March 11, 2022

Ai, Machine Learning, Deep Learning: What's the Difference?

Technology is making a huge impact on the way businesses are run. From enhancing productivity to helping you market your business better, there are many ways to utilize technology to create a well-oiled machine.  

An advanced technology we talk a lot about is artificial intelligence (Ai) and machine learning. But what do these terms mean? And how do they compare to the technology deep learning? Let’s take a look at all 3 terms and how they can benefit your business.  

What is Artificial Intelligence (Ai)?  

Artificial intelligence (Ai) is the part of science and engineering that makes machines intelligent, particularly computer programs. This intelligence is comparable to how humans learn. Just as we can learn more by studying, the more data that is fed into the program the more intelligent it will become.  

The difference between computer and human intelligence, however, is how we process information. Humans have short term memories and the ability to form retrievable long term memories. Computers are the same but the speed at which they can process this information is much faster. Where humans have the upper hand is creatively processing the information and formulating ideas that can generate new ways of doing things.  

Computers are currently only able to work off the information they have been programmed for. With new technological advances, however, that could change and computers could begin to seemingly “think” for themselves. But humans will never be replaced in the workforce as someone will always be needed to provide oversight. These advances will just make those human employees that much more efficient.  

What is Machine Learning?

Machine learning can handle large datasets that would be impractical to process manually. It takes the computer’s ability to process information and places it into models, or use cases, that allow insights to be formed from the data. The model then improves over time by automatically learning through experience.  

Machine learning algorithms become a model when you add the data to make predictions or decisions without being explicitly programmed to do so. The machine learning model then represents what was learned by the algorithm. Model is the output of applying machine learning algorithms on “training data” and it is used to make predictions on new data. At ElectrifAi, we use the term model to describe a use case that solves a real-world business problem and consists of a model or ensemble of models.  

For example, natural language processing (NLP) is a part of machine learning that extracts text data from large quantities of unstructured text. That machine learning model learns over time by providing feedback after the insights are generated. If the model is programmed to sift through thousands of contracts to search for key phrases, the model can learn to flag abbreviations or misspellings of the word by parsing the information around the word.  

There are many use cases that can be used by companies to help them understand their business problems better and generate recommendations on how to improve. Some of those use cases include:  

  • Customer Experience  
  • Fraud Management  
  • Marketing and Cross Sell
  • Spend and Contract Management  
  • Dynamic Pricing  
  • Demand Forecasting
  • Operational Efficiencies  
  • Computer Vision
  • And much more!  

The use cases don’t stop there. Machine learning developments are always taking place and the only limitations are your imagination. The difference is how these use cases are applied. You can try to tackle these problems on your own, but wouldn’t it be much easier to have a trusted partner help you implement them into your business?

What is Deep Learning?  

Clients sometimes ask us about deep learning and how it’s different from machine learning. Deep learning is actually not different than machine learning, it’s a subset of machine learning.  

Deep learning is the part of machine learning that teaches the computer to learn by example. It’s the technology behind self-driving cars that teach the cars to recognize stop signs versus people or other obstacles.  

Deep learning enables machine learning to achieve such impressive results as it helps the model achieve recognition accuracy at much higher levels than before. Deep learning is even improving so much that it can achieve higher scores than humans at classifying objects in images.  

The term “deep” in deep learning actually refers to the number of hidden layers in a neural network. A neural network is a series of algorithms that can identify relationships within a dataset by mimicking the way the human brain operates. Neural networks usually only contain 2-3 hidden layers, whereas deep networks can have as many as 150 layers. That’s what makes deep learning so valuable as it can process much more information than normal machine learning could on its own.  

How ElectrifAi Can Help

At ElectrifAi, we are different than other machine learning providers or what you can create on your own. We have machine learning models pre-built and business ready. These models have been battle tested in real business situations, not just theoretically in a lab environment.  

Building machine learning is risky and time-consuming, not to mention very expensive. We can get you started with machine learning for less than the cost of a single data scientist. We deliver superior, fast and reliable results you can depend on for your company. Our models also seamlessly integrated into existing workflows so there is less downtime and no learning curve for new platforms.  

We provide rapid time to value and generate real business value with a very high return on investment (ROI). Want to learn more about how your data can help you achieve the benefits of machine learning? Reach out to us today!  

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