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.

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.

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.

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.

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.

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.

Create flute, saxophone, trumpet, or violin reproductions of any 15-second piece of audio using machine learning.

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.

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.

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.

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.

Deepfakes are media created using generative AI tools that intentionally misrepresent real people or events—often for malicious purposes—at the cost of public trust, security, and privacy.

Once limited to expert studios, automated tools can create convincing deepfakes

Although the best deepfakes are engineered using generative adversarial networks where systems compete to create and assess fake data until the two cannot be distinguished, simpler tools have allowed nonexperts to develop deepfakes for financial scams, political deception, and nonconsensual pornography.

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

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.'

AI-generated avatars allow users to create deepfakes in real time

Synthetic media tools can map voices and faces to make individuals appear as someone else. Although they open the door to new opportunities in immersive education and entertainment, they also raise concerns about the misuse of personal identity.

The ease of deepfakes creation may soon overwhelm our sense of digital truth

Shortly after the public release of related technology in 2017, some experts saw the technical limitations of deepfakes as preventing them from becoming widespread tools of disinformation. Since then, the neural networks behind diffusion models and generative AI have eliminated the barriers to creating convincing synthetic media for propaganda.

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.

Deepfake potential ranges from destabilizing democracies to improving accessibility

The technology can revolutionize education, medicine, and art through actions such as helping a disabled person express themselves. However, fictional audio and video can also jeopardize a person's reputation and life or quickly spread misinformation, even without content being flawless recreations.

Digital re-aging and de-aging of faces in film and TV has become increasingly common

Traditional techniques for accomplishing this process were slow and labor-intensive, and often required manual frame-by-frame edits, forcing them to be reserved for "hero characters" and close-up shots. Deep learning methods used by Disney and other production companies now provide fully-automatic and production-ready methods to produce such deepfakes for entertainment purposes.

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."

Neural networks mimic the structure and behavior of real physical systems

Early generative AI systems were inspired by scientific models such as the Ising model, which explains how atoms interact with one another and align in magnets. As these networks have expanded, their behavior has more closely resembled that of quantum fields, enabling them to be used in exploring complex particle physics interactions.

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.

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.

Malicious use of deepfakes can be found in scams, misinformation, and disinformation

Artificial neural networks can identify an individual's physical characteristics when provided with training data, such as real footage of the person speaking. Graphics techniques overlay these with matching characteristics from another real or generated person to create novel footage of things that have never taken place in reality.

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.

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.

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

Because they are built on content produced by humans, who possess biases, generative AI tools inherit prejudices and stereotypes from training data, which later appear in their outputs.

Fair use may shield AI companies from potential copyright violations

AI models are trained on enormous datasets built from content scraped from websites across the internet. Copyright law includes a four-factor test to determine whether copies of content fall under fair use, but the law's enforcement has been inconsistent.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

Nvidia Omniverse can be used to accelerate 3D workflows and explore creative ideas

This video explores different apps available within Nvidia Omniverse that artists can use to create cinematic, virtual worlds. Some of the tools are AI-powered, like a feature that converts spoken audio into a video of a character speaking.

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.

Find out how important you are to AI tools when they generate text responses with this tool, which estimates the weights of people's names.

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.

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.

'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.

Digital twins create real-time 3D models of smart cities

These re-creations allow city planners to track changes and manage infrastructure more easily by testing strategies in virtual environments. Geospatial technologies, such as mobile mapping and satellite imaging, help cities assess urban layouts, plan for disasters, and manage resources more efficiently.

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.

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-powered models can provide low-cost, localized and accurate weather forecasting

These models can run on standard laptops, reducing the need for expensive supercomputers and expanding access to developing regions. AI forecasts can help farmers make informed planting decisions, improving crop yields and reducing costs.