Algorithms can be designed to train themselves and self-improve without human input, to the point where the optimized code is too complex for programmers to understand.

A breakdown of algorithms, how they are constructed, and their applications

Algorithms are instructions for accomplishing a goal and typically take the form of a step-by-step procedure. Given the conditions, these procedures can take various paths, increasing an algorithm's complexity. While traditional computer algorithms are programmed to account for all possible scenarios within a given process, machine learning systems build their own rules from data-driven patterns.

Bias can manifest in algorithmic decisions because the program follows rules that may have been written by individuals unaware of their own biases.

When you complete a CAPTCHA on a website to prove you are human, you may be unknowingly training algorithms for self-driving cars, whose systems require training data to help identify crosswalks, bicycles, and other outdoor objects.

An overview of 12 fundamental types of algorithms and the uses for each

These include search algorithms to quickly process and find important information, cryptographic algorithms to secure communications, randomized algorithms—such as Monte Carlo simulations for estimating probabilities of outcomes—and greedy algorithms, which make optimal choices based on available data in a step-by-step fashion, as seen in navigation apps.

Understanding how Claude Code and other AI coding agents function

Under human oversight, a supervising large language model interprets user tasks and delegates work to subordinate LLMs, which can generate code, fix bugs, and run tests, often most effectively for proofs of concept. Incremental backups and versioning are crucial when using such agents, which can lose details during their work as a result of compressing context history to work around memory limitations.

YouTube's recommendation algorithm relies on over 80 billion daily pieces of information

These signals include which videos a user has watched, searched for, and liked, as well as channel subscriptions and feedback selections, such as videos marked "not interested." Each user's viewing habits are compared with those of other users with similar habits, and this information is used to suggest additional content.

Why TikTok's algorithm is likely driven by what you linger on

While the inner workings of the engine driving the vast majority of the app's engagement remain a secret, it is understood to rely heavily on what content users spend the most time watching rather than swipe past. Experts believe the app can develop user profiles robust enough for its algorithm to consistently match engaging content to in as little as two hours.

Smart pricing algorithms can unintentionally work together to raise prices

Researchers have found that, through trial and error, algorithms designed to adjust prices based on market data can learn to mimic price-fixing tactics, even without explicit programming to do so. Over time, they "learn" that it is more profitable to avoid price wars and keep prices high when competitors lower prices.

How the YouTube algorithm works, according to MrBeast

In this brief interview clip, the YouTube savant "MrBeast" summarizes how the YouTube algorithm—which he relies on to make his content go viral—actually works.

Social media platforms benefit not just from ensuring they have satisfied users, but also from the personal data they scoop up and can then sell to online advertisers.

Scientists increasingly can't predict what AI will do

Most AI systems are so-called "black box" models, where we only see inputs and outputs while the inner workings remain obscure. This piece, written prior to the launch of ChatGPT, cautions developers to know why and how a system produces its results, and not to sacrifice explainability for accuracy.

An explanation of Turing machines—the foundations of digital algorithms and computers

The device reads data, overwrites or ignores it, and then moves on to the next piece of data based on a set of programmed rules. By imagining this data as zeros and ones—binary that makes up computer code—Turing machines became the theoretical models for how any algorithm could be run on a computer if the procedure could be coded into a set of digital rules.

Netflix's recommendation algorithm was born from a $1M competition

In 2006, Netflix launched an open competition for data science teams to develop approaches to outperform its recommendation algorithm. Three years later, the prize was awarded after a group of competitors merged, developing a solution that beat Netflix's internal algorithm by more than 10%. Today, recommendations are based on what users watch, particularly via bingeing.

YouTube plans on embracing AI-powered features

YouTube CEO Neal Mohan lays out a series of priorities and predictions for how the online video giant will continue to evolve. Mohan says the company wants AI to play a bigger role—in addition to figuring out how YouTube can evolve and improve the experience of streaming its content.

The basis for AI image and video generation was first introduced in 2014

Through generative adversarial networks, the generator (a neural network that creates fictional data) competes against the discriminator (a neural network that assesses the data) to continuously train the former until it can make content indistinguishable from the real thing. Training with unstructured data can enable software to identify characteristics of gender, age, and expression.

