For many of us, using a computer is a part of life. Whether sending messages to loved ones, completing work assignments on a laptop, or playing our favorite video games, computers provide a world of opportunities to connect. In recent years, however, there has been a revolution in the technologies that support modern computer users. Gone are the days of Clippy – instead, sophisticated neural networks and AI models such as Siri, Alexa, and Cortana can help us do all sorts of things, from ordering groceries to finding places.
Often, we take these tools for granted – but understanding how these models work can be instrumental in working in the jobs of tomorrow, including those who are completing an online masters in information technology. Let’s take a look at one such AI model, looking to shape the way we use computers in the 2020s – the neural network. How does it work, and why does this radical technology look set to change the world – for the better?
Machine Learning and AI
Before diving into the world of neural networks, we must first understand machine learning. Artificial intelligence and machine learning are often used in the same sentence; however, it’s essential to recognize that while all machine learning involves artificial intelligence, not all artificial intelligence requires machine learning. It’s very much OK if you’re confused at this point – artificial intelligence models can sometimes be downright confounding.
First of all, think of any computer model as data. It could be something as simple as weather data – the location of weather stations, geographic location, altitude, barometric pressure, rainfall in the past day, etc.
Machine learning uses many processes to create a model that can help predict a result when adding new data. At a very basic level, a machine learning model contains a few layers – a decision layer, where the model makes an estimate; an error function, where the estimation is compared to assess its accuracy; and finally, an optimization layer, where the model can be adjusted to reduce the difference between the estimation and the actual result.
For example, let’s say we have the weather patterns for a farmer’s field—and the pumpkins, planted just a few months ago, are nearly ripe. Machine learning, having learned from historical data, can be used to help predict the optimal time to pick the pumpkins – potentially helping to avoid extreme weather events that decimate sensitive crops each year and maximizing the yield and, therefore, the potential profitability of the crop.
The Next Level of Machine Learning – Neural Networks
Neural networks take machine learning to another level. Consider the human brain and its billions of neurons – even the most well-versed of neuroscientists cannot tell you exactly how the layers of neurons interact and engage with each other to make decisions.
Neural networks attempt to use layered nodes, working with a range of different mathematical models to assign weight and relevance. Instead of a single layer of decisions, such as that in traditional machine learning, neural networks consider various conditions and rules to reproduce the thought process that a human brain goes through.
Think of it like when you go to a vending machine, wanting to purchase a drink. A set of decisions typically come to mind when deciding what beverage you wish to consume – is it a hot drink? Is it a cold drink? Have you recently come back from a run? – which can help to influence exactly what you’d like from the vending machine.
Neural networks work similarly—assigning different weights for the variables in question and then using those weightings to get the most accurate possible result. While they are not the only type of AI algorithm, there are situations in which this level of decision-making can be incredibly useful.
Applications of Neural Networks
Neural networks are a potent form of AI model. Where machine learning applications typically focus on interpreting simple data, such as written and spoken language, neural networks can be used on much more complex systems.
One of the most common applications of neural networks is natural language processing (NLP). For virtual assistants like Siri and self-help chatbots to work, underlying systems need to understand human-generated text and its context.
An industry where neural networks have incredible potential is the healthcare sector. Medical professionals do an amazing job but are faced with two limitations – their own personal learning and time.
A neural network trained on a dataset of cancer detections can then use that knowledge to help identify if a patient scan warrants further investigation. This may seem like a trivial process, but it uses the work of thousands of oncologists and practitioners to develop a highly accurate model.
A Promising Moment
Neural networks are a promising new technology in AI. When harnessed effectively, they have incredible potential in many fields, such as healthcare and meteorology.
Consider this – in just one state in Australia, it’s estimated that there were some four thousand missed cancer diagnoses during 2020 and 2021. While cancer survival rates are high, that is typically contingent on being correctly diagnosed to allow for enough time for treatment and care – neural networks could be used in tandem with more expansive diagnostic programs to help close the gap.
Artificial intelligence and machine learning look set to change the world in incredible ways. As one critical application of AI, expect neural networks to become more and more commonplace – even if you don’t recognize them at first.
Rashan is a seasoned technology journalist and visionary leader serving as the Editor-in-Chief of DevX.com, a leading online publication focused on software development, programming languages, and emerging technologies. With his deep expertise in the tech industry and her passion for empowering developers, Rashan has transformed DevX.com into a vibrant hub of knowledge and innovation. Reach out to Rashan at [email protected]
