Artificial Intelligence (AI), Machine Learning, and Deep Learning are being used more frequently, quite often being interchanged for one another when they shouldn’t be.
So, what are the differences between the three and how should they apply?
AI is a branch of computer science aimed at the simulation of intelligent behavior in computers and the capability to imitate intelligent human behavior. By providing pre-defined input and programming a system that can ‘think’, and carry out tasks similar to humans.
A subset of AI, Machine Learning focuses on how to make machines learn on their own using pattern recognition, statistical modeling and .
At its core, machine learning is a collection of algorithms that can learn from and make predictions based on recorded data, optimize a given utility function under uncertainty, extract hidden structures from data and classify data into concise descriptions. – Amazon Web Services
Where typical computer programming generates code-specific outputs (e.g. if this, then that), Machine Learning uses ever-changing data inputs to generate statistical models that predict the correct result based on prior patterns and positive outcomes. Companies like Amazon have highly leverage machine learning to help grow their business – yes, even know which products to suggest based on your buying history and others.
A subfield of Machine Learning is Deep Learning is machine learning at a much deeper level, based on deep neural networks. It uses layers of nodes to perform calculations, each node connected to a network, each providing information and calculations back to cohesively solve a problem end-to-end. With each node working simultaneously, reaction and processing time is greatly decreased, allowing for faster response time. Deep learning is part of the driverless car effort and even chatbots like Amazon’s Alexa or Google Home carrying out a conversation with you.
Since the first use of the term artificial intelligence in 1956, the field of AI has grown and has the attention of all industries, splintered into specialized areas and evolved into creating AI tools and services that complement humans. Here are 6 definitions of AI and a look at.
Learn how Artificial Intelligence (AI) solves cognitive problems commonly associated with human intelligence through machine learning and deep learning. Broadly, these techniques are separated into “supervised” and “unsupervised” learning techniques, where “supervised” uses training data that includes the desired output, and “unsupervised” uses training data without the desired output.
The term “Artificial Intelligence” has been floating around for a while. We see it in sci-fi movies, “AI” game bots we play against, Google search, and, oh yeah, those robots that are some day going to take over the world. Off late, though, “Machine Learning” and “Deep Learning” have surfaced, with many asking what exactly each of these are.
Deep learning is basically machine learning on a “deeper” level (pun unavoidable, sorry). It’s inspired by how the human brain works, but requires high-end machines with discrete add-in graphics cards capable of crunching numbers, and enormous amounts of “big” data. Small amounts of data actually yield lower performance.