Artificial Intelligence (AI) technology matured over the last few years into a disruptive technology. Breakthroughs in a technique called deep learning set new performance levels for various AI applications including speech recognition, language translation, recommendation and computer vision. It has enabled a first wave of successful consumer applications driven by companies like Google, Uber, Facebook and Netflix. Now a second AI wave of industrial applications is on the horizon, including predictive maintenance, quality control, smart automation and process optimization.
Machine Learning is a new way of programming. In the traditional way of programming, a human expert creates a program by defining rules that processes data into some output. Machine learning uses a different approach to create a program. By giving it a dataset with the desired output, the machine learning algorithm learns by itself how to generate an output for new unseen data.
ML is adopted by almost all industry segments, from finance to manufacturing. Predictive models help reducing cost, improving quality and enabling new applications.
Deep Learning is a new disruptive technology. There are four different techniques, each optimized for specific applications:
Artificial Neural Networks – A machine learning AI algorithm that learns nonlinear relationships from data.
Convolutional Neural Networks – AI algorithms specialized for learning from image data. Used a.o. for detecting objects and people in photo’s and analyzing medical or scientific images.
Recurrent Neural Networks – AI algorithms specialized for learning from time series data. Typically used for language and speech processing, sensor data processing and video processing.
Reinforcement learning – AI algorithms specialized for learning to take optimal sequences of actions. Typically used for process optimization tasks and (robotic) automation.
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