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Reddit is a website known as a message-board with a page for trending posts. Their algorithm was created to show only the best and most relevant posts to users. They do this by calculating the amount of upvotes, or likes, a post has relative to the amount of time it has been up. Furthermore, the Reddit algorithm also calculates a percentage of user engagement, which simply means that Reddit calculates what percent of users who see the post interact with it. This algorithm is designed to only show posts to users that are engaging and will keep the user on the platform for as long as possible.

Reddit also personalizes the posts that people see. Depending on what types of posts the user has engaged with in the past, Reddit will try to show posts which are similar in nature to keep the user engaged.

Image source: https://medium.com/hacking-and-gonzo/how-reddit-ranking-algorithms-work-ef111e33d0d9

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Since there are over 2 billion users on Youtube, how does the algorithm show people what they want to see? When Youtube started in 2005, the algorithm favored clicks overwatch time. This means that if a person clicked on the video, the algorithm would pick up that the user was interested in the topic. But some problems with that were the click-baiting and putting false titles. Youtube fixed the algorithm in 2012, when they decided to use the watch time - how long the user spends on a video - to decide what videos to recommend. This was a lot better, and it helped deliver better recommendations to the users. Finally, in 2016, Youtube released the machine learning part of the algorithm, (shown in the picture below), demonstrating the funnel that the algorithm filters through to recommend the right videos to the right people.

Source: https://blog.hootsuite.com/how-the-youtube-algorithm-works/

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The full details of how exactly the ranking of apps from a search is made is unknown but most of what occurs is either fairly obvious or can be inferred. For example when you enter a query in the App Store, it tries to pull together apps with the same or similar/relevant names. For example if you were to type "news", apples' news app might appear alongside other media sources. If you were to type "noticias", Spanish news media would appear. Apps with relevant or similar names are sorted on the basis of Downloads, Ratings, Keywords, and updates. The popularity off an app will determine where it is. Someone is much more likely to find and be interested in a 5-star app with 300,000 reviews over its 2-star, 2 review counterpart. The subtitle of an app might also lead your search for it. If the potential query "Made for awesomeness" is created, then in the list of apps that appear, some may have the subtitle "Made for awesomeness". Apps are also sorted based on how recently and frequently they update. Most people aren't looking for some outdated food delivery service no longer in business from 10 years ago, they only worked in certain areas and were pretty small compared to current business anyway. You either want door dash or some kind of equivalent because they are relatively modern apps that are actually still being used, and if you were to order something the app would work whereas one from 10 years ago no longer in business may not.

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Search engines, as well as app stores, like Google Play Store, Apple App Store, Amazon App Store or Windows Store, use complex algorithms in order to sort search results based on the relevance of an app to a specific search query.

The Google Play Store and Apple App Store keep don't openly reveal how exactly how their algorithm works and which ranking factors have the most weight. Your app store rank is determined by several components in accordance to the user search query and keywords used in the app store listing metadata.

App Store Ranking Factors Summarized

Although the exact ranking algorithms are not publicly available, the following known factors highly influence your app store rank.

Apple App Store Ranking Factors:

App Name

App URL

App Subtitle

Keyword field

In-App Purchases

Rating and Reviews

Update Cycle

Downloads and Engagement

Some Hidden factors

Google Play Ranking Factors

App Title

App Description

In-App Purchases

Rating and Reviews

Update Cycle

Downloads and Engagement

Some Hidden factors

App Store Ranking Factors for App Store and Google Play
Source: App Radar

There are major differences in ranking algorithms in Apple App Store and Google Play Store. However, there are some similarities. In the following you will learn which app store ranking factors apply for both stores.

App Name or App Title

App Store and Google Play index keywords that are in your app name or title. Research also shows that keywords in your app name have stronger rank strength than those in other fields such as subtitle. That means you're more likely to rank higher for keywords that are in your app name. iOS apps can have 30 characters for the name, while Google Play gives you 50 characters.

Examples of app names for iOS and Android apps
Source: App Store

Rating and Reviews

Google Play and Apple App Store take app ratings and user reviews into consideration when ranking your app. The better your ratings and reviews are, the higher your app will rank. Google will also comb through user feedback and find keywords there. You can also find keyword inspiration from the words that your users use to describe your app.

App ratings and reviews affect app store ranking
Source: Pokemon Go on Google Play

Although getting reviews is vital to app success, you should avoid asking for them too often or at the wrong times. A good time to ask would be after your users have completed an achievement. They'll be feeling good and more likely to leave a positive review. Also, the better your average app rating is, the higher your keyword rankings will be for relevant keywords.

For iOS apps, Apple restricts how often developers can ask for reviews. You're allowed to do so, according to Apple's guidelines, only three times a year. This forces you to perfect your timing when prompting users for a review. You also have the option of resetting you average app rating every time you release an app update. However, think carefully about resetting it and use the capability sparingly. People will still be able to see old reviews, even after the reset.

Tip: Ask the right question. You can start with a question to know if the user is enjoying the app. If he says yes, you can ask him to rate you. However, if he says no, you can ask him some feedback without taking him into the review option.

ASO factors, like keywords placed in the title, keyword field or description, are components, you can optimize by yourself. While other ASO factors like app installs, ratings and reviews, are more dependent on your users. But, there are still many strategies that you can leverage to increase your downloads, improve your ratings, and boost user engagement.
Dive Deeper:
App User Reviews: The Do's and Don'ts of Replying
Google Play Reviews
How to Get Users to Leave an App Store Review
How to Reply to App Store Reviews
Apps that are constantly updated usually have better reviews. That's because users understand that the developers are working to improve the app and listening to their feedback.

Both Apple App Store and Google Play consider the frequency of updates when ranking apps. Therefore, the more committed you are to your product, the better your rank position will be.

Tip: App Radar saves your old metadata versions, so you can compare them with the new updates and decide which ones had a large impact on app growth. This is a good way to keep track of your updates.

App Downloads

It may sound obvious, but it's always good to point out that your app downloads also affect your app store rank. The more downloads and user engagement your app has, the better its rank position will be. You want to keep increasing your download velocity, meaning the amount of downloads your app gets within a certain amount of time. The higher the velocity, the higher the rank. That's because Apple and Google like to show off apps that are clearly popular with users.

The first step to accelerating your download velocity is to identify where the downloads are coming from. In other words, which app conversion rate is the highest.

It may be that most people are installing after they've clicked into your app store listing rather than directly from the search results page. If this is the case, you'll want to increase your keyword rankings, improve your average app rating, and also see if you can better optimize or localize your text and visuals.

You can also track these 6 app metrics to measure how your downloads and user engagement is doing.

See your app conversion rates and other metrics on App Radar
Source: App Radar App Store Optimization tool

You're able to see your app metrics and also compare conversion rates across different traffic sources in your App Radar account.
Dive Deeper:
6 Basic Mobile App Marketing terms for User Acquisition beginners
6 App Metrics You Absolutely Need to Measure
How to Improve App Retention Rate with Organic Installs
Google Firebase

Google Firebase can be used for apps in both Google Play and App Store. By enabling the Google Firebase, you are allowing your app content to be shown in mobile search browsers.

People will be able to see your app in relevant search results even if they don't have your app already downloaded. By integrating Firebase and making sure your app shows up in mobile searches, you improve your discoverability and thus, install rates.

Both app stores have some hidden factors in their algorithm that nobody knows what they are exactly. Bear this in mind and that Google Play's ranking algorithm is a little more complex than the one by Apple.

App Store Ranking Factors for iOS Apps

App Name

We already went over this above. But here's a short recap. Keywords in the App Name have the strongest ranking weight. You can add up 30 characters. Be mindful of that limit when you're naming your app.

App Subtitle

iOS 11 introduced the Subtitle field. Apple allows you to have 30 characters here. Use the space wisely because the keywords written in this field are indexed by the App Store search algorithm.

Keywords in the subtitle have the second strongest app store ranking weight
Source: App Store

iOS Keyword Field

You might be asking, "if keywords are so important, why can't I indicate to Apple which ones I want to rank for?". Well you're in luck. Apple does take this into consideration and as a result offers a specific Keyword field.

The keyword field is unique to Apple's App Stor e and is not found in Google Play.

You're allowed 100 characters here. Additionally, these keywords are hidden from users. They're not shown on your public app store listing. So use this space to implement keywords that you haven't already put in the App Name and App Subtitle.

When writing your keywords in App Store Connect, separate them by comma and avoid spaces, articles, and prepositions. Use singular OR plural, and do not repeat keywords across any fields. As mentioned before, App Radar has a keyword indicator, which helps you choose the right keywords for your app.

In-App Purchases (IAP)

The names of your In-App Purchases (IAPs), including subscription plans, are going to be indexed by Apple as keywords tool. Each IAP has its own display name, promotional image, and description. Your IAPs can show up in the app store search results and even be featured in the Today tab. So make sure that you don't forget to optimize them too.

Apple's Spotlight Search

Through this tool, the users can search the apps installed in their mobile. Enabling this feature in your app helps improve the engagement because users are going to be reminded of using your app more frequently. The higher your engagement rate is, the higher your position on the rank will be.

Google Play Ranking Factors for Android Apps

App Title

Again, we already went over the app title above. So I'll be brief. Google Play allows 50 characters for your app title. Keywords placed here have the strongest ranking weight.

App Description

Google Play scans your app description to pick your keywords. Be aware that keywords written in the first lines are more relevant than the ones in the rest of the description.

Short Description

Also, in Google Developer Console, you can differentiate between Short Description and Full Description. The Short Description thereby is visible above the fold Google Play Store. This makes this field not only relevant for keyword rankings, but crucial for attracting users. Make sure to point out what your app is about within these 80 characters.

Repeat keywords in the long description 3-5 times to rank in Google Play
Source: Strava on Google Play

Long Description

You can go up to 4000 characters in the Long Description. Try to repeat the keywords you want to rank for several times. We recommend 3-5 times. This will help Google recognize what search terms are relevant to your app and what your app's main functionalities are.

