As back to school season is upon us, it provides a great framework for achieving a better understanding of artificial intelligence (AI) and how it relates to machine learning (ML) and deep learning (DL). These terms are often incorrectly used interchangeably, but they are in reality very distinct and different concepts.
Just like our own need as people and a society for ongoing learning is unending, the opportunities for adoption and implementation of AI seems everywhere. Growing numbers of executives believe AI will be of critical importance within the next two years, and ML as well as DL are being adopted by more than half of the commercial companies today. These findings, based on survey input from major industry leaders, along with other crucial factors such as the low cost of computation and our current data avalanche spurred by the massive amount of unstructured data being created every minute by mobile devices, the Internet of Things (IoT) and the growth of connected devices, all contribute to the hype around AI.
While this interest in and demand for AI grows, as often is the case with rapidly emerging technologies, we see a great need for those within government and industry to better understand what AI, ML, and DL are, and how they differ.
The federal government has a trove of data that could, and rightly should, benefit from the promise of AI. But the first step to realizing that promise is to understand what each mean and how they are connected. The reality is that DL is a type of ML algorithm, and ML is a type of AI. But what’s the difference?
Artificial Intelligence is Like a School
AI is often thought of as a system. However, it actually is implemented within a system. AI is the overarching umbrella technology, a school per se, in which ML and DL reside. Simply put, AI is used to train computers to do things better than humans. It is programming intelligence to create a library of knowledge to increase the chance of success in problem solving. By applying AI and this set of knowledge to the massive amounts of data collected by an agency, it can help to improve over time without manual intervention.
Machine Learning is Like a Classroom
ML is a field of study, in which computers can learn by their own without being explicitly programmed. Kind of like a classroom for computers, ML is an environment that is set up that allows the system to understand the data as part of the program. This application of AI gives the system the ability to automatically learn and improve from experience. Its algorithms are iterative in nature and constantly learning and seeking to optimize outcomes.
Deep Learning is Like the Student, Brain Included
DL is the closest imitation of how the human brain works and includes the use of neural networks. If AI is the school, and ML is the classroom, DL is the student – complete with their very own brain. As a subset of ML, DL involves running data through a large network of neurons to solve core AI problems, such as distinguishing images. The main difference between ML and DL is that with ML, the model still needs some guidance. In ML, if there is an inaccurate prediction, the programmer must fix that problem. In the case of DL, the model corrects the problem itself.
Putting AI into Action
As our government agencies, similar to the commercial sector, increase their integration of AI, ML and DL into systems and applications to help use, analyze and manage the ever-increasing amount of data being generated all around us, the platform and processes for effectively managing and storing that data and those functions becomes an even more important consideration.
Because of the opportunities that AI offers for playing an important role in the management and use of data, every agency should be considering AI – and, possibly, its subsets – for better leveraging data for mission success and greater constituent service. However, the first step toward effective adoption and use of AI is understanding what you need to accomplish and which of the components of AI will help you achieve it.
