:23+00:00</dc:date>
    <link>https://www.vice.com/en/article/epzyva/ai-chatgpt-tokens-words-break-reddit</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[“I've just found out that several of the anomalous GPT tokens ("TheNitromeFan", " SolidGoldMagikarp", " davidjl", " Smartstocks", " RandomRedditorWithNo", ) are handles of people who are (competitively? collaboratively?) counting to infinity on a Reddit forum. I kid you not,” Watkins tweeted Wednesday morning. These users subscribe to the subreddit, r/counting, in which users have reached nearly 5,000,000 after almost a decade of counting one post at a time. ]]></description>
<dc:subject>ai tokenization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:fefee2d4048d/</dc:identifier>
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</item>
<item rdf:about="https://mastodon.social/@maxkreminski/109645043162353389">
    <title>Max Kreminski: &quot;somehow missed this excellent …&quot; - Mastodon</title>
    <dc:date>2023-01-20T22:41:17+00:00</dc:date>
    <link>https://mastodon.social/@maxkreminski/109645043162353389</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[“the outputs we most value are the ones where the machine accidentally says more than it knows how to intend” ]]></description>
<dc:subject>simulation AI poetry meaning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:116a33bb1ba7/</dc:identifier>
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</item>
<item rdf:about="https://textual-inversion.github.io/">
    <title>An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion</title>
    <dc:date>2022-11-01T21:39:18+00:00</dc:date>
    <link>https://textual-inversion.github.io/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favorite toy? Here we present a simple approach that allows such creative freedom.

Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new "words" in the embedding space of a frozen text-to-image model. These "words" can be composed into natural language sentences, guiding personalized creation in an intuitive way. Notably, we find evidence that a single word embedding is sufficient for capturing unique and varied concepts.]]></description>
<dc:subject>ai images nlp concepts</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:8eb67d04ce25/</dc:identifier>
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<item rdf:about="https://karpathy.medium.com/software-2-0-a64152b37c35">
    <title>Software 2.0. I sometimes see people refer to neural… | by Andrej Karpathy | Medium</title>
    <dc:date>2022-08-26T21:04:41+00:00</dc:date>
    <link>https://karpathy.medium.com/software-2-0-a64152b37c35</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[To make the analogy explicit, in Software 1.0, human-engineered source code (e.g. some .cpp files) is compiled into a binary that does useful work. In Software 2.0 most often the source code comprises 1) the dataset that defines the desirable behavior and 2) the neural net architecture that gives the rough skeleton of the code, but with many details (the weights) to be filled in. The process of training the neural network compiles the dataset into the binary — the final neural network. In most practical applications today, the neural net architectures and the training systems are increasingly standardized into a commodity, so most of the active “software development” takes the form of curating, growing, massaging and cleaning labeled datasets.]]></description>
<dc:subject>ai software datascience inls201</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:70cbfab106fa/</dc:identifier>
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</item>
<item rdf:about="https://course.fast.ai/">
    <title>Practical Deep Learning for Coders - Practical Deep Learning</title>
    <dc:date>2022-08-10T21:47:57+00:00</dc:date>
    <link>https://course.fast.ai/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.]]></description>
<dc:subject>machinelearning ai course</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:59862f9c9900/</dc:identifier>
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</item>
<item rdf:about="https://statmodeling.stat.columbia.edu/2021/07/07/top-10-ideas-in-statistics-that-have-powered-the-ai-revolution/">
    <title>Top 10 Ideas in Statistics That Have Powered the AI Revolution « Statistical Modeling, Causal Inference, and Social Science</title>
    <dc:date>2021-07-07T17:37:55+00:00</dc:date>
    <link>https://statmodeling.stat.columbia.edu/2021/07/07/top-10-ideas-in-statistics-that-have-powered-the-ai-revolution/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Each idea below can be viewed as a stand-in for an entire subfield. We make no claim that these are the “best” articles and books in statistics and machine learning, we’re just saying they’re important in themselves and represent important developments. By singling out these works, we do not mean to diminish the importance of similar, related work. We focus on methods in statistics and machine learning, rather than equally important breakthroughs in statistical computing, and computer science and engineering, which have provided the tools and computing power for data analysis and visualization to become everyday practical tools. Finally, we have focused on methods, while recognizing that developments in theory and methods are often motivated by specific applications.]]></description>
<dc:subject>statistics AI machinelearning history</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7c05799c15b7/</dc:identifier>
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</item>
