Machine Learning, Artificial intelligence (AI), data mining, all these terms are being thrown around willy nilly lately. Oddly enough, as AI gets more attention the more confusing it gets.
As the tech industry remains under the spotlight we’re getting bombarded with unclear information and hearsay on AI. We’re left feeling overwhelmed and unmotivated to actually understand how AI works.
It seems every time we turn on the news someone is either praising AI or running it down. There’s no escaping the future of technology, by we can start by understanding why machine learning can greatly improve our relationship with technology.
Machine learning vs Human learning: What’s the difference and why will it impact how we learn?
Machine learning is a subset of Artificial intelligence (AI) that trains machines how to learn. Machine learning is a form of Data mining that constructs specific algorithms to improve machines learning and prediction process. Data mining is the process of discovering patterns and relations in large data sets.
The goal of Machine learning is to to use data to make our machines as efficient as humans in completing different tasks. The focus is on finding the right data so machines can be more accurate in their predictions without being programmed.
We can see that machines are able to learn in the exact way that humans do. When we are first born we know absolutely nothing, but with repetition and experience, we eventually develop into capable human beings. With the right data and testing, machines are able to learn in the same way we do.
To do that there is a Machine Learning cycle that uses the necessary data to then train the algorithm to do a task. Then there is testing to see if the machine was able to fulfil the task. Then they take the results and get feedback on how well the machine performed. Then finally, they use that feedback to improve the data to make the machine’s predictions/decisions more and more accurate.
We can see how this cycle takes place in a common example of image classification by training a machine to tell the difference between a picture of a cat and a dog. The machine will learn to understand the difference by being fed statistical data based on the anatomy of the two animals.
For example, many dogs have floppy ears where cats have pointed ears and the computer on its own can use all the data to correctly pick which one is a cat and which one is a dog. Inevitably there will be errors in this process which is why the feedback stage in the cycle is crucial to ensure the computer can keep learning, just like we did when we were kids.
So why should we care if a machine can successfully distinguish a dog from a cat?
Well, we should focus on how machine learning is changing the world around us. The more machines can do the mundane low-priority tasks for us the more time and energy we can dedicate to learning and working on our passions. Ultimately, the smarter we can make our machines, the smarter we make ourselves.
Breaking down how Machine learning works
As a general overview, Machine learning’s algorithms are divided into two sections:
Supervised learning and Unsupervised learning.
Supervised Learning means that the algorithm has a data analyst helping to give the necessary feedback during the training stage. This way once the training is done the algorithm can apply everything it learnt to the new data. All data is labelled and the data analyst knows the correct answers and is acting as teacher to evaluate if the machine got the answers right.
Unsupervised Learning is used in Deep Learning, a subset of Machine Learning where machines use an iterative approach and don’t need training. This means that machines can make decisions and predictions on their own.
Our relationships with machines are more intimate than we thought
Many companies are using machine learning because it can help them achieve one essential goal that all companies want: to improve user satisfaction. Machine learning’s advanced algorithms offer us the ability to truly personalize everyone’s experience using the most common online companies. Looking at these three influential companies; Google, Amazon and Facebook, we see how machine learning impacts our lives daily.
Google has been heavily relying on machine learning to improve its understanding of the user’s search intent. Machine learning makes it possible to give the most accurate selection of content to the user because they have the data to better know the specific users intent.
Amazon uses machine learning to focus on pattern recognition. Machine learning’s algorithms help Amazon predict users product interests and can offer the user suggestions for products that they think they’d actually want.
Facebook also strategically applies machine learning techniques to personalize users News Feed to ensure a better user experience. Like the other companies, Facebook uses predictive analysis to give us an individualistic feel by filtering who we pay more attention to on Facebook to show the most relevant information to us on our News Feed first.
Machine learning helps us become more efficient with our technology by getting to know each one of us better. Since we are all becoming more dependant on the internet why should we all have a generic experience if we are all different?
Our differences are what make us human so our tools should represent us all separately. Machine learning’s role in understanding our individual traits improves our relationship with technology.
How JAYA uses Machine learning to learn more about our clients
We at JAYA use machine learning to help us put people first. That’s why we want to use machine learning in a completely transparent way to break this barrier. With machine learning, we are able to understand the people by using tactics such as pattern recognition and predictive analysis to really get to know them. This guarantees everyone gets the experience they deserve.
Our products are geared to understand the future of your business. For example, our product Keybe focuses on getting to know your clients so you can build relationships based on trust. With Keybe your clients come first, they can choose how they want to be treated to ensure you can connect with them the most accurately possible.
Smarter machines mean smarter human beings
There are so many complexities underneath the surface of AI that the mere idea of understanding how it works is discouraging. Rather than getting caught up in the technical jargon, we need to step back and think about why we care about machine learning.
When we welcome machine learning the more we can benefit from their abilities. Many mundane tasks can be lifted from our shoulders, giving us more time to focus on the important things. More personalized experiences make our relationship with our devices more meaningful. These are only a few examples of how having smarter machines can help us to live smarter lives.