Machine Learning is growing in almost every sector of business on a global scale. But what is machine learning and how can it benefit your business?
Machine Learning (ML) and Artificial Intelligence (AI) are not new forms of technology, they’ve actually been around for quite some time. But because of the recent advances and innovations in technology, the way they are developed and used is very different today than it was when they were first born. AI and ML are not the same thing, they are not interchangeable terms. ML is a branch of AI, and it’s very important to the progression of AI as a whole. So how does machine learning work and how can it benefit your business?
According to ZDNet, “At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.
Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video.”
Meanwhile, artificial intelligence is the ability of a machine to perform the same task as a human without any human being involved. The developments in ML over the last few years largely contribute to the advancements in AI. When you can train a machine to make decisions based off of learned patterns, you’re creating a new avenue of automation, freeing up humans to complete tasks that require deeper knowledge-based thinking.
This is how ML can benefit businesses worldwide. Every business owner seeks ways to make business more efficient, more productive, and faster. Machine Learning enables businesses to do all of those things, which will increase revenue. At the end of the day, that’s the goal of every business: to fill a need and make money doing it.
Machine learning applications are already in use every day. Consumers are subjected to ads on a regular basis, whether it’s on social media sites or somewhere else. The ads generated for us to view are typically based on our regular internet activity. The more you search and surf, the more information is fed into an algorithm that’s dedicated specifically to put ads in front of people who might actually find a product useful. If you are an avid classic car fan, you enjoy restorations and finding those rare gems, wouldn’t you rather see ads catered to that subject instead of seeing ads for gardening?
One key thing to remember about machine learning is that it takes a LOT of big data to work properly. If you’re a startup and don’t have a lot of data, that’s okay, you should still keep an eye toward investing in machine learning. Why? Because when you have enough data, your algorithm will start with having every single piece of data you’ve collected since the beginning of your operation. The more data an algorithm has to work with, the more accurately it will respond. And if you’re a large corporation, or even a medium-sized business that hasn’t started with ML yet, rest assured it’s not too late. You still have the data needed, you just have to feed it into the algorithm from where that data is stored, and any new data that comes in can be fed directly into it.
Every business can benefit from machine learning. It allows businesses to be more efficient, more productive and respond faster. But, like anything in technology, it is also incredibly complex. If you’re not technical, hire an expert. If you’re not sure if you’re technical, hire an expert. If you question anything about the process at all, even if you trust your internal tech team and just want a second opinion, hire an expert. This is not something you want to try on your own unless you have the background to do so. Research what you can, learn what products and processes are already out there for use and determine what you are interested in using. When you’re ready, consult an expert to make sure everything is done properly and have them teach you (and your team) how to manage and maintain the system.