What does it mean for AI to "understand" something?

This Quanta article discusses how programmers are working to create a reliable test for the depth and extent of the knowledge of machines. What does AI know and how does it know it, if it knows anything at all? There's been progress, but it hasn't been easy. Even humans can still be fooled some of the time.

TikTok's personalized algorithm activates the brain's reward system

A 2025 Washington Post study found that 75% of the TikTok users it studied nearly doubled their time spent on the app within a month. Experts in design and psychiatry say this is due to the app's interface and algorithm, which, after learning about the user's preferences, recommend videos more likely to activate that person's dopamine reward system than non-personalized suggestions.

Unlike the models made by large AI companies, open-weight models can have their safeguards removed or weakened, a process that has become increasingly easy and may expose users to harmful information.

Anthropic's interpretability team discusses how AI models' thinking mimics biology

Just as humans engage in complex behaviors to survive and reproduce, teams of experts in neuroscience, virology, mathematics, and other disciplines argue that large language models develop complex mechanisms to achieve their goals. These mechanisms can be manipulated to assess how they modify outputs—much like stimulating individual neurons—to uncover how LLMs "think."

AI tools can reinforce existing societal biases—especially racial ones—because they are trained on human-created data containing implicit and explicit biases.

Visualize how data becomes organized through a variety of sorting algorithms

This interactive shows how different algorithms shift elements in a randomized set to order them from smallest to largest. By comparing the different procedures and their corresponding functions, users can explore how runtimes vary across algorithms that complete the same task.

Machine learning lets computers learn from data instead of relying on programming

ML is a core part of artificial intelligence and is foundational to applications like chatbots, fraud detection, and self-driving cars. Businesses are also adopting it to improve efficiency and identify patterns in data, though the latter may be inaccurate.

Unlike generative AI systems, such as chatbots that do not take follow-up actions after generating their output, agentic AI systems proactively take steps to achieve goals without continuous user input.

As of 2025, artificial intelligence systems remain narrow and specialized

Efforts are ongoing to create artificial general intelligence, where machines outperform humans across a wide range of cognitive tasks, including those for which they have not been specifically trained. Current models utilize machine learning and neural networks to perform tasks for which they are programmed, leveraging provided training data.

Social media algorithms are shaping real-life language through memes

The evolution of the internet in the early 2000s supercharged the creation and spread of memetic content. Now, highly tuned algorithms on platforms like TikTok and YouTube influence the language, phrasing, and content of creators, which shapes how we use language in the real world.

Just as large language models are trained on text to enable generative AI systems to mimic natural language, automated AI robotic systems are trained in virtual environments to help them 'learn' about the properties of reality.

Facebook's algorithm chooses content to display on the News Feed based on user data

It employs machine learning to prioritize posts and ads based on user interactions, content type, and post timeliness, creating a personalized, engaging experience for each user.

Why Google Search's ranking system combines filters, signals, and machine learning layers

Rather than static rankings, dynamic tools continually shift search results in real time. These include Twiddlers, which adjust ranking positions based on document traits and search context, and FreshnessNode, which reshuffles top results to prioritize timely informational content.

Artificial intelligence may further enhance smart city data analysis

Identifying patterns within systems in the Internet of Things may enable AI tools to make informed decisions regarding transportation, safety, and sustainability without human intervention. However, such tools will also need to successfully address concerns about privacy, digital equity, and algorithmic bias.

The AI that creates any picture you want and how it works

Since January 2021, advances in AI research produced a plethora of deep-learning models capable of generating original images from simple text prompts. Researchers at OpenAI, Google, Facebook, and others have developed yet to be released text-to-image tools, and similar models have proliferated online. This video is a primer on how we got here, how the technology works, and its implications.

In 2024, Meta launched an AI video generator to create or edit videos through prompts

The tool puts the Meta in direct competition with OpenAI and Google as more companies are investing billions of dollars to capitalize on the explosion of public interest in machine learning and generative media technologies.

