the AI created Music learned from got inspiration from the hit songs and came up with a great new hit song for you and then kind of you 00:13:21 know what we'll call those those artifacts or the little similarities here and there might get picked up by Content ID on YouTube

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.

Actor-critic is a temporal difference algorithm used in reinforcement learning. It consists of two networks: the actor, which decides which action to take, and the critic, which evaluates the action produced by the actor by computing the value function and informs the actor how good the action was and how it should adjust. In simple terms, the actor-critic is a temporal difference version of policy gradient. The learning of the actor is based on a policy gradient approach.

Leaky Bucket is implemented similarly to Token Bucket where OVER_LIMIT is returned when the bucket is full. However tokens leak from the bucket at a consistent rate which is calculated as duration / limit. This algorithm is useful for metering, as the bucket leaks allowing traffic to continue without the need to wait for the configured rate limit duration to reset the bucket to zero.

Do you know if I'm real? An experiment to benchmark human recognition of AI-generated faces

Nieuws kwam tot ons via een combinatie van kranten, bladen en radio of tv. Papieren media hadden het te doen met beperkte fysieke ruimte omdat papier geld kost, ook iets weegt en meer papier is ook nog duurder te vervoeren. Bij radio- en tv-zenders was het niet anders door beperkte tijd, een beperkt aantal kanalen en zeer hoge kosten. Dus een redactie maakte een beperkte selectie voor ons: een filter.Ook informatie-uitwisseling onderling ging per post en ook dat was bewerkelijk en bepaald niet gratis. Iets dergelijks gold eigenlijk voor alle vormen van informatie die tot ons kwam.En sinds een tijdje worden die filters minder belangrijk of ze verdwijnen compleet. Het zelf massaal verspreiden van (nep-)nieuws en andere informatie kost niets meer, dus iedereen kan iedereen onbeperkt bekogelen met extreme hoeveelheden informatie.In veel gevallen is er geen enkel filter meer op die toestroom van informatie. En al is dat filter er wel, dan moet je dat zelf maar zien in te stellen. Of, nog erger, het filter is er, maar functioneert niet in jouw belang en is daarmee onbetrouwbaar

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.

As Threads "soars", Bluesky and Mastodon are adopting algorithmic feeds. (Tech Crunch) You will eat the bugs. You will live in the pod. You will read what we tell you. You will own nothing and we don't much care if you are happy.

Instrumental to this era of speed cubing was Dr. Jessica Fridrich, who in 1997 developed a method for solving the cube faster than ever. Most of the fastest cube solvers nowadays use some version of the Fridrich method.

no algorithm can flip a single edge in place

There aren’t any algorithms that can swap only a pair of corners, nor only a pair of edges.

swapping a pair of edges and a pair of corners cancel each other out, since there’s an algorithm to undo that.

We'll start from the bottom and work our way up to modern load balancing algorithms.

TikTok offers an online resource center for creators seeking to learn more about its recommendation systems, and has opened multiple transparency and accountability centers where guests can learn how the app’s algorithm operates.

“The reality is that tech companies have been using automated tools to moderate content for a really long time and while it’s touted as this sophisticated machine learning, it’s often just a list of words they think are problematic,” said Ángel Díaz, a lecturer at the UCLA School of Law who studies technology and racial discrimination.

个人学习可能取决于他人行为的主张突出了将学习环境视为一个涉及多个互动参与者的系统的重要性

Recommendation media is the new standard for content distribution. Here’s why friend graphs can‘t compete in an algorithmic world.

Of course, the product itself is a small part of the equation. It’s the community of creators that really matters. People like the influencer family are the fuel that make these engines run. The next generation of social platforms could care less if your personal network is signed up and more about the vibrancy of a indentured creator class that that can be endlessly funneled into the video feed. Like media, social platforms are generational. Social is shifting from connections to entertainment. The next gen wants their MTV.

Media is a game of intent and attention. The most valuable platforms dominate one or the other. Few win at both. On the internet, our intent is funneled into commercial action.

5.11 Convert your principles into algorithms and have the computer make decisions alongside you.

Explore more Thinking Routines at pz.harvard.edu/thinking-routines

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.

