Generative AI tools excel at pattern recognition, not contextual accuracy

Technologies labeled “AI” have historically lost that title after widespread adoption

Generative AI is the latest entry in a recurring cycle where emerging tools start as "AI" until they become common software, like databases or machine learning. Generative AI and large language models may be the next platform shift after smartphones and the Web.

LLMs are the backbone of AI tools that process and generate natural language

Large language models identify patterns in massive datasets of books, code, and unlabeled text from which to generate coherent responses. GPT-3 was trained on about 45 terabytes of data and uses 175 billion parameters—each parameter being a value the model adjusts as it learns.

Transformer architecture can recognize and predict patterns in language

The underlying software powering text generation in AI tools associates each word or subword—called a token—with a set of values corresponding to how often it appears near other tokens. By recognizing these associations in prompts, the LLM can infer meaning.

Large language models are examples of foundation models

Such models are trained on massive amounts of data, which enable them to perform a variety of tasks, rather than being provided task-specific data to complete a narrow function. Such models come with high compute costs and potential trust issues due to the nature of their unorganized training data.

'Context rot' explains why AI conversations degrade the longer they run

Research shows that as a large language model's context window fills up, performance declines, and models begin over-indexing on content at the start and end of a conversation while losing track of what's in the middle. The practical fix is simpler than you'd think: start a fresh chat more often.

How ChatGPT learns to read

The introduction of ChatGPT last fall jumpstarted public interest in the future capabilities of artificial intelligence and what it means for society. Of many somewhat "jargony" terms thrown around, the phrase "Large Language Model" is commonly seen—but also incredibly unclear as to what it means. In the simplest sense, the programs comb through mountains of internet data in response to a question and guess at what should come next (subscription needed).

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."

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.

Agentic AI systems can proactively achieve user goals without user direction

Unlike large language models, which do not take follow-up actions after generating their output, agentic AI uses an initial prompt to take multiple steps, learn from its environment and outcomes, and continue working without follow-up human prompting.

Neural networks are modeled after an idealization of the brain

These networks—including the backbone of ChatGPT-3, which has approximately 175 billion parameters—naturally discover the implicit rules of grammar and syntax within language by identifying patterns. Deviations in word patterns add creativity to the text generation.

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.

How generative AI differs from machine learning

Compared to machine learning - which is sort of like teaching a toddler how to recognize what a dog looks like by showing them images of one - generative AI is like teaching that same child how to actually draw a dog. Unlike machine learning, generative AI can actually generate. Read this article for a more in-depth comparison between these two types of artificial intelligence.

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.

ChatGPT may struggle with factual inaccuracies, despite writing accurately in English

In a classroom experiment, a journalist enrolled in an AP English Literature course submitted an essay written by ChatGPT, which confused characters and setting when comparing two texts. The tool was also unfamiliar with texts outside of its training data.

Model context protocol allows large language models to connect to external data

Using a unified interface, MCP simplifies how AI systems securely connect to external data, tools, and services. The protocol enables AI agents to perform tasks without ongoing human refinement or updates to manually integrate new data.

Artificial general intelligence equals or surpasses human intelligence

AGI incorporates sensory perception, memory, and advanced logical inference systems to move beyond the narrow tasks seen in large language models and chatbots. One of the most significant hurdles in developing AGI is designing systems that learn and can flexibly apply learning across domains.

AI excels at assisting with structured tasks involving logistics and planning

Tools like ChatGPT, Gemini, and Perplexity can help organize chaotic calendars, troubleshoot bedtime routines, and assist with salary planning and moving. However, specific quotes, like insurance, and local cost comparisons can be flawed since AI draws from nonlocalized datasets.

Surreal backgrounds, asymmetry, and indecipherable text can help identify deepfakes

Differences in the backgrounds of training data with the same subject introduce variability, resulting in background textures and text that lack realistic detail. Difficulties with managing long-distance dependencies, the complexity of hair, and other real-world phenomena introduce errors that can be used to flag AI-generated content.

AI tools are trained on human-created data containing implicit and explicit biases

Because AI tools reflect patterns in their training data, their outputs may reinforce existing societal biases—especially racial ones—rather than offering neutral or fair ones. Adequate training is necessary to undermine these biases whenever new data is incorporated.

AI is not good at understanding satire

Social media has become rife with misinformation. In response, some organizations have deployed AI-assisted programs trying to curb the spread of this inaccurate content. The problem is that misinformation and satire, rhetorically speaking, often use the same kind of language, something AI struggles to understand.