Google Tags functions similarly to categories in App Store. You can pick up to 5 tags for your app from a predefined list. Then Google will use your selected tags to categorize your app. Although this has no direct affect on your Google Play ranking, it certainly does help the algorithm rank you app for the right keywords.

The rumor is that Google Play includes some SEO strategies in their search ranking.

However, having more places where people can discover your app is never a bad thing. The better web SEO you have, the more people are finding and downloading your app, the higher you will rank in Google Play. So make sure you're at least linking to your app from your website.
Dive Deeper:
Differences for app videos on Google Play Store and Apple App Store
Differences in iOS & Android ASO You Need to Know (Google Play vs. App Store)
How To Optimize Your ASO Keyword Strategy

Get Your ASO Score

See how optimized your app store listings are.
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App Store Ranking Factors: 🍎 App Store vs. 👾 Google Play

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Search engines, as well as app stores, like Google Play Store, Apple App Store, Amazon App Store or Windows Store, use complex algorithms in order to sort search results based on the relevance of an app to a specific search query.

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Netflix has been making sure that its algorithms highlights their newest shows, as well as some older shows and movies that they think a consumer will enjoy based off of the users past viewing history, along with their location. For example, say a user watched more romantic comedies, and lives in Japan. The Netflix algorithm will be sure to recommend more romantic comedies, like the ones they have liked in the past, that are in Japanese, to the user. This algorithm ensures that consumers will intake new shows and movies constantly, and this results in exposure to new content. Strong recommendation algorithms also boost viewership on their platform. By their titles that attract public attention, alongside their marketing being on numerous media platforms, they are guaranteed a viewer spike in the first week when a show comes out due to fueled interest that can start months before a show or movie is even out. Furthermore, they make sure to put shows and movies on your content library that they assume you will like due to your viewing history, which, in turn, has an outcome of more clicks, or really just spikes in views of their content. This corporations estimates that about 75% of their viewership is based off of recommendations too. Last December, Netflix decided to remove their region-based algorithm in their recommendation system because of their current efforts for global expansion. Because of this, they are making shows in multiple languages, which has currently broadened the choices the recommendation algorithm can recommend to users of all kind.
Source:
https://www.martechadvisor.com/articles/customer-experience-2/recommendation-engines-how-amazon-and-netflix-are-winning-the-personalization-battle/#

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Customer experience personalization is all about data first. Get the data right and you can shape the overall customer experience by applying data science and machine learning. Recommendation engines are very powerful personalization tools because it's a great way to do "discovery" - showing people items they will like but are unlikely to discover by themselves. They improve a visitor's experience by offering relevant items at the right time and on the right page. In the immortal words of Steve Jobs - "a lot of times, people don't know what they want until you show it to them."

Because of how well recommendation engines boost subscriber numbers through engagement and stickiness, facilitating such serendipitous discovery has turned into a high stakes multi-billion-dollar race for the world's biggest digital companies. Personalized suggestions are implemented via software programs that crunch massive amounts of data to "learn" user preferences and come up with a list of recommended items for the user.

The customer personalization journeys of Amazon and Netflix demonstrate just how powerful recommendation engines can be. See how these online giants built cutting edge recommendation engines that keep subscribers coming back for more.
Amazon
( The image describes the recommendations across the buying experience -- from product discovery to checkout)

A lot of Amazon's fantastic revenue growth has been built on successfully integrating recommendations across the buying experience -- from product discovery to checkout. Enabling personalized suggestions in e-commerce, is perhaps the number one reason for recommendation engines, because of what is known as the long tail problem - rare, obscure items that are not very popular and don't drive the bulk of revenue. Recommending long tail items to shoppers is critical because if successful it has the potential of giving ROI on slow-moving inventory.

The retail giant's recommendation algorithms are based on seemingly few elements: a user's purchase history, items in their shopping cart, items they've rated and liked, and what other customers have viewed and purchased. However, for a retailer with as many items as Amazon, the challenge becomes which recommendations to present and in which order - a problem known as "learning to rank" in data science. A secondary problem is one of diversity - how to show a diverse selection of items in your recommendation. Amazon's is able to achieve a high level of customer relevance though algorithms based on a process called item-to-item collaborative filtering.
Learn More: What is Customer Experience (CX)? Definition, Design, Management, Best Practices and Examples
The importance of suggesting the right item to the right user can be gauged by the fact that 35% of all sales are estimated to be generated by the recommendation engine. Amazon is investing a large amount of talent and resources on getting better to AI - specifically "deep learning" technology to make recommendation engines which learn and scale even more efficiently. Deep learning involves massively networked computing power to enable more complex forms of machine learning.

In May 2016, Amazon opened up its sophisticated AI technology as a cloud platform. The company unveiled DSSTNE (pronounced "destiny"), an open-source artificial intelligence framework that Amazon developed to power its own product recommendation system. "We are releasing DSSTNE as open source software so that the promise of deep learning can extend beyond speech and object recognition to other areas such as search and recommendations," said the company.

It's easy to understand why Amazon has a strong incentive for open sourcing its AI, thus increasing the probability that a developer outside Amazon will find a way to make the recommendation system better. Ultimately, the retail giant wants to create a system that can make get better at predicting products based on lesser data and of course products that customers are more likely to click on and buy.
Netflix

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Shabana Arora Former Editor, MarTech Advisor

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Recommendation Engines: How Amazon and Netflix Are Winning the Personalization Battle

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Customer experience personalization is all about data first. Get the data right and you can shape the overall customer experience by applying data science and machine learning. Recommendation engines are very powerful personalization tools because it's a great way to do "discovery" - showing people items they will like but are unlikely to discover by themselves.

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PageRank is an algorithm devised by Larry Page and Sergey Brin (the founders of Google) in 1998 while they were working on creating a new search engine at Stanford. PageRank was later utilized by Google Search to rank pages within its search engine results. Essentially, PageRank is a relative measure of the importance of a specific page on a site by counting the relative number and quality of links connecting to a page to determine the relative importance/significance of said page. The more important the PageRank algorithm deems the webpage, the higher it appears in Google's search results. Although it is no longer the only algorithm used by Google to filter search results, it was the first algorithm that was used to do so and is the most widely known as a result of it being very unique for its time. This algorithm has had significant positive and negative effects on the world, as it ushered in a new era of search engines and technology, being responsible for Google's success, one of the largest companies in the world. Unfortunately, today there is much controversy over the power of Google in the lives of billions of people around the world, which is a negative effect of the PageRank algorithm. The math behind the PageRank algorithm is explained in the image below.

Source:
https://interestingengineering.com/15-of-the-most-important-algorithms-that-helped-define-mathematics-computing-and-physics

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League of Legend's matchmaking Algorithm

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League of Legend's matchmaking Algorithm

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As silly as it sounds, I've been recently getting into matches on this game called "league of legends", where I'd be paired up with one or two players with a low win rate in match-making (there are 5 people per team). This was causing me to lose more games then win even if my skills are great. Out of curiousity, I was able to figure out that League of legend's match making algrotihtm does this on purpose to balance out players winrates. If I, the user, have a winrate higher than 50%, I will be placed into a team with 1-2 players with winrates lower than 50%. On the otherhand, if the user has a winrate less than 50%, they will most likely be placed with a player with a high winrate. I can see why league of Legend's had decided to implement this algorithm, however, it gets annoying quickly knowing that you are losing more games due to your teamates.

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Matchmaking is the existing automated process in League of Legends that matches a player to and against other players in games.

The system estimates how good a player is based on whom the player beats and to whom the player loses. It tries to make matches where it thinks a player has a 50/50 chance of winning. It knows pre-made teams are an advantage, so it gives pre-made teams tougher opponents than if each player had queued alone (or other premades of a similar total skill level) Riot Games Inc. uses some formulas in an attempt to make the premade teams vs solo players matching fair.

The basic concept is that the system over time understands how strong of a player you are, and attempts to place you in games with people of the same strength. As much as possible, the game tries to create matches that are a coin flip between players who are about the same skill.

The basic priorities of the system are, in order of importance:

1. Protecting new players from experienced ones.

2. Fairness & creating competitive matches.

3. Finding a match at all. The longer you wait, the more important this priority is.

There are three steps on that the matchmaking system follows:

1: Determine the player's strength

If solo queued, it's just based on personal rating (i.e. ranked team rating for ranked team, normal games rating for normal games).

If it's a pre-made team, the rating is the average of the team members, along with an increase based on the type of pre-made to make sure that the team gets tougher opponents, because being a pre-made is an advantage.

2: Determine eligible opponents

Initially, the system will only match you with very similar players based on the rating you have been assigned. Eventually, it gives you less ideal matches because it doesn't want you to be in line forever.

New players get special protection to increase the likelihood of being matched with and against other new players.

3: Find a match

Eventually, the system finds a match that is deemed "fair" for everyone involved, and puts the players into a game.

The system then tries to balance the team to make two teams with 50% odds of winning. In our mid-beta pool, which has very few players relative to what is in release, the majority of games created by the system had teams predicted to be extremely fair (no team with odds greater than 55% to win). We expect that in release, that 98%+ of the time we will see games where no team is thought by the system to have a higher than 55% chance to win. This is because adding a small amount of players to the system dramatically improves the quality of matches made, and we expect a lot more people to be playing at release than in closed beta...

The Matchmaking System works along with a modified version of the Elo system. The basic gist of the Elo system is that it uses math to compare two player ratings to guess the game result - like, "Player A will win vs Player B 75% of the time". From there, the game is played. If a player wins, the player gain points. On the contrary, if the player loses, he loses points. If the win was "unexpected" (i.e. the system expects you to lose), the points you gain are larger.

