By Jimmy Cantrell
The human brain contains millions of neurons that allow us to process vast amounts of information from multiple sources. Our senses feedback data from our experiences to our brains, which organizes the data into patterns to allow us to understand the world around us.
Deep learning is a subset of machine learning that uses algorithms built on neural networks modeled after the human brain. With multiple layers working together inside the computer, artificial neurons or ‘nodes’ use mathematical calculations to process data to solve complex problems, much like how our own brains do.
In this short guide, we explore how deep learning works in more detail and how it can be useful in our day-to-day lives.
How it works
Firstly, the computer is given input, which could be a problem or a task. The data is passed through hundreds of internal, hidden layers which work at lightning speed to compare the input to millions of data sources in an attempt to categorize it.
Imagine being asked to identify an animal – your brain would compare the fur, size, shape, features and physical indicators to your internal knowledge of the animal kingdom. Similarly, if a computer was asked to do the same, it could use the internet to scan all available information on animals in order to accurately identify the answer.
The output is the final layer, and this can be as simple as ‘yes’ or ‘no’ or have multiple possible answers. All of this happens within seconds, enabling deep learning machines to quickly surpass the speed of humans when given specific tasks. Of course, the potential power of deep learning software can seem scary, but researchers assure us that we’re far from the robotic uprising portrayed in futuristic sci-fi movies.
According to Gartner, only 54% of AI projects reach the production stage. Any AI software also requires an immense amount of human input to create, maintain and train the software in order for it to run effectively and accurately, though deep learning software does require less input than traditional machine learning.
Benefits of deep learning software
Where traditional machine learning software can recognize patterns and provide speedy solutions in many instances, it relies on supervised learning which limits the software to providing only predetermined answers. In contrast, deep learning networks can organize data into new categories, subsets or lists without a human needing to input all the options first. Much like how our brains work, these innovative neural networks can create new answers and give solutions based on their own research.
There are several different deep learning neural networks, which are usually built for specific tasks such as natural language processing. This technology allows computers to comprehend text and speech in multiple different languages and can be revolutionary for bridging communication gaps in education, business and healthcare.
For example, an intermediate care facility in Montgomery County received a $44,000 humanoid robot to help bridge communication gaps and entertain the children, as well as to support staff by performing simple medical tasks such as double-checking dosage information.
AI in education
With AI clearly here to stay, educators are looking for ways to integrate the new technologies into their schools and institutions. With the potential to save teachers time, speed up lesson planning and provide personalized support to children from different backgrounds, the possibilities are endless. AI is transforming the way we interact with each other, work and solve problems in the modern world – and it’s fast becoming its own academic category in itself.
Dan Fitzpatrick, a key speaker at FETC 2024, spoke of the importance of ensuring educators receive opportunities for training in AI, to enable them to introduce this innovative technology to their students and make the most of its capabilities.
Learn more about AI for education
Jimmy Cantrel
As an educator and technology enthusiast, Jimmy enjoys sharing the benefits and challenges of AI with others to help integrate it into society and dispel common fears and myths surrounding new software.
