38 Matching Annotations
  1. Oct 2024
  2. Jul 2024
  3. Jun 2024
    1. To boost its search engine rankings, Thai Food Near Me, a New York City restaurant, is named after a search term commonly used by potential customers. It’s a data layer on top of reality. And the problems get worse when the relative importance of the data and reality flip. Is it more important to make a restaurant’s food taste better, or just more Instagrammable? People are already working to exploit the data structures and algorithms that govern our world. Amazon drivers hang smartphones in trees to trick the system. Songwriters put their catchy choruses near the beginning to exploit Spotify’s algorithms. And podcasters deliberately mispronounce words because people comment with corrections and those comments count as “engagement” to the algorithms.These hacks are fundamentally about the breakdown of “the system.” (We’re not suggesting that there’s a single system that governs society but rather a mess of systems that interact and overlap in our lives and are more or less relevant in particular contexts.)
  4. Apr 2024
    1. Unlike traditional search engines that rely on keywords, Perplexity AI focuses on understanding your intent. It analyzes your query, the context of your previous interactions, and your overall knowledge base to determine what you're truly seeking. 
    1. If one user marks a word as bold and another user marks the same word as non-bold, thereis no final state that preserves both users’ intentions and also ensures convergence

      Having "bold" mean some semantics, e.g., important.

      Then they merge. Alice does not consider it important, Bob does -> render both. E.g., Bob's "importance" expressed as bold, Alices "not important" as grayed text.

    2. If the context has changed

      E.g.,

      "Ny is a great city."

      Alice removes "great".

      Bob wonts to replace it with "gorgeous", by removing "reat" and adding "orgeous".

      Having merged:

      "Ny is a orgeous city."

      Begs for semantic intent preservation, such as "reword great to gorgeous".

    3. Fig. 4

      Uhh, I'd imagine "remove" would refer to "bold" annotation.

      Otherwise, there can be another "bold" with t < 20, that would be accidentally removed.

      Syntactic intent is not preserved.

    4. For example, a developer mightdecide that colored text marks should be allowed to overlap, with the overlap region rendering ablend of the colors

      That would be better for user to decide.

      I think a good default is to express semantics explicitly.

      I.e., for Bob to not automatically state his utterance as important just because it's atop of Alice's, that she considers important.

      If Bob tries to reword - ok. If Bob want to add - no.

    5. No

      Given Bold, Italic, Colored are syntactic representation of semantics that we do capture - they can overlap.

      Moreover, in Bob's user-defined mapping from semantics to syntax, Bob's "important" can be bold, while Alice's "important" can be italic.

    6. Conflicts occur not only with colors; even simple bold formatting operations canproduce conflicts

      Again, let them capture semantics.

      Say, "Alice considers "The fox jumped"" as important. Alice changes mind, only "The" is important. Bob considers "jumped" as important.

      Result: Alice considers "The" important. Bob considers "jumped" important.

    7. Consider assigning colored highlighting to some text

      Color is meant to convey some semantics. Like "accent", or "important". These semantics can coexist, just like italic and bold.

      So a solution may be to: 1. Let users express semantics of their annotations 2. Give user-customizable defaults of how they are to be rendered.

      Ensuring, that semantics's render is composable. I.e., it conveys originally asigned semantics.

    8. Furthermore, as with plain text CRDTs, this model only preserves low-level syntactic intent,and manual intervention will often be necessary to preserve semantic intent based on a humanunderstanding of the text

      Good remark of syntactic vs semantic intent preservation.

      Semantics are in the head of a person, that conveys them as syntactic ops. I.e., semantics get specified down to ops.

      Merging syntactically may not always preserve semantics. I.e., one wants to "make defs easier to read by converting them to CamelCase", another wants the same but via snake-case. Having merged them syntactically, we get Camel-Snake-Case-Hybrid, which does not preserve any semantic intent. The semantics intent here are not conflict-free in the first case, though.

      Make defs readable | | as CamelCase as Snake Case | | modify to CC modify to SC They diverged at this point, even before getting to syntactic changes.

      The best solution would be to solve original problem in a different way - let defs be user-specific. But that's blue sky thinking. Although done in Unison, we do have syntactic systems around.

      So staying in a syntactic land, the best we could do is to capture the original intent: "Make defs readable".

      Then we need a smart agent, human or an AI, specify it further.

    9. The key idea is to store formatting spans alongside the plaintext character sequence,linked to a stable identifier for the first and last character of each span, and then to derive the final formattedtext from these spans in a deterministic way that ensures concurrent operations commute

      I.e., let's capture user's intent as ops, not their result.