The system was modified for team use, and basically, the concept is that a player gets a team Elo based on whoever is on the team, and if the player wins, it is assumed that everyone on the team was "better" than the guess, and gains points. There are some problems with this, but it generally works out, especially if people use pre-mades a little bit. The System do a few little things to nudge the Elo rating in the right direction when you start out so that people get where they need to get faster.

Various proprietary methods to identify players that are significantly more skilled than a new player, and boot their rating up a bunch behind the scenes when we notice this.

Gaining levels boosts a player's Elo rating a lot. This further helps separate level 30 summoners from low level summoners.

Additionally, newer players gain and lose points more rapidly so that they are able to play in their skill level faster. Over time, this means that good players end up high rated because they do better than the system expects, until the system is guessing correctly how often they will win.

Zileas, the VP of game design, has made a detailed post on the matchmaking engine

References

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Matchmaking is the existing automated process in League of Legends that matches a player to and against other players in games. 1 Details 1.1 1: Determine the player's strength 1.2 2: Determine eligible opponents 1.3 3: Find a match 2 Elo System 3 See also 4 References The system estimates how good a player is based on whom the player beats and to whom the player loses.

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The YouTube algorithm is, essentially, a real-time feedback loop that suggests videos based on a viewer's interests. The loop suggests such videos by examining a user's searches, previous videos watched, and user patterns. Ultimately, the algorithm has a couple goals it wants to accomplish: making sure the user is continuing to use its platform, and find the right video for the user.
The impact of the YouTube algorithm is positive. This is because it enhances the user/consumer experience through a simple, yet efficient, algorithm.

Source: https://blog.hootsuite.com/how-the-youtube-algorithm-works/#:~:text=According%20to%20YouTube%2C%20the%20algorithm,get%20viewers%20to%20keep%20watching.

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Euclidean Algorithm is an algorithm that use used to find the great common factor (GCF) between two numbers. It uses the pattern when two number divide and has a remainder, the  denominator can be used as the numerator, and the remainder can be used as the denominator to write a new division calculation. These steps repeat until the remainder is 0. The GCF will then be the denominator of the final division with 0 remainder. Discovered by Greek mathematician Euclid, the Euclidean Algorithm has an positive impact by providing an efficient method to find the GCF between two numbers.

source: https://www.inchcalculator.com/euclidean-algorithm-calculator/

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The recommendation system for Netflix uses data collected from the person watching. Recommended shows are the result of viewing habits. Every time someone watches a movie or TV show, Netflix is collecting data for the algorithm. The algorithm keeps refreshing every time a person is active on Netflix and is constantly up to date. The algorithm takes into account the time of day you watch, what genres are being watched and for how long. Additionally, it sees if your viewing habits are similar to another Netflix watchers habits. In fact if you tend to watch the same things as another Netflix user, the algorithm will take note of this and recommend shows based on the other person’s viewing habits as well. Netflix wants to keep you engaged and interested to keep watching. They use the data collected and run it through the algorithm to show us shows and movies they think will intrigue us. Every few days a new thing to watch pops up in the recommended section for people to watch.
Source: https://uxplanet.org/netflix-binging-on-the-algorithm-a3a74a6c1f59

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Youtube's Algorithm to keep users engaged

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Youtube's Algorithm to keep users engaged

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Youtube's Algorithm has changed a lot since the creation of the platform. Up until recently, (2016 - 2020) Youtube used a machine-learning algorithm to recommend and push videos to the users. According to youtube,  their current algorithm is a "real-time feedback loop that tailors videos to each viewer’s different interests". Youtube has not revealed the inner workings and calculations of its algorithm to prevent people from cheesing the system, but according to https://blog.hootsuite.com/how-the-youtube-algorithm-works/ (a very "legit" source that I found), Youtube's video algorithm takes in a variety of variables when recommending videos to its users.  Some of the variables that youtube takes into consideration include:

Amount of users clicking on the video
Watch time of each individual
Likes and dislikes
The age of the video

More user-based information like subscribed channels liked and disliked videos, what videos do the users ignore, and what channels do they engage most with are also included when calculating what videos they recommend to the user.

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Youtube's recommendation algorithm is extremely complicated, involving multiple equations and functions that the user's data is put through in order to create a set of recommended videos. Generally, there are two different algorithms that determine recommended videos. One algorithm compiles a large list of videos, while the other algorithm ranks the video, placing the most important data at the top of the feed. There is a lot that goes into this decision making, including harsh equations, rigorous preference monitoring, and popularity counters. An example of an equation is listed down below, where "i" is the item, "c" is the context, and where "u" is the user. The algorithm's complexity is what makes it so effective; however, it can still make non-perfect decisions from time to time.

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Google Maps uses an algorithm to find the shortest path to the users end destination. For example, when you type in the location of somewhere you would like to go, the algorithm searches for the most optimal, shortest and fastest distance in which it would take the least amount of time to get to the destination given. This is done by using Dijkstra's Algorithm. The algorithm is a greedy algorithm which attempts to find the shortest path between nodes on a graph. Google maps uses this algorithm to find the shortest path between destinations.

Source: https://medium.com/@yk392/dijkstra-algorithm-key-to-finding-the-shortest-path-google-map-to-waze-56ff3d9f92f0

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Netflix uses machine learning, algorithms and creativity to find recommendations for you in order to accommodate the best they can to your interests. Netflix collects data from you such as the duration you spend watching a certain genre, what you watch after, what was watched before, what is watched right now. They also take time of day into consideration when giving recommendations. They categorize every movie, every show and choose the ones that best fit you. They then display it on your screen with the ones in the top left being most recommended because they are most likely to be seen while the ones in the bottom right are less likely to be seen. This algorithm is positively impacting the viewing audience because it always is updating and bringing new and interesting content to the viewer.
Source: https://uxplanet.org/netflix-binging-on-the-algorithm-a3a74a6c1f59

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The YouTube recommendation algorithm is one that has changed over time ever since its foundation in 2005. During the years of 2005-2012, YouTube used a system where they would push videos with the most views onto users' recommendation pages. However, this led to "clickbait" (misleading title and thumbnail) and user engagement was poor. YouTube then changed the system to user engagement in 2012. This new system promoted the message of "make videos that people want to watch". This allowed people's recommendation pages to be far more interesting and allowed for higher user experience quality. Then, from 2016-2020, YouTube started to demonetize videos that violated community guidelines as well as stop recommending videos that were "borderline" content (content that did not violate guidelines but was harmful and misleading). In the end, YouTube's recommendation algorithm has gone through some change and has allowed users to be able to watch the videos that they enjoy.

site: https://blog.hootsuite.com/how-the-youtube-algorithm-works/

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Autopilot in vehicles is something that goes quite unappreciated. The complexity of Tesla's algorithms within autonomous cars is exceptional. The core algorithms used operate on the data coming from the many sensors and cameras placed around the vehicle. The data is sent into the CPU where the algorithms represent the surroundings in a true-to-size virtual space. This virtual space is subject to even more rigorous algorithms which break down the data and analyze the choices and best possible decision to fulfill while under autopilot. The algorithms act on real-world situations to determine an outcome. This is where the grey area of the algorithms is found. The real world is a place of uncertainty. Some of the decisions that the car is forced to make can be questionable while looking at it from a moral standpoint. If the car is moving on the highway and is forced to make an abrupt turn to avoid a truck near the front of the car. Does it swerve left where a van full of adults are driving, or into the helmet-less motorcyclist on the right? What if the motorcyclist was wearing a helmet, would that affect the decision? Overall, autonomous vehicles and the Tesla algorithms are extremely beneficial and have a huge positive impact. There will always be difficult situations that pose a threat to others or even yourself, but such a complex algorithm should not be invalidated due to a moral conflict.

Source: https://www.tesla.com/autopilotAI

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With the rapid expansion of social media's influence, a precise advertising algorithm is necessary to maximize profit. Social media websites collect user's data including videos you watch and posts you like. They then use this data to estimate what type of person you are. This estimate is auctioned off to different companies that appeal to your estimated type. Multiple factors contribute to a company's "value", these include how much text they have, or how much clickbait it has. Unfortunately, there isn't much information on the code of these advertising algorithms

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This fascinating algorithm sorts through countless sites and directs users to the sites they are most likely to visit. While Google does not provide the math behind the algorithm, we are able to know some of the factors that determine the search results. First, the series of algorithms look at the input query from the user. It then searches for results using keywords and synonyms of the keywords in the query. It takes into account timeliness as well. For example, if you were to search for a trending topic or sports scores, the search results would be very recent because the user is likely searching for recent information. These complex algorithms are even capable of assessing what the user is searching for with only a few keywords. Instead of assessing relevance by counting how many times the article mentions keywords, the algorithm returns articles that specifically answer a query. The algorithm also takes into account the reliability and usability of relevant sites before the results are returned. In order to increase user-friendliness, the algorithm promotes websites that run quickly. Finally, the algorithm uses past user data in order to determine what site the user is most likely going to click on. The algorithms have been incredibly beneficial, giving access to knowledge to millions and allowing millions more to spread knowledge. The algorithms can potentially be harmful with the existence of intentional or unintentional biases. Because the algorithm promotes sites that are "trustworthy", it makes it easy for Google to potentially censor information. That being said,  so far Google has maintained incredible integrity with its practices.

Information from: https://www.google.com/search/howsearchworks/algorithms/

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The algorithm takes all of the data from the cameras and sensors around the car and combines all of the data to make an in-depth view of the world around the car. In order to train the neural networks to predict such representations, algorithmically create accurate and large-scale ground truth data by combining information from the car's sensors across space and time. This version of data collecting is not as in-depth of a view as LIDAR witch most if not all other self-driving car companies use Tesla’s way of detecting and putting the world around the car together allows it to operate in poor weather conditions such as rain. The LIDAR has a hard time seeing the world around it in poor weather conditions.

