In the modern high-tech world, Machine Learning (ML) and Artificial Intelligence (AI) technologies are perpetually making headlines across the world. Machine learning is supposed to revolutionize the world – from the way we diagnose diseases, to how we get around our cities, and so much more.
But, like any hot topic, it can be hard to distinguish the facts about machine learning from fiction – and understand what it really is. In this article, we’ll discuss X of the most common myths about machine learning. Let’s get started.
Myth 1: Machine Learning And AI Are The Same Thing
Machine learning and AI are often used as synonyms, and to mean the same thing – but they are not the same thing. Machine learning is a much more specialized field than AI, and while they are related, they are not the same thing.
AI is a very broad field, and it covers areas such as self-driving cars, robotics, natural language processing, and other complicated tasks. Essentially, AI is anything that can make a machine seem “smart”. AI uses technology, algorithms, and modern software innovations to enhance the ability of machines to respond naturally to human input.
Machine learning is different. Machine learning is all about understanding data – recognizing patterns in data, predicting outcomes from large data sets, and creating algorithms that can help filter this data more efficiently.
Myth 2: Machine Learning Allows Computers To Learn Autonomously
This myth could not be farther from the truth. Machine learning is completely reliant on humans – humans are the ones who feed datasets and other information to the machine learning algorithms, thereby “educating” it.
In reality, teams of programmers must feed large amounts of specially structured data into an algorithm and learning architecture – with appropriate training parameters, filters, and other such steps taken, to ensure that the machine “learns” properly.
Without proper data sets, machines can’t “learn”. For example, if you were training an algorithm to recognize a picture of a car, you would need to give it a large database of pictures of cars.
Not only that, you would need to control for other factors – for example, you may not want to include pictures of cars around people until the algorithm has begun to recognize cars, as the people could be miscategorized, and throw off the learning process.
Once a machine learning algorithm has been “educated” in this way, it can often be allowed to parse through data sets on its own. However, data scientists are still responsible for feeding this data to the machine. There is no such thing as truly “autonomous” learning.
Myth 3: Machine Learning Can Be Used For Any Task
This is also not true. Because machine learning requires an algorithm to be created out of hundreds, thousands, or even millions of pieces of data, it’s usually restricted to fields in which this data can easily be collected, sorted, and fed into the machine learning algorithm.
This means that machine learning algorithms are the best at tasks which involve static, large data sets – such as text interpretation, voice analysis and transcription, image recognition, and other such tasks. This input data is simple – allowing for a quick response by the algorithm.
This also means that most machine learning tools are limited to tasks where data is digitized. For example, though machine learning is heralded as a breakthrough technology in the medical field, many X-rays are not digitized regularly – meaning that the data sets used for a task like recognizing a tumor in an X-ray scan may be severely restricted or incomplete.
Recognize What’s Fact – And Fiction – About Machine Learning!
Machine learning and artificial intelligence are exciting fields which allow us to use our powerful computing technology for the greater good. But it’s important to realize the limitations of current AI and ML models – and what tasks they’re truly useful for.
So don’t be fooled! Understand what’s true about machine learning – and what is still in the realm of science fiction.