<item rdf:about="https://catalog.lib.unc.edu/catalog/UNCb2336204">
    <title>H.M. Collins. - Artificial experts : social knowledge and intelligent machines</title>
    <dc:date>2020-11-15T23:51:07+00:00</dc:date>
    <link>https://catalog.lib.unc.edu/catalog/UNCb2336204</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A machine is what we make of it. It can mimic us if we can mimic it, or help it out, or overlook its mistakes. In "Artificial Experts sociologist Harry Collins explains what computers can't do, but also studies the ordinary and extraordinary things that they can do. He argues that although machines are limited because we cannot reproduce in symbols what every community knows, we give them abilities because of the way we embed them in our society. He unfolds a compelling account of the difference between human action and machine intelligence, the core of which is a witty and learned exploration of knowledge itself, of what communities know and the ways in which they know it. In the course of his investigations, Collins derives enlightening metaphors for the relation between artificial intelligence and prosthetic technologies such as artificial hearts. He provides an intriguing explanation of why pocket calculators work and shares his own experience in constructing an expert system designed to teach people to grow specialized semiconductor crystals. He describes a novel development of the Turing protocol for the definition Of intelligence, a new classification of human skill, and an original way of understanding our relationship to machines. From an AI point of view, the acquisition of knowledge and the selection of applications are critical to the success of expert systems. Collins offers an original approach to both problems for AI researchers and practitioner]]></description>
<dc:subject>book AI sociology STS</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:72668040c2f6/</dc:identifier>
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<item rdf:about="https://twitter.com/i/web/status/1115813185968267264">
    <title>Twitter</title>
    <dc:date>2019-04-11T07:17:06+00:00</dc:date>
    <link>https://twitter.com/i/web/status/1115813185968267264</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[RT @chadloder: TW: racist and homophobic slurs.

I've just finished some very sophisticated #MachineLearning and #AI analysis whic… ]]></description>
<dc:subject>AI MachineLearning</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:08aebe080d5e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:AI"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WSCollection.html">
    <title>Collection of Winograd Schemas</title>
    <dc:date>2017-08-22T16:04:06+00:00</dc:date>
    <link>http://www.cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WSCollection.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This online document is a collection of 146 Winograd schemas.]]></description>
<dc:subject>inls201 language AI</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:37751c31f4a8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls201"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:AI"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://twitter.com/andrewyng/status/788548053745569792">
    <title>Twitter</title>
    <dc:date>2016-10-19T13:18:35+00:00</dc:date>
    <link>https://twitter.com/andrewyng/status/788548053745569792</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[RT @nasrinmmm: @AndrewYNg a normal person understands anything in natural language in <1sec yet no #AI has basic NLU of a 5year old ]]></description>
<dc:subject>AI</dc:subject>
<dc:source>https://twitter.com/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:724c878c7c1c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:AI"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://karpathy.github.io/2015/05/21/rnn-effectiveness/">
    <title>The Unreasonable Effectiveness of Recurrent Neural Networks</title>
    <dc:date>2015-12-09T20:01:00+00:00</dc:date>
    <link>http://karpathy.github.io/2015/05/21/rnn-effectiveness/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[There's something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I've in fact reached the opposite conclusion). Fast forward about a year: I'm training RNNs all the time and I've witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. This post is about sharing some of that magic with you.]]></description>
<dc:subject>ai machinelearning deeplearning datastudies</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:05f330370e5c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datastudies"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://blog.wikimedia.org/2015/11/30/artificial-intelligence-x-ray-specs/">
    <title>Artificial intelligence service gives Wikipedians ‘X-ray specs’ to see through bad edits « Wikimedia blog</title>
    <dc:date>2015-12-02T20:00:20+00:00</dc:date>
    <link>https://blog.wikimedia.org/2015/11/30/artificial-intelligence-x-ray-specs/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Wikipedia is edited about half a million times per day. In order to maintain the quality of Wikipedia, this firehose of new content needs to be constantly reviewed by Wikipedians. The Objective Revision Evaluation Service (ORES) functions like a pair of X-ray specs, the toy hyped in novelty shops and the back of comic books—but these specs actually work to highlight potentially damaging edits for editors. This allows editors to triage them from the torrent of new edits and review them with increased scrutiny.
