How ChatGPT learns to read

AI gained mainstream attention with tools like IBM Watson and Apple’s Siri

With the release of ChatGPT in 2022, which drew over 100 million weekly users in just two months, natural language processing and understanding could be achieved at scale via machine learning. Unlike earlier artificial intelligence that could pull stored knowledge, generative AI produces text, images, or sounds.

ChatGPT is trained on most of the world's digitally accessible text

As with other AI chatbots, ChatGPT is primarily trained through non-supervised pretraining, which identifies underlying structure and patterns within input data. Then, human intervention addresses harmful or biased patterns that should not be propagated to generated outputs.

In 2023, OpenAI spent $520 million on ChatGPT

The ongoing development and refinement of new AI models requires larger datasets and processing more parameters between words and subwords to improve the accuracy of generated content. New startups are expected to harness this technology to provide unthought-of products and services.

Try OpenAI's ChatGPT for free

In late 2022, OpenAI launched a chatbot that quickly became one of the most viral tech products of all time. The software uses code to interpret natural language to produce sophisticated content based on simple user prompts.

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.

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.

AlexNet was the AI tipping point that set the stage for ChatGPT

AlexNet is a neural network created in 2012 that laid the groundwork for modern AI models. Before AlexNet, the ideas behind neural networks existed, but the computational power to run them didn’t.

Comparing ChatGPT with Claude, the latter excels in writing and coding projects

As of December 2025, Claude Code—Anthropic's AI-powered coding assistant—accounts for 54% of the enterprise coding market, and, while subjective, some report that Claude provides more natural-sounding writing. However, ChatGPT can create images and videos, not just generate text, and provides a broader set of applications via custom GPTs.

Explore noteworthy use cases for ChatGPT

From asking the chatbot to sort grocery lists to bird-watchers using it to help identify birds, the official ChatGPT Instagram account spotlights interesting and creative use cases for the tool, especially in the days after a significant update or model has been released.

Generative AI tools excel at pattern recognition, not contextual accuracy

Large language models are trained on vast amounts of unstructured data, from which they develop parameters for grammar and associations between words. These connections can introduce errors due to inapplicable reasoning when used on new data in unfamiliar contexts.

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.

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.

LLMs explained simply and quickly

Large language models generate responses by predicting one token at a time, based on patterns they’ve learned from massive datasets. This explainer covers their core components: data, architecture, training, and how transformer models use context to simulate conversation.

Sam Altman says OpenAI released ChatGPT early as a public service

As the company was developing ChatGPT-4, version 3 was released to allow everyone to adjust to the new tool. Since then, he and his colleagues have called for oversight and regulation to address fears of misuse and AI-enabled pathogens.

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."

Should you use ChatGPT for budgeting?

Some people using AI to help them budget—but is it a good idea? In this article, a personal finance editor explains what happened when they tried to use ChatGPT to help them create a budget. Read the article here.

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.

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.

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.

Pharmaceutical companies can use ChatGPT to improve drug development

Moderna has incorporated the tool into its workflows to accelerate analysis and identify optimal drug dosage from clinical data. The company's legal team also uses a custom GPT—Contract Companion—to break down and summarize contracts quickly.

ChatGPT was conceived over dinner at a Silicon Valley hotel

A guest list of tech leaders, including Elon Musk, Peter Thiel, and LinkedIn cofounder Reid Hoffman, met to discuss how to counter Google's efforts to create human-level cognition systems. The group initially committed a reported $1B to launch the effort.

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.

There’s an artificial intelligence model trained on bankruptcy law

Although it's designed for lawyers, the tool is able to help everyday browsers synthesize important cases and point users to relevant documents about them. One sample using it asks Chatlaw to explain the significance of a recent case and then to define relevant terminology.

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.

Breaking down Anthropic's products and why their models are named after literary forms

Since March 2024, the Claude series of large language models includes three varieties—Haiku, Sonnet, and Opus—named to metaphorically illustrate increasing complexity and computing power. Claude Code can perform software engineering tasks for developers and, as of January 2026, writes nearly all of Anthropic's code alongside Opus 4.5.

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.

DALL-E generates images by turning words into shared picture meanings

DALL-E first uses a model called CLIP to associate pictures with specific words, followed by a model called GLIDE, which slowly removes noise from random dots until a clear image appears that matches the prompt.

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.

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.

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.

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

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

In 2025, four executives from large tech companies were given positions in the US Army Reserves—including Palantir Chief Technology Officer Shyam Sankar, which reinforced Palantir's ties with the US military and its role as a leading defense partner.

Learn how to read a prospectus, the juicy document that any company must file with the SEC before going public.

Clever Hans was a 1900s horse that was believed to be able to solve math equations, tell time, and read and comprehend German, until a psychology student discovered how he got his answers.

Telehealth demand for GLP-1s for weight loss is booming as consumers seek easier access.

Children are now three times more likely to say they want to be a YouTuber when they grow up than an astronaut, with some even attending 'YouTube camps' that teach kids how to create content for the platform.

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.

Some Pokémon have been named after real and fictional organisms, while some real-world species have been named after Pokémon, such as Chilicola charizard and Binburrum zapdos.

Researchers believe the brain consolidates memories, processes emotions, and plans for the future during dream states

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

Learn how monoclonal antibodies, which mimic the body's antibodies, can fight diseases like Ebola.

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

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

The Mona Lisa's celebrity status and repeated restorations have obscured what made the painting so revolutionary for its time

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.

In 1939, baseball star Lou Gehrig delivered his inspirational 'Luckiest Man' speech after retiring due to the health condition that now bears his name.

Apeirophobia is an overwhelming fear of living forever, perhaps stemming from the brain's inability to conceptualize an infinite life.

The syncytiotrophoblast is the outer layer of the placenta and the largest human cell, spanning an area about twice that of a king-sized bedsheet.

The Great Salt Lake's distinct dual coloration results from high-salinity organisms that only live in the northern portion and produce a purple protein.