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The Instagram algorithm usually goes through a cycle of things that are interest, timeliness, frequency, following, and usage. These are all things that add up to what will pop up on your Instagram feed. It is also based on old things you may have liked a long time ago. The more similar type of posts you like the more those types of posts will continue to pop there. In conclusion, the algorithm for your Instagram feed is basically a combination of all your Instagram behaviors and searches.
source:https://later.com/blog/how-instagram-algorithm-works/

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The company Cogniac uses algorithms to recognize flaws in manufactured parts in order to identify weak points or cracks. It uses machine learning by being told what the thing it's looking for is so it can find it again even if it looks different. This can be used to find cracks by identifying what a proper part looks like and then comparing it to the part that's damaged.

https://cogniac.co/use-cases/rail

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In stocks the market is very volatile, so it's quite hard to tell when and how the stock will either increase or decrease. By the use of algorithmic trading, the computer recognizes certain patterns of stocks that may occur when it's about to go up/down so they either buy or sell the said stock. This helps stock brokers make decisions faster without their judgement being clouded by emotions. The way this algorithm works is by identifying certain trends of the graph and then buying before the trend usually goes up and selling before the stock goes down. It uses statistics and the highest likely chance of trends succeeding and testing numerous different trends to further better itself. This not only benefits people who invest into their own stocks they prefer but also stock brokers who are helping other people make money as well. This is a positive impact as it helps people generate more wealth
https://www.quantstart.com/articles/Best-Programming-Language-for-Algorithmic-Trading-Systems/

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One algorithm I'm interested in is the algorithm used to generate Tetris pieces in guideline Tetris games. The algorithm used is a certain kind of randomizer used to give the player consistent yet random pieces. The way the algorithm works is by grouping up the 7 Tetris pieces into groups of 7. Each group is called a bag, and the player is given consecutive bags of pieces. Each bag consists of one of every 7 Tetris pieces albeit in random order. This algorithm ensures a certain degree of randomness but also prevents the player from getting too many of the same piece consecutively or not getting a certain piece for too long.

One interesting consequence of this algorithm is that players can use the properties of the randomizer to create patterns that can be built semi-consistently. This can also be used to create a pattern that guarantees that the player never dies - this pattern results in a clear board at the end and the pattern can be repeated indefinitely:

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China is a world leader in facial recognition technology. The Chinese government has nearly all of it's 1.4 billion people in a facial recognition database. China also has roughly 200 million surveillance cameras. According to CNBC, Shanghai's YITU Technology can identify a face out of nearly 2 billion its database in just seconds (CNBC).

The algorithm detects faces using key facial features. It then uses statistics and other information such as location to shrink down the possibilities and select a final match. The impacts are both positive and negative. Big cities in China, such as Shanghai, are typically very safe due to the prevalence of facial recognition technology and police. It's hard to commit violent crimes if the authorities see almost your every move. On the other hand, these systems may invade the privacy of innocents who haven't done anything wrong. I personally think I would be okay with facial recognition technology, as long as the data wasn't be used for something else, because it would be what keeps me safe. In the future, this technology is likely to spread across the world and will hopefully help discourage crime in areas all over the planet.

https://www.cnbc.com/2019/05/16/this-chinese-facial-recognition-start-up-can-id-a-person-in-seconds.html

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Job recruiting algorithms collect, scan, and rank the applicant's resumes for a job opportunity. Many large corporations use these algorithms to efficiently deal with thousands of resumes. Unqualified candidates can consistently and effectively be eliminated using job recruiting algorithms. The math behind the algorithm is that it looks for keywords and qualifications that can translate to a good candidate. In other words, the job recruiting algorithms will determine the strength of a candidate by their skills, which is based on the keywords featured in their resume. I think job recruiting algorithms are positive as long as it is balanced with human recruiting.
https://www.topresume.com/career-advice/what-is-an-ats-resume

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The strukturtensor algorithm is one used to process images, and divide pixels into different categories, such as pixels in regions with similar hues, pixels on the edge of a block of color, and those on a vertex. By splitting pixels into these groups, the algorithm is able to create a black and white image which shows the outlines of objects. In order to do this, it finds the differences between color values over a certain area. Different color differences correspond to different values for the new drawing.

https://www.cs.cmu.edu/~sarsen/structureTensorTutorial/

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The TikTok "FYP" or the For you page is a set of videos uniquely tailored to the user's interests. The system recommends content by ranking videos based on a combination of factors. The algorithm is based on the video you like, user interactions such as commenting and following, video information, and the user's account settings. Each of these factors are carefully weighted to create your personalized "FYP" This is part of the reason why TikTok is so popular among the newer generations because they find videos tailored to their interests and find people who are similar

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The YouTube algorithm gives the user videos to watch 70% of the time. And proven by Pew Research Center, 81% of YouTube users watch what is recommended to them by the YouTube algorithm.

At the start of YouTube's existence, for seven years, YouTube had a system where they rewarded video creators that got the most views instead of the ones that kept their watchers engaged. This led the video creators to use clickbait, which could mean misleading titles and thumbnails proliferated. The watchers would often get mad and feel tricked after clicking a video and watching something that wasn't what they expected, leading them to click off the video before finishing. Youtube realized this issue and changed its system many times after this. But still today, there are still video creators using clickbait.

Today's algorithm that YouTube uses is what they call a “real-time feedback loop that tailors videos to each viewer’s different interests.” It chooses the videos for each individual user. The algorithm tries to accomplish two things at the same time: find the right video for each viewer, and get viewers to keep watching. This means the algorithm is watching user behavior as well as video performance. The two main places that the algorithm affects are the search results and recommendation streams.

How the YouTube algorithm influences search results

Two different people could search for the same thing but get different results because of the algorithm. This is because of 2 reasons:

Your video’s metadata (title, description, keywords) and how well those match the user’s query

Your video’s metadata (title, description, keywords) and how well those match the user’s query

How the YouTube algorithm influences recommended videos

First, the algorithm gives each video s score based on performance analytics data.

Second, it shows videos to people depending on what they have watched in the past and what people similar to them have watched.

The algorithm's goal is not to show the users "good" videos but rather videos they want to watch.

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In Among Us, the game determines who in the lobby will be the imposter, and which players will be the crewmates. For example, if the lobby settings are set to 1 imposter and 10 players, each player has a 10% chance of being the imposter in the game because the algorithm multiplies the number of imposters set for a lobby by 100, and then divides that by the number of players joining. This algorithm makes Among Us exciting and fun because no one knows who the imposter will be, but everyone has a fair shot at it. This makes everyone sus during the game because anyone can be the imposter.

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Twitter, like other social media platforms, uses machine learning to sort out content based on ranking signals. The ranking signals include relevance, engagement, rich media, and recency. The algorithm calculates what you would like to see based on what you have liked in the past and what people like you have liked in the past. This leads to you mainly getting stuff you want to see. This can be both positive and negative. It can be positive by entertaining people but it can lead to people not making correct assumptions about certain topics.

https://blog.hootsuite.com/twitter-algorithm/

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Netflix uses machine based learning to curate a user experience based on the users interests. It does this by looking at nuanced plot threads and connecting them to similar content that the user likely wouldn't have otherwise chosen. This algorithm is thought of by many as the reason why Netflix is successful so it is highly important.

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The Netflix algorithm provides users of the streaming service a more personalized and entertaining viewing experience. The algorithm utilizes a variety of data such as viewer ratings/history, the time duration of a viewer watching a certain genre/show, the device the viewer is using, and the time of day the viewer is active. When a new user joins the website, it asks them what shows or movies they would like to watch, and the site then recommends shows and movies of a similar genre to those chosen by the user. Another interesting application the algorithm has on the site is the selection of thumbnails/artwork of suggested titles. For example, if the user watches a lot of action movies, an action scene from the movie will play if you hover over it, catching the user's attention. The algorithm has a positive impact because it provides a fresh and entertaining experience for the users.
source: https://medium.com/@springboard_ind/how-netflixs-recommendation-engine-works-bd1ee381bf81#:~:text=Netflix's%20machine%20learning%20based%20recommendations,and%20accurate%20the%20algorithm%20is.

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The Instagram algorithm decides which posts people see when they open their app. The Instagram algorithm has three primary ranking signals: Relationship, interest, and timeliness. Relationship means between the two accounts. Do you like their posts? Do you DM often? etc. Interest is if you have interreacted with posts like it in the past, you will be more likely to see this post. The more recent the post was, the more likely a person is to see it at the top of their feed. Obviously there are more important algorithm then if people see your instagrams posts or not. However, if you use Instagram to make money or promote your business and you don't know how the algorithm works, you are probably not reaching your full potential.
Website: https://blog.hootsuite.com/instagram-algorithm/#how

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The Akinator website is an algorithm that asks general questions to try and find out what famous celebrity or character the user is thinking about. The engine analyzes your answers is slowly narrows down its options and questions that it asks the user. Finally, after asking around 10-20 questions, the engine reveals the character or person the user is thinking of. The impact is positive because the engine makes the game fun and cool as it is able to slowly find out the celebrity you are thinking about.
Link: https://en.akinator.com/game

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The BOA app used an algorithm engine that can make an conversation with the user to help them search for what they want or help then to do things. The engine analyze the key word entered by the user and return relative result that can help you to narrow down you problem. I can compare the characters you entered in the search box with the services in the app to determine what the of question are you asking to give you a few choice that can help you to move on with your question. The good thing about it is that you can find answers for questions that you might not even sure about what you are looking for.
Link: https://promo.bankofamerica.com/erica/

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The google search algorithm is an algorithm used to determine which websites show up in response to your search -- and in what order they show up. As an algorithm that affects Google, a search engine used by tons of people around the globe, it is very important. This algorithm is actually a whole group of algorithms to provide an article that has the answers you are looking for and is also high quality (has lots of information, is reliable, etc.) The specific words you use, the language you write the question in, your settings, your location, and even how easy it is for users with different devices or low internet speed to use this website all play a part, and more!