By combining open data and open source machine learning algorithms, our goal is to make quality control in Wikipedia more transparent, auditable, and easy to experiment with.
Our hope is that ORES will enable critical advancements in how we do quality control—changes that will both make quality control work more efficient and make Wikipedia a more welcoming place for new editors.]]></description>
<dc:subject>ai wikipedia editing machinelearning datastudies</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:310d3d88c861/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:wikipedia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:editing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datastudies"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://whoo.ps/2015/02/23/futures-of-text">
    <title>Futures of text | Whoops by Jonathan Libov</title>
    <dc:date>2015-03-01T17:05:44+00:00</dc:date>
    <link>http://whoo.ps/2015/02/23/futures-of-text</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Text is an incredibly comfortable medium. Text-based interaction is fast, fun, funny, flexible, intimate, descriptive and even consistent in ways that voice and user interface often are not. Always bet on text:

Text is the most socially useful communication technology. It works well in 1:1, 1:N, and M:N modes. It can be indexed and searched efficiently, even by hand. It can be translated. It can be produced and consumed at variable speeds. It is asynchronous. It can be compared, diffed, clustered, corrected, summarized and filtered algorithmically. It permits multiparty editing. It permits branching conversations, lurking, annotation, quoting, reviewing, summarizing, structured responses, exegesis, even fan fic. The breadth, scale and depth of ways people use text is unmatched by anything.]]></description>
<dc:subject>text ui messaging nlp analysis design mobile AI</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c46daf81681d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:text"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ui"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:messaging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:mobile"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:AI"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://web.stanford.edu/group/SHR/4-2/text/agre.html">
    <title>phil agre - the soul gained and lost: artificial intelligence as a philosophical project</title>
    <dc:date>2014-08-22T21:53:33+00:00</dc:date>
    <link>http://web.stanford.edu/group/SHR/4-2/text/agre.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Humanistic critical practice can take up numerous relationships, cooperative or not, to this cycle of research. My own analysis in this paper has employed a relatively old-fashioned set of humanistic methods from the history of ideas, tracing the continuity of certain themes across a series of authors and their intellectual projects. Since formalization is a fundamentally metaphorical process, discursively interrelating one set of things with another, mathematical set, it can be particularly fruitful to trace the historical travels of a given metaphor among various institutional sites in society, technical and otherwise.[30] The purpose in doing so is not simply to debunk any claims that technical institutions might make to an ahistorical authority, but to prevent the passage to formalism from forgetting the underlying commitments that a given way of speaking about human activities draws from its broader cultural embedding.[31] This contextual awareness will be crucial when the technical research reaches an impasse and needs to be diagnosed as a manifestation of internal tensions within the underlying system of ideas. Any given set of ideas will be more easily given up when they are seen as simply one path among many others not taken. Indeed, this awareness of context will be crucial for recognizing that an impasse may have occurred in the first place. Viewed in this way, technical impasses are a form of social remembering, moments when a particular discursive form deconstructs itself and makes its internal tensions intelligible to anyone who is critically equipped to hear them.

