Machine Learning and its partner in progress Artificial Intelligence have no doubt been popping up on your radar daily. These are exciting times, with big changes in the way we go about our jobs and lives incoming at a rapid clip. It’s easy to get overwhelmed by it all, particularly if you don’t really understand what the terms getting thrown around mean.
Today we’re going to take some of that bewilderment away and take a good look at machine learning, what it means and how it’s going to shape the future. Check it out!
What is Machine Learning?
Machine learning is a branch of AI that allows systems (machines) to learn from experience, accurately predicting outcomes without being explicitly trained to. Although you may only be hearing about it recently, it’s not that new. Checkers master Robert Nealy lost a game of checkers to a computer in 1962 – a major moment in the origins of machine learning.
Today, it’s far more prevalent. How do you think Netflix curates such an enticing list of TV shows for your weekend binge-watching? Their algorithm has learned from historical data, predicting recommendations that align with your tastes. And how do you think self-driving cars work? Their decisions are made by a complex set of algorithms that recognise road signs, objects, dangers and directions.
Machine learning basically allows machines to become better and what they do without the input of humans. They learn and develop their own programs, becoming more human-like in their behaviour.
This isn’t just a passing fad, people. This is going to shape our future in more ways than we can imagine! Before we go down a Terminator-style rabbit hole, it’s important to note that this isn’t a complete wild west, thanks to something called ‘Explainable AI’.
Explaining the Unexplainable
Machine learning has been shrouded in mystery due to its complex decision-making processes. Explainable AI, or XAI, is here to change that. Designed to make machine learning more transparent and accountable, XAI answers the ‘how’ and ‘why’ around machine learning and AI. It’ll help humans comprehend the decisions AI makes and engender more of a trust towards them. We feel this is important, particularly when you consider how prevalent machine learning will be in our futures!
Methods of Machine Learning
There are three distinct models of machine learning. We’re going to try and keep these definitions as easy to understand as possible!
Supervised Machine Learning
Supervised machine learning uses data that’s intended to train algorithms… kind of like a teacher-student relationship. This method uses ‘already answered’ questions (labelled data) to train the machine to correctly classify data, predict outcomes and answer questions. This is already widely used, for example moving incoming spam mail into your spam folder.
Unsupervised Machine Learning
Not unlike an explorer heading off to unseen lands, unsupervised learning sifts through unknown information (unlabelled data) to find linked differences and similarities. There’s no prior guidance and no human intervention, it’s all on the machine. This approach is ideal for identifying groups within data or recognizing patterns. It’s often used in market analysis or image recognition.
Semi-supervised learning is a hybrid approach, leaning on both above methods. It uses a small amount of labelled data to guide the understanding and categorization of a larger unlabelled dataset. It’s useful when you don’t have enough labelled data to train the algorithm on, but still have large volumes of data that needs analysing!
Impressive Machine Learning Applications
You’re hearing more about machine learning because it’s getting faster, smarter, and more applicable to the world today. Here are a few instances where it’s really making an impact in the world today.
Healthcare is a constantly evolving industry where there’s a heavy focus on technological progression to help save lives and support healthcare professionals. Machine learning has proved particularly valuable here, helping make sense of huge amounts of data generated every day in this space. This has served to unify the system and allow for better care delivery and patient outcomes. Diagnosis of diseases such as cancer has been advanced, as have clinical practice guidelines and clinical decision support.
Machine Learning and Climate Change
Machine Learning models can help in the fight against climate change, which resonates particularly strongly in these times of extreme heat waves and wild weather! Not only can machine learning help us make our energy use more efficient, it can also predict complex weather patterns and natural disasters like floods and earthquakes!
Cybersecurity and Machine Learning
Those machine learning algorithms are becoming stealthy warriors in the fight against cybercrime. They can proactively sift through huge swathes of data, identifying malware, predicting system vulnerabilities, and shutting down threats in real-time. It’s going to be essential in the fight against hackers in the years to come!
The Future of Machine Learning
We hope that gives you an insight into machine learning and what it is and can achieve. It has so much potential, but there are also many challenges it has to face and overcome. A major one of these is privacy concerns, which is something that the whole AI field is going to have to closely navigate.
We’re excited about this technology and where its heading, we hope that it gets there in a manner rooted in fairness and trust. Whatever happens, it’s going to be a thrilling adventure! If you have any questions about machine learning, why not run them past one of our resident tech-heads at Smile IT? We love chatting about all things AI!
When he’s not writing tech articles or turning IT startups into established and consistent managed service providers, Peter Drummond can be found kitesurfing on the Gold Coast or hanging out with his family!