Cutting Edge Technology Can Also Cut Through the Rougher Parts of Your Business
Most of the general public associates artificial intelligence with science fiction. A smaller group of people might think of it as little more than digital playthings. Both of these views had validity in a certain time and place. But artificial intelligence, also known as machine learning, is now fairly commonplace.
It’s simply that people often don’t even notice AI within their lives. But when you do, it’s quite apparent that machine learning can help your business in a number of different ways. However, it’s best to focus on the five most interesting in order to really get things moving.
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General machine learning infrastructures
This application of machine learning isn’t the flashiest. And in fact, it’s not even customer facing. But it’s important to create a solid library for use with your company’s projects. It’s important to keep in mind that machine learning is both an end product and an aspect of practical software engineering. The latter is what creates the former.
This becomes technical fairly quickly. But in general your on-site programmers should create a software library. This is something which they’ll be able to quickly expand to meet any new need which might arise. It also allows for easier leveraging of third party libraries into your own internal codebase.
Artificial intelligence has a longer history than most people realize. And very few realize just how much work goes into properly decoding human speech. Understanding and processing speech is an incredibly processor intensive task. But it’s finally within the reach of most businesses.
We’ve only recently reached a point where voice recognition is in a usable state. But modern science is learning more and more about how human neural networks process linguistic data. And as our understanding improves there, it improves within artificial neural networks as well.
Customer facing natural language processing
We’ll also soon seen why it’s important to have an internal codebase. Because this can help you leverage a single solution in multiple directions. Here we’re going to examine two uses of natural language processing. One facing outward and one inward.
In an outward direction you’ll want to use it alongside the voice recognition. Voice recognition only takes customer interaction so far. Natural language processing is what helps a machine actually understand what’s been said. In this example it’d take something a person spoke aloud. That data would then be processed and an appropriate action taken in response.
Internal natural language processing
This is a rich area for machine learning. Public interaction usually happens in fairly uncontrolled environments. However, you have much more control of what happens inside your own company. This means you can ensure that the data pushed to a machine learning system is properly formatted.
You can also ensure that the data is properly formatted and dealt with. This all comes together to make it quite easy for your machine learning systems to essentially handle a good chunk of what would normally take a full office. For example, a paystub generator can easily work with the fairly limited datasets contained within a billing system. This is especially true for modern offices where most financial information is kept in a carefully sorted database.
Imagine being able to have someone standing by every single location your product is sold in. An employee who could monitor sales and then change it accordingly. That’s the realized dream of dynamic pricing systems.
You can have an AI bot who essentially just watches over the financial climate and purchasing decisions in various markets. If and when action could make you more per sale it will step in and make those changes. And likewise, it can change prices back if and when that’s needed.
Going with everything at once
It’s important to remember that machine learning is one of the fastest growing fields. New advances are coming from directions that few ever saw coming. Likewise, changing hardware specifically designed for machine learning is creating even more possibilities. One shouldn’t limit himself to just one of these solutions. Instead, consider any or all of these as a foundation to build upon.