This is an algorithm with an extremely good impact. In ranking the websites so that users can easily find what they are looking for within the first few links they click on, Google has it made it much easier for all it's users to find information when they are looking for it.

Of course, this all assumes that the algorithm is working properly. If there was in fact something wrong with the code that led it to not effectively find the best sites or be more biased towards/against some type of site, that would certainly be a bad impact - and it would affect a lot of people, too!

link: https://www.google.com/search/howsearchworks/algorithms/

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Google's Search Algorithm and Ranking System - Google Search

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With the amount of information available on the web, finding what you need would be nearly impossible without some help sorting through it. Google ranking systems are designed to do just that: sort through hundreds of billions of webpages in our Search index to find the most relevant, useful results in a fraction of a second, and present them in a way that helps you find what you're looking for.

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The Euclidean Algorithm is an algorithm first described by the Greek mathematician, Euclid. It's purpose is to find the GCD (Greatest Common Divisor) between two whole integers. The importance of this algorithm stems from the fact that it is among the oldest algorithm devised that is still somewhat in use today. The algorithm was written by Euclid in the year 300 B. C.

Source: https://www.khanacademy.org/computing/computer-science/cryptography/modarithmetic/a/the-euclidean-algorithm

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The BOA app used an algorithm engine that can make an conversation with the user to help them search for what they want or help then to do things. The engine analyze the key word entered by the user and return relative result that can help you to narrow down you problem. I can compare the characters you entered in the search box with the services in the app to determine what the of question are you asking to give you a few choice that can help you to move on with your question. The good thing about it is that you can find answers for questions that you might not even sure about what you are looking for.

Link: https://promo.bankofamerica.com/erica/

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Nowadays finding a residency or fellowship position/ program can be very difficult. So , in order to address this issue, the NRMP uses a mathematical algorithm to help match applicants to programs that would suit them best. It takes into account your location, qualifications, and your general needs. I have attached a link to a video better explaining the algorithm below:
https://www.nrmp.org/matching-algorithm/

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Google's search engine uses an algorithm to return relevant results to a user search. It takes information from search such as language inputed, specific information needed, and the meaning of the query to search for matching webpages. The webpages are analyzed for things like keywords on the page. Machine-learned algorithms also change over time to better match searches to results. The algorithm also attempts to prioritize reliable results with using a ranking system called PageRank and choosing sites that other users prefer for similar searches.

A possible negative impact may be biases in search results

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Google's Search Algorithm and Ranking System - Google Search

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With the amount of information available on the web, finding what you need would be nearly impossible without some help sorting through it. Google ranking systems are designed to do just that: sort through hundreds of billions of webpages in our Search index to find the most relevant, useful results in a fraction of a second, and present them in a way that helps you find what you're looking for.

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The Tesla Autopilot system is one of the most advanced forms of self-driving artificial intelligence. The autopilot system uses algorithms to use the data that has been taken over hundreds of thousands of miles of using vehicles on the road and is able to process the data and calculate the path that a car should take. The data is compiled through Tesla's cars being driven and sending information through the network in addition to other sources of information that gathers data on different roads and highways. Using this data, the car will calculate which paths will have a risk of putting the car and passengers and danger and which is the safe path in addition to calculating what objects are cars and walls. Based on these calculations, the algorithm will plan out the best driving path and route for the car. This can have both a positive and negative impact, as some times the algorithm will work perfectly and the car won't have any problems, however other times the algorithm can cause problems and the car may need to be taken over by a human driver which can cause danger if the driver is not looking.

Link: https://digital.hbs.edu/platform-rctom/submission/machine-learning-the-engine-inside-teslas-automated-driving-technology/

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The Centers for Medicare and Medicaid Service (CMS) risk adjustment model uses the Hierarchal Condition Category (HCC) method to assess the different Medicare patients and calculate their risk scores. Certain medical diagnoses are put into groupings based on the use of resources that is required. The higher the anticipated healthcare costs, the higher the risk score. This system is used to adjust plan payments to ensure fair payment for providing healthcare and benefits for patients. These algorithms help to reduce the workload on office staff and to calculate an accurate cost for health plans.
Link: https://www.foreseemed.com/medicare-risk-adjustment

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As one of the current largest and most popular streaming services, Disney+ has thousands of shows and hundreds of films, making it tough to decide and find what to watch next. To resolve this, Disney+ uses multiple algorithms that feed off behavioral data, making it smarter as time goes on. By studying people's program preferences, what they watch, don't watch, likes, and dislikes, the data can help decide which is the best algorithm that should be used for that user at the moment. The algorithms predict what a person might want to watch next and the relationship between various content to improve and personalize the user experience. The algorithms' positive impact is that the data allows Disney+ to understand their viewers better, enabling the service to make things more engaging for users. With the algorithms, the users can find what they desire to watch while also seeing and understanding what's available on the service and what it provides.

Link: https://www.forbes.com/sites/insights-teradata/2020/04/21/how-disney-plus-personalizes-your-viewing-experience/?sh=2bd76e4e3b6e

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TikTok's FYP or "ForYou Page" is seen by many users and is the first page you see when you get onto the app. This page is generated to show videos similar to recent videos you've watched or to show you a certain genre of videos you regularly watch. The algorithm of this page is to real you in to stay on the app, if you keep seeing videos you're interested in as you keep scrolling down the ForYou page, the more you want to watch similar videos to the one previously shown. This algorithm keeps its users on the app and allows for more usage of the app from its consumers worldwide. This ForYou page also shows videos you've recently interacted with (liking, sharing, commenting), so if you've recently interacted with one of Charli D'amelio's dance TikTok's you're most likely to see her later again on your ForYou page.

Link: https://www.yrcharisma.com/2020/01/06/tiktok-fyp-algorithm/

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Instagram, the social media app used by millions around the world, uses an algorithm for their personal "Discover" page.  The posts or reels that are displayed on the discover page are geared towards what your activity on the app was or what you find the most interesting.  The main points of what Instagram looks at is:
1. Interest
2. Relationship
3. Timeliness
4. Frequency
5. Following
6. Usage
Most of the time, the more posts you interact with such as liking it or commenting on it. This leads to the relationship aspect of it. Instagram wants to prioritize posts from your friends and family. Another part is the timelines and frequency which calculates the number of times you open the app or even look at a post. In Instagram, there's another page called the discover page which delivers content that Instagram thinks you’ll be most interested in. Sometimes it's made up of content from accounts you already follow or to things you like.
Link: https://later.com/blog/how-instagram-algorithm-works/

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Randomness is apart of our daily lives, so it’s no surprise that it exists within the world of computer science too. Randomness can be in any way, shape or form. It can be numbers, letters, words, you name it. However, not all random number generators are truly random. Most random number generators use an algorithm to create a random number, and while this may be random enough, it isn’t truly random. People have found out how to create true randomness within the world of computers, using the most random thing of all, real life.

Link: https://www.howtogeek.com/183051/htg-explains-how-computers-generate-random-numbers/

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Algorithms in video games are used to control movement patterns and behaviors in a complex environment. Every time  the instructions of the steering algorithm are put inn place, a controlled object in the game moves. These video games use mathematical processes to mimic behavior that us humans have. For example, these algorithms use mathematical justification to figure out the exact amount of space or time that the character needs to make s certain move in a fighting game. I think that this will have a positive impact especially since the gaming industry is one that is thriving right now. Many people are interested in video games and the thought of another "world". By this I mean the complexity of another reality built because of these algorithms.

Link to the article: https://towardsdatascience.com/how-a-chess-playing-computer-thinks-about-its-next-move-8f028bd0e7b1

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Middle out compression is something I first came across when watching HBO's Silicon Valley, but now Dropbox's Lepton is using the algorithm for real lossless image compression. Dropbox's Lepton uses a middle-out compression algorithm to reduce the size of JPEG-encoded images while being completely lossless. From the dropbox website "The JPEG format encodes an image by dividing it into a series of 8×8 pixel blocks, represented as 64 signed 10-bit coefficients. Thus the following 16×16 image would be encoded as 4 JPEG blocks.", this explains how the algorithm works to get this lossless compression. The impact of this code is that now images can be reduced without losing any of the original bits.
Implementing Middle-Out compression here is the start of a wave of middle-out compression being used for all kinds of things from video chat to live streaming.  The code for this compression is open source and can be found on GitHub for anyone to play around with.

Link to the article : https://techcrunch.com/2016/07/14/dropboxs-lepton-lossless-image-compression-really-uses-a-middle-out-algorithm/

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This algorithm was made to control sensor and navigation systems on aircraft. It is fairly new, and tested first in Dec. 2020 on a U-2 Dragon Lady spy plane (the extremely high altitude spy plane).  Not much is known about this algorithm at the moment as the U-2 pilot and air force declined to say what the specific tasks are. To put it broadly, the algorithm was put in charge of the plane's radar senses and tactical navigation with very narrow duties. In fact,  it was said that this was the first time that a military system integrated AI, so obviously the pilot still had majority of the control as this is very fresh. While being trained against an opposing computer, the AI looked for oncoming missiles and launchers. For the in flight test, it got to decide where to direct the planes sensors. Of course, it will be a while before humans and AI team up on the battle field, but this allowed all of the branches to see the potential. I feel like the impact of this could be positive or negative, depending on your stance regarding war. Personally, I believe this will positively impact the air force pilots soon by adding an extra layer of security.

Link: https://www.washingtonpost.com/business/2020/12/16/air-force-artificial-intelligence/

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To forecast price fluctuations, the financial industry has long used algorithms, but they are now being used in the burgeoning practice of high-frequency stock trading. This form of rapid-fire trading requires algorithms that can make decisions in the order of milliseconds, also called bots. By comparison, to both identify and react to possible dangers, it takes a person at least a full second. As a result, human beings are increasingly being cut out of the trade loop, and an entirely new digital world is emerging.

