Organizations are turning to machine learning in droves to differentiate and innovate their offerings. You might recognize it. It is what Apple uses (along with many other online vendors) to present customers with relevant apps or products. Gartner Fellow and Vice President Tom Austin recently noted that about half of large enterprises are experimenting with “smart computing” projects.
There are also some vendors that categorize their solutions as machine learning, whether they meet the definition or not. With all the jargon, there is bound to be confusion. Approaches like cognitive computing, come in many flavors – machine learning, natural language processing and deep learning. And old terms, like neural networks, are coming back.
Unlike standard algorithms that are designed to perform a particular task, machine learning methods are designed to learn how to perform a task – learning as they are exposed to data. Just as humans have different learning styles, machines can learn in different ways. These learning methods include supervised learning, semi-supervised learning, unsupervised learning, and reinforcement techniques.
Machine learning lies at the heart of many advanced intelligence solutions, from AI to deep learning neural networks to natural language processing (NLP) and cognitive computing.
Today’s market is seeing an explosion in machine learning – from AI to deep learning to cognitive computing. Why now? We owe these breakthroughs to advances in inexpensive commodity hardware that can be chained together to form massively parallel computational environments. Machine learning software can now execute across hardware clusters, running learning processing in tandem- whether in-memory, in-database or both. These environments can hold all the big data necessary to feed greedy methods like deep learning. By centralizing input data, these systems give algorithms unprecedented maneuverability to cycle through neural layer iterations, test reinforcement rewards and fuse different types of data – while delivering answers at human-like speed.
>>Read more by Fiona McNeill and Dr. Hui Li, Datanami, January 31, 2017