Deep Learning vs. Machine Learning — What’s the Difference?

What is artificial intelligence (AI), exactly?

Artificial Intelligence (AI), is a science that enables machines to think and behave like humans.

Although it may seem simple, no computer can match the complexity of human intelligence. Computers are skilled at applying rules and performing tasks. However, sometimes an action that seems simple for someone might seem extremely complicated for computers.

Serving customers is not something that servers do every day. However, it is possible to carry a tray of drinks around a busy bar and serve them to the right customer.

Computers aren’t there yet, but machine learning and deep learning are steps towards a key element of this goal: analyzing large volumes of data and making decisions/predictions based on it with as little human intervention as possible.

What is machine learning?

Machine Learning is a subset in artificial intelligence that focuses on one goal: enabling computers to perform tasks without explicit programming.

Computers receive structured data in most cases and are taught to ‘learn’ how to evaluate and act on that data.

Structured data can be described as inputs that you can place in columns or rows. A category column might be called “food” in Excel. Row entries could include ‘fruit’ and’meat. This type of structured data is extremely easy to use by computers.

A computer can store new data and sort it out without human intervention once it is programmed.

The computer might eventually recognize that “fruit” is a type or food, even if your data has stopped being labeled. This self-reliance is so important to machine learning that it breaks down the field into subsets depending on how much ongoing human assistance is required.

Semi-supervised and supervised learning

Supervised learning, a subset in machine learning, requires the most continuous human involvement — hence the name “supervised”. A model is created to teach the computer how to respond to training data.

Once the model has been established, additional data can be input into the computer to check its response. The programmer/ data scientist can make accurate predictions or correct any errors. Imagine a programmer trying teach computer image classification. They would input images and ask the computer to classify them, correcting any errors or confirming each one.

This supervision over time helps refine the model to be able handle new data sets that follow the ‘learned patterns’. It is inefficient to monitor the computer’s performance continuously and make adjustments.

Semi-supervised learning is when the computer receives a mix of correctly labeled and unlabeled data. It then searches for patterns by itself. Although the labeled data is provided as guidance from the programmer they don’t issue any ongoing corrections.

Unsupervised learning

This is unsupervised learning, which uses unlabeled data. Computers are allowed to discover patterns and make associations, sometimes generating results that would have been impossible for a human data analyst.

Unsupervised learning is common with clustering. This allows the computer to organize the data into common themes or layers. This technology is used by e-commerce and shopping websites to determine which recommendations to send to particular users based upon their past purchases.

Reinforcement learning

Unsupervised or supervised learning has no consequences for the computer if it does not understand data correctly. What if the computer received positive feedback for doing the right thing and negative feedback for the wrong thing? It would then presumably learn how to accomplish specific tasks through trial and error, and know it is on the right track when it gets a reward (for instance, a score that reinforces its “good behavior”.

This reinforced learning is crucial for helping machines master complicated tasks that involve large, flexible and unpredictable data sets. Computers can use this information to perform surgery, drive cars, scan luggage, and other tasks.

What does machine learning do today?

It might surprise you to learn that machine learning tools are used every day by many people. It is used by Google to filter spam, malware and attempted phishing email out of your inbox. It is used by your bank and credit card to alert you about suspicious transactions. Machine learning is used to drive Siri and Alexa’s speech and voice recognition systems. Machine learning could also be used to scan X-rays or blood tests results for abnormalities like cancer, if your doctor refers you.

Machine learning is being used to deal with increasingly complex data as the number of applications grows. Computers that can process unstructured data such as images and video are in high demand. This is where deep learning comes in.

What is deep learning?

Machine learning refers to computers being able perform tasks without having been explicitly programmed… but computers still think and behave like machines. They are still far below what humans can do, even though they can perform complex tasks like gathering data from images or videos.

Deep learning models are a sophisticated way to use machine learning. They have been specifically designed to mimic the human brain and will be able to address these problems. Deep neural networks are complex, multi-layered systems that allow data to flow between nodes (like neurons), in highly connected ways. This results in a non-linear transformation that is becoming increasingly abstract.

Although it requires a lot of data to “feed and build” such a system it can start to produce immediate results and require very little human intervention once it is in place.

Different types of deep learning algorithms

These new goals are possible thanks to a growing number of deep-learning algorithms. Two examples will be shown to show how data scientists and engineers apply deep learning in the field.

Convolutional Neural Networks

Convolutional neural network algorithms are specifically designed for working with images. The process of applying a weight-based filter to every element in an image helps the computer understand and respond to them.

This is useful when scanning large quantities of images for one item or feature. For example, images of the sea floor to find signs of a shipwreck or photos of a crowd to show a single person’s face.

This science is known as computer vision and has been a major growth area for the industry in the last 10 years.

Recurrent Neural Networks

Recurrent neural networks introduce an important element to machine learning that is missing in simpler algorithms: memory. The computer can keep past decisions and data points ‘in memory’ and use them to review current data, introducing context.

Recurrent neural networks have become a key focus in natural language processing. The computer can understand a section of text better than a human if it has the same tone and content as the one before it. The computer can also make driving directions more accurate by remembering that it takes twice as much to drive along a Saturday night route than if everyone follows the recommended route.

Five key differences between deep and machine learning

There are many differences among these subsets, but here are five that are most important.

1. Human Intervention

Machine learning is more dependent on human intervention in order to achieve results. Deep learning is more difficult to set up, but only requires minimal intervention.

2. Hardware

Deep learning algorithms are more complex than machine learning programs. Machine learning programs can be run on most computers. However, deep learning requires far more resources and hardware. This increased power demand has led to an increase in the use of graphic processing units. The GPU’s high bandwidth memory and thread parallelism (the ability for multiple operations to run efficiently simultaneously) are reasons they are so useful.

3. Time

Although machine learning systems are easy to set up and use, their power may be limited. While deep learning systems can take longer to set up, they can produce results instantly (although their quality will likely improve as more data is available).

4. Approach

Machine learning requires structured data, and traditional algorithms such as linear regression are used. Deep learning uses neural networks and can handle large amounts of unstructured data.

5. Applications

Machine learning is already being used in your bank, email, and doctor’s offices. Deep learning technology allows for more complex and autonomous programs like self-driving cars, or robots that can perform advanced surgeries.

Machine learning and deep learning: The future

Deep learning and machine learning will have a profound impact on our lives for many generations. They will transform virtually every industry. Machine involvement could replace dangerous jobs such as space travel and work in harsh environments.

People will also turn to artificial intelligence for rich entertainment experiences that look like science fiction.

Machine learning and deep learning are great career options

To help machine learning and deep learning achieve their highest potential, it will require the ongoing efforts of skilled individuals. There are key career paths that have attracted top talent, despite the fact that each field has its own unique needs.