Large language models struggle to play video games, despite coding them easily

As of 2025, given clear tasks, specific feedback, and test results to learn from, LLMs have been successful at coding simple video games that resemble pre-existing ones. However, given the variety of games, the lack of training datasets, and the need for spatial awareness, which LLMs find challenging, some of these models experience greater difficulty navigating and "winning" games.

AI models incorporate a randomness parameter when generating responses to prompts

AI models use this parameter to prevent repeated outputs by sometimes choosing less likely next words during sequential generation. However, this randomness—alongside insufficient data and training—may cause hallucinations of incorrect results.

AI data center growth has created shortages and price hikes in computer parts

The parallel processing needed to power large language models for AI chatbots and other tools drove demand for high-speed graphics processing units to spike in 2025, enabling capital-rich technology companies to price out consumers. Similar demands for the computer memory used in storage drives are expected to keep shortages from easing through 2028.

Algorithms built to detect credit card fraud can deny charges if they perceive the attempted purchase is unfamiliar or unusual based on historical data, even when a person has enough credit to make the purchase.

Since fraudulent charges are rare compared with the much greater number of transactions across purchase histories, these models have been shown to be overly sensitive, lacking sufficient training data on what legitimate fraud looks like. Models also perform less reliably for groups in low-income areas or communities of color, given less available data from which to build an accurate baseline.

Explore a gallery of DALL-E creations

OpenAI's text-to-image generator can create content in a wide range of image styles based on simple prompts. Its utility has also given rise to an organic, passionate user base who employ the tool both for fun and for professional purposes.

Watch how 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.

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.

English mathematician Ada Lovelace wrote the first computer algorithm a century before computers existed and hypothesized that computing devices would one day be able to compose music—foreseeing the generative AI systems of today.

A vending machine run with Anthropic's AI went out of business after it was convinced to give away items for free and sell a video game console.

This site reaches millions of visitors who are tired of AI chatbots and seeking human connection. Pretend to be an AI chatbot and ask questions, provide answers, and draw for people in real time.

Radioactive elements can introduce software errors because the ionizing radiation they produce can detach electrons from atoms, and computer code relies on the distribution of electrons.

Electrical grids worldwide must double their power delivery capacity by 2050 to meet surging demand from AI and electric vehicles.

Indefinite cloning of some endangered species may not sustain their longevity because cloning success rates decrease as genetic mutations accumulate.

Crows are believed to have intelligence on par with a seven-year-old child and demonstrate complex behaviors, from problem-solving with tools to recognizing human faces.

How Amazon operates a 'flywheel' business model, a self-reinforcing cycle that drives continuous growth

Chaos theory—informally known as the butterfly effect—is the reason why weather forecasts are presented with probabilities rather than certainties.

Browse a detailed visual analysis of over 200,000 similes—comparisons that often use 'like' or 'as'—from popular fiction to see how frequently writers the same idiomatic expressions.

Search a massive database of over 4 decades of clues from the TV quiz show 'Jeopardy!'

Recent research found that the brain doesn't seem to massively rewire following amputation—upending earlier beliefs about the cause of phantom limb pain.

Art theft is the third-largest source of criminal revenues worldwide, thanks in part to the subjective nature of valuing a work of art.

Listen to an in-depth conversation on the links between Parkinson's disease and herbicide exposure and the challenging pace of scientific investigation.

Memecoins are a type of cryptocurrency—a form of digital currency with a wide variety of uses—created around popular 'memes,' or viral internet jokes.

The Brothers Grimm were lawyers before they were storytellers, but became interested in fairy tales as they began investigating how rules and customs had been passed down through cultures.

Gerrymandering works through two techniques: cracking opposing voters across districts to dilute their power, and packing them into a few to waste their votes in lopsided majorities.

The power and influence of the American presidency have expanded significantly over time—far beyond what the founders envisioned.

Marburg virus, related to Ebola, is also extremely deadly, and has no available vaccine.

The word 'jazz' evolved from 'jasm,' 19th-century American slang for 'energy'

Use this tool to calculate the dollar value of your parental mental load, from meal planning to coordinating after-school care, then see resources that help generate solutions.

Corals are themselves not coral reefs—the term refers to the tiny marine invertebrates that build the rigid reef.

Most of the observable universe's volume consists of voids and supervoids—enormous regions of space with little to no galaxies, stars, or light.