91 Matching Annotations
  1. Jan 2024
    1. Lenstra’s elliptic curve algorithm for fac-toring large integers. This is one of the recent applications of elliptic curvesto the “real world,” to wit, the attempt to break certain widely used public keyciphers.
  2. Sep 2023
  3. Aug 2023
    1. N+7 algorithm used by the Oulipo writers. This algorithm replaces every noun—every person, place, or thing—in Hacking the Academy with the person, place, or thing—mostly things—that comes seven nouns later in the dictionary. The results of N+7 would seem absolutely nonsensical, if not for the disruptive juxtapositions, startling evocations, and unexpected revelations that ruthless application of the algorithm draws out from the original work. Consider the opening substitution of Hacking the Academy, sustained throughout the entire book: every instance of the word academy is literally an accident.

      How might one use quirky algorithms in interestingly destructive or even generative ways to combinatorially create new things?

  4. Jul 2023
    1. In traditional artforms characterized by direct manipulation [32]of a material (e.g., painting, tattoo, or sculpture), the creator has a direct hand in creating thefinal output, and therefore it is relatively straightforward to identify the creator’s intentions andstyle in the output. Indeed, previous research has shown the relative importance of “intentionguessing” in the artistic viewing experience [33, 34], as well as the increased creative valueafforded to an artwork if elements of the human process (e.g., brushstrokes) are visible [35].However, generative techniques have strong aesthetics themselves [36]; for instance, it hasbecome apparent that certain generative tools are built to be as “realistic” as possible, resultingin a hyperrealistic aesthetic style. As these aesthetics propagate through visual culture, it can bedifficult for a casual viewer to identify the creator’s intention and individuality within the out-puts. Indeed, some creators have spoken about the challenges of getting generative AI modelsto produce images in new, different, or unique aesthetic styles [36, 37].

      Traditional artforms (direct manipulation) versus AI (tools have a built-in aesthetic)

      Some authors speak of having to wrestle control of the AI output from its trained style, making it challenging to create unique aesthetic styles. The artist indirectly influences the output by selecting training data and manipulating prompts.

      As use of the technology becomes more diverse—as consumer photography did over the last century, the authors point out—how will biases and decisions by the owners of the AI tools influence what creators are able to make?

      To a limited extent, this is already happening in photography. The smartphones are running algorithms on image sensor data to construct the picture. This is the source of controversy; see Why Dark and Light is Complicated in Photographs | Aaron Hertzmann’s blog and Putting Google Pixel's Real Tone to the test against other phone cameras - The Washington Post.

  5. Apr 2023
    1. While past work has characterized what kinds of functions ICL can learn (Garg et al., 2022; Laskin et al., 2022) and the distributional properties of pretraining that can elicit in-context learning (Xie et al., 2021; Chan et al., 2022), but how ICL learns these functions has remained unclear. What learning algorithms (if any) are implementable by deep network models? Which algorithms are actually discovered in the course of training? This paper takes first steps toward answering these questions, focusing on a widely used model architecture (the transformer) and an extremely well-understood class of learning problems (linear regression).
  6. Mar 2023
    1. we have turned to machine learning, an ingenious way of disclaiming responsibility for anything. Machine learning is like money laundering for bias. It's a clean, mathematical apparatus that gives the status quo the aura of logical inevitability. The numbers don't lie.

      Machine learning like money laundering for bias

  7. Feb 2023
    1. Floyd-Warshall Algorithm,

      floyd warshall algorithm

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  8. Nov 2022
    1. it became clear that Fermat's Last Theorem could be proven as a corollary of a limited form of the modularity theorem (unproven at the time and then known as the "Taniyama–Shimura–Weil conjecture"). The modularity theorem involved elliptic curves, which was also Wiles's own specialist area.[15][16]

      Elliptical curves are also use in Ed25519 which are purportedly more robust to side channel attacks. Could there been some useful insight from Wiles and the modularity theorem?

