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The world we live in today is becoming increasingly data driven, and machine learning methods are being applied in almost every industry. From healthcare to retail, to financial services, to education, the world around us is more and more intelligent at a rapid rate. While machine learning was once considered only for science fiction novels like I, Robot, we are beginning to see its true potential in our daily lives. This article discusses artificial intelligence and machine learning and presents some reasons why companies should adopt machine learning in their business.

1. Getting started

Let's first discuss two important concepts:artificial intelligence (AI) and machine learning (ML). Using AI, machines can learn to think and act just like humans. AI systems are getting smarter all the time and increasingly capable of performing complex tasks. This means that our business processes could soon be completely handled by robots! ML on the other hand, is the science of teaching computers to understand data and create value from it. Quoting Tom Mitchell, Professor and Former Chair of the Machine Learning Department at Carnegie Mellon University:

Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.

From these definitions, you can notice that AI and ML are very closely related and connected. This is absolutely the case. However, there are not the same. ML is considered a subset of AI.

2. Why do ML and AI matter today?

AI is finding its way into our lives in more ways than we could have ever imagined. Google Now, Siri, Alexa, Cortana and Facebook's Messenger are all AI-based. There is no doubt that these technologies bring convenience to our lives. As ML becomes more prevalent across industries, it will continue to create new opportunities and challenges.

2.1 Machine learning in predictive Analytics

Predictive analytics is a set of statistical tools used to analyze past events and build predictive models using data mining techniques. An example would be predicting customer churn rates based on previous purchase history, or predicting whether a loan applicant will default on future payments. Predictive modeling relies on historical and real time data to project future trends. We use advanced statistics to develop predictive models that rely on algorithms to extract insights from complex datasets. Once developed, the model is tested through simulation methods to ensure its effectiveness. Then, the model can be deployed in production environments.

2.2. Machine learning in many other fields

ML algorithms have been applied in many fields including medicine, business, finance, transportation, education, etc. We have seen its rise everywhere we look: in chatbots, self-driving cars, voice recognition software, and natural language processing. Chatbots for example, which are computer programs designed to engage in human conversations through text, email, phone calls or social media, have some benefits. These standalone programs allow companies to save time and focus on other important aspects of their business. In addition, they provide instant responses and quick answers to customers.

But, perhaps the biggest change is happening in the medical field where doctors are being replaced (or assisted) by AI. In this case, ML models, which is created by studying patterns in the data, could help identify disease patterns, recognize cancer cells, and determine whether a patient should have surgery directly instead of undergoing additional tests. The diagnosis for millions of people around the world could be improved, from the United States to India to China to Africa.

From these examples, we can even deduce the following relationship between data, ML and AI: Machine learning models are created by studying the patterns in the data. And an AI system is built using machine learning. ML will be more and more present in our daily life. From simple things, like a recommendation to buy a piece of clothing, to more complex stuff, like implants. For example, neuralink is designing the first neural implant that will allow people to control a computer or mobile device wherever they are.

3. A few reasons to consider using machine learning for your business

There are numerous reasons why you should use machine learning to boost your business. We will cover seven of them.

3.1. Better customer experience

ML helps companies understand their customers' behavior, helping them make smarter decisions about what products they offer and how they can best serve those customers. ML can help predict consumer purchase intent, recognize unusual events like product recalls, and find trends across data sets.

3.2. Better marketing

Marketing automation technology lets businesses personalize messages based on individual customers' interests and preferences. As a result, it helps save time and money spent crafting marketing materials and reaching out to people who will not respond to traditional advertising.

3.3. Increased revenue

When well-automated, marketing campaigns generate sales conversions at a higher rate than human-driven strategies. Machine learning can even allow marketers to automate repetitive tasks, freeing up employees to focus on more complex projects.

3.4. Better employee engagement

According to this Forbes article, 70 percent of workers are disengaged from their jobs, impacting productivity and job satisfaction. When organizations adopt AI tools and processes, they're able to create more engaging work environments that inspire their teams to put forth maximum effort.

3.5. Reduced operational costs

As machine learning becomes increasingly sophisticated, it's able to learn from historical patterns to avoid costly mistakes. In turn, this frees up companies to spend more time thinking strategically instead of spending additional resources on reactive maintenance.

3.6. Increased brand loyalty

The rise of social media has created a heightened awareness among consumers. However, brands do not have to rely solely on word-of-mouth for increased exposure; they can utilize ML systems to develop digital interactions that capture attention and build brand affinity.

3.7. Faster decision making

Businesses need to adapt quickly to changing conditions, but often find themselves overwhelmed by the amount of information they receive. Machine learning algorithms give businesses access to real-time analysis and insights that can help them address issues sooner rather than later.

4. Final thoughts

All industries require data analysis in order to find insights and make decisions. But humans are not great at interpreting massive amounts of data. That is where machine learning comes in. That is where ArerSoft comes in. We use among others the combination of statistics, and math, along with knowledge of business and industry to analyze and solve complex problems in order to help organizations achieve their goals.

Whether you have little or a large amount of data, we can help you.

We develop powerful and sophisticated algorithms to analyze data. In the end, we are able to make sense of huge amounts of data and transform them into useful information and actionable recommendations.