Babel is a metaphor for what some forms of social media have done to nearly all of the groups and institutions most important to the country’s future—and to us as a people.

very specific choices made by the companies that have come to dominate the internet generally and social media platforms in particular

More importantly, these companies are still way too guarded about how exactly their standards operate, or how their engagement ranking systems influence what goes viral and what doesn’t.

There are some bugs in the software.

Russians could study and manipulate patterns in the engagement ranking system on a Facebook or YouTube.

Algospeak refers to code words or turns of phrase users have adopted in an effort to create a brand-safe lexicon that will avoid getting their posts removed or down-ranked by content moderation systems. For instance, in many online videos, it’s common to say “unalive” rather than “dead,” “SA” instead of “sexual assault,” or “spicy eggplant” instead of “vibrator.”

has the operator return its first defined argument, then pass over the next defined one in case of a dead-end, in a depth-first selection algorithm.

The evaluation may result in the discovery of dead ends, in which case it must "switch" to a previous branching point and start over with a different alternative.

Ambiguous functions

In preparing these instructions, Gaspard-Michel LeBlond, one of their authors, urges the use of uniform media for registering titles, suggesting that “ catalog materials are not diffi cult to assemble; it is suffi cient to use playing cards [. . .] Whether one writes lengthwise or across the backs of cards, one should pick one way and stick with it to preserve uniformity. ” 110 Presumably LeBlond was familiar with the work of Abb é Rozier fi fteen years earlier; it is unknown whether precisely cut cards had been used before Rozier. The activity of cutting up pages is often mentioned in prior descrip-tions.

Because of the constantly growing number of volumes, and to minimize coordination issues, Gottfried van Swieten emphasizes a set of instructions for registering all the books of the court library. Written instructions are by no means common prior to the end of the eighteenth century. Until then, cataloging takes place under the supervision of a librarian who instructs scriptors orally, pointing out problems and corrections as every-one goes along.

Checks are usually done in this order: 404 if resource is public and does not exist or 3xx redirection OTHERWISE: 401 if not logged-in or session expired 403 if user does not have permission to access resource (file, json, ...) 404 if resource does not exist or not willing to reveal anything, or 3xx redirection

What Fukuyama and a team of thinkers at Stanford have proposed instead is a means of introducing competition into the system through “middleware,” software that allows people to choose an algorithm that, say, prioritizes content from news sites with high editorial standards.

In an internal presentation from that year, reviewed by the Wall Street Journal, a company researcher, Monica Lee, found that Facebook was not only hosting a large number of extremist groups but also promoting them to its users: “64% of all extremist group joins are due to our recommendation tools,” the presentation said, predominantly thanks to the models behind the “Groups You Should Join” and “Discover” features.

Using chemicals to improve our economy of attention and become emotionally "fitter" is an option that penetrated public consciousness some time ago.

They have become more significant because social interaction is governed by social convention to a much lesser extent than it was fifty years ago.

The possibility of pharmacological intervention thus expands the subjective autonomy of people to act in their own best interests or to their own detriment. This in turn is accompanied by a new form of self-reflection, which encompasses both structural images of the brain and the ability to imagine the neuro-chemical activity that goes on there. What is alarming is that many of the neuroscientific findings that have triggered a transformation in our perception of ourselves are linked with commercial interests.

Given the racist algorithmic codes of the internet (Noble, 2018)

Algorithms of Oppression: How Search EnginesReinforce Racism

What I want to suggest to you is that, in some improbable way, this page is as much of an heir to the structure of a commonplace book as the most avant-garde textual collage. Who is the “author” of this page? There are, in all likelihood, thousands of them. It has been constructed, algorithmically, by remixing small snippets of text from diverse sources, with diverse goals, and transformed into something categorically different and genuinely valuable. In the center column, we have short snippets of text written by ten individuals or groups, though of course, Google reports that it has 32 million more snippets to survey if we want to keep clicking. The selection of these initial ten links is itself dependant on millions of other snippets of text that link to these and other journalism-related pages on the Web.

In a certain sense, this is a search algorithm, a defined series of steps that allows the user to index the text in a way that makes it easier to query.

Koo's discovery makes it possible to peek inside the black box and identify some key features that lead to the computer's decision-making process.

Neural nets learn and make decisions independently of their human programmers. Researchers refer to this hidden process as a "black box." It is hard to trust the machine's outputs if we don't know what is happening in the box.