    1. From the Introduction to Ed25519, there are some speed benefits, and some security benefits. One of the more interesting security benefits is that it is immune to several side channel attacks: No secret array indices. The software never reads or writes data from secret addresses in RAM; the pattern of addresses is completely predictable. The software is therefore immune to cache-timing attacks, hyperthreading attacks, and other side-channel attacks that rely on leakage of addresses through the CPU cache. No secret branch conditions. The software never performs conditional branches based on secret data; the pattern of jumps is completely predictable. The software is therefore immune to side-channel attacks that rely on leakage of information through the branch-prediction unit. For comparison, there have been several real-world cache-timing attacks demonstrated on various algorithms. http://en.wikipedia.org/wiki/Timing_attack

      Further arguments that Ed25519 is less vulnerable to - cache-timing attacks - hyperthreading attacks - other side-channel attacks that rely on leakage of addresses through CPU cache Also boasts - no secret branch conditions (no conditional branches based on secret data since pattern of jumps is predictable)

      Predicable because underlying process that generated it isn't a black box?

      Could ML (esp. NN, and CNN) be a parallel? Powerful in applications but huge risk given uncertainty of underlying mechanism?

      Need to read papers on this

    2. More "sales pitch" comes from this IETF draft: While the NIST curves are advertised as being chosen verifiably at random, there is no explanation for the seeds used to generate them. In contrast, the process used to pick these curves is fully documented and rigid enough so that independent verification has been done. This is widely seen as a security advantage, since it prevents the generating party from maliciously manipulating the parameters. – ATo Aug 21, 2016 at 7:25

      An argument why Ed25519 signature alg & Curve 25519 key exchange alg is more secure; less vulnerable to side attacks since the process that generates is have been purportedly verified and extensively documented.

  9. Oct 2022
    1. An assessment method for algorithms. In een sessie werd dit genoemd in combinatie met IAMA als methoden voor assessment.

  10. Aug 2022
    1. The Medianizer algorithm

      makerdao is not open to syntetix-like attack <- the latter only had two price discovery sources.

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  11. Jul 2022
    1. “Algorithms are animated by data, data comes from people, people make up society, and society is unequal,” the paper reads. “Algorithms thus arc towards existing patterns of power and privilege, marginalization, and disadvantage.”
    1. “replace ‘algorithm’ with ‘audience.'” Instead of positioning videos to perform well for an algorithm, how can they perform best with an audience?
  12. May 2022
  13. Apr 2022
    1. Before 2009, Facebook had given users a simple timeline––a never-ending stream of content generated by their friends and connections, with the newest posts at the top and the oldest ones at the bottom. This was often overwhelming in its volume, but it was an accurate reflection of what others were posting. That began to change in 2009, when Facebook offered users a way to publicly “like” posts with the click of a button. That same year, Twitter introduced something even more powerful: the “Retweet” button, which allowed users to publicly endorse a post while also sharing it with all of their followers. Facebook soon copied that innovation with its own “Share” button, which became available to smartphone users in 2012. “Like” and “Share” buttons quickly became standard features of most other platforms.Shortly after its “Like” button began to produce data about what best “engaged” its users, Facebook developed algorithms to bring each user the content most likely to generate a “like” or some other interaction, eventually including the “share” as well. Later research showed that posts that trigger emotions––especially anger at out-groups––are the most likely to be shared.

      The Firehose versus the Algorithmic Feed

      See related from The Internet Is Not What You Think It Is: A History, A Philosophy, A Warning, except with more depth here.

    1. Algorithms in themselves are neither good nor bad. And they can be implemented even where you don’t have any technology to implement them. That is to say, you can run an algorithm on paper, and people have been doing this for many centuries. It can be an effective way of solving problems. So the “crisis moment” comes when the intrinsically neither-good-nor-bad algorithm comes to be applied for the resolution of problems, for logistical solutions, and so on in many new domains of human social life, and jumps the fence that contained it as focusing on relatively narrow questions to now structuring our social life together as a whole. That’s when the crisis starts.

      Algorithms are agnostic

      As we know them now, algorithms—and [[machine learning]] in general—do well when confined to the domains in which they started. They come apart when dealing with unbounded domains.

  14. Mar 2022
    1. computers might therefore easily outperform humans at facial recognition and do so in a much less biased way than humans. And at this point, government agencies will be morally obliged to use facial recognition software since it will make fewer mistakes than humans do.

      Banning it now because it isn't as good as humans leaves little room for a time when the technology is better than humans. A time when the algorithm's calculations are less biased than human perception and interpretation. So we need rigorous methodologies for testing and documenting algorithmic machine models as well as psychological studies to know when the boundary of machine-better-than-human is crossed.

    1. In less than 6 hours after starting on our in-house server, our model generated 40,000 molecules that scored within our desired threshold. In the process, the AI designed not only VX, but also many other known chemical warfare agents that we identified through visual confirmation with structures in public chemistry databases. Many new molecules were also designed that looked equally plausible.

