Neural Networks

Extracted Attributes

• Category

  Computational Model

• Model type

  Probabilistic model

• Inspiration

  Biological neural networks; Human brain structure and functions

• Also known as

  Artificial Neural Network (ANN), Neural Net, Simulated Neural Network (SNN)

• Field of study

  Machine learning, Deep learning

• Core components

  Interconnected artificial neurons (nodes) and edges (synapses)

• Primary function

  Process data, learn patterns, make decisions, classify and cluster data

Timeline

• Neural networks are utilized as probabilistic models, often combined with deterministic models to form hybrid models, with AlphaGo and AlphaZero serving as key examples. (Source: Related Documents)

  Ongoing

• Google DeepMind deploys models like Gemini, which leverage neural networks, to billions of users as a central AI engine for Google and Alphabet Inc. (Source: Related Documents)

  Ongoing

• AlphaFold technology, powered by neural networks, is actively used to accelerate scientific discovery. (Source: Related Documents)

  Ongoing

• Isomorphic Labs, a spinout company, leverages AlphaFold (which uses neural networks) to revolutionize drug discovery through partnerships with major pharmaceutical companies. (Source: Related Documents)

  Ongoing

• Genie 3, an Interactive World Model utilizing neural networks, is unveiled as a groundbreaking example for advancing robotics and embodied AI. (Source: Related Documents)

  Recent/Ongoing

• Demis Hassabis predicts that Artificial General Intelligence (AGI), which will require fundamental breakthroughs in areas like AI creativity and continual learning (and thus advanced neural networks), is likely 5-10 years away. (Source: Related Documents)

  Predicted (5-10 years away)

• AGI, enabled by advanced neural networks, is envisioned to usher in a new Golden Age of Science and create numerous scientific breakthroughs. (Source: Related Documents)

  Predicted (within next decade)

Web Search Results

• Neural network (machine learning) - Wikipedia

  In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure and functions of biological neural networks. [...] A neural network consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain. Artificial neuron models that mimic biological neurons more closely have also been recently investigated and shown to significantly improve performance. These are connected by edges, which model the synapses in the brain. Each artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. The "signal" is a [...] ANNs are composed of artificial neurons which are conceptually derived from biological neurons. Each artificial neuron has inputs and produces a single output which can be sent to multiple other neurons. The inputs can be the feature values of a sample of external data, such as images or documents, or they can be the outputs of other neurons. The outputs of the final output neurons of the neural net accomplish the task, such as recognizing an object in an image.[citation needed]

• What is a neural network? | Types of neural networks - Cloudflare

  A neural network, or artificial neural network, is a type of computing architecture that is based on a model of how a human brain functions — hence the name "neural." Neural networks are made up of a collection of processing units called "nodes." These nodes pass data to each other, just like how in a brain, neurons pass electrical impulses to each other. [...] Neural networks are used in machine learning, which refers to a category of computer programs that learn without definite instructions. Specifically, neural networks are used in deep learning — an advanced type of machine learning that can draw conclusions from unlabeled data without human intervention. For instance, a deep learning model built on a neural network and fed sufficient training data could be able to identify items in a photo it has never seen before. [...] Neural networks come in various types based on their structure and function. Common types include shallow and deep neural networks, perceptrons, multilayer perceptrons, feed-forward and recurrent neural networks, modular networks, radial basis function networks, liquid state machines, and residual networks. Each type differs in how data flows and how layers interact, from simple forward-only designs to complex architectures that loop, skip, or combine outputs.

• What is a Neural Network? - AWS

  A neural network is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning (ML) process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like

• What is a Neural Network? - GeeksforGeeks

  # What is a Neural Network? Last Updated : 07 Aug, 2025 Suggest changes 59 Likes Neural networks are machine learning models that mimic the complex functions of the human brain. These models consist of interconnected nodes or neurons that process data, learn patterns and enable tasks such as pattern recognition and decision-making. [...] Neural networks are important in identifying complex patterns, solving intricate challenges and adapting to dynamic environments. Their ability to learn from vast amounts of data is transformative, impacting technologies like natural language processing, self-driving vehicles and automated decision-making. [...] 5. Gaming and Autonomous Systems: Neural networks enable real-time decision-making, enhancing user experience in video games and enabling autonomous systems like self-driving cars.

• What is a Neural Network? | IBM

  Neural networks are sometimes called artificial neural networks (ANNs) or simulated neural networks (SNNs). They are a subset of machine learning, and at the heart of deep learning models. Industry newsletter ### The latest AI trends, brought to you by experts Get curated insights on the most important—and intriguing—AI news. Subscribe to our weekly Think newsletter. See the IBM Privacy Statement. ### Thank you! You are subscribed. [...] My IBM Log in Subscribe # What is a neural network? ## What is a neural network? A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions. [...] Neural networks rely on training data to learn and improve their accuracy over time. Once they are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the best-known examples of a neural network is Google’s search algorithm.

• Instance Of

  Q737498

• Inception Date

  1/1/1988