We've developed scientific methods to study black boxes for hundreds of years now, but these methods have primarily been applied to [living beings] up to this point

A 2018 MIT study co-authored by the company’s own former chief media scientist found that false news stories on Twitter are 70 percent more likely to be retweeted than true stories and spread across the network at least six times faster.

Documents examined by the Wall Street Journal last May show Facebook’s internal research found 64 percent of new members in extremist groups joined because of the social network’s “Groups you should join” and “Discover” algorithms.

Recent patent filings show that Microsoft has been exploring additional ideas to monitor workers in the interest of organizational productivity. One filing describes a “meeting insight computing system” that would generate a quality score for a meeting using data such as body language, facial expressions, room temperature, time of day, and number of people in the meeting.

the aggregation algorithm is roughly equivalent to the following pseudo code

A more active stance by librarians, journalists, educators, and others who convey truth-seeking habits is essential.

When Wojcicki took over, in 2014, YouTube was a third of the way to the goal, she recalled in investor John Doerr’s 2018 book Measure What Matters.“They thought it would break the internet! But it seemed to me that such a clear and measurable objective would energize people, and I cheered them on,” Wojcicki told Doerr. “The billion hours of daily watch time gave our tech people a North Star.” By October, 2016, YouTube hit its goal.

The conundrum isn’t just that videos questioning the moon landing or the efficacy of vaccines are on YouTube. The massive “library,” generated by users with little editorial oversight, is bound to have untrue nonsense. Instead, YouTube’s problem is that it allows the nonsense to flourish. And, in some cases, through its powerful artificial intelligence system, it even provides the fuel that lets it spread.#lazy-img-336042387:before{padding-top:66.68334167083543%;}

Safiya Noble, Algorithms of Oppression (New York: New York University Press, 2018). See also Mozilla’s 2019 Internet Health Report at https://internethealthreport.org/2019/lets-ask-more-of-ai/.

You know Goethe's (or hell, Disney's) story of The Sorceror's Apprentice? Look it up. It'll help. Because Mark Zuckerberg is both the the sorcerer and the apprentice. The difference with Zuck is that he doesn't have all the mastery that's in the sorcerer's job description. He can't control the spirits released by machines designed to violate personal privacy, produce echo chambers, and to rationalize both by pointing at how popular it all is with the billions who serve as human targets for messages (while saying as little as possible about the $billions that bad acting makes for the company).

Many of the book’s essayists defend freedom of expression over freedom from obscenity. Says Rabbi Arthur Lelyveld (father of Joseph, who would become executive editor of The New York Times): “Freedom of expression, if it is to be meaningful at all, must include freedom for ‘that which we loathe,’ for it is obvious that it is no great virtue and presents no great difficulty for one to accord freedom to what we approve or to that to which we are indifferent.” I hear too few voices today defending speech of which they disapprove.

Beware online "filter bubbles"

As the largest Voronoi regions belong to the states on the frontier of the search, this means that the tree preferentially expands towards large unsearched areas.

So what's our total time cost? O(nlog⁡2n)O(n\log_{2}{n})O(nlog2​n). The log⁡2n\log_{2}{n}log2​n comes from the number of times we have to cut nnn in half to get down to sublists of just 1 element (our base case). The additional nnn comes from the time cost of merging all nnn items together each time we merge two sorted sublists.

Alexander Samuel reflects on tagging and its origins as a backbone to the social web. Along with RSS, tags allowed users to connect and collate content using such tools as feed readers. This all changed with the advent of social media and the algorithmically curated news feed.

BFS looks at each adjacent node and doesn't consider the children of those adjacent nodes. DFS looks at each adjacent node, and looks at all the children of the current adjacent nodes. It again, looks at the children of the next adjacent node (adjacent to the children of the prevoius)

Machine learning models are basically mathematical functions that represent the relationship between different aspects of data.

concepts that we have never yet imagined

One reason is that products are often designed in ways that make us act impulsively and against our better judgment. For example, suppose you have a big meeting at work tomorrow. Ideally, you want to spend some time preparing for it in the evening and then get a good night’s rest. But before you can do either, a notification pops up on your phone indicating that a friend tagged you on Facebook. “This will take a minute,” you tell yourself as you click on it. But after logging in, you discover a long feed of posts by friends. A few clicks later, you find yourself watching a YouTube video that one of them shared. As soon as the video ends, YouTube suggests other related and interesting videos. Before you know it, it’s 1:00 a.m., and it’s clear that you will need an all-nighter to get ready for the following morning’s meeting. This has happened to most of us.