      Although the model was driven "towards compounds such as the nerve agent VX", it found VX but also many other known chemical warfare agents and many new molecules...that looked equally plausible."

      AI is the tool. The parameters by which it is set up makes something "good" or "bad".

  15. Jan 2022
  16. Dec 2021
    1. Standard algorithms as a reliable engine in SaaS https://en.itpedia.nl/2021/12/06/standaard-algoritmen-als-betrouwbaar-motorblok-in-saas/ The term "Algorithm" has gotten a bad rap in recent years. This is because large tech companies such as Facebook and Google are often accused of threatening our privacy. However, algorithms are an integral part of every application. As is known, SaaS is standard software, which makes use of algorithms just like other software.

      • But what are algorithms anyway?
      • How can we use standard algorithms?
      • How do standard algorithms end up in our software?
      • When is software not an algorithm?
  17. Nov 2021
    1. Now that we've made peace with the concepts of projections (matrix multiplications)

      Projections are matrix multiplications.Why didn't you sayso? spatial and channel projections in the gated gmlp

    2. Computers are especially good at matrix multiplications. There is an entire industry around building computer hardware specifically for fast matrix multiplications. Any computation that can be expressed as a matrix multiplication can be made shockingly efficient.
  18. Aug 2021
    1. A friend of mine recently took his teenage daughter on vacation to San Francisco, where he'd once lived but she'd never been. As they drove to the tourist mecca of Fisherman's Wharf, he made a few detours, taking in some of the old sights to brighten his fading memories. Every time he departed from the route Google Maps offered, though, he noticed that his daughter grew anxious. He pondered her reactions and realized then that when they were driving in a strange place, she normally saw her parents dutifully following the directions offered up by the app. Disobeying it in what were to her unfamiliar surroundings clearly made her uncomfortable.
    1. there’s no spec for a search engine, since youcan’t write code for “deliver links to the 10 web pages that best match the customer’s intent”
  19. Jun 2021
  20. Mar 2021
  21. Feb 2021
  22. Jan 2021
  23. Dec 2020
    1. ; e-commerce sites have an economic incentiveto use personalization to induce users into spending moremoney

      personalize of algorithm can make people spend more money, which help to improve the benifical of both website and merchant.

  24. Nov 2020
  25. Sep 2020
  26. Aug 2020
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  28. Jun 2020
  29. May 2020
  30. Apr 2020
  31. Jan 2020
  32. Oct 2019
    1. An algorithm is a step by step list of instructions that if followed exactly will solve the problem under consideration

      Là 1 danh sách hướng dẫn chính xác từng bước được dùng để giải quyết vấn đề.

  33. May 2019
    1. enginethatistheproblembut,rather,theusersofsearchengineswhoare.Itsuggeststhatwhatismostpopularissimplywhatrisestothetopofthesearchpile
      • I wanted to highlight the previous sentence as well, but for some reason it wouldn't let me*

      I understand why the author is troubled by the campaign's opinion of "It's not the search engines fault". It makes it seem as if there was nothing that could be done to stop promoting those ideas, and that if something is popular it will just have to be the result at the top.

      This can be problematic, as people who were not initially searching that specific phrase may click through to read racist, sexist, homophobic, or biased information (to just name a few) that perpetuates inaccuracies and negative stereotypes. It provides easier access into dangerous thinking built on the foundations of racism, sexism, etc.

      If the algorithms are changed or monitored to remove those negative searches, the people exposed to those ideas would decrease, which could help tear down the extreme communities that can build up from them.

      While I do understand this view, I also think that system can be helpful too. All the search engine does is reflect the most popular searches, and if negative ideals are what people are searching, then we can become aware and direct their paths to more educational and unbiased sources. It could be interesting to see what would happen if someone clicked on a link that said "Women belong in the kitchen", that led them to results that spoke about equality and feminism.

  34. Jan 2019
    1. Contrary to mainstream thinking that this new technology is unregulated, it’s really quite the opposite. These systems apply the strictest of rules under highly deterministic and predictable models that are regulated through mathematics. In the future, industry will be regulated not just by institutions and committees but by algorithms and mathematics. The new technology will gradually out-regulate the regulators and, in many cases, make them obsolete because the new system offers more certainty. Antonopoulos explains that “the opposite of authoritarianism is not chaos, but autonomy.”

