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.'

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.

Audio deepfake tools can now impersonate anyone in real time

A cybersecurity company developed a tool that mimics a person's voice with no noticeable delay using only publicly available software, hardware, and voice samples, allowing users to have seemingly authentic conversations while pretending to be someone else.

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.

Audio deepfakes have fooled voice biometrics at banks

Limitations in facial and body movements as well as mismatches between audio and lip movements can sometimes help flag a video as a deepfake, but removal of the video component significantly improves AI mimicry.

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.

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 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.

Using AI to restore speech 30 years after a stroke

Through a novel surgical procedure involving electrodes implanted in the brain, along with the help of artificial intelligence, a stroke patient is regaining her ability to speak. This technology converts her thoughts into speech through a digital avatar, allowing her to verbally express herself for the first time in decades.

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.

The journalist who let AI speak with his voice

Journalist Evan Ratliff became the object of his own story: He opted to clone his voice, feed it into an AI chatbot, and have it do the talking. What could seem like a fun experiment ended up revealing just how far the capacity of artificial intelligence can go in imitating our identities. Hear the ins and outs of how Ratliff achieved this in this fun and thought-provoking episode.

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.

Governments are now using generative AI to manipulate public opinion

A Freedom House report found that AI-powered disinformation campaigns and censorship tactics are spreading, with 16 countries utilizing the technology to shape online narratives or suppress dissent, as of 2023. In Venezuela, deepfake software was used to push pro-government propaganda with fake news anchors.

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

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.

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.

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.

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.

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.

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

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.

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.

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.

Listen to a deepfake robocall of President Joe Biden

Bad actors are not solely using deepfake technology to manipulate images. They can also manipulate audio for political purposes. In this example, eligible voters are asked to skip the 2024 New Hampshire Democratic primary.

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.

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.

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."

Decoding the hype around AI

2022 was the year AI broke onto the scene, with applications like ChatGPT amazing users with its ability to generate realistic-sounding answers. Technologist Arvind Narayanan presents a clear-eyed view of what the technologies can (and can't) do.

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.

In 2024, deepfake audio of London Mayor Sadiq Khan nearly caused real-world disorder

The audio included incendiary remarks related to the ongoing Palestinian protests and played on the public's preexisting political biases. The technology has since destabilized the concept of truth in elections, where politicians blame the use of real gaffes on AI and deepfakes to spread misinformation.

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.

The speech software's origins at MIT led to Hawking's trademark voice being American

Stephen Hawking's trademark voice is called "Perfect Paul," but it mimics Dennis Klatt, an American researcher at MIT who developed the speech synthesis technology. Klatt used his voice during this development, which was immortalized through Hawking.

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.

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.

How Stephen Hawking’s voice became iconic

Stephen Hawking relied on a voice synthesizer after losing the ability to speak due to complications with ALS. Despite technological advances, he refused to change the "Perfect Paul" voice, making it a cornerstone of his public identity.

Researchers found that by mid-2025, around 35% of newly published websites utilized AI for language generation

The study also found that pages using AI narrow the range of ideas and perspectives and skew more positively, though the measurable impacts on factual accuracy and writing style variation remain unclear.

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.

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.

One man used an AI band called the Velvet Sundown to conduct what he called an elaborate social experiment.

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.

Listen to the 'world's first' song made by a quantum computer and AI

UK startup, Moth, collaborated with electronic artist ILĀ to create the world's first song using quantum-powered generative AI. The technology has the potential to help create more personalized content based on input parameters, such as music or dialogue in games.

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.

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.

A brief history of artificial intelligence

The concept of artificial intelligence and what it may mean for humanity has evolved as computer technologies have grown increasingly powerful through the decades. Beginning with the first (massive) computers in the 1940s, scientists have sought to make the machines as intelligent as humans. But it wasn't until computing power made exponential gains have computers begun to imitate human behavior.

An expert recreates the voice of a 3,000-year-old mummy

Ear, nose, and throat expert David Howard discusses how his research team extracts vocal folds from preserved human tissue, which allows them to model the deceased person's larynx. From there, they can create a synthetic voice based on the organ's geometry.

Microsoft has integrated AI across its software products with Copilot

It utilizes an underlying model from OpenAI—makers of ChatGPT—to assist users in creating documents, writing code, locating files, and more across Windows, Microsoft 365, Bing search and WhatsApp on desktop and mobile devices. Copilot Voice and Vision offer accessibility features for users who prefer not to use mouse/keyboard navigation.

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.

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

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.

Take a quiz to identify whether you can spot deepfakes

Advances in artificial intelligence have made deepfakes increasingly difficult to identify, as they are intentionally designed to appear real. The technology has exacerbated some of the problems brought on by digital editing tools like Photoshop.