Neural networks mimic the structure and behavior of real physical systems

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.

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.

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.

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.

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."

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.

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.

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.

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.

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 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.

Quantum computers harness weird physics to explore unsolvable problems

Classical computers manipulate bits as either 0 or 1, but quantum computers use qubits to hold combinations of both, enabling new algorithms. This new approach could one day help with breakthroughs in medicine, chemistry, and AI.

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.

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.

What are neural networks and why do they matter?

Learn how a neural network works, why it matters, and how it can be trained to recognize patterns, classify data, and forecast future events.

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.

Learn how natural systems exhibit characteristics of chaos theory

In biology, some mathematical models of ecosystem populations and heart irregularities show strong sensitivity to initial conditions, which scientists can simulate to determine quantitative tipping points at which these systems become unstable. Within the social sciences, crowd behavior and the virality of social media posts have shown how human behavior fits within the framework of chaos theory.

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.

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.

Can physics explain where consciousness comes from?

Consciousness is a complex emergent phenomenon of the brain, which is primarily composed of neurons and glial cells. Since all matter is composed of atoms that follow the laws of physics, there may be quantum processes beyond classical electrochemical interactions that can explain where consciousness comes from.

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.

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.

Particle interactions follow rules related to specific quantum properties

Like a business whose purchased inventory matches sold inventory, physicists identified specific patterns across decades of particle accelerator data, which became new conservation laws for elementary particles. These laws help identify which particle decays are expected or forbidden.

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.

View 3D renderings of atomic orbitals created with quantum mechanics

Scientists use the Schrödinger equation to determine how a system governed by a wavefunction—an equation that predicts the probability of certain outcomes—evolves with time. Computers can animate atomic structure and behavior by depicting the wavefunction as a fluid that guides a particle, like water moving a speck of dust.

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.

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.

Explore an interactive 3D model of the brain

The brain contains about 86 billion neurons—nervous system cells—and controls vital bodily functions via electrochemical signals. Neurons form circuits through axons, and different brain regions use these circuits to perform specific tasks.

Neural rhythms shape how we move, focus, remember, and interact with the world

Millions of neurons in the brain produce rhythmic electrical signals, or brain waves, that guide physical movement and help form and store memories. Researchers also use these rhythms to create brain-computer interfaces, letting people control devices using only their thoughts.

Emergent quantum phenomena can produce nonfundamental quasiparticles

While physicists have discovered new particles by colliding known ones at high speeds, placing known particles in extreme environments or configurations can allow quantum mechanics to produce bizarre behaviors, making them seem like new types of matter.

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.

AI expert shows the cyclical nature of AI hype—in 1988

Rodney Brooks wrote several decades ago about the boom and bust cycles of artificial intelligence. Starting from the 1950s excitement over ENIAC's multiplication through the second launch of then-novel neural networks, Brooks suggests AI progresses incrementally despite the hype.

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.

Quantum field theory, which has made predictions accurate to 12 decimal places, explained

Instead of being made of indivisible particles, all visible matter in the universe is built from underlying quantum fields—smooth, invisible entities that behave like waves and extend throughout space and time. The theory suggests these fields are fundamental, and particles are disturbances that move through them.

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.

Quantum mechanics explains how particles behave like both matter and waves

In the quantum world, particles such as electrons are “quantized,” meaning they can only hold specific values of energy or momentum. These particles also behave like waves, with their wave function predicting where they might be found at any moment as probability clouds called orbitals.

The term 'artificial intelligence' was coined in 1956

Dartmouth professor John McCarthy introduced the term during a summer workshop aimed at exploring how to make machines perform human-like tasks. Two years later, Perceptron Mark I became one of the first neural network machines.

Learn why modern scientific patterns have been described as theories rather than laws

After confirming that the accuracy of some laws of physics is limited to certain domains, science became more hesitant to categorize identified mathematical relationships, such as quantum mechanics, as unchanging laws. Instead, these recent, complex frameworks are designated as theories to acknowledge that they are conditional and may be part of a larger, more complete future framework.

The Standard Model reveals an underlying organization to the universe

Using the framework of quantum field theory, it structures all known particles into 12 matter particles, alongside five particles that carry three of the four fundamental forces. Although remarkably precise and predictive, it cannot yet explain gravity, dark matter, or dark energy.

Scientists are experimenting with ways to probe the multiverse theory

Researchers are simulating vacuum bubbles—the theorized starting points of universes—using quantum computers, digital models, and exotic lab setups to study how they form, expand, and possibly collide. One experiment aims to "blow" artificial universe-like bubbles inside ultra-cold quantum gases, potentially capturing real-time glimpses of cosmic birth processes in a lab.

Quantum field theory reveals that particles are ripples in universal fields

Instead of being made of indivisible particles, all visible matter in the universe is built from underlying quantum fields—smooth, invisible entities that behave like waves. The theory suggests these fields are fundamental, and particles are disturbances that move through them.

Neural implants blur the line between personal intention and machine execution

A key ethical dilemma—the contemplation conundrum—explores whether implants might misinterpret mere imagination as intent to act. Neuroscientists have found no brain signal that clearly marks when someone decides to act, making intent hard to detect in neurotechnological systems.

Efforts to find Majorana particles may finally lead to reliable quantum computers

Though the particle—which is its own antiparticle—remains theoretical, scientists from Microsoft to Google continue developing systems to mimic this property towards building error-resistant quantum computers. Scientists at UCSB have been attempting to get the particle using just pencil lead and Scotch tape.

Explore interactive simulations of strange attractors, the graphs that help visualize chaos

Although simple, linear equations reach an explicit value for a given input, such as *f(x) \= x* + 1 equaling 4 when *x* equals 3, some chaotic, nonlinear systems tend to evolve towards multiple values simultaneously. These simulations allow users to observe the unusual mathematical artifacts created by systems as their outputs evolve toward these distinct values.

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.

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 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.

Take a deep dive into the neuron

Neurons are the basic unit of the brain and nervous system—electrically excitable cells that sends signals throughout the body. They can communicate with each other, fire in sequence, and conspire to do everything from drive body movement to help form memories. Check out this intro-level overview to learn more about their structure and how they work.

Virtual particles are bookkeeping tools used to describe force interactions

Physicists mathematically describe the electromagnetic, weak nuclear, and strong nuclear forces as the result of these particles being exchanged between particles of matter. Despite not being real, observable particles, this method has provided experimental predictions with an accuracy of over 12 decimal places.

What does it mean for AI to "understand" something?

This Quanta article discusses how programmers are working to create a reliable test for the depth and extent of the knowledge of machines. What does AI know and how does it know it, if it knows anything at all? There's been progress, but it hasn't been easy. Even humans can still be fooled some of the time.