Link: https://io9.gizmodo.com/the-10-algorithms-that-dominate-our-world-1580110464

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Recommendation Engines are used in many places around the internet and other places that involve computers. Recommendation engines play a big role in how in internet is personalized for everyone. The engine is used to determine what a person may or may not like about a given topic. The math behind this engine is very simple because it uses the list of items and depending on what is selected makes predictions on what is liked and not, from this it can make more assumptions along the way as more items are selected. This can has a good impact because it allows for the scale of the internet narrowed down the user.

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Page Rank was a program that Google used to decide what websites showed up first when something was searched. The way the algorithm would work is it would assign a website a number ranging from 0 to 10 based on how many links were pointed to the website. The higher the number the more important and authoritative the website was deemed by the algorithm. Resulting in the website being ranked higher in search results. The more links that pointed to a website the higher the number that was assigned became. 0 is a low-quality website and 10 being a very high-quality website. At the time this was not the only program that was used to determine the rank of search results. But it has not really been used for a few years but I am sure that it has had an influence on the program that is being used primarily for search result rankings now.
link: https://www.semrush.com/blog/pagerank/#header2

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The social media app TikTok, uses an algorithm for the users ForYou page. The videos that come up on your ForYou page is specialized to each user. The factors that are included in choosing the next video for you to watch are based off your interactions, the videos information(content), and your device and account settings. The TikTok algorithm pays attention to every little thing, like how long you watch the video from begging to end, so everything is in account for. Also, TikTok algorithm is used to put your videos on the ForYou page. It is more likely to occur if your a brand, business, or a well known creator already on the app. Here are a few factors if you want your video on the ForYou page that the TikTok algorithm uses, if you use more hashtags in your video, the most popular ones at the time will work better(look on the discover tab). Another factor is what your caption is, ones that are like "Part 1", or one that makes it seem more mysterious. Lastly, the TikTok algorithm will more likely pick your video if you use the trending sounds or songs. As TikTok uses this algorithm, it it used to keep you hooked on the app so you spend more time on it.
This image below shows types of content that are on peoples ForYou page.
Source:https://later.com/blog/tiktok-algorithm/

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The platform Youtube pulls billions of users per month. Its recommendation algorithm uses the users watch history to further recommend videos for the user. It is this algorithm that makes Youtube so addicting and it is reported that the algorithm is what decides what people watch 70% of the time. Youtube says that its algorithm is a "real time feedback loop that tailors videos to each viewer's different interests." To use the recommendation algorithm, Youtube first ranks videos based on performance analytics data. This data contains clickthrough rate, watch through rate, retention rate, engagement (likes, dislikes, comments), view rate of growth, and more factors. It then matches these videos to people. This matching system is based on the users watch history and what people of similar interests have watched. For this, they look at data from the user pertaining to what channels and topics they watch, how much time they spend watching topics, how many times the video has appeared already, and what they don't watch. The user activity not only influences the users recommendation stream, but also what pops up for the users search results. As a result of the recommendation algorithm, Youtube's platform has gained a lot of traction and users since its release.
The image below is simple flowchart released by Youtube in 2016 regarding its recommendation algorithm:
Source: https://blog.hootsuite.com/how-the-youtube-algorithm-works/

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Millions of people around the world listen to music. One of the most popular applications that people use to listen to music is Spotify. There is a good reason for this too.  A big reason why Spotify is so popular is that it is unique to each user. To make the listener's experience more enjoyable, Spotify recommends songs, playlists, artists, and albums to the user using BaRT.  BaRT takes into account things like what you listen to, your music history, your age, location, and the artists or genres of music that you listen to the most. If you listen to any of the songs or playlists that it offers you for more than 30 seconds, the algorithm sees that as a successful recommendation and will continue to recommend music like that to you. BaRT also keeps diversity within the recommended playlists. It does this by taking into account what other people your age, similar location, and people around the world are currently listening to. It then offers the music to you under a title like "Something New". I think that the impact of this can only be positive because it makes the experience much more enjoyable, unique, and special for the user.

https://onezero.medium.com/how-spotifys-algorithm-knows-exactly-what-you-want-to-listen-to-4b6991462c5c

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Every year self driving cars seem to become more accessible and popular so it is important for them to have very good algorithms because of how much they have to manage.  The algorithm doesn't just hold the data and commands that it is given but it actually learns new things as it is used. This means that the algorithm can notice and learn about little things that are important while driving such as navigation, obstacles that may interfere with the car, and even the drivers behavior based on their driving.  This ability for the algorithm to learn is known as machine learning and the way it works is done through a process called supervised or unsupervised learning. These types of learnings use a combination of predictions, input data, output data, clustering, classification, and regression to allow the car to learn.

https://medium.com/@intellias/how-machine-learning-algorithms-make-self-driving-cars-a-reality-4180a528121c

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New AI algorithm that automates artificial pancreas system

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New AI algorithm that automates artificial pancreas system

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A research team from POSTECH's Department of Convergence IT Engineering and Electrical Engineering has created an AI based algorithm for an artificial pancreas system. The pancreas' are used by people with type 1 diabetes, where they have to input every meal intake of carbohydrates, so the pancreas can produce insulin. The algorithm (AlphaGo) makes this much simpler and easier as it automatically gives the person the required amount of insulin that they need without needing to input the intake.

The algorithm works by eliminating the inconvenience of inputting the meal intake by sensing the glucose level of the person, then releasing how much insulin the person needs to deal with it. The algorithm has the beneficial effect of  "enabl[ing] fully automated blood sugar control without the hassle of inputting meal or exercise information." This saves a lot of time for a person and could potentially spread to other treatments, making them more efficient as well.

https://www.news-medical.net/news/20210223/New-AI-algorithm-to-fully-automate-the-artificial-pancreas-system.aspx

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Have you ever been trying to buy something online, and then something pops up on the side that catches you attention?  This is due to product recommendation engines that use algorithms to entice their buyers with certain products.  These algorithms take search history, shopping history, and other customer's purchase history into account, and then filter through their products to give you suggestions based on what they think you would like.

These algorithms are extremely beneficial to retailers because they cause customers to spend more money.  Amazon stated that 35% of their sales were due to the recommendation engines.

https://www.theukdomain.uk/online-retailers-can-use-algorithms-grow-business/

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Risk Assessment algorithms are formulas that predict a convict's likelihood of recidivism. The algorithms are very prevalent in the criminal justice system and can be used to replace cash bail systems, determine sentence length, and even decide conditions of parole and probation. These risk assessments use factors like age, criminal history, and employment to make judgements on whether a criminal will commit another crime. This algorithm could be incredibly useful in replacing the outdated and regressive bail system, allowing those of lower socioeconomic classes who are lower risk to  spend time at home instead of in jail before their trial.

However, in practice, this algorithm has proven to be inaccurate and racially biased. A study done by the Equal Justice Initiative found that just 20 percent of those who were chosen as likely reoffenders were actually charged with another crime. Furthermore, Black defendants were 77% more likely to be chosen as high risk, and 45% more likely than white defendants to be predicted as a recidivist.

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Understanding risk assessment instruments in criminal justice

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Editor's Note: his report from The Brookings Institution's Artificial Intelligence and Emerging Technology (AIET) Initiative is part of "AI and Bias," a series that explores ways to mitigate possible biases and create a pathway toward greater fairness in AI and emerging technologies. Algorithmic tools are in widespread use across the criminal justice system today.

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To help people find good matches,  Tinder has an algorithm. The algorithm primarily focuses on location and age; you get matched with your preferences.  Tinder has something called "super likes" that enable you to like someone "louder" aka when they see your profile back, there will be a blue star indicating that they swiped right on you. The algorithm then has to pause and add your profile with the super like closer to the top of the pile of profiles the other person is given. As you swipe through profiles and get closer to the end, the Tinder algorithm starts to recycle previous people, as someone you rejected before might seem better after looking through thousands of other people. It will also recycle previous match ups, too. Although love isn't something you can solve with an algorithm, the Tinder algorithm does help some people find others.

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If there's one thing I know about love, it's that people who don't find it have shorter life spans on average. Which means learning how the Tinder algorithm works is a matter of life and death, extrapolating slightly.

According to the Pew Research Center, a majority of Americans now consider dating apps a good way to meet someone; the previous stigma is gone. But in February 2016, at the time of Pew's survey, only 15 percent of American adults had actually used a dating app, which means acceptance of the tech and willingness to use the tech are disparate issues. On top of that, only 5 percent of people in marriages or committed relationships said their relationships began in an app. Which raises the question: Globally, more than 57 million people use Tinder - the biggest dating app - but do they know what they're doing?

They do not have to answer, as we're all doing our best. But if some information about how the Tinder algorithm works and what anyone of us can do to find love within its confines is helpful to them, then so be it.

The first step is to understand that Tinder is sorting its users with a fairly simple algorithm that can't consider very many factors beyond appearance and location. The second step is to understand that this doesn't mean that you're doomed, as years of scientific research have confirmed attraction and romance as unchanging facts of human brain chemistry. The third is to take my advice, which is to listen to biological anthropologist Helen Fisher and never pursue more than nine dating app profiles at once. Here we go.

The Tinder algorithm basics

A few years ago, Tinder let Fast Company reporter Austin Carr look at his "secret internal Tinder rating," and vaguely explained to him how the system worked. Essentially, the app used an Elo rating system, which is the same method used to calculate the skill levels of chess players: You rose in the ranks based on how many people swiped right on ("liked") you, but that was weighted based on who the swiper was. The more right swipes that person had, the more their right swipe on you meant for your score.

Tinder would then serve people with similar scores to each other more often, assuming that people whom the crowd had similar opinions of would be in approximately the same tier of what they called "desirability." (Tinder hasn't revealed the intricacies of its points system, but in chess, a newbie usually has a score of around 800 and a top-tier expert has anything from 2,400 up.) (Also, Tinder declined to comment for this story.)

