While machine learning can add enormous value to a huge variety of different fields, it is also often overrated. We've all seen science fiction movies, and it can be tempting to think of machine learning as something that gives machines human-level intelligence. In fact, while machine learning can help you in many ways, it is better regarded as a specialized tool for analyzing data than a silver bullet to solve any problem.
What is Machine Learning?
Machine learning is ultimately about finding patterns in structured data and making predictions. Predictions can be (and often are) about what will happen in the future. But this is not the only way to find the term "predictions" used in machine learning solutions. Often it also means predicting the answer to questions such as: "What kind of dog is in this image?" The latter prophecy is not a time-based prophecy (looking into the future), but rather a prophecy in its context: "What would an omniscient oracle answer if asked this specific question?"
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How is machine learning different from traditional software?
Machine learning can also be considered "learning from data". Traditional software solutions are built around deduction (a smart person identifies a set of rules and codes them as a series of if statements, and then these rules can be applied to the data), whereas Machine learning solutions are built around induction (a machine learning). The algorithm automatically discovers rules by looking at a large number of examples, and these rules can then be applied to more data).
Tradition Programming Vs. machine learning
Traditional Programming Vs. machine learning
How is Machine Learning different from AI?
As an aside, we will be using the term "machine learning" throughout this article, but in common usage the terms "artificial intelligence" or "AI" are often used instead. For a more detailed discussion, see Machine Learning vs Artificial Intelligence. They are not the same.
Why is machine learning important?
Machine learning, especially with recent advancements, can certainly bring new opportunities to your business, no matter what field you are in. One of the reasons machine learning is getting so much attention is because it is being used for many seemingly unrelated breakthroughs. Fields, including:
Image processing (for example, facial recognition);
sound processing (for example, auto captioning videos with subtitles);
text processing (for example, translation between different natural languages);
time series processing (for example, predicting future energy use);
Numerical modeling (for example, estimating the fair value for a home, or the probability that a particular customer will purchase a certain product).
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Each of these areas extends to more subregions (for example, the algorithms we use for facial recognition can detect cancer even in X-rays), and more importantly, Very similar algorithms can be used in all of these areas, which means that progressing in one algorithm can lead to progress in many areas. This applies to almost every field, including medicine, marketing, finance and business.
Machine learning in each of these areas is enabled by vast (and growing) datasets. Improvements in machine learning unlock more value of this data, so the data itself becomes more valuable. And as more attention is paid to data, machine learning models get even better, creating a virtuous cycle.
Is machine learning a good fit for your company and project?
Machine learning has so much potential that almost every company wants to use it. But that doesn't mean it's always a good idea.
As a business manager, you take on two risks:
forcing machine learning into a solution where it cannot add value in an effort to remain modern and relevant;
Fail to leverage machine learning in a solution where it will add value.
While every situation is unique, there are some good rules about whether machine learning is good for your company or project.
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