What are neural networks and why do they matter?

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.

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.

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.

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.

Why does the stock market matter?

Open any newspaper, turn on your local TV news station, or listen to a daily business radio segment, and you’re bound to hear about how the stock market is performing. Netflix created a 17-minute (free) explainer that dives deep into the nitty-gritty of how we got here.

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.

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.

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.

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.

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."

Using deep learning to analyze tumors

The explosive growth of life sciences data is driving artificial intelligence methods to gain traction in biology and medicine — including cancer research.

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.

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.

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.

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.

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.

The debate on why humans are conscious—and what that means

Understanding what consciousness is, and why and how it evolved, is perhaps the greatest mystery known to science. Watch interviews with the world's leading experts on what it is and whether we'll ever know.

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.

Why cryptocurrency matters from Andreesen Horowitz general partner

In this hourlong video, Andreessen Horowitz (a16z) General Partner Chris Dixon discusses "Crypto Networks and Why They Matter," giving an overview of the cryptocurrency space, the transformative implications of its technology, and the potential for cryptocurrency networks to lead a new wave of innovation.

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.

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.

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.

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.

Nvidia entered its AI era after learning that its chips could be used in industries beyond gaming

Its semiconductor chips were used in the automotive industry, for data centers, and by crypto miners. And starting in 2006, Nvidia took steps that would ultimately transform it into the AI company it's known as today. This podcast is the second part of a three-part series diving deep into Nvidia's company evolution.

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.

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.

Ledgers—what are they good for?

It might seem strange that a ledger—a dull and practical document associated mainly with accounting—would be described as a revolutionary technology. But the blockchain matters because ledgers matter. Read (or listen to) this 14 minute breakdown on why ledgers matter.

CUDA was Nvidia’s first step into artificial intelligence

CUDA (Compute Unified Device Architecture) is a parallel computing platform that was developed by Nvidia in the mid-2000s. By letting people perform computations in parallel, rather than sequentially, CUDA allowed people to compute large amounts of data quickly. CUDA has been used to build machine-learning models as well.

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.

See how computer memory really works—from binary bits to modern RAM

The video explains how computers store information as 1s and 0s, how RAM functions as fast working memory, and why systems utilize different types of storage. The video demonstrates how this process enables computers to operate.

How neurons give rise to thoughts

Neurons in the brain are singular nerve cells that flicker on and off or connect to each other based on incoming stimuli. The more neurons communicate by firing electrical impulses through tendrils known as synapses, the stronger their connection becomes. The electrical energy zipping along these interneural connections in an attempt to classify and respond to incoming stimuli may be what gives rise to thoughts.

Neurons send signals through electrical impulses and chemical messengers

By converting electrical signals into chemical messages at synapses, neurons allow information to travel throughout the nervous system. With repeated use, synapses grow stronger, enhancing the associations between neurons.

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.

By 2024, Nvidia had become one of the world’s most valuable companies

Nvidia started as a graphics chip designer for PCs and consoles. This explainer traces its rise from early GPU innovation to powering AI breakthroughs and fueling the data centers behind today’s biggest tech platforms.

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.

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.

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.

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.

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.

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.

Breaking down how and why 5G will change the world

The jump from 3G to 4G networks was pretty huge, and 4G to 5G is many times greater and is almost difficult to comprehend. This nine-minute video dives into how 5G will transform our world.

What's with the teen brain, anyway

In this video NIDA explores the intriguing similarities between the processes of brain development and computer programming. This analogy helps us understand why toxic environmental factors like drugs, bullying, or lack of sleep strongly affect a teenager's life The information provided by this video can empower your children or students with information they need make better decisions.

A breakdown of algorithms, how they are constructed, and their applications

Algorithms are instructions for accomplishing a goal and typically take the form of a step-by-step procedure. Given the conditions, these procedures can take various paths, increasing an algorithm's complexity. While traditional computer algorithms are programmed to account for all possible scenarios within a given process, machine learning systems build their own rules from data-driven patterns.

The first neural devices were cochlear implants, designed to treat hearing loss

Neural interfaces connect with our nervous system to restore or enhance abilities, such as hearing or movement. These neuroprostheses have enabled the restoration of mobility, communication, and activities of daily living that might otherwise require a caregiver.

An overview of 12 fundamental types of algorithms and the uses for each

These include search algorithms to quickly process and find important information, cryptographic algorithms to secure communications, randomized algorithms—such as Monte Carlo simulations for estimating probabilities of outcomes—and greedy algorithms, which make optimal choices based on available data in a step-by-step fashion, as seen in navigation apps.

The dreaming brain, as understood by scientists

Technology has advanced to where the seemingly separate fields of psychology and neuroscience are asking the same questions about dreams, such as whether they're more closely related to perception or visualization. Some field-intersecting findings include research that shows people with impaired face perception don't dream of faces, those with brain legion-related cognition difficulties deal with similar issues in dreams, and children's dreams change in parallel to their cognitive development.

Why patterns in art are so pleasing

Repeated visual cues like shapes or lines derive from the brain's innate compulsion to identify faces, places, bodies, and more. With repeated shapes, the brain seeks out resolution to the subtle variations between them. See examples in art and music.

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.

Memories are formed through attention, repetition, and emotional significance

Our brains create memories by taking in sensory input, filtering it through short-term memory, and storing it in long-term memory. Emotional events are more likely to become vivid, lasting memories because they activate stronger neural connections.

What is psychology? An overview

What is psychology and why should we study it? This short video from Macat explains how the subject has developed over the years and introduces some of the key thinkers and ideas.