The cycle of reaching and interpreting technical impasses, moving back and forth between technical design and critical inquiry, can be practiced on a variety of scales, depending upon the acuity of one's critical methods. The example I traced in the body of this paper was extremely coarse: whole decades of research could be seen in hindsight to have been working through a single, clear-cut intellectual problem. The difficulty was not that AI practitioners were insulated from the philosophical critiques of Cartesian reason that might have provided a diagnosis of their difficulties and defined the contours of alternative territories of research. To the contrary, Hubert Dreyfus was articulating some of these critiques all along. The real difficulty was that the critical apparatus of the field did not provide its practitioners with a living, day-to-day appreciation for the contingent nature of their formalisms. Although they viewed formalization as conferring upon language a cleanliness and precision that it did not otherwise possess, the effect was precisely the reverse. Lacking a conscious awareness of the immense historicity of their language, they could not understand it as it called out to them the very things they had discovered. A reformed technical practice would employ the tools of critical inquiry to engage in a richer and more animated conversation with the world.]]></description>
<dc:subject>ai critique humanities technology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:852ed3e35691/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:critique"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:humanities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:technology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/pdf/1305.2254.pdf">
    <title>Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic</title>
    <dc:date>2013-06-18T14:33:28+00:00</dc:date>
    <link>http://arxiv.org/pdf/1305.2254.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In many probabilistic first-order representation systems, inference is performed by "grounding"—i.e., mapping it to a propositional representation, and then performing propositional inference. With a large database of facts, groundings can be very large, making inference and learning computationally expensive. Here we present a first-order probabilistic language which is well-suited to approximate "local" grounding: every query Q can be approximately grounded with a small graph. The language is an extension of stochastic logic programs where inference is performed by a variant of personalized PageRank. Experimentally, we show that the approach performs well without weight learning on an entity resolution task; that supervised weight-learning improves accuracy; and that grounding time is independent of DB size. We also show that order-of-magnitude speedups are possible by parallelizing learning.]]></description>
<dc:subject>statistics logic ai inference naacl icml graph</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c7c9d22083b5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:logic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:naacl"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:icml"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:graph"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.iro.umontreal.ca/~lisa/publications2/index.php/attachments/single/104">
    <title>Learning Deep Architectures for AI</title>
    <dc:date>2013-06-03T19:13:43+00:00</dc:date>
    <link>http://www.iro.umontreal.ca/~lisa/publications2/index.php/attachments/single/104</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Theoretical results strongly suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one needs deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difﬁcult optimization task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.]]></description>
<dc:subject>deeplearning AI</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7c9338829f1e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:AI"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.bilkent.edu.tr/~akman/jour-papers/jop/jop2000-2.pdf">
    <title>Rethinking context as a social construct</title>
    <dc:date>2012-06-14T17:01:58+00:00</dc:date>
    <link>http://www.cs.bilkent.edu.tr/~akman/jour-papers/jop/jop2000-2.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This paper argues that in addition to the familiar approach using formal contexts, there is now a need in artificial intelligence to study contexts as social constructs. As a successful example of the latter approach, Idraw attention to 'interpretation' (in the sense of literary the- ory), viz. the reconstruction of the intended meaning of a literary text that takes into account the context in which the author assumed the reader would place the text. An important con- tribution here comes from Wendell Harris, enumerating the seven crucial dimensions of con- text: knowledge of reality, knowledge of language, and the authorial, generic, collective, spe- cific, and textual dimensions. Finally, two recent approaches to interpretation, due to Jon Barwise and Jerry Hobbs, are analyzed as useful attempts which also come to grips with the notion of context.

