Artificial intelligence (AI)

is intelligence exhibited by machines. In computer science, an ideal “intelligent” machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal.

 

Artificial superintelligence

is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds.

 

Autoencoder

or Diabolo network is an artificial neural network used for unsupervised learning of efficient codings. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. Recently, the autoencoder concept has become more widely used for learning generative models of data.

 

Computer vision

is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.

 

Convolutional neural networks

In machine learning, a convolutional neural network (CNN, or ConvNet) is a type of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex.

 

Decision tree

is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.

 

Deep learning

(also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data.

 

Generative adversarial networks

are a branch of unsupervised machine learning, implemented by a system of two neural networks competing against each other in a zero-sum game framework. They were first introduced by Ian Goodfellow et al. in 2014. This technique can generate photographs that look authentic to human observers.

 

Image synthesis

or rendering is the process of generating an image from a 2D or 3D model (or models in what collectively could be called a scene file) by means of computer programs. Also, the results of such a model can be called a rendering.

 

Machine learning

is the subfield of computer science that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).

 

Natural language processing

is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve: natural language understanding, enabling computers to derive meaning from human or natural language input; and others involve natural language generation.

 

Neural networks

(also referred to as connectionist systems) are a computational approach, which is based on a large collection of neural units (AKA artificial neurons), loosely modeling the way a biological brain solves problems with large clusters of biological neurons connected by axons.

 

Recurrent neural network

is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. This makes them applicable to tasks such as unsegmented connected handwriting recognition or speech recognition.

 

Reinforcement learning

is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics, and genetic algorithms.

 

Supervised learning

is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

 

Unsupervised learning

is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution – this distinguishes unsupervised learning from supervised learning and reinforcement learning.

Popular AI platforms and libraries A-Z

OpenCV

Open Source Computer Vision

http://opencv.org

OpenAI’s Universe

Universe contains an ever-expanding catalog of environments. Environments will soon include programs from EA, Microsoft Studios, Valve, Zachtronics

https://universe.openai.com

OpenAI’s Gym

OpenAI Gym lets you upload your results or review and reproduce others’ work. Each task is versioned to ensure results remain comparable in the future. 

https://gym.openai.com

Tensorflow

An open-source software library for Machine Intelligence. 

https://www.tensorflow.org

Scikit-learn

is a free software machine learning library for the Python programming language. 

http://scikit-learn.org

Theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

https://github.com/Theano

Keras

is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. 

https://keras.io

Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. 

http://caffe.berkeleyvision.org