In March 2019, Tinder published a blog post explaining that this Elo score was "old news" and outdated, paling in comparison to its new "cutting-edge technology." What that technology is exactly is explained only in broad terms, but it sounds like the Elo score evolved once Tinder had enough users with enough user history to predict who would like whom, based solely on the ways users select many of the same profiles as other users who are similar to them, and the way one user's behavior can predict another's, without ranking people in an explicitly competitive way. (This is very similar to the process Hinge uses, explained further down, and maybe not a coincidence that Tinder's parent company, Match, acquired Hinge in February 2019.)

But it's hard to deny that the process still depends a lot on physical appearance. The app is constantly updated to allow people to put more photos on their profile, and to make photos display larger in the interface, and there is no real incentive to add much personal information. Most users keep bios brief, and some take advantage of Spotify and Instagram integrations that let them add more context without actually putting in any additional information themselves.

The algorithm accounts for other factors - primarily location and age preferences, the only biographical information that's actually required for a Tinder profile. At this point, as the company outlined, it can pair people based on their past swiping, e.g., if I swiped right on a bunch of people who were all also swiped right on by some other group of women, maybe I would like a few of the other people that those women saw and liked. Still, appearance is a big piece.

As you get closer and closer to the end of the reasonable selection of individuals in any dating app, the algorithm will start to recycle people you didn't like the first time. It will also, I know from personal experience, recycle people you have matched with and then unmatched later, or even people you have exchanged phone numbers with and then unmatched after a handful of truly "whatever" dates. Nick Saretzky, director of product at OkCupid, told me and Ashley Carman about this practice on the Verge podcast Why'd You Push That Button in October 2017. He explained:

Hypothetically, if you were to swipe on enough thousands of people, you could go through everyone. [You're] going through people one at a time ... you're talking about a line of people and we put the best options up front. It actually means that every time you swipe, the next choice should be a little bit worse of an option.

So, the longer you're on an app, the worse the options get. You'll see Tinder, Bumble, OkCupid, we all do recycling. If you've passed on someone, eventually, someone you've said "no" to is a much better option than someone who's 1,000 or 10,000 people down the line.

Maybe you really did swipe left by accident the first time, in which case profile recycling is just an example of an unfeeling corporation doing something good by accident, by granting you the rare chance at a do-over in this life.

"Every time you swipe, the next choice should be a little bit worse of an option"

Or maybe you have truly run out of options and this will be a sort of uncomfortable way to find out - particularly unnerving because the faces of Tinder tend to blur together, and your mind can easily play tricks on you. Have I seen this brown-haired Matt before? Do I recognize that beachside cliff pic?

Don't despair, even though it's tempting and would obviously make sense.

The secret rules of Super Likes and over-swiping

One of the more controversial Tinder features is the Super Like. Instead of just swiping right to quietly like someone - which they'll only discover if they also swipe right on you - you swipe up to loudly like someone. When they see your profile, it will have a big blue star on it so they know you already like them and that if they swipe right, you'll immediately match.

You get one per day for free, which you're supposed to use on someone whose profile really stands out. Tinder Plus ($9.99 a month) and Tinder Gold ($14.99 a month) users get five per day, and you can also buy extra Super Likes à la carte, for $1 each.

Tinder says that Super Likes triple your chances of getting a match, because they're flattering and express enthusiasm. There's no way to know if that's true. What we do know is that when you Super Like someone, Tinder has to set the algorithm aside for a minute. It's obligated to push your card closer to the top of the pile of the person you Super Liked - because you're not going to keep spending money on Super Likes if they never work - and guarantee that they see it. This doesn't mean that you'll get a match, but it does mean that a person who has a higher "desirability" score will be provided with the very basic information that you exist.

We can also guess that the algorithm rewards pickiness and disincentivizes people to swipe right too much. You're limited to 100 right swipes per day in Tinder, to make sure you're actually looking at profiles and not just spamming everyone to rack up random matches. Tinder obviously cares about making matches, but it cares more about the app feeling useful and the matches feeling real - as in, resulting in conversation and, eventually, dates. It tracks when users exchange phone numbers and can pretty much tell which accounts are being used to make real-life connections and which are used to boost the ego of an over-swiper. If you get too swipe-happy, you may notice your number of matches goes down, as Tinder serves your profile to fewer other users.

I don't think you can get in trouble for one of my favorite pastimes, which is lightly tricking my Tinder location to figure out which boys from my high school would date me now. But maybe! (Quick tip: If you visit your hometown, don't do any swiping while you're there, but log in when you're back to your normal location - whoever right-swiped you during your visit should show up. Left-swipers or non-swipers won't because the app's no longer pulling from that location.)

There are a lot of conspiracy theories about Tinder "crippling" the standard, free version of the app

There are a lot of conspiracy theories about Tinder "crippling" the standard, free version of the app and making it basically unusable unless you pay for a premium account or add-ons, like extra Super Likes and Boosts (the option to serve your profile to an increased number of people in your area for a limited amount of time). There is also, unfortunately, a subreddit specifically for discussing the challenges of Tinder, in which guys write things like, "The trick: for every girl you like, reject 5 girls." And, "I installed tinder 6 days ago, ZERO matches and trust me, im not ugly, im not fucking brad pitt but what the fuck?? anyways i installed a new account with a random guy from instagram, muscular and beautiful, still ZERO matches ..."

I can't speak to whether Tinder is actually stacking the deck against these men, but I will point out that some reports put the ratio at 62-38 men to women on the app. And that ratio changes based on geography - your match rate depends a lot on your local population dynamics.

How the other swiping apps and algorithms are different (even though Tinder's is the best)

Of course, Tinder's not the only dating app, and others have their own mathematical systems for pairing people off.

Hinge - the "relationship app" with profiles more robust than Tinder's but far less detailed than something like OkCupid or eHarmony - claims to use a special type of machine learning to predict your taste and serve you a daily "Most Compatible" option. It supposedly uses the Gale-Shapley algorithm, which was created in 1962 by two economists who wanted to prove that any pool of people could be sifted into stable marriages. But Hinge mostly just looks for patterns in who its users have liked or rejected, then compares those patterns to the patterns of other users. Not so different from Tinder. Bumble, the swiping app that only lets women message first, is very close-lipped about its algorithm, possibly because it's also very similar to Tinder.

The League - an exclusive dating app that requires you to apply using your LinkedIn - shows profiles to more people depending on how well their profile fits the most popular preferences. The people who like you are arranged into a "heart queue," in order of how likely the algorithm thinks it is that you will like them back. In that way, this algorithm is also similar to Tinder's. To jump to the front of the line, League users can make a Power Move, which is comparable to a Super Like.

Tinder may come off as a superficial hot-or-not app, but there's no proof that a more complicated matchmaking algorithm is a better one

None of the swiping apps purport to be as scientific as the original online dating services, like Match, eHarmony, or OkCupid, which require in-depth profiles and ask users to answer questions about religion, sex, politics, lifestyle choices, and other highly personal topics. This can make Tinder and its ilk read as insufficient hot-or-not-style apps, but it's useful to remember that there's no proof that a more complicated matchmaking algorithm is a better one. In fact, there's a lot of proof that it's not.

Sociologist Kevin Lewis told JStor in 2016, "OkCupid prides itself on its algorithm, but the site basically has no clue whether a higher match percentage actually correlates with relationship success ... none of these sites really has any idea what they're doing - otherwise they'd have a monopoly on the market."

In a (pre-Tinder) 2012 study, a team of researchers led by Northwestern University's Eli J. Finkel examined whether dating apps were living up to their core promises. First, they found that dating apps do fulfill their promise to give you access to more people than you would meet in your everyday life. Second, they found that dating apps in some way make it easier to communicate with those people. And third, they found that none of the dating apps could actually do a better job matching people than the randomness of the universe could. The paper is decidedly pro-dating app, and the authors write that online dating "has enormous potential to ameliorate what is for many people a time-consuming and often frustrating activity." But algorithms? That's not the useful part.

This study, if I may say, is very beautiful. In arguing that no algorithm could ever predict the success of a relationship, the authors point out that the entire body of research on intimate relationships "suggests that there are inherent limits to how well the success of a relationship between two individuals can be predicted in advance of their awareness of each other." That's because, they write, the strongest predictors of whether a relationship will last come from "the way they respond to unpredictable and uncontrollable events that have not yet happened." The chaos of life! It bends us all in strange ways! Hopefully toward each other - to kiss! (Forever!)

The authors conclude: "The best-established predictors of how a romantic relationship will develop can be known only after the relationship begins." Oh, my god, and happy Valentine's Day.

The chaos of life! It bends us all in strange ways! Hopefully toward each other - to kiss!

Later, in a 2015 opinion piece for the New York Times, Finkel argued that Tinder's superficiality actually made it better than all the other so-called matchmaking apps.

"Yes, Tinder is superficial," he writes. "It doesn't let people browse profiles to find compatible partners, and it doesn't claim to possess an algorithm that can find your soul mate. But this approach is at least honest and avoids the errors committed by more traditional approaches to online dating."

Superficiality, he argues, is the best thing about Tinder. It makes the process of matching and talking and meeting move along much faster, and is, in that way, a lot like a meet-cute in the post office or at a bar. It's not making promises it can't keep.

So what do you do about it?

At a debate I attended last February, Helen Fisher - a senior research fellow in biological anthropology at the Kinsey Institute and the chief scientific adviser for Match.com, which is owned by the same parent company as Tinder - argued that dating apps can do nothing to change the basic brain chemistry of romance. It's pointless to argue whether an algorithm can make for better matches and relationships, she claimed.

"The biggest problem is cognitive overload," she said. "The brain is not well built to choose between hundreds or thousands of alternatives." She recommended that anyone using a dating app should stop swiping as soon as they have nine matches - the highest number of choices our brain is equipped to deal with at one time.

Once you sift through those and winnow out the duds, you should be left with a few solid options. If not, go back to swiping but stop again at nine. Nine is the magic number! Do not forget about this! You will drive yourself batty if you, like a friend of mine who will go unnamed, allow yourself to rack up 622 Tinder matches.