It must be noted that there has been a considerable body of contributions connecting lin- guistic structure with social context. For example, anthropological linguistics, from Bronislaw Malinowski onwards, has underlined the cultural context of discourse as essential to meaning.  This viewpoint became prominent with the emergence of the ethnography of speaking in anthropology. Thus, conversation analysis represents a consistent formal effort to contribute to an analysis of the nature of context. While this paper emphasizes and reviews the literary theory approach, it makes various contacts with works of the latter kind (e.g., the landmark contributions of Erving Goffman, John Gumperz, William Hanks, John Heritage, Dell Hymes, et al.) in order to deliver a more balanced and complete study of the dimensions of context.]]></description>
<dc:subject>nlp ai context</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:b3ba0d96a92b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:context"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://metaoptimize.com/qa">
    <title>Training Examples Q&amp;A - machine learning, natural language processing, artificial intelligence, text analysis, information retrieval, search, data mining, statistical modeling, and data visualization</title>
    <dc:date>2010-06-30T00:53:11+00:00</dc:date>
    <link>http://metaoptimize.com/qa</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Where data geeks ask and answer questions on machine learning, natural language processing, artificial intelligence, text analysis, information retrieval, search, data mining, statistical modeling, and data visualization!]]></description>
<dc:subject>ai machinelearning nlp textanalysis ir datamining search statistics infoviz reference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:8360af74d3ea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ir"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:infoviz"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:reference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://cci.mit.edu/">
    <title>MIT Center for Collective Intelligence</title>
    <dc:date>2006-10-23T02:46:10+00:00</dc:date>
    <link>http://cci.mit.edu/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[How can people and computers be connected so that—collectively—they act more intelligently than any individuals, groups, or computers have ever done before?
]]></description>
<dc:subject>collaboration social ai organization metadata</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:4952a12fc5fa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:collaboration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.imagemattersllc.com/">
    <title>Image Matters LLC</title>
    <dc:date>2006-03-25T18:54:58+00:00</dc:date>
    <link>http://www.imagemattersllc.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[We specialize in Semantic Web, Internet, geospatial and sensor technologies. For example, our unique knowledgeSmarts™ technology is unparalleled as a geospatial-temporal-semantic reasoning platform.
]]></description>
<dc:subject>semweb companies ubicomp locative ai</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:5137afeb4d77/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:companies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ubicomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:locative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.lab7.kuis.kyoto-u.ac.jp/">
    <title>Ishida Laboratory</title>
    <dc:date>2006-02-12T20:18:14+00:00</dc:date>
    <link>http://www.lab7.kuis.kyoto-u.ac.jp/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Ishida Toru's lab at Kyoto University's Department of Social Informatics. Research focuses on agent-mediated communication.
]]></description>
<dc:subject>japan research HCI ai socialinformatics</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:32516de150fb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:japan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:HCI"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:socialinformatics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://groups.csail.mit.edu/dig/2005/09/6.898/">
    <title>6.898 Notions &amp; Notations of the Semantic Web</title>
    <dc:date>2006-01-16T00:36:21+00:00</dc:date>
    <link>http://groups.csail.mit.edu/dig/2005/09/6.898/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This is a reading seminar that will prepare students to do graduate-level research on the applications and control of automated reasoning on the World Wide Web.
]]></description>
<dc:subject>semweb course logic ai</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:52bb5336856b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:course"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:logic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nhm.ac.uk/about-us/news/2005/august/news_6268.html">
    <title>DAISY, the Museum's vision for the future of systematics - Natural History Museum</title>
    <dc:date>2005-11-10T23:37:52+00:00</dc:date>
    <link>http://www.nhm.ac.uk/about-us/news/2005/august/news_6268.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[DAISY uses artificial intelligence and computer vision technologies to produce virtual collections of authoritatively identified specimens.
]]></description>
<dc:subject>ai computervision museum photography image metadata</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:a6e5d4cf8e5c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:computervision"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:museum"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:photography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:image"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://homepages.cwi.nl/~media/demo/IWA/">
    <title>VOX POPULI: automated documentaries</title>
    <dc:date>2005-11-10T04:19:40+00:00</dc:date>
    <link>http://homepages.cwi.nl/~media/demo/IWA/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A system for automatic generation of biased video sequences.
]]></description>
<dc:subject>video ai research smil documentary rhetoric opinion amsterdam multimedia semweb</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:8115712abf21/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:video"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:smil"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:documentary"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:rhetoric"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:opinion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:amsterdam"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:multimedia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.swarm.org/wiki/Main_Page">
    <title>SwarmWiki</title>
    <dc:date>2005-10-03T19:52:24+00:00</dc:date>
    <link>http://www.swarm.org/wiki/Main_Page</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Swarm is a software package for multi-agent simulation of complex systems.
