They place two neural networks--a model and a discriminator--in a competitive setting. The discriminator's objective is to correctly label samples from either the model or the training data. The model's objective is to deceive the discriminator, in other words, to produce

Recently, a new class of machine learning algorithms has emerged, where models and discriminators are generated in a competitive setting. The most prominent example is Generative Adversarial Networks (GANs). In this paper we examine how these algorithms relate to the Turing test, and derive what--from a Turing perspective--can be considered their defining features. Based on these features, we outline directions for generalizing GANs--resulting in the family of algorithms referred to as Turing Learning. One such direction is to allow the discriminators to interact with the processes from which the data samples are obtained, making them "interrogators", as in the Turing test.

Recently, a new class of machine learning algorithms has emerged, where models and discriminators are generated in a competitive setting. The most prominent example is Generative Adversarial Networks (GANs). In this paper we examine how these algorithms relate to the Turing test, and derive what - from a Turing perspective - can be considered their defining features. Based on these features, we outline directions for generalizing GANs - resulting in the family of algorithms referred to as Turing Learning. One such direction is to allow the discriminators to interact with the processes from which the data samples are obtained, making them "interrogators", as in the Turing test. We validate this idea using two case studies. In the first case study, a computer infers the behavior of an agent while controlling its environment. In the second case study, a robot infers its own sensor configuration while controlling its movements. The results confirm that by allowing discriminators to interrogate, the accuracy of models is improved.

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.

A breakthrough will allow machines to learn by observing. This Turing Learning, as its inventors have named it, promises smarter drones that could detect militants engaging in behavior that could endanger troops, like planting roadside bombs. Still in its infancy, the new machine learning technique is named for British mathematician Alan Turing, whose famous test challenges artificial intelligences to fool a human into thinking he or she is conversing with another human. In Turing learning, a program dubbed the "classifier" tries to learn about a system designed to fool it. In certain ways, Turing Learning resembles many existing machine-learning systems.

It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield. This could mean advances in the world of technology with machines able to predict, among other things, human behaviour. The discovery takes inspiration from the work of pioneering computer scientist Alan Turing, who proposed a test, which a machine could pass if it behaved indistinguishably from a human. The interrogator has to find out which of the two players is human. If they consistently fail to do so - meaning that they are no more successful than if they had chosen one player at random - the machine has passed the test, and is considered to have human-level intelligence.

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models.

It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield. This could mean advances in the world of technology with machines able to predict, among other things, human behaviour. The discovery takes inspiration from the work of pioneering computer scientist Alan Turing, who proposed a test, which a machine could pass if it behaved indistinguishably from a human. The interrogator has to find out which of the two players is human. If they consistently fail to do so – meaning that they are no more successful than if they had chosen one player at random – the machine has passed the test, and is considered to have human-level intelligence.

A new breed of robots could someday learn to act human without being told how to behave. These machines are able to learn both how natural and artificial systems work by simply watching them. This could mean advances in the world of robotics with machines able to predict, among other things, human behaviour, as well as imitate it. A new breed of robots could someday learn to act human without being told how to behave. The researchers put a swarm of robots under surveillance, and wanted to find out the rules which governed their movements.