  5. Nov 2023
  6. Mar 2023
  7. Nov 2022
  8. Jun 2022
    1. which often come into my mind ‘out of the blue’ rather than deliberate intent.

      but where does even "deliberate intent" ultimately come from?

  9. Mar 2022
  10. Sep 2021
  11. Jun 2021
    1. A Gould proof rarely endeavored to influence in any manner the structure or thesis of a piece, and was not meant to. Its purpose, according to Miss Gould, was to help a writer achieve an intent in the clearest possible way.

      There's something interesting in this take on writing.

      It also brings up the looming question: "What is your intent?"

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    Annotators

  12. May 2021
  13. Mar 2021
  14. Feb 2021
    1. An intent filter is an expression in an app's manifest file that specifies the type of intents that the component would like to receive. For instance, by declaring an intent filter for an activity, you make it possible for other apps to directly start your activity with a certain kind of intent. Likewise, if you do not declare any intent filters for an activity, then it can be started only with an explicit intent.
  15. Nov 2020
    1. (What I’m saying is, if I pass you a game state with a cheap stock, maybe take a look into that horse’s eyes.)
  16. May 2020
  17. Apr 2020
    1. While these particular indictments refer to credit card data, the laws do also reference authentication features. Two of the key points here are knowingly and with intent to defraud.
    2. I could have released this data anonymously like everyone else does but why should I have to? I clearly have no criminal intent here. It is beyond all reason that any researcher, student, or journalist have to be afraid of law enforcement agencies that are supposed to be protecting us instead of trying to find ways to use the laws against us.
    3. For now the laws are on my side because there has to be intent to commit or facilitate a crime
  18. Feb 2020
  19. Mar 2019
    1. Language Understanding

      目标是根据一个用户utterance/query 得到其对应的语义slot。slots是预先根据场景定于的。通常来说有两种类型的表示,一个是句子级别的类别,例如用户的意图和utterance的类别。另外一个是单词级别的信息抽取,例如命名实体和槽位填充。

      意图识别是根据一句话来检测用户的意图。 基于深度学习的意图识别: L. Deng, G. Tur, X. He, and D. Hakkani-Tur. Use ofkernel deep convex networks and end-to-end learningfor spoken language understanding. InSpoken Lan-guage Technology Workshop (SLT), 2012 IEEE, pages210–215. IEEE, 2012

      G. Tur, L. Deng, D. Hakkani-T ̈ur, and X. He. Towardsdeeper understanding: Deep convex networks for se-mantic utterance classification. InAcoustics, Speechand Signal Processing (ICASSP), 2012 IEEE Interna-tional Conference on, pages 5045–5048. IEEE, 2012.

      D. Yann, G. Tur, D. Hakkani-Tur, and L. Heck. Zero-shot learning and clustering for semantic utteranceclassification using deep learning. 2014.

      尤其是这个用CNN来抽取query vector进行query分类。 H. B. Hashemi, A. Asiaee, and R. Kraft. Query intentdetection using convolutional neural networks. InIn-ternational Conference on Web Search and Data Min-ing, Workshop on Query Understanding, 2016

      P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero, andL. Heck. Learning deep structured semantic modelsfor web search using clickthrough data. InProceedingsof the 22nd ACM international conference on Confer-ence on information & knowledge management, pages2333–2338. ACM, 2013

      Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil.Learning semantic representations using convolutionalneural networks for web search. InProceedings of the23rd International Conference on World Wide Web,pages 373–374. ACM, 2014.

  20. Feb 2019
  21. Jan 2019
  22. Dec 2018
    1. The issue of whether something ‘works’ goes beyond questions of technical or practical efficacy to address a host of social, cultural, aesthetic and ethical concerns.

      Intent is the critical factor for design work, not its function.

  23. Oct 2015
    1. Vanessa, the executive direc-tor of Youth Rising, said that she felt pressure for groups like hers to appear“youth-led,” but that this was sometimes unproductive because youth need supportto develop certain skills necessary for political action.

      How is political activism being modeled for these youths? Was there intent participation from which they can observe and learn until they built confidence to proceed independently or were they expected to figure it out on their own with minimal guidance? This is a departure from the examples we have been looking at until now.

  24. Apr 2015
  25. Oct 2013
    1. (b) they invest a speech with moral character.

      Maybe the facade of moral character, but if used just because Aristotle's guidebook told the speaker to... I'd question that, anyway.