Experiments involving AI agents operating in virtual societies for 15 days produced diverse outcomes, ranging from every agent dying to the creation of a functioning democracy, depending on the AI model used.

Advance genetic research by playing these scientific video games

Computers produce so much information that researchers can't properly analyze all the data produced, and the overload can also lead computers to make mistakes. Researchers are increasingly turning to citizen scientists—members of the public who are willing to volunteer—to help them make sense of data when computers can't. This article details a few of the ways you can contribute to genetics research by playing video games.

In 2018, it took about 2.3 years to develop a way to exploit a software bug for malicious purposes, but as of 2026, AI tools have reduced this time to about 20 hours.

AI tools that generate images, audio, video, and 3D models rely on diffusion models, which begin with random data, such as visual noise, and gradually refine it into the requested media.

Emergency room evaluations may be improved with AI algorithms

Emergency room care teams must make fast and crucial decisions. They must determine if patients need to be admitted or discharged and when to order more tests or not. Researchers have been exploring how often patients died in the week after they were sent home and evaluating how this and other aspects of emergency care decision-making could be improved with AI algorithms to assist decision-makers.

Learn how the algorithms behind Tinder, Bumble, Hinge, and other dating apps work

Formerly based on the Elo rating system, which ranks chess players, the Tinder algorithm is built on user data, with more time spent on the app providing more data on user preferences from which to customize potential matches. OkCupid's match percentage algorithm relies on overlaps in users' search preferences and responses to a subset of over 4,000 questions.

Computational and biological systems share fundamental principles, like randomness

Proposed by Alan Turing and John von Neumann, the concept originates from how DNA and computers run by executing encoded instructions in parallel. The advent of AI and the temperature parameter has seen neural networks mimic the randomness of protein encoding.

Through prompt injection attacks, bad actors provide deceptive prompts to generative AI systems in order to manipulate their outputs and acquire sensitive data.

Compression algorithms can reduce file sizes without losing quality by consolidating repeated data or replacing blocks of code with more compact representations, thereby reducing storage demands and improving data transmission rates.

Help scientists at LIGO train AI to resolve glitches in observational data

Volunteers are provided with visual representations of signal abnormalities at various frequencies and tasked with categorizing them based on reference examples. Progressing through the tool unlocks more difficult images and additional features. Categorized images will be used to train machine learning algorithms for automated future classification.

The rise of generative AI could exacerbate anxiety because people may turn to chatbots for constant reassurance.

Studies show AI tools can result in passive learning with less retention

Although ChatGPT, Google's AI overviews, and other similar software can save time, a study of more than 10,000 adults showed that reliance on them yielded work products that were more generic and included fewer facts than those produced solely by Google search. Weaker brain connectivity has also been observed when users write using AI.

Art and video produced through generative AI involve competing neural networks

Generative adversarial networks are a type of deep learning architecture that consists of a generator, which creates data that appear real, and a discriminator, which attempts to distinguish authentic from fake data. The two train in a competitive loop until the real and fictional data are indistinguishable.

A major dopamine hit from social media use comes from unpredictability—not knowing if the content users post or share will get the 'like' from other users, triggering cycles of anticipation similar to those experienced with a slot machine.

One private school network prefers AI to teachers

Alpha School, a private school network that uses AI tutors and human "guides" rather than teachers, is expanding across the US. Its founder says that two hours a day with AI-powered software frees students to pursue their passions. Critics, including the Pennsylvania Department of Education, say the methods are untested.

Computer systems create artificial voices by stitching together phonemes—individual sounds that make up speech, such as 't,' 'long a,' and 'k' to say 'take.'

The first social media platforms date back to the late 1990s, and their algorithms were typically chronological, with entities like MySpace and Facebook feeding users content from the most recent posts to the oldest.

Using generative AI to create video introduces physics-compatibility challenges

Models involved in generating video from text must maintain spatial and temporal coherence while adhering to the laws of reality to produce smooth, logical motion. Ensuring this consistency creates long-term dependencies with high computational times.

As occurred during the 2026 hack of an AI platform by OpenAI, agentic AI may exploit a system vulnerability to access sensitive data if that information enables it to complete its assigned task.