As industries scale, automation has become a big chunk of efficient manufacturing and service. Artificial Intelligence (AI) can be looked at as an efficient automation simulated as human intelligence. It enables machines to sense, comprehend, learn and act in a situation and teach themselves according to the outcome.
Broadly speaking, all AI technologies fall under the two umbrella definitions:
Carries out simple and result oriented tasks. Examples include personal assistants like Siri, IBM’s Deep Blue, industrial robots etc.
Is able to simulate human intelligence more closely by engaging in fuzzy logic to apply cross-domain expertise and finding a solution without human intervention.
Machine Learning in AI
Applying various domain principles from Deep Learning to parallel data processing, an AI is able to fine-tune its algorithm based on case outcomes to better suit human feedback. Machine Learning and big data algorithms allow AI software to sift through terabytes of data to appropriate a proper response. Automation and adaptive learning are core AI principles powered by their roots in machine learning.
Applications of AI Technology
The ability of an AI system to sift through massive loads of data provides it with wide applications in nearly all fields of industry:
However, as the data set for Machine Learning is chosen by a human, some bias inadvertently slips in, especially when training an AI for deep learning or a Generative Adversarial Networks (GAN).