There are a ton of concepts that are being utilized right now – pattern recognition, neurocomputing, deep learning, machine learning, etc. All of these really come under the general concept of artificial intelligence but the terms are sometimes mistakenly swapped. One that stands out is that people often interchange artificial intelligence with machine learning. Machine learning is a subset category of AI, but AI doesn't always have to incorporate machine learning.
Artificial intelligence (AI) and machine learning (ML) are transforming how product teams form development and marketing strategies. Investments in AI and Machine Learning continue to increase exponentially year over year.
What is Artificial Intelligence?
AI is the capacity of a computer to perform operations analogous to learning and decision making in humans, as by an expert system, a program for CAD or CAM, or a program for the perception and recognition of shapes in computer vision systems.
What is Machine Learning?
Machine learning is a branch of artificial intelligence in which a computer generates rules underlying or based on raw data that has been fed into it.
Machine learning is a process whereby data is mined and knowledge is discovered from it utilizing algorithms and adjusted models. The process is:
- Data are imported and segmented into training data, validation data, and test data.
- A model is built utilizing the training data.
- The model is validated against the validation data.
- The model is tuned to improve accuracy of the algorithm utilizing additional data or adjusted parameters.
- The fully trained model is deployed to make predictions on new data sets.
- The model continues to be tested, validated, and tuned.
Within marketing, machine learning is helping to predict and optimize sales and marketing efforts. As an example, you might be a large company with thousands of representatives and touchpoints with prospects. That data can be imported, segmented, and an algorithm created that scores the likelihood that a prospect will make a purchase. Then the algorithm can be tested against your existing test data to assure its accuracy. Finally, once validated, it can be deployed to help your sales team prioritize their leads based on their likelihood of closing.
Now with a tested and true algorithm in place, marketing can deploy additional strategies to see their impact on the algorithm. Statistical models or custom algorithm adjustments can be applied to test multiple theorems against the model. And, of course, new data can be accumulated that validate that the predictions were correct.
In other words, as Lionbridge illustrates in this infographic – AI vs. Machine Learning: What’s the Difference?, marketers are able to drive decision making, gain efficiencies, improve results, deliver at the right time, and perfect customer experience.