If you’re involved in IT, you’ve probably heard the buzzwords “deep learning” “machine learning” and “AI” all over. But what do those terms really mean?
Artificial intelligence is the way that we train computers to learn and act based on the knowledge they get from data. From the data that machines get they are able to understand more about their environment. Machine learning and deep learning are types of artificial intelligence (AI) technology used all around the world for software and programming. These kinds of artificial intelligence help machines and programs learn from the data they collect. They’re able to get smarter, having a fake form of intelligence, based on how they are used. The learn from the information we input, our online reactions, the things we spend time doing, and more. Everything that you do on a computer or smartphone is utilized in artificial intelligence to help organizations and programs better serve you.
In the IT world, AI is an exciting element of the future that continues to grow and develop. You may not realize it, but AI is all around you, and will continue to appear around you as the technology continues to progress. From your smartphone to your home alarm, AI is in technology all around you. Learn more about the definitions of machine learning and deep learning, and how they’re used in the IT world today.
Machine learning is an algorithm which “learns” or adjusts itself based on the data that it processes. It’s somewhat self explanatory; it's a technique when a machine gets more information and becomes smarter based on the data it gets. The computer or machine can then adjust how it performs and operates based on what it’s learned. Algorithms are used to enhance the predictions and computing of machines. This kind of AI can’t function on its own; it requires a programmer for correction if the program starts to make incorrect assumptions based on the info it has.
There is great potential when it comes to AI for all kinds of organizations, from business and healthcare to government and tech. It can help create continuous improvement, it makes data processing and learning easier with automation, it can help easily identify trends and patterns, and can result in overall better experiences for everyone as the machine learns and adapts to enhance an organization.
While there are many advantages of this kind of learning, there are some disadvantages as well. This kind of learning takes time, and can be incorrect unless human intervention is there. There is a high chance for errors, so programmers have to be diligent with their technique to keep errors to a minimum. Artificial intelligence is also a large drain on time and resources, so organization’s need to be prepared for the expense.
Overall however, these algorithms are a great option for organizations as they work to incorporate data and improve their technology every day. This is a technology that will not go away anytime soon, and more computers and machines than ever utilize this AI to better serve. It’s important to understand how AI works all around you, and how it is changing technology and businesses around the world.
You may understand what machine learning is, but how does it work in real life? Take a look at these examples all around you.
Smart home assistants. Google, Siri, and Alexa are great examples of machine learning. When you tell your smart home assistant about your routine, that routine is remembered and implemented for the future. The algorithms used in smart home devices are extremely advanced, and getting smarter all the time.
Social media services. Your Twitter and Facebook feed learn about your preferences and serve you more of the content you want. Similarly, you are served ads based on the data your social media services learn about you.
Music services. Your favorite music provider like Spotify or Apple Music learns about the kind of music you like, and suggests new things that you might enjoy listening to as a result. Algorithms are key in determining the genre of music to suggest similar options for you.
Deep learning is a type of machine learning, but it’s far more advanced and capable of self-correction. That's the main difference these two kinds of learning—the need for computing intervention and the kinds of algorithms used. Deep learning doesn’t require human intervention, while basic machine learning may interpret data incorrectly and need fixing, deeper kinds of learning don't have that issue. It works well with larger sets of data so it’s beneficial for organizations that really want to parse huge data sets.
Deep Neural Networks (DNN).
When we talk about deep learning, we have to mention deep neural networks (DNN). They are part of what's known as artificial neural networks that are an important technique for how machines are able to store data. The term “deep” is referring to the number of hidden layers in a neural network. Normal neural networks have two or three hidden layers, while deep neural networks have as many as 150. Deep models use the large sets of labeled data and artificial neural network architectures so they can learn directly from the data, instead of needing a manual extraction.
These deep neural networks allow for a lot more space for data to live, and the program can continue to learn with all the deeply hidden data its storing. The neural networks help a deep learning program self-correct. If it detects that something is wrong, if it’s assuming incorrectly or learning incorrectly, it’s able to call on the deep neural networks to correct. Neural networks are a subset of all kinds of artificial intelligence, but the depth of the neural network will vary based on the kind of computer being used.
here are many examples in the world around us including:
Translation services. Many language translation services rely on deep learning to help them translate quickly and accurately. The deep learning element of this AI allows the program to look into the neural networks to find the right translation. Prediction is an important element of translation services and is possible thanks to neural networks. The algorithms used in translation services are important in making sure the grammar, not just the words, are translated correctly.
Color addition. Adding color to black and white images is another example of deep learning programming. This process would normally be done manually and take extensive time, but deep learning and stored neural networks can recognize and learn about the colors and do it much faster.
Autonomous vehicles. Autonomous vehicles rely heavily on deep learning to work. They need to learn about traffic signs, pedestrians, other vehicles, and more. All of these is needed to keep autonomous vehicles safe. Neural networks are crucial in autonomous vehicle technology.
Both of these kinds of learning are going to keep developing over time. Due to the increasing complexity and demand for AI and similar technologies, there will be an increasing demand for people with IT degrees who can work with these technologies. As industries adapt to leverage machine learning and AI, there is still a huge need for data analytics experts and data science professionals to drive the development of these systems, and enable organizations to take advantage of the explosion of data.
Many people are concerned about what AI means for the future. Artificial intelligence and automation doesn't mean that jobs are going to disappear overnight. However, they do pose the reality that certain jobs will be automated over time, and that professionals with IT and tech training will be in much higher demand. So a degree in information technology could help you be prepared for this future.
There are many career paths you can follow if you find machine learning and deep learning interesting, including:
Machine learning engineer
Business intelligence developer
Machine learning designer
If you’re ready to take the chance and leap into machine learning, WGU offers IT degree programs that will help prepare you for a future in this exciting technological realm.