Machine Learning vs. Deep Learning in AI Systems
The advanced digital landscape is becoming more and more crowded with new technologies and new buzzwords to describe them. Some of these are artificial intelligence (AI), machine learning or cognitive learning, and deep learning.
But, what do they mean?
What is machine learning?
Machine learning was first to arrive on the AI scene. It has been the force that has driven AI development forward. At its core, machine learning transfers the onus of knowledge from human to machine, making it possible for the machine to learn from experience rather than continual human input.
The next frontier: Deep learning
In our effort to teach machines to think and learn as we humans do, neural networks were created. These systems are designed – at least loosely – to replicate the human brain in its capacity to retain comprehensive sets of data. At a deeper level than machine learning, it classifies this information into patterns or collections, which are then used to logically, and with a high degree of probability, interpret future datasets to produce conclusions similar to those reached by humans.
Advancing AI systems
Both machine learning and deep learning are the engines driving the advancement of artificial intelligent devices and systems. In the first instance, a machine’s ability to learn from its experiences has moved AI beyond its science fiction roots to the real world. Using machine learning algorithms, AI systems are able to look for patterns in malware files to report anomalies and predict security breaches ahead of time.
Deep learning elevates AI to the next level. Capable to take on more human-like thought processes, deep neural networks can determine and predict actions based by breaking down less quantifiable elements like pictures, audio, video, speech, written words, machine signals and more. This can result in driverless cars, better preventative healthcare and more efficient enterprise IT departments.