What is Machine Learning? What is Artificial Intelligence? Why does it matter to small business?

Machine Learning (ML) is a field of Artificial Intelligence (AI), which is concerned with creating computers or software that can perform tasks that usually require human intelligence. In other words, ML is a branch of AI.

So what’s the difference between AI and ML?

AI is a broad term that describes algorithms that can learn on their own. This means that AI doesn’t require humans to program or train it. It’s usually associated with deep learning. Deep learning is a subset of neural networks. Neural networks have been around since the 1950s but only recently has there been enough computing power available to make them useful in industry.

ML is another broad term that describes different types of algorithms. These algorithms are designed by humans and then trained to perform specific tasks.

Machine Learning has become a buzzword in recent years. The term was coined in 1959 by John McCarthy at Dartmouth College. Since then, it has gained popularity due to its ability to solve complex problems.

How machine learning works

In order for an algorithm (a series of instructions telling a computer how to transform a set of facts into useful information) to work effectively, it needs to be trained on some data. This data could be historical data, real-time data, or any combination of both. Once the algorithm is trained, it will be able to make predictions based on new data.

There are three main steps involved in the process of using ML: data collection, model building, and prediction.

Data Collection

The first step involves collecting data from the source. Data collection can be done manually, automatically, or a combination of both. For example, you might have a database of customer information where each record contains their name, address, phone number, email address, etc. You would need to collect this data manually. However, if your company already has a CRM system, you can use it to gather all the necessary data.

Model Building

Once the data has been collected, the next step is to build a model. A model is essentially a mathematical equation that describes how the data should be used. For example, if you want to predict whether someone will buy a product, you may need to look at several factors including the person’s age, gender, income level, location, and so forth. Each factor may contribute differently to predicting the outcome. Therefore, you would need to create a model that takes into account all these variables.

Prediction

Once the model is built, it can be tested against new data. If the results match what the model predicts, then the model is considered accurate. On the other hand, if the model fails to predict correctly, then there is room for improvement.

Types of Machine Learning Algorithms

Supervised vs Unsupervised Machine Learning

Unsupervised ML is useful when we do not have much data available. It helps us understand patterns within our data without having to label them beforehand. An example of an unsupervised ML algorithm is clustering. Clustering refers to grouping similar items together.

Supervised ML is more effective than unsupervised ML because it allows us to train models based on labeled data. Labeled data means that we know the correct answer before applying the model. Examples of supervised ML include regression analysis and classification.

Regression Analysis

Regression analysis is a type of supervised ML that calculates the relationship between one variable (the independent variable) and another (the dependent variable). For example, if I wanted to calculate the average weight of people who live in New York City, I would take my data set of weights and divide it by the population of New Yorkers. The result would give me the average weight per person living in NYC.

Classification

Classification is a type of supervised learning that classifies objects into categories. For example, let’s say I had a dataset of customers’ names, addresses, and purchases. I could classify the customers as either male or female based on their gender. This would allow me to group customers according to their gender.

How are businesses using Machine Learning?

Here are some common uses for AI/ML today.

Customer Service – Customer service agents can now get answers to questions via chatbots. Chatbots can answer simple questions such as how much is shipping cost. But they can also handle complex problems like finding the best flight or hotel deal.

Fraud Detection – Companies are starting to use AI/ML to detect fraudulent activity. A good example is fraud detection within credit card transactions. If you’ve ever tried to pay for something online and you got a message saying your transaction has been declined, this is because it detected fraudulent activity.

Product Recommendations – Many companies have started to integrate recommendations into their websites. Amazon recommends products based on what you already bought. Netflix suggests movies based on what you watched last time. Google search results suggest relevant pages based on what you searched for before.

Financial Services – Financial services firms are starting to use AI to analyze financial statements and make predictions about the future. They can then provide better advice to clients.

Sales – Salesforce’s Einstein platform combines machine learning and artificial intelligence to help salespeople close deals faster. It analyzes all the information in a lead’s profile to predict whether they will buy.

Healthcare – The healthcare industry is one of the most active areas for AI/ML. One example is IBM Watson which helps doctors diagnose diseases by analyzing medical images. Another example is Apple’s Siri which provides health information through its smart speaker.

Marketing – Marketing is another area where ML is making waves. Marketers are able to target specific groups of consumers with personalized ads. They also use ML to analyze trends and make predictions about future consumer behavior.

Retail – Retailers are using ML to optimize inventory management and enhance customer experience. They’re also using ML to develop predictive analytics that will help them better anticipate demand.

Transportation – Transportation companies are leveraging ML to reduce congestion and increase efficiency. Some of these companies include Uber, Lyft, and Logistics tracking apps.

Manufacturing – Manufacturers are using ML to design products, automate processes, and streamline operations.

Security – IT & Telecommunications companies are using ML to detect threats and protect against cyberattacks.

Utilities – Energy companies are using ML to forecast energy consumption and find new ways to generate power.

Agriculture – Agricultural companies are using AI to improve crop yield and animal health.

Insurance – Insurance companies are using ML to assess claims and determine insurance coverage and forecast claim events.

Construction – companies are using ML to build more efficient buildings and plan projects before they begin construction.

Real estate – companies are using ML to provide real-time property information and recommendations.

Automotive – Automotive companies are using ML to develop self-driving cars.

Home automation – companies are using ML to control smart home devices and appliances.

Smart cities – companies are using technology like IoT (Internet of things) and ML to improve public safety and transportation.

We even used the Machine Learning tool Frase to write this blog post!

Deep Learning Ai

Businesses use AI/ML to help them make better decisions. They do this through two main methods: 1) Predictive analytics. 2) Explainable AI.

Predictive Analytics

Predictive analytics is when you take data from your company and apply an algorithm to predict future outcomes. The goal here is to reduce risk and increase efficiency. An example of predictive analytics would be if you’re a retail store owner and you want to know which items customers will buy next. You could collect customer purchase history and run that through an algorithm to find patterns in buying behavior. Then you might recommend products based on those patterns.

Explainable AI

Explainable AI is where you take data from your business and apply an algorithm to explain why something happened. In other words, you want to show people why you made a decision instead of just saying “this is why.” An example of explainable AI would be if you’re working with a customer service team and you need to figure out why they didn’t resolve a problem. Instead of blaming the user, you’d want to look at the data and see if there was anything else going on.

Machine Learning vs Artificial Intelligence

There is some overlap between these terms. However, they mean completely different things.

Machine Learning is a broader term than artificial intelligence. Machine learning is about applying algorithms to data without any programming.

Artificial intelligence is a narrower term that refers to systems that think like humans. This includes natural language processing, speech recognition, image classification, object detection, robot navigation, game playing, etc.

The Future of AI & ML

Artificial intelligence and machine learning are growing rapidly. We expect to see more applications across industries as well as new ways to combine multiple technologies together.

Business owners need to take the time to understand the technology behind these processes so that they can leverage them in their business operations.

Photo by Michael Dziedzic on Unsplash