Training of machine learning systems takes time, and requires rote learning, parameter adjustment, macro-operators, chunking, explanation-based learning, clustering, mistake correction, case recording, multiple model management, back propagation, and the previously mentioned algorithms.
Machine learning can be included in the demonstrational programming ideal, where an end-user development technique is created by teaching a computer about new behaviours based on concrete examples.
Applications of machine learning include, but are not limited to, syntactic pattern recognition, structure health monitoring, bioinformatics, bank fraud detection, online advertising, and so on.
Some commonly used applications today are personal assistants (such as Google Now, Microsoft Cortana or Apple Siri), Google’s driverless car and governmental surveillance systems.
All in all, the AI machine learning market is one where trillions of dollars will be made. For example, according to BCC Research and Siemens, the machine learning digital assistant market alone is expected to reach a market value of $41bn by 2024, up from $6bn in 2014.
The same sort of values repeat themselves in expert systems, autonomous robots, embedded systems and neurocomputers that use machine learning to carry out that functions.
Growth rates are also expected to accelerate across all five machine learning verticals.. Between 2014 and 2019, the median yearly growth rate is set to be 20.8%. Between 2019 and 2024, this will increase to a median of 22.8%.