Roth, now 67, gravitated to matching markets, where parties must choose one another, through applications, courtship and other means. In 1995, he wrote a mathematical algorithm that greatly improved the efficiency of the system for matching medical school graduates to hospitals for their residencies. That work led him to improve the matching models for law clerkships, the hiring of newly minted economists, Internet auctions and sororities. “I’m a market designer,” he says. “Currently, I’m focused on kidneys. We’re trying to match more donor kidneys to people globally.”

If you do not like the price you’re being offered when you shop, do not take it personally: many of the prices we see online are being set by algorithms that respond to demand and may also try to guess your personal willingness to pay. What’s next? A logical next step is that computers will start conspiring against us. That may sound paranoid, but a new study by four economists at the University of Bologna shows how this can happen.

We have developed quite a few concepts and methods for using the computer system to help us plan and supervise sophisticated courses of action, to monitor and evaluate what we do, and to use this information as direct feedback for modifying our planning techniques in the future.

I think it could be a big mistake to have the population at large play around with algorithms.

In other words, when YouTube fine-tunes its algorithms, is it trying to end compulsive viewing, or is it merely trying to make people compulsively watch nicer things?

Algorithms will privilege some forms of ‘knowing’ over others, and the person writing that algorithm is going to get to decide what it means to know… not precisely, like in the former example, but through their values. If they value knowledge that is popular, then knowledge slowly drifts towards knowledge that is popular.

Do we want technology to keep giving more people a voice, or will traditional gatekeepers control what ideas can be expressed?

Once products and, more important, people are coded as having certain preferences and tendencies, the feedback loops of algorithmic systems will work to reinforce these often flawed and discriminatory assumptions. The presupposed problem of difference will become even more entrenched, the chasms between people will widen.

We want to make our model temporally-aware, as furtherinsights can be gathered by analyzing the temporal dy-namics of the user interactions.

Reproducibility:We ran our experiment on a single com-puter, running a 3.2 GHz Intel Core i7 CPU, using PyTorchversion 0.2.0.45. We run the optimization on GPU NVIDIAGTX 670. We trained our model with the following parame-ters:= 0:04,= 0:01,K= 120. All code will be madeavailable at publication time6.

interest in understanding how web pages are rankedis foiled: in particular, users cannot know whether ornot a high ranking is the result of payment – andagain, such secrecy reduces trust and thereby theusability and accessibility of important information

The basic dilemma is simple. If the algorithms areopen – then webmasters (and anyone else) interestedin having their websites appear at the top of a searchresult will be able to manipulate their sites so as toachieve that result: but such results would then bemisleading in terms of genuine popularity, potentialrelevance to a searcher’s interests, etc., therebyreducing users’ trust in the search engine results andhence reducing the usability and accessibility ofimportant information. On the other hand, if thealgorithms are secret, then the legitimate public

Leading thinkers in China argue that putting government in charge of technology has one big advantage: the state can distribute the fruits of AI, which would otherwise go to the owners of algorithms.

use algorithms to decide on what individual users most wanted to see. Depending on our friendships and actions, the system might deliver old news, biased news, or news which had already been disproven.

2016 was the year of politicians telling us what we should believe, but it was also the year of machines telling us what we should want.

ConvexHull

It assumes computational statistical inferences derived from machine learning algorithms doesn’t threaten the privacy of children.

How do we reassert humanity’s moral compass over these alien algorithms? We may need to develop a version of Isaac Asimov’s “Three Laws of Robotics” for algorithms.

algorithm has preprocessing time f(n)

“Design it so that Google is crucial to creating a response rather than finding one,”

top-down dynamic programming

Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment

A team at Facebook reviewed thousands of headlines using these criteria, validating each other’s work to identify a large set of clickbait headlines. From there, we built a system that looks at the set of clickbait headlines to determine what phrases are commonly used in clickbait headlines that are not used in other headlines. This is similar to how many email spam filters work.

QUICKSELECTADAPTIVEbased

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