AI vs. Deep Learning: What’s the Difference?

New technologies like artificial intelligence (AI) and deep learning play a vital role in shaping industries across the globe, from agriculture and manufacturing to transportation. To understand the differences between AI and deep learning, you can think of AI as a broad field of technology that focuses on creating machines capable of performing human tasks and deep learning as a machine-teaching method within the field of AI. Learn more about AI and deep learning and how these technologies significantly benefit various industries.

What is AI?

AI technology enables machines to perform tasks that humans usually do. Several examples of these tasks include:

• Analysis

• Decision making 

• Learning and comprehension

• Object identification

• Pattern recognition

• Problem-solving

Although some AI systems need human supervision to “learn,” others have designs that don’t require human supervision. 

What industries use AI, and how do they use it?

Industries of all types, including education, retail, and finance, use AI in various ways. Understanding the broad applications of AI helps to consider how it impacts various industries. Just a few of the industries that benefit from AI (and specific applications of use) include:

• Agriculture: Detection of disease, drought, pest infestation for healthier crops

• Education: Development of an individualized curriculum focusing on each learner’s strengths, weaknesses, and personal tastes

• Entertainment: Movie or music recommendations based on former picks to keep customers engaged

• Finance: Constant transaction monitoring to protect customers from nefarious banking activity

• Gaming: Realistic simulation of virtual gaming worlds and characters for more enjoyable gaming

• Manufacturing: Use of robots to perform complex or hazardous tasks so humans don’t have to

• Retail: Quick analysis of inventory and sales data for optimal pricing and profitability

• Transportation: Engine sensor monitoring in trains, planes, and other forms of transport to help prevent accidents

What are some advantages of AI technology? 

No matter the industry, AI delivers many benefits, including automating repetitive tasks and reducing the odds of human error. Consider just a few of the advantages of AI:

• AI performs repetitive and time-consuming tasks like assembly work or data entry, allowing human workers to focus on more interesting or creative tasks.

• AI reduces human error, impacting industries that rely on accuracy, such as accounting, finance, sales, or data management. 

• AI can streamline processes for greater efficiency, productivity, and cost-effectiveness.

What are some challenges of AI technology? 

Although AI offers many benefits across industries, this new technology may present a few challenges, such as susceptibility to human error. Consider the following examples.

• The efficiency and output of AI-enhanced machines can displace some human workers from their jobs.  

• Nefarious actors can use AI technology to create fake photos, videos, or audio recordings, putting people or businesses at risk.

• AI programmed and trained by humans may be susceptible to human biases or errors.

What is deep learning?

Deep learning refers to a type of machine learning that uses an artificial system called deep neural networks to process data. It helps with various tasks, including:

• Improving the customer experience with self-service platforms and chatbots

• Converting text into images and image classification

• Powering autonomous vehicles

How do neural networks work?

Neural networks process data similarly to the human brain, which explains how it got its name. Neural networks consist of various layers of connected nodes (or artificial neurons). Each node has its own weight and threshold, and when a node’s output surpasses its threshold, it “fires” (like a neuron), passing data along to other nodes.

The different layers of neural networks include:

• An input layer: Information first enters through this layer.

• One or more hidden layers: The input layer and other hidden layers send information to nodes in these layers.

• An output layer: This layer may have one or more nodes, which receive information from the hidden layers and produce a final result (or output).

Read more: 4 Types of Neural Network Architecture

What are some deep learning applications?

As a machine learning method, deep learning works particularly well for certain processes and applications ranging from natural language processing to making predictions. Popular applications include:

• Image recognition: Detection of pedestrians, traffic lights, and road signs in self-driving cars; machine “vision” used for making advanced medical diagnoses

• Processing of written and spoken language: Use of chatbots and automated customer service agents; foreign language translation apps

• Analysis and prediction making: Analysis of past consumer activity to make personalized customer recommendations for future purchases; analysis of past stock performance to predict future stock prices

What are some advantages of deep learning technology?

As a form of machine learning, deep learning offers a few key advantages to users:

• Due to their neural network design, deep learning models deliver exceptional results when performing complex tasks.

• Deep learning models respond to various learning methods, including supervised, unsupervised, semi-supervised, reinforcement, and transfer learning.

• Deep learning models get smarter with more data, just like people.

What are some challenges of deep learning technology?

You will need large volumes of data to derive benefits from deep learning, which is one of the potential drawbacks. You may undergo a few challenges like these not-so-convenient features of deep learning:

• Deep learning models require massive data sets of high-quality information for accurate performance.  

• Deep learning models require expensive and (at times, hard to find) hardware.

• Due to a lack of knowledge about how some unsupervised deep learning models learn, data scientists and other users may need help understanding their output.

AI vs. deep learning: Tips for choosing a field of study

Whether you’re intrigued by the broad nature of AI or prefer a narrower field of study like deep learning, a few tips may help you choose the field for you. Consider the following strategies:

• Take time to learn about the different available fields of study.

• Get advice from a mentor or a current or former learner.

• Explore different topics by taking introductory courses in fields that interest you. 

• Consider your strengths and how they’ll benefit you in a particular field.

• Follow your passion and choose a field of study that will hold your interest over the long term. 

Getting started in AI and deep learning on Coursera 

Technologies like AI and deep learning offer important applications and benefits across many industries, and learning more about them can help you get started down an educational path. 

Begin your journey into AI and deep learning with foundational courses on Coursera. Break into the field of AI and master the fundamentals of machine learning and deep learning with the Deep Learning Specialization offered by the experts at Deep Learning AI. You can complete this cutting-edge course within three to six months, depending on your schedule. This program covers neural networks, deep learning, sequence models, and more.