Cluster: Artificial intelligence

Artificial intelligence (AI) is intelligence exhibited by machines. This is a cluster of topics in AI or related to AI.

Types of artificial intelligence

Artificial Intelligence (AI) is classified into types based on the degree to which an AI system can replicate or go beyond human capabilities. One classification system uses four types: reactive machines, limited memory machines, theory of mind and self-aware AI. Another classification divides AI into two divisions: Weak AI or Narrow AI and Strong AI or General AI or Artificial General Intelligence. Different branches of AI are referred to by the method used to achieve AI.

Machine learning

Machine learning is a technique for realizing AI and it is an application of AI where machines are given access to data from which they learn for themselves.

Machine learning tools

Tools, algorithms, libraries and interfaces for machine learning.

Neural networks

Artificial neural network (ANN) processing devices can be algorithms or actual hardware that are loosely modeled after the neuronal structure of the mammalian cerebral cortex. Neural networks are used in the branch of machine learning called deep learning. The following are types of neural networks used in machine learning as well as topics associated with neural networks.

Deep learning frameworks

A Deep Learning Framework is an interface, library or a tool which allows users to build deep learning models more easily and quickly, without getting into the details of underlying algorithms. Libraries are useful for individuals who want to implement Deep Learning techniques but don’t have robust fluency in back-propagation, linear algebra or computer math. These libraries provide pre-written code for functions and modules that can be reused for deep learning training for different purposes.

Reinforcement learning

Reinforcement learning is an area of machine learning focusing on developing agents that can learn from their environment over time by taking actions and receiving rewards. The following are algorithms, tools and research topics related to reinforcement learning.

Supervised learning

Supervised learning is a type of machine learning in which data is fully labelled and algorithms learn to approximate a mapping function well enough that they can accurately predict output variables given new input data. This section contains supervised learning techniques. For example, Gradient Descent is a technique to optimize neural networks in supervised machine learning. Gradient descent optimization algorithms are used to speed up the learning process of deep neural networks. Another example, Support Vector Machine (SVM), is a type of algorithm that is a discriminative classifier formally defined by a separating hyperplane used for regression and classification tasks.

Decision trees

A decision tree is a model for supervised learning that can construct a non-linear decision boundary over the feature space. Decision trees are represented as hierarchical models of "decisions" over the feature space, making them powerful models that are also easily interpretable.

Unsupervised learning

Unsupervised learning is a branch of machine learning focused on structuring data that has not been labeled. The following are methods used in unsupervised machine learning.

Clustering

In unsupervised machine learning, clustering is the process of grouping similar entities together in order to find similarities in the data points and group similar data points together.

Ensemble learning

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model. The purpose is to decrease variance (bagging), bias (boosting), or improve predictions (stacking).

Dimensionality reduction

In machine learning classification problems when there are too many factors or variables, also called features. When most of the features are correlated or redundant, dimensionality reduction algorithms are used to reduce the number of random variables. Certain features are selected and others are extracted.

Parameterized statistical models

Machine learning models are parameterized to tune their behavior for a given problem. Noise contrastive estimation (NCE) is an estimation principle for parameterized statistical models. NCE is a way of learning a data distribution by comparing it against a defined noise distribution. The technique is used to cast an unsupervised problem as a supervised logistic regression problem. NCE is often used to train neural language models in place of Maximum Likelihood Estimation.

Computer Vision

Computer vision is the ability of artificially intelligent systems to “see” like humans. In the computer vision field machines are developed that automate tasks that require visual cognition. Deep learning and artificial neural networks are used to develop computer vision. The following are topics related to computer vision as well as tools and libraries. Companies developing or selling computer vision products are under the Computer Vision subheading under the AI applications and companies section.

Natural language processing

Natural language processing is a branch of AI that helps computers understand, interpret and manipulate human language. The following are tools and topics related to NLP. NLP companies developing or selling NLP applications are found in the AI applications and companies section under Natural language processing.

Quantum Computing

Advances in deep learning are expected to increase understanding in quantum mechanics. It is thought that quantum computers will accelerate AI. Quantum computers have the potential to surpass conventional ones in machine learning tasks such as data pattern recognition. The following are topics, companies and technologies that link quantum computing and AI.

Semantic Computing

Semantic computing deals with the derivation, description, integration and use of semantics (meaning, context and intention) for resources including data, document, tool, device, process and people. Semantic computing includes analytics, semantics description languages, integration of data and services, interfaces and applications. In AI, semantic computing involves the creation of ontologies that are combined with machine learning to help computers create new knowledge. Semantic technology helps cognitive computing extract useful information from unstructured data in pattern recognition and natural-language processing.

IoT (Internet of Things)

The Internet of Things (IoT) refers to objects that connect and transfer data via the internet and the sharing of information between devices. IoT based smart systems generate a large volume of data including sensor data valuable to researchers in healthcare, bioinformatics, information sciences, policy and decision making, government and enterprises. AI can be combined with machine learning for analysis of data and prediction.

Artificial life and evolutionary computation

While some lines of AI research aim to simulate the human brain. Artificial life or animate approach is concerned with the conception and construction of artificial animals as simulations or actual robots. It aims to explain how certain faculties of the human brain might be inherited from the simplest adaptive abilities of animals. Evolutionary computation is a generic optimization technique that draws inspiration from the theory of evolution by natural selection.

Artificial intelligence companies

The following are companies using AI to develop products or producing AI software for various applications. AI programs designed for a specific applications are also listed.