For companies that are looking to get involved in their first machine learning (ML) investments, the whole process can seem a little cryptic and esoteric. For many people, it's really hard to visualize how machine learning actually works, and exactly what it will do for a business.
In some cases, someone who's researching machine learning can have quite an epiphany when they consider why large numbers of image files, collected into neat digital containers, are so important for ML projects. That's because the "image file" concept helps to visualize ML. Thinking about this allows us to understand more about how these sorts of technologies will be applied to our world very soon.
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The short answer is that these large numbers of image files are important to machine learning because they represent training sets – sets of initial data that the computer has to work on as it learns. But there is a little more to it than that. Why are images so valuable?
One reason that images are so valuable is that scientists have made a lot of progress in image processing. But beyond that, they've also made progress in helping machines to identify outcomes based on what is in a picture.
For example, anyone who has heard about deep stubborn networks with both generative and discriminative engines understands a little about how computers can read and comprehend visual data and images. They're not reading the pixels like they used to do – they're actually "seeing" the image and identifying components. For example, think of Facebook's face recognition – the computer learns what you look like, and identifies you in pictures – as well as those around you. This is often made possible through the aggregation of many images and iterative training that forms the basis for a machine learning project.
When the stakeholders have identified a plan and concept, and gone out and collected all of the images that are relevant, and put them into the machine learning algorithms, they can leverage the immense power of artificial intelligence to run business processes.
A company might send a web crawler out onto the internet looking for pictures that may contain a particular customer, to build a file showing that customer's identity and his or her preferences and tendencies. The company might even use this information to automate direct mail or other direct marketing. When you start thinking about it like this, it's easy to see how just that process of image recognition and identification can be tied to all sorts of functionality that will let computers do so many of the things that humans have been used to doing for all of our recorded history. Taking the example of customer research, with the above types of setups, humans don't have to be involved at all: the computer can "go out on the web" and report back to its owners or the holders of the data.
For anyone who's involved in wading into the deep waters of machine learning, understanding the concept of mass image data mining provides a good first step in a road map to harnessing machine learning power and figuring out how to use it to benefit an enterprise.