      <big>评:</big><br/><br/>1933 年德国包豪斯设计学院被纳粹关闭,大部分师生移民到美国,他们同时也把自己的建筑风格带到了美利坚。尽管人们在严格的几何造型上感受到了冷漠感,但是包豪斯主义致力于美术和工业化社会之间的调和,力图探索艺术与技术的新统一,促使公众思考——「如何成为更完备的人」?而这一点间接影响到了我们现在所熟知的美国式人格。<br/><br/>区块链最终会超越「人治」、达到「算法自治」的状态吗?类似的讨论声在人工智能领域同样不绝于耳。「绝对理性」站到了完备人格的对立面,这种冰冷的特质标志着人类与机器交手后的败退。过去有怀疑论者担心,算法的背后实际上由人操控,但随着「由算法生成」的算法,甚至「爷孙代自承袭」算法的出现,这样的担忧逐渐变得苍白无力——我们有了更大的焦虑:是否会出现 “blockchain-based authoritarianism”?

  35. Nov 2018
    1. how does misrepresentative information make it to the top of the search result pile—and what is missing in the current culture of software design and programming that got us here?

      Two core questions in one? As to "how" bad info bubbles to the top of our search results, we know that the algorithms are proprietary—but the humans who design them bring their biases. As to "what is missing," Safiya Noble suggests here and elsewhere that the engineers in Silicon Valley could use a good dose of the humanities and social sciences in their decision-making. Is she right?

  36. Jul 2018
    1. The new organs process this enormous amount of information to break you down into categories, which are sometimes innocuous like, “Listens to Spotify” or “Trendy Moms”, but can also be more sensitive, identifying ethnicity and religious affiliation, or invasively personal, like “Lives away from family”. More than this, the new organs are being integrated with increasingly sophisticated algorithms, so they can generate predictive portraits of you, which they then sell to advertisers who can target products that you don’t even know you want yet. 
    1. Perelman says his Babel Generator also proves how easy it is to game the system. While students are not going to walk into a standardized test with a Babel Generator in their back pocket, he says, they will quickly learn they can fool the algorithm by using lots of big words, complex sentences, and some key phrases - that make some English teachers cringe. "For example, you will get a higher score just by [writing] "in conclusion,'" he says.
    2. "The idea is bananas, as far as I'm concerned," says Kelly Henderson, an English teacher at Newton South High School just outside Boston. "An art form, a form of expression being evaluated by an algorithm is patently ridiculous."
  37. Apr 2018
    1. This fall, my colleagues and I released gobo.social, a customizable news aggregator. Gobo presents you with posts from your friends, but also gives you a set of sliders that govern what news you see and what’s hidden from you. Want more serious news, less humor? Move a slider. Need to hear more female voices? Adjust the gender slider, or press the “mute all men” button for a much quieter internet. Gobo currently includes half a dozen ways to tune your news feed, with more to come.

      Gobo, a proof of concept.

  38. Mar 2018
  39. Mar 2017
    1. for not very large numbers

      Would an approach using the Sieve or Eratosthenes work better for very large numbers? Or the best shot would be a probabilistic primality test?

  40. Dec 2016
  41. Oct 2016
  42. Aug 2016
  43. Apr 2016
    1. While there are assets that have not been assigned to a cluster If only one asset remaining then Add a new cluster Only member is the remaining asset Else Find the asset with the Highest Average Correlation (HC) to all assets not yet been assigned to a Cluster Find the asset with the Lowest Average Correlation (LC) to all assets not yet assigned to a Cluster If Correlation between HC and LC > Threshold Add a new Cluster made of HC and LC Add to Cluster all other assets that have yet been assigned to a Cluster and have an Average Correlation to HC and LC > Threshold Else Add a Cluster made of HC Add to Cluster all other assets that have yet been assigned to a Cluster and have a Correlation to HC > Threshold Add a Cluster made of LC Add to Cluster all other assets that have yet been assigned to a Cluster and have Correlation to LC > Threshold End if End if End While

      Fast Threshold Clustering Algorithm

      Looking for equivalent source code to apply in smart content delivery and wireless network optimisation such as Ant Mesh via @KirkDBorne's status https://twitter.com/KirkDBorne/status/479216775410626560 http://cssanalytics.wordpress.com/2013/11/26/fast-threshold-clustering-algorithm-ftca/

  44. Nov 2015
    1. I don't totally agree with the fact that writers the creation of language is the target of a writer, I think language is just a means, the "algorithm" that "plays" with words/semanthincs, as any machine can do