16 Matching Annotations
  1. Nov 2021
  2. Jan 2021
    1. チャットbotやレコメンデーション、質問への回答、検索、パーソナルアシスタント、顧客サポート自動化、コンテンツ生成など、人と機械、人と人の自然言語によるやりとりを含む幅広いシナリオを支えるためだ


  3. Nov 2020
    1. For both the tailor-customer and doctor-patient examples, personal data is an input used to improve an output (dress, suit, medical treatment) such that the improvement directly serves the interests of the person whose information is being used.

      This reminds me of "Products are functions" where your personal data is a variable than enters into the function to determine the output.

    1. Preserving user privacy is difficult when detectingmore nuanced forms of censorshipSome forms of softcensorship might involve intentional performance degrada-tion or content manipulation. Detecting this type of behav-ior would require comparing performance or content acrossgroups of users, but such a comparison also implies thateach user must reveal their browsing history.

      If you want to investigate whether content for a user was manipulated or performance was degraded, there may be no other way but to access detailed logs of their usage. This might raise privacy concerns.

      Not only is personalization difficult to disambiguate from manipulation and censorship, personalization also makes it more costly to compare the personalized experience to some baseline value to determine if manipulation or performance degradation has taken place.

  4. Oct 2020
    1. Personalisation in educational technology: the absence of underlying pedagogies

      The International Journal of Educational Technology in Higher Education is available on Springer Open. I do not find the layout to be user-friendly, but I appreciate open access to the articles. The authors explore literature related to personalized experiences with educational technology. Personalized education involves adjusting objectives, content, and approaches to the learner. The literature did not provide a large enough sample to be representative, but it did provide a fascinating look at two approaches to personalization: a system that guides learning and an approach where the students guide decisions about learning. 7/10

  5. May 2020
    1. Previously, Google has said that the data captured from reCaptcha is not used for ad targeting or analyzing user interests and preferences. After this story was published, Google said that the information collected through reCaptcha will not be used for personalized advertising by Google.
  6. Mar 2020
    1. "users are not able to fully understand the extent of the processing operations carried out by Google and that ‘the information on processing operations for the ads personalization is diluted in several documents and does not enable the user to be aware of their extent."
    1. Now, if you intend to serve personalized ads to users, you’ll need to ensure that explicit consent to ad personalisation is collected before you can display personalised ads for end-users (where this consent is not collected, Google will default to serving non-personalized ads, potentially impacting your ad revenue).
  7. Jan 2020
  8. May 2018
    1. algorithms to personalize results and make them available to other parties for political or commercial purposes

      Algorithms personalize results for political/commercial purposes

  9. Nov 2017
  10. Jan 2017
  11. Jun 2016
  12. Jan 2016
    1. There is no single correct way to implement personalized learning

      You mean Knewton isn’t the only way to do things? Or that Knewton will adopt all sorts of different methods?

  13. Dec 2015
  14. Jul 2015
    1. The keystone that makes all this possible is 'data', which is becoming an endgame for a few and an enigma for others. In simpler words, while ecommerce and tech enabled brands are leveraging data to make better decisions and pursue customers, conventional & brick-n-mortar business lack such granular information about their own customer. How can this situation be evened out?

      Learn from marketing domain!