You may have heard this term a few times over the past little bit. It is becoming an increasingly popular form of computer science that promises to improve our everyday lives. But what is machine learning?
Here’s another term you’ve likely heard: artificial intelligence, or AI for short. Machine learning is a type of AI that allows software to become more accurate at predicting outcomes over time. The difference is, the program, or more specifically the algorithm, often doesn’t need humans to help.
However, it does go deeper than that.
No Skynet Here
You have likely seen movies set in a dystopian future overrun by AI, so you can be forgiven for thinking that artificial intelligence is scary. However, transforming machines into robots that can truly think for themselves is quite a long way off.
Machine Learning is strictly based on data and not human knowledge. Their level of intelligence is solely based on the amount of data we give them to train with. As a result, machine learning could not attain human-level intelligence.
It is highly likely you’ve experienced a form of machine learning already. Whether you’ve used a chatbot or predictive text, or even the shows Netflix suggests, there is plenty of machine learning going on behind the scenes.
Moreover, manufacturers, retailers, banks, and even bakeries are using machine learning to find efficiencies in their processes. Hospitals are using machine learning to identify illnesses from images alone.
A recent survey found that 67% of companies are using machine learning, while 97% had plans to implement the technology in the near future.
How Does It Work?
Just as humans depend on experience to learn about the world around us, machines use input to increase their knowledge. Machine learning generally begins with data. Observations, examples, instructions, or direct experiences are fed into machines which then look for patterns.
From those patterns, inferences can be made. Sometimes, those inferences can be made without any human involvement beyond starting the process itself. It depends on which of the four basic types of machine learning is being used:
Here, labelled training data and defined variables are fed into an algorithm, essentially applying past lessons to new data to predict future outcomes. This allows for greater control and less bias.
This type of machine learning algorithm is used to analyze and cluster unlabelled training data. These algorithms can discover hidden patterns or data grouping without any human intervention.
A mix of the two, semi-supervised learning involves feeding some labelled training data into an algorithm, while it is free to explore data on its own and try to come up with its own understanding.
This form of learning is primarily used to teach a machine to complete a multi-step process that has defined rules. Hence, an algorithm is programmed to complete a task, and may receive positive or negative cues from operators during the process, but it is free to find its own way to complete the task.
There are Limitations
As useful as it is to have machines independently find patterns and solve problems for us, it is far from a perfect technology. The algorithms involved are difficult to train, the process is prone to data issues, and the results are often biased.
However, the technology is constantly developing and improving. With more insight into what machines are learning, and more importantly, why, it could become an even more powerful tool in transforming how we use data in our day-to-day lives.