]]></description>
<dc:subject>ai research tools simulation wiki community</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:e9ad8b809cf6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:wiki"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:community"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bruce.edmonds.name/">
    <title>Bruce Edmonds</title>
    <dc:date>2005-10-03T19:49:14+00:00</dc:date>
    <link>http://bruce.edmonds.name/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Social and socially-situated intelligence; measures and characterisations of complexity; evolutionary processes; nature and application of context in cognitive and AI domains; social simulation; philosophy of science (particularly modelling); etc.
]]></description>
<dc:subject>simulation social cognition research people economics ai</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:363c7c1c036e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.echonest.com/index.htm">
    <title>The Echo Nest</title>
    <dc:date>2005-09-03T00:35:18+00:00</dc:date>
    <link>http://www.echonest.com/index.htm</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Brian Whitman's new "future music" startup.
]]></description>
<dc:subject>music ai commercial future boston mit stealth</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:5d43d0328c57/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:music"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:commercial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:future"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:boston"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:mit"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:stealth"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.research.ibm.com/UIMA/">
    <title>UIMA: Unstructured Information Analysis Architecture</title>
    <dc:date>2005-08-09T15:46:47+00:00</dc:date>
    <link>http://www.research.ibm.com/UIMA/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[It is an open, industrial-strength, scaleable and extensible platform for creating, integrating and deploying unstructured information management solutions from combinations of semantic analysis and search components.
]]></description>
<dc:subject>ai architecture community knowledge management nlp opensource research search semantics semweb standards</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:9501509db066/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:community"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:knowledge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:opensource"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semantics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:standards"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://brachman.org/">
    <title>Ron Brachman</title>
    <dc:date>2005-08-05T21:20:52+00:00</dc:date>
    <link>http://brachman.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In addition to working in AI, he has been active in the Knowledge Discovery in Databases research community and has published in the information retrieval, database management, and software engineering literatures.
]]></description>
<dc:subject>semweb ai yahoo research people</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:1dbb5ba1a0e6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:yahoo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:people"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://longtail.typepad.com/the_long_tail/2005/07/six_kinds_of_fi.html">
    <title>The Long Tail: Filters 101</title>
    <dc:date>2005-07-28T20:05:21+00:00</dc:date>
    <link>http://longtail.typepad.com/the_long_tail/2005/07/six_kinds_of_fi.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[There are, as I see it, two main categories of filters (or, to be precise, "post-filters")--Software and People--with several subtypes and loads of different variation in the wild.
]]></description>
<dc:subject>media filters search social ai</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:ccb5f41d6851/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:filters"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:search"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mallet.cs.umass.edu/index.php/Main_Page">
    <title>Mallet</title>
    <dc:date>2005-07-19T20:02:53+00:00</dc:date>
    <link>http://mallet.cs.umass.edu/index.php/Main_Page</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[MALLET is an integrated collection of Java code useful for statistical natural language processing, document classification, clustering, information extraction, and other machine learning applications to text.
]]></description>
<dc:subject>ai bayes java linguistics nlp tools</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:a6377e480c51/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:bayes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:java"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.umbriacom.com/aaai2006_weblog_symposium/">
    <title>AAAI Spring 2006 Symposia :: Computational Approaches to Analysing Weblogs</title>
    <dc:date>2005-06-24T15:19:04+00:00</dc:date>
    <link>http://www.umbriacom.com/aaai2006_weblog_symposium/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The fast growing blogosphere is a vast resource which is a fruitful domain for AI investigations.
]]></description>
<dc:subject>conference spring2006 ai blog nlp semweb social metadata</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:c92fe55173ac/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:conference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:spring2006"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:semweb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stimtv.com/technology.html">
    <title>stimTVnetwork™</title>
    <dc:date>2005-06-06T15:58:55+00:00</dc:date>
    <link>http://www.stimtv.com/technology.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The stimTV™ Network is built on patented technology that facilitates the storage and retrieval of video clips in response to a viewer’s profile or to specific requests by delivering fully assembled, entertaining, broadcast-quality media programming.
