Artificial intelligence, machine learning, and deep learning are interrelated, but built on different layers of abstractions. One thing they have in common is that all of these applications—such as self-driving cars or computer programs that help physicians determine the chance of a patient having a heart attack—need ever-increasing volumes of big data and computational power to produce impactful results.
Fundamentals of Machine Learning now.
Artificial intelligence (AI) is the theory and development of machines mimicking human intelligence to perform tasks. AI tries to replicate part or all of human intelligence in an application, system, or process. Examples of AI systems include speech recognition, visual perception, and language translation. Machine learning and deep learning are subsets of artificial intelligence.
Machine Learning and Deep Learning
Machine learning (ML) is a subfield of AI that uses artificial neural networks (ANNs) to mimic how humans make decisions. Machine learning enables computers to learn — on their own, without being programmed — from large datasets. It is used to identify trends in large volumes of data and statistical modeling.
Drilling down one layer further is deep learning (DL) — one of several approaches to machine learning. Deep learning uses deep neural networks to learn patterns from massive amounts of data. Neural networks are sets of algorithms, modeled after the biological structure of the human brain, that each focus on a specific layer of the task to learn. Examples include Netflix’s recommendation system and MIT’s algorithm that can very quickly predict future behavior.
A simple way to understand the difference between each is to visualize the problem of how to train a computer to recognize a picture of a cat:
- Artificial intelligence would require a programmer to write all the code required for a computer to recognize a cat.
- Machine learning would require programmers to teach the system how to learn what a cat looks like by feeding it images and correcting its analysis until the computer became accurate.
- Deep learning would divide the task of recognizing a cat into different layers — one layer of the algorithm learns to recognize the eyes, one the general shape, etc. The connected layers, then, produce the machine learning capability.
Machine learning and deep learning make AI smarter and more accessible.
AI, ML, and DL in the Cloud
Advancements in cloud technologies are making AI, ML, and DL more accessible. Cloud AI service providers such as Amazon Machine Learning, Microsoft Azure, and Google Cloud AI provide shared resources (network, compute, memory, disk) that are cost-effective, easy-to-use, and scalable.
Cloud integrated technology platforms — IaaS, PaaS, SaaS, and iPaaS — allow even small- and mid-sized companies to harness the power of big data storage and analytics. AI APIs, ML algorithms, deep learning, facial recognition, data visualization, computer vision, and natural language processing techniques are integrated into the service, with computations done remotely by the data center. Specialized training in data science is not required.
These improved big data integration solutions and platforms continue to accelerate the development of AI, ML, and DL.
Artificial Intelligence and Talend
The impactful AI business applications available today are dependent upon relevant, reliable, quality data. You cannot have one without the other. As volumes of big data and computing power increase, and technologies advance, the realization of full AI — autonomous sentience — gets closer every day.
To learn more about artificial intelligence, machine learning, and deep learning, and using Spark machine learning components with Talend, don’t miss the on-demand webinar Fundamentals of Machine Learning.