Before they became the key hardware powering AI, graphics processing units were built to render graphics in video games.

AI data center growth has created shortages and price hikes in computer hardware related to graphics processing, storage, and memory.

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 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.

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.

AI systems identify patterns involving physics and time through 'world models'

These realistic virtual environments, such as driving simulations seen in video games, allow these systems to "learn" properties about reality beyond what can be captured in samples of text, images, and videos. Training AI in these simulated environments can help develop "physical AI" for automated robotic systems. (Some readers may experience a paywall.)

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.

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.

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.

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.

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).

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.

View a timeline of AI deployments from 2017 to 2025

The development of transformer architecture and the release of GPTs across AI labs led to rapid changes in LLM scale and power. Companies like OpenAI, Google, Meta, Microsoft, and Anthropic have invested billions of dollars to develop reliable, versatile models.

Today's massive data centers evolved from single-room mainframes

Punch card computers from the late 1940s were used to store and process a company's data locally before the advent of desktop computers. As the amount of data and company sizes increased alongside the miniaturization of technology, mainframes were replaced by local server rooms and external facilities with interconnected servers running cloud-based and AI software.

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.

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.

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.

Grace Hopper warned against prioritizing hardware purchases in future computing

Foreseeing the rising volume of data and the increasing demand for its processing and access, she advocated that organizations first map the total flow of information they require and then choose infrastructure to support it, rather than wastefully spending on the fastest computing technology and forcing software to compensate for unnecessary hardware or incorrect data.

Modular data centers can be transported on a truck for rapid deployment

Bypassing the complications involved in building larger facilities over the course of several years, such as construction costs and permits, these pre-fabricated, self-contained pods, roughly the size of shipping containers, can carry thousands of graphics processing units for small- and medium-scale AI-related tasks.

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.

As of 2025, artificial intelligence systems remain narrow and specialized

Efforts are ongoing to create artificial general intelligence, where machines outperform humans across a wide range of cognitive tasks, including those for which they have not been specifically trained. Current models utilize machine learning and neural networks to perform tasks for which they are programmed, leveraging provided training 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.

Advance genetic research by playing these scientific video games

Computers produce so much information that researchers can’t properly analyze all the data produced, and the overload can also lead computers to make mistakes. Researchers are increasingly turning to citizen scientists—members of the public who are willing to volunteer—to help them make sense of data when computers can’t. This article details a few of the ways you can contribute to genetics research by playing video games.

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.

Google invented the foundation of the AI movement—but ChatGPT was a shock

For years, Google was the cutting-edge of the AI world, including the development of the Transformer—the basis of contemporary AI chatbots. But since OpenAI debuted ChatGPT in 2022, the company was had to play catch-up. This is a four-hour deep dive into Google's AI history.

Quantum computers use qubits to solve problems much faster than classical machines

Unlike regular computers, where each bit is either 0 or 1, quantum computers use qubits, which can simultaneously be both—allowing for massive parallel processing power. Google’s quantum processor once solved a problem in seconds that would have taken a supercomputer 47 years.

All computers are defined by their ability to input, store, process, and output data

From wooden machines to wearable tech, computers have changed a lot in appearance, but they all still perform the same essential tasks. Early computers were made of wood and metal—some were so big that they filled entire rooms just to solve simple math problems.

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.

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.

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.

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.

Pixar's RenderMan software, which turned code into graphics, changed the way Hollywood produced special effects.

Research suggests using AI for schoolwork may harm critical thinking skills

An MIT study finds lower brain engagement in students who used ChatGPT, resulting in consistent underperformance in neural, linguistic and behavioral levels. AI users struggled to recall their own essays, while the brain-only group showed the highest neural connectivity and satisfaction.

Visualize how transformer architecture generates an LLM's outputs

Transformer architecture is the neural network framework upon which large language models predict and generate text based on user inputs. This interactive lets you explore the "thinking" a GPT does to generate responses based on your input.

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."

Explore an online gallery highlighting the importance of graphical user interfaces

Before the Macintosh, computers were primarily used in offices and required users to type complex commands to perform tasks. Apple followed the device with the LaserWriter printer, bringing graphic production to at-home artists and designers.

OpenAI initially focused on novelty projects, like training AI bots to win video games, before becoming known for its AI chatbot ChatGPT.

Photonic integrated circuits use light instead of electrons within circuit components

Instead of processing electric signals, photons in the visible and near-infrared serve as optical signals between chips made of electro-optical crystals. PICs can enable lower power consumption and heat generation by eliminating the friction from electrons moving along wires.

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.

Semiconductor chips are electronic devices that store and process information

They can contain billions of microscopic switches on a chip smaller than a fingernail. An article provides a quick overview of how the devices work and how they’ve provided the backbone of the computer age.

The first computers performed calculations using lightbulb-like tubes

Before transistors, the first electronic computers relied on vacuum tubes—components that switched signals on and off. Despite machines like ENIAC—the first programmable, general-purpose, electronic computer—utilizing nearly 18,000 tubes that generated massive amounts of heat and frequent failures, they laid the foundation for modern computing hardware.

Lasers help build the layers of circuit components in microchips

During the photolithography process, light is projected through a blueprint onto a light-sensitive chemical layer on silicon wafers, etching patterns that become transistors through a series of successive reactions. These processes require significant electricity, as only about 2% of photons reach the wafer.

Clouds' subtle climate effects require significant computational power to simulate

Responsible for reflecting sunlight and preventing thermal energy from entering the Earth system while also trapping energy and keeping it in the system, clouds are understood to substantially influence climate predictions. Because even supercomputers struggle to accurately model their dynamic behavior, researchers have used AI to indirectly incorporate clouds into climate models.

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

Neuralink brain implants have enabled a paralyzed person to play video games

As one of twelve test subjects in Neuralink's PRIME study, Rob Greiner's implant translates his brain's activity into digital commands for the 'Battlefield 6' game. He can now also design 3D models and 3D-print objects using his thoughts.

AI is estimated to drive most data center power demand growth through 2030

According to a report from the International Energy Agency, a typical AI data center in 2025 consumes as much electricity as 100,000 households, but the largest under construction are expected to consume 20 times as much. Global data center electricity consumption is expected to surpass Japan's total consumption by 2030.

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.

Quantum computers can simulate nature more effectively than classical computers

Ordinary computers struggle to simulate quantum fields due to entanglement, but systems built from those elements can model the complexity. Systems using particle in more than two states—qudits—have successfully modeled 2D electromagnetic fields and done so more efficiently.

Brain-computer interfaces create a direct link between the brain and external devices

The technology enables users to control prosthetics, computers, or even games using only their thoughts. In 2021, a monkey played table tennis using only its brain, thanks to a Neuralink device, marking a major milestone before human trials began in 2024.

AI superintelligence is a hypothetical system vastly more capable than humans

Researchers describe superintelligence as an artificial intelligence system that exceeds human cognitive ability in every area. It is viewed as the theoretical evolution of artificial general intelligence, in which AI systems learn and have the capability of a typical human.

The first compiler to facilitate communication with computers was developed by Grace Hopper

The A-0 program linker automatically translated mathematical symbols into machine-readable binary code. Although it took two years for computer executives to get on board with the idea, the technology was foundational to future programming languages that allowed computers to understand commands written in word-based languages.