In today’s world, it is almost impossible to live a week without hearing at least a couple of news about AI (Artificial Intelligence) and machine learning. Such complex software systems are used by almost every top corporation, including Amazon, Google, Microsoft, Apple and so on. But what exactly is Machine Learning? How does it work?
Machine learning originates with simple pattern recognition, and although the algorithms existed for a while, only over a couple of previous years, it has become a complex system that steadily revolutionizing virtually every major industry. The central concept of AI is quite simple, the machine needs to be able to analyze previous data and adapt based on it. It might sound simple but the actual technical realization is as far more complex, as you might imagine.
Brief History of Artificial Intelligence
To better understand how machine learning platforms can be used nowadays, we should take a closer look at where it all began. At the very foundation of computational technology lies simple binary (“1” and “0”) logic, upon which all the computer algorithms are built. In the 50s, Arthur Samuel, a researcher at IBM, has performed what can probably be called first AI experiments, which involved teaching early computers to play checkers.
Unfortunately for him, nobody saw the importance of his work at that time. It was not until his retirement from IBM, when he received recognition from the scientific community for his progress, or better say, for laying the foundation of what we now call machine learning.
Basics of Artificial Intelligence Programs
Although it is fun to think of artificial intelligence as a sentient being that will eventually rise up and conquer humanity, the reality is far less colorful. And to understand the capabilities of AI, we should understand its very basics.
It’s all about data
The sad reality is that famous artificial intelligence phrase is not much more than a couple of buzzwords, which sound really nice and have a deep Sci-Fi feel to them. However, machine learning tends to live up to people’s expectations. There are numerous business and social challenges that can be solved by applying this technique. New and exciting algorithms, such as deep learning, are constantly emerging in today’s world. But it all comes down to the data in the end. Without high-quality data the entire process becomes useless.
So naturally, the most complex and most important part of any AI is data transformation. Filtering through the noise, creating appropriate parameters and feature engineering are what takes most of the resources. Therefore, such systems are very often integrated with a CRM platform.
Machine learning isn’t flawless
Apart from being highly reliant on the quality of data, machine learning platforms have other flaws.
- The quantity of data does play a significant role in machine learning since it builds its knowledge base on informational patterns. And having a limited amount of data to learn from would very fast lead to mistakes in the future. So unless you are working with vast amounts of data, it is much more preferable to use simpler models. And this is part of the reason why AI didn’t find its way in our everyday lives and SMB. With a limited amount of data to work with, the machine can be inefficient and even counterproductive.
- Machines can be biased. Yes. For most machine learning platforms, the choice you make today would affect the outcome in the future. If the specified learning parameters are based on some assumption, the training data generated can reinforce this assumption, creating essentially a confirmation bias. Therefore a foundational algorithm needs to be flexible.
- Machine learning is still vulnerable to a simple human error. In most cases when the system fails, it is due to a human error that was introduced into training data or a learning algorithm.
Machine learning methods
There are numerous models that are at the very core of machine learning, but most noticeable and widespread are:
- Supervised learning. This model is based on giving a machine a set of data, where the desired output is known. And learning algorithm needs to select inputs which correspond to correct results. Basically, it is a pattern learning technique, and this model is highly effective in systems where previous data is highly likely to predict future events. This is how machines learn to play chess.
- Unsupervised learning. The learning technique that does not have a known correct output and data has no historical labels. The machine needs to identify structure within data. It is widely used in sales and financial sectors, where the large amounts of data need to be filtered and patterns, such as customer behavior, can be identified.
- Reinforcement learning is based on a trial and error technique. The goal is to find strategies that lead to the best possible outcome. This model is widely used in robotics, navigation, and gaming.
Complex machine learning today usually incorporates a hybrid of models, since business challenges that are presented today require a highly sophisticated approach.
Machine Learning Tools for Business
Today’s market is far larger and far more sophisticated than ever before, and as a consequence, it means that there is an immense amount of data, for any major industry. For companies, the ability to collect this data and turn it into meaningful information is key to working more efficiently and gaining a competitive edge.
Perhaps the first industry to truly adopt the machine learning technology was the financial sector, followed shortly after by marketing and sales.
For financial institutions, it is obvious, since everything in this business is just a number, even some abstract concepts, like risk can be quantified with data mining. Gaining an insight through complex unsupervised learning can show great investment opportunities. Banks also utilize machine supervised learning models to identify fraudulent activities and prevent them.
Marketing and sales industry is a little bit trickier, up to a point when you start quantifying customer behavior. Recommendations based on your previous purchases and search history are products of machine learning. Companies in this line of business invest in AI to capture customer data and analyze it to create the most responsive and pleasant shopping experience.
Not every company requires a constant data analysis through AI, and some companies simply don’t have IT resources to sustain it. So we now see a rise of a new business model – Machine-Learning-as-a-Service. Which doesn’t have the same ring as Software-as-a-Service (SaaS). But nevertheless, it is a model that is gaining great popularity. It acts virtually the same as SaaS, your company pays the subscription fee and uses AI that someone else is managing.
Although the technology behind machine learning tools is far from perfection, your company can take advantage of an existing application to grow your business and succeed in the market. Salesforce platform provides a great number of applications and analytics tools, such as Salesforce Einstein, that can give you a competitive edge.
Here at OMI, we specialize in delivering state-of-the-art Salesforce solutions that are tailored to your organization. We are also offering a set of machine learning services for the CRM platform along with Salesforce customization, integration and so on. Want to start a project? Contact us today and we’ll come up with a solution that will be just right for your business.