]]></description>
<dc:subject>commercial multimedia ai streaming web video msmdx</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:2c2a221980cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:commercial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:multimedia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:streaming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:video"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:msmdx"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stimtv.com/npowr/properties.html">
    <title>NPOWR</title>
    <dc:date>2005-06-06T15:54:44+00:00</dc:date>
    <link>http://www.stimtv.com/npowr/properties.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A granted U.S. patent entitled A System for the Automated Generation of Media which represents a breakthrough in the ability to generate customized viewing experiences from a database of stored media assets.
]]></description>
<dc:subject>ai multimedia commercial msmdx</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:c9de5939db8b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:multimedia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:commercial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:msmdx"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://teach-computers.org/kcvc05.html">
    <title>Collecting Knowledge from Volunteer Contributors</title>
    <dc:date>2005-02-05T22:02:47+00:00</dc:date>
    <link>http://teach-computers.org/kcvc05.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Knowledge-starved AI applications can potentially benefit from collecting knowledge from Web users, to enable a variety of applications (knowledge acquisition, common sense reasoning).
]]></description>
<dc:subject>ai social metadata msmdx</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:ef2db26cb6e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:msmdx"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jbpm.org/state.of.workflow.html">
    <title>The State of Workflow</title>
    <dc:date>2004-12-08T01:23:31+00:00</dc:date>
    <link>http://www.jbpm.org/state.of.workflow.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A workflow management system (WFMS) is a software component that takes as input a formal description of business processes and maintains the state of processes executions, thereby delegating activities amongst people and applications.
]]></description>
<dc:subject>ai code commercial</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:bd7ec0127516/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:code"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:commercial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.gonze.com/weblog/story/11-8-4#11-8-4">
    <title>Computer-assisted reputation</title>
    <dc:date>2004-11-09T02:54:15+00:00</dc:date>
    <link>http://www.gonze.com/weblog/story/11-8-4#11-8-4</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Methods for designing social systems so that humans do the mediation. These are partially automated systems where humans make the decisions and automata amplify the value of those decisions.
]]></description>
<dc:subject>ai ideas social tools web</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:d800cd6da157/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ideas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:web"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.agentisolutions.com/products/products.htm">
    <title>Agent iSolutions: Brahms</title>
    <dc:date>2004-10-14T02:42:36+00:00</dc:date>
    <link>http://www.agentisolutions.com/products/products.htm</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Brahms is a data driven (forward chaining) discrete event environment usable for simulation purposes as well as for agent-based software solutions requiring the use of intelligent agents.
]]></description>
<dc:subject>ai social tools</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:0938d70f285e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://cfpm.org/">
    <title>Centre for Policy Modelling</title>
    <dc:date>2004-10-14T02:22:04+00:00</dc:date>
    <link>http://cfpm.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[One of the UK’s leading research groups exploiting the synergies between distributed artificial intelligence and social simulation to analyse areas like marketing, organisational design and strategic decision-making.
]]></description>
<dc:subject>academia ai policy social research labs simulation</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:04f58535a357/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:labs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:simulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://web.media.mit.edu/~barbara/">
    <title>Barbara Barry</title>
    <dc:date>2004-09-09T18:57:07+00:00</dc:date>
    <link>http://web.media.mit.edu/~barbara/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Working on augmenting consumer cameras with artificial intelligence to create new partnerships between videographers, their tools and their content.
]]></description>
<dc:subject>academia ai authoring mit people video</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:2b0e7aa24643/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:authoring"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:mit"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:video"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://web.media.mit.edu/~push/">
    <title>Push Singh</title>
    <dc:date>2004-09-09T18:55:20+00:00</dc:date>
    <link>http://web.media.mit.edu/~push/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[My research is focused mainly on finding ways to give computers human-like common sense.
]]></description>
<dc:subject>ai mit people academia</dc:subject>
<dc:identifier>https://pinboard.in/u:rybesh/b:c4641dc15415/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:mit"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:academia"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>