AI power generation
The concept of artificial intelligence employs the application of machine learning, subject to mimicking the aspect of human intelligence. The computer aspect is tasked with learning the approach to responding to certain actions; thus, it employs the use of algorithms alongside the historical data subject to the creation of the propensity model whereby the propensity model is then tasked with the onset of making decisions. The aspect of artificial intelligence assists in data processing efficiently and quickly (Ahmet, 2018).
Various differences exist between the concept of deep learning and machine learning. This is based on the fact that the concept of machine learning employs the application of algorithms with regards to data phrasing, earning with subject to data, and making informed decisions subject to the content learned. On the other hand, the concept of deep learning employs the structuring of algorithms with subject layers that enhance the creation of an artificial neural network capable of learning and individually making intelligent decisions. Additionally, the machine learning aspect requires human intervention, while the aspect of deep learning doesn’t need human intervention subject to recording a program. Deep learning requires high computing power, while machine learning has the possibility of running on lower-end machines without the presence of complex computing power. The machine learning approach is different in that the application of algorithms subject to machine learning parses data subject to all the parts, whereby there is a consequent combination of the parts to develop a solution. On the other hand, the deep learning systems approach takes a look with regards to the entire problem subject to one fell swoop (Dangeti, 2017).
References
Ahmet, C. (2018). Artificial intelligence: How to advance machine learning will shape the future of our world. Shockwave Publishing via PublishDrive.
Dangeti, P. (2017). Statistics for machine learning. Packt Publishing.