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

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.

Type any prompt and watch Craiyon—the free AI image generator that began in 2021 as DALL·E mini—turn words into pictures.

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.

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.

Play 'Quick, Draw!' to help train Google's neural networks

Players are tasked with drawing objects, and Google’s AI—which has been trained on millions of user-submitted doodles—tries to guess what’s being sketched. Over 50 million drawings across 345 categories have been created and can be reviewed by users.

Watch an AI-generated short film

The Waymark production company created "The Frost" using the AI platform DALL-E 2. Set in an apocalyptic future where the weather has been stalled, the film was made by stitching together hundreds of clips, each only a few seconds long.

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.

Visualize how transformer architecture generates a large language model's outputs by running words and subwords assigned to specific numbers through multiple rounds of mathematical processing.

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.

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.

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.

Test your ability to identify AI-generated images

This interactive project from MIT challenges users to distinguish between AI-generated images and real ones.

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 vs. machine learning vs. deep learning

Machine learning is best understood with visuals. A collection of the best 9 images, videos and flowcharts that explain what it is and how it works.

Deepfakes images can be identified by visual elements that do not match real-world phenomena, including asymmetric eyes or earrings, surreal backgrounds, and indecipherable background text.

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.

LLMs are trained via modeling, imitation learning, and reinforcement learning

Training large language models begins with pretraining the model to predict the next word in a sequence based on finding patterns in massive amounts of text. Patterns are then fine-tuned to model human-written dialogue and to align responses with user preferences.

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.

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.'

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."

Teaching a robot to laugh

Researchers have designed a shared-laughter AI system that responds to human laughter in order to build a sense of empathy into dialogue. Learn how they did it with this explainer.

The world's first chatbot, ELIZA, was developed in the 1960s as a technical demonstration exploring human-computer communication, with one of its scripts allowing it to emulate a psychotherapist.

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.

Graphics processing units, which process large amounts of data in parallel, have become the foundational hardware for AI systems that rely on hardware capable of processing millions of text parameters simultaneously.

Turning 2D illustrations into 3D models with AR

Coloring books capture the imagination of children and provide them with one of their earliest opportunities for creative expression. This two-minute video shows an augmented reality coloring book app in which children color characters in a printed coloring book, their drawing is detected and tracked, and the video stream is augmented with an animated 3-D version of the character that is textured according to the child's coloring.

Office Assistants, including Clippy, were digitally edited with Macintosh computers

The animated characters served as intelligent user interfaces for Microsoft Office and were sketched by hand before being scanned. Clippy was selected as the default assistant after social psychologists at Stanford University identified it as the most trustworthy, likable, and humorous character based on public testing.

AI hallucinations are inaccuracies or falsehoods in generated content

AI hallucinations are a by-product of the sequential creation of content based on patterns in text, which may not reflect the context or meaning behind a prompt. Back-end issues like biased datasets and overfitting of model data contribute to hallucinations.

Machine learning and deep learning are successive subsets of artificial intelligence

ML allows systems to learn from data, while DL uses complex neural networks to analyze large amounts of unstructured data. Neural networks enable capabilities like image and speech recognition through their use of algorithms to identify patterns in data.

Transformer architecture was the breakthrough that made AI chatbots possible

It changed how text was translated from a sequential, "one word at a time" method to one where every word in a text is processed in parallel. Advances in positional encoding and self-attention also helped models recognize context and word order better.

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.

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.

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.

Watch video samples created by AI

Sora, OpenAI's video generation tool, can create up to 60 seconds of photorealistic video footage featuring highly detailed scenes, complex camera motion, and multiple characters with vibrant emotions from a text prompt.

Create professional-quality spoken audio from text

Generative AI now makes it possible to create everything from computer-generated written content to beautiful imagery and hyper-realistic videos in seconds from a user’s simple text prompt. Add to that list: High-quality spoken audio, which the AI software company ElevenLabs specializes in. Its natural-sounding speech synthesis and text-to-speech tech can produce human-like speech that mimics a variety of different tones, styles, and accents.

The next technology epoch is here

From ChatGPT to DALL-E, 2022 saw the mainstream emergence of artificial intelligence for consumer use. Here's how these platforms will disrupt today's five biggest tech companies—Apple, Amazon, Meta, Google, and Microsoft.

See the news with AI-generated data visualizations

Realtime uses generative AI to create immediate data visualizations of news items on its regularly-updated feed, pulling from public data feeds. Peruse stories around sports, economics, politics, and more with the helpful aid of charts and graphs. See the shape of the news with this clever AI tool here.

Tokens—numbers assigned to words and subwords—are the basic units of data that AI tools process to generate outputs that mimic language without understanding the text itself.

Roy Lichtenstein popularized benday dots in his famous comic-inspired paintings

American newspaper publisher Benjamin Henry Day Jr. developed the "benday" printing and photoengraving technique to produce grays and colors with fine patterns of ink of equal size and distribution. The process would become widespread in newspaper printing and define the work of Roy Lichtenstein.

Particle detection has evolved from cloud and bubble chambers to digital 3D displays

As invisible particles passed through cloud and bubble chambers, they disrupted supersaturated vapor and superheated liquid, respectively, creating trails that served as proxies for cosmic rays. The transition from photographed events to digital sensors enhanced data collection and accuracy.

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.

Explore persistent 3D environments with Marble

Marble is a spatial AI system built by World Labs, a San Francisco startup focusing on world models (rather than large language models). Using a text prompt (or a single image or video), users can generate a "world" and move through it, edit it, or export it. The company and project are part of the broader spatial intelligence area of AI, where AI can simulate environments.

Pretend to be an AI chatbot and answer real prompts

This site, which allows people to answer prompts in real time, has millions of visitors. Join the crowd of people tired of AI chatbots and seeking human connection. You can ask questions, answer questions, and draw for each other.

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.

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.

Six pigeons were trained to detect cancer from looking at CT scans—now that work is the basis for training new AI medical tools.

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.

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.

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.

Draw or drag on your screen, and this browser experiment matches your gesture to real Google Earth satellite imagery of rivers, coastlines, and highways.