To sum up: Don't over-swipe (only swipe if you're really interested), don't keep going once you have a reasonable number of options to start messaging, and don't worry too much about your "desirability" rating other than by doing the best you can to have a full, informative profile with lots of clear photos. Don't count too much on Super Likes, because they're mostly a moneymaking endeavor. Do take a lap and try out a different app if you start seeing recycled profiles. Please remember that there is no such thing as good relationship advice, and even though Tinder's algorithm literally understands love as a zero-sum game, science still says it's unpredictable.

Update March 18, 2019: This article was updated to add information from a Tinder blog post, explaining that its algorithm was no longer reliant on an Elo scoring system.

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If there's one thing I know about love, it's that people who don't find it have shorter life spans on average. Which means learning how the Tinder algorithm works is a matter of life and death, extrapolating slightly.

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There are thousands of movies and shows on Netflix which makes it hard for a user to find one that suits them. To assist that, Netflix uses an algorithm that is based on information about media you've watched (genre, actors, categories etc.), when you watch, how long you watch and some other factors to create a recommendation list personalized for you. By using this information, the algorithm filters out different shows and movies that fit your description. This is a great algorithm that all streaming services need because finding everything on your own with no help will be tiring and difficult.

For more information: https://help.netflix.com/en/node/100639

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Sports teams in leagues such as the NFL, MLB, and NBA have recently been using algorithms and advanced statistics to determine the best coaches to hire, players to sign, and in basketball what shots to take. Nowadays massive amount of athlete data are collected through things like cameras and wearable devices that create refined performance metrics that gives coaches all the information they need on players such as when to rest them, what lineups are the most effective, and who needs to score more for the team to win.

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Sport algorithms aren't working for business. Shutterstock

You may have heard that algorithms will take over the world. But how are they operating right now? We take a look in our series on Algorithms at Work.
Replacing human decision-making with algorithms seems to make sense. People tend to rely on unreliable cognitive shortcuts, get fatigued or distracted, and can be swayed by subjective opinion and inter-personal alliances.

On the other hand, algorithms are bearers of encoded logic which consistently execute pre-determined decision criteria. Therefore they are immune to emotional influences; they can rise above social relationships to objectively analyse data and optimise decisions.

But, while the application of algorithms has produced remarkable outcomes in the sporting world, this success does not fully translate to the business world. The reason is rooted in the different levels of complexity of these environments.

Algorithms in sports

One of the earliest and most successful applications of algorithmic thinking has been in professional sports. It was in the middle of the previous century when American baseball teams started using player statistics to make decisions such as which players to hire, develop, and trade.

Known as Sabermetrics, this analytical approach has since expanded to other sports and grown in sophistication.

Today, massive amounts of athlete data are collected through elaborate camera systems and wearable devices to create increasingly refined performance metrics that coaches can leverage in real time. This allows coaches to decide when to rest a player, which combinations of players are most effective, or how many points their key players need to score in order to win a game.

The profound impact of this algorithmic approach is evident in that many professional teams regard "sports analytics" to be crucial and view algorithmic innovation as key to their success.

Can the success be replicated in the world of business?

Some of the best known companies in the world utilise algorithms to enhance consumer services and optimise internal processes.

Walmart's e-Commerce arm, Jet.com, employs algorithms that adjust prices based on the items in a customer's checkout cart. Amazon and Netflix's touted recommendation systems are themselves algorithms applied to vast quantities of customer data.

Algorithm-based chat bots are now commonplace in the banking and insurance industries. Uber's pricing and ride-allocation systems are exclusively managed by algorithms that match ride demand and supply in different geographical areas in real time.

Despite their widening adoption, algorithms can have unintended negative consequences. For instance, Google, Facebook, Twitter, and Instagram have all come under fire for employing algorithms that promoted racist and abusive behaviour.

Read more: Do computers make better bank managers than humans?

Some hiring algorithms have been shown to be racially biased whereas others denied loans to eligible applicants. In another example, rogue algorithms led to the firing of high-performing teachers.

These failures occurred not simply due to poor algorithmic design, but because of unanticipated interactions between algorithms and their complex environments.

Compared to sports, business environments are equivocal, unstructured, and dynamic. There are many possible measures for organisational success (profit, growth, market share, stock value, etc.), which cannot be easily traced back to the performance of individual employees. In fact, there are multiple dimensions along which employee performance can be assessed. Many of them are not readily observable and can be measured in various ways (engagement, commitment, innovation etc.).

Additionally, a lot of industries are characterised by hyper-competition, where disruptive innovations can render existing strategies obsolete and require rapid and profound technological and organisational changes.

Sports and business are different worlds

On the other hand, sporting competitions have a small and finite number of outcomes. The rules that govern athletes' actions are known, accepted, explicitly enforced, and stable. Success is easily defined in terms of points, wins, and championships. Individual and team performance indicators are unambiguous and can be calculated from direct observation (points scored, hits made, assists per minute, punches per round, etc.), and their importance can be measured in terms of their relative contribution to the success of the athlete or team.

The relative stability, simplicity, and predictability of the sporting environment make it suitable for algorithmic technologies. Such technologies aim to emulate human decision-making by generating options within a well-defined problem area, identifying criteria to evaluate them, assigning weights to each option, calculating their scores, and choosing the option with the highest score.

Read more: How marketers use algorithms to (try to) read your mind

This process faithfully mirrors decision-making processes made by team managers and coaches and can improve them due these technologies' ability to process and analyse large datasets (a basketball coach must select five starters from his roster. The number of options is limited and his choice can be optimised by using an algorithm that analyses individual player performance data against the opposing team's performance data).

However, research has shown that in complex, ambiguous, and dynamic conditions, such as those that characterise businesses, people engage in naturalistic decision-making. Here, people do not use algorithmic strategies to generate utility estimates for different courses of action.

In fact, no explicit action of making a decision is taken. Instead, people perceive a situation through pattern-matching against previous experience. Based on this classification, certain actions present themselves as plausible because they have been applied in previous similar situations ("I have a feeling this employee is in distress. I should call him in for a meeting").

Read more: Ethics by numbers: how to build machine learning that cares

Because naturalistic decision-making involves little explicit deliberation and is mostly intuitive, current algorithmic technologies cannot reliably replicate it. This explains the mixed results from applying these technologies in business.

While we should not entirely exclude algorithmic technologies from business decision-making, companies would be well-advised to apply them to support analytical decision-making rather than replace naturalistic decision-making.

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What businesses can learn from sports about using algorithms

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You may have heard that algorithms will take over the world. But how are they operating right now? We take a look in our series on Algorithms at Work. Replacing human decision-making with algorithms seems to make sense. People tend to rely on unreliable cognitive shortcuts, get fatigued or distracted, and can be swayed by subjective opinion and inter-personal alliances.

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In Pacman the ghosts that run towards the pacman are controlled by an algorithm using x and y coordinates. Each ghost has a different sort of "personality" which means they have different algorithms that decide how and where they move. The ghosts are driven by algorithms about certain tiles that they have to reach in the maze.

The impact of this algorithm is that the ghosts all have different ways that they move around the maze in pacman, and if the algorithms are studied it is possible to find patterns in their movements to understand how to beat the game faster and easier.

https://gameinternals.com/understanding-pac-man-ghost-behavior
101computing.net/pacman-ghost-algorithm/

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AI Detecting Students’ Emotions While Learning

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AI Detecting Students’ Emotions While Learning

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A startup called Find Solution AI in Hong Kong launched 4 Little Trees, a software designed to detect a student’s emotions to help with their learning. While using the software, the AI uses an algorithm that detects the student’s emotion by measuring muscle points. The software also takes their results on assignments and creates a report on their strengths and weaknesses. With this data, tests designed like a game are offered for the students to make school fun. Founder Viola Lam says students using this software perform 10% better than if they didn’t use the program.

There are positive benefits, as exam grades can be improved through this software. With the reports on the student’s strengths and weaknesses and data about the student’s emotions, this can definitely help the student’s learning process. The program can detect primary emotions, like happiness, sadness, or disgust, with a high accuracy. However, this software cannot detect secondary emotions, such as discomfort or signs of anxiety. Privacy concerns also arise, as students may not feel comfortable showing their face while using the software. Overall, this program can be useful for students to improve exam scores, especially with online learning in the current pandemic.

Article Link: https://www.cnn.com/2021/02/16/tech/emotion-recognition-ai-education-spc-intl-hnk/index.html

Video Link(linked below): https://www.cnn.com/videos/business/2021/02/15/emotion-recognition-students-find-solution-ai-spc-intl-hnk.cnn/video/playlists/business-evolved/

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how ai that reads emotions is changing the online classroom - cnn video

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How AI that reads emotions is changing the online classroom - CNN Video

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A Hong Kong company has developed facial expression-reading AI that monitors students' emotions as they study. They say the technology could make the virtual classroom as good as - or better than - the real thing.

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The Boeing 777 checklists can calculate the engine temperatures and can detect if there is an engine failure/fire. If there is a certain high temperature, it's an indicated Engine failure. That then prompts a full checklist for the pilots to safely take the aircraft to the ground, and contain the engine fire.

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Engine Failure and Driftdown in a Boeing 777

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Companies are selling admission algorithms arguing they are fair. Analysis of student's  data and  college's definition of 'student success' will drive the analysis.  The  companies are looking at even your game playing behavior to 'tease out' and measure chosen traits. Basically anything a student is doing online - posting, watching videos , gaming,  posting student centered videos can be used to measure desired  student characteristics.  Selected data  can be used to make predictions about student  and their  chance of accepting the admission to that college.

Negative consequences of these algorithm include data bias and data privacy issues. I will add more here

For more information:
https://www.edsurge.com/news/2020-07-10-as-colleges-move-away-from-the-sat-will-admissions-algorithms-step-in