Artificial intelligence is not in itself a separate, individual technology but consists of a range of different technologies and methods. These include machine learning and its various forms, the visual recognition of objects and speech recognition. The AI technologies and methods that are currently receiving the most attention are described below (Stanford University, 2016):
Large-scale machine learning: concerns the design of learning algorithms and the scaling of existing algorithms to work with extremely large data sets
Deep learning: A subclass of machine learning methods (see above), has enabled machine object recognition in images and activity recognition in videos and has led to significant advances in other areas of machine perception such as audio and speech recognition and natural language processing
Reinforcement learning: concept in which the focus of machine learning shifts from recognizing patterns to experience-based sequential decision making; reinforcement learning can drive AI applications to perform actions in the real world. While the concept has been limited to science in recent decades, there are now also practical and applied successes from the real world.
Computer vision (machine image recognition): Currently the most widespread form of machine perception, computer vision is a subfield of AI that has been significantly influenced by the emergence of deep learning in particular: For the first time, computers are able to perform some perceptual tasks better than humans. Much of the current research is focused on automatic image and video analysis.
Furthermore, the following AI technologies and methods are also among those currently receiving the most attention:
Natural Language Processing, Collaborative Systems, Robotics, Internet of Things, Crowdsourcing and Human Computation as well as Neuromorphic computing.
Source: Pöchhacker Innovation Consulting (2017)
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