Artificial Intelligence (AI) and Machine Learning (ML): How do they differ?

BI Connector Team |

Artificial Intelligence AI and Machine Learning ML Differences


In this blog post, we’ll see the basic differences between Artificial Intelligence (AI) and Machine Learning (ML) with examples.

Though the terminologies, AI, and ML are usually used interchangeably in the business world by the non-technical folks, they both are slightly different from each other indeed. 

If you’re new to AI and ML technologies, you might even wonder how a preprogrammed solution is different from an AI solution. No worries! We’ll also cover how a preprogrammed app differs from an AI-driven solution.

In short, we’ve created this piece as simple as possible, specifically for a non-techie to clearly understand and differentiate a preprogrammed app, AI solution, and ML solution from each other.

Ok, let’s break the ice!

What’s Artificial Intelligence (AI)

Artificial Intelligence is the field of programming machines to make decisions based on dynamic, real-world scenarios. An AI solution is unlike an app for anticipated scenarios, where the decision is coded within the program itself.

Before we see the examples for AI, let’s first take a quick look at how an AI solution differs from a preprogrammed solution with a simple example!

Artificial Intelligence vs Preprogrammed solution

A flight ticket booking system is an example of a preprogrammed solution, where the program is developed to take the users through a predefined, fixed set of processes. 

The possible scenarios are all foreseen and the corresponding decisions are all implemented within the program. There is no scope for the program to rely on inputs from the real-world to make a decision by itself. 

Flight ticket booking app

From a technical perspective, the app just interacts with a database to check if flight(s) are available between the from and to cities you entered on the selected date. 

If no, it displays a message as flight not available on the selected date. 

If yes, it then checks if the number of seats you requested in the selected class is available, and displays the list of flights meeting the condition. Then you’re taken through the process of entering the travelers’ details and making the payment. 

The decisions for the ‘Yes’ and ‘No’ conditions for the different use-cases of this flight booking app are all foreseeable and predictable. Hence the decisions to all these foreseen conditions are preprogrammed within the app, and the app doesn’t rely on real-world inputs to make a decision. 

Artificial Intelligence (AI) Solution Examples

In this section, we’ll see 2 examples of AI applications.

  1. Cab riding app, say Uber
  2. e-Commerce app, say Amazon

The examples provided below about how AI could be used by Uber and Amazon will help you understand AI better.

AI applications in Uber

Uber was one of the early adopters of AI. This Forbes article lists the different ways Uber leveraged AI back in 2018! It’s quite evident that Uber is leveraging AI much more now.

When you open the Uber app, you see the different cab options listed, with the time taken by each of them to reach the pickup spot you entered. When you enter the destination, you can see the drop-off ETA as well for each of these cab options.

Artificial Intelligence Use case in Uber cab riding

This time taken is calculated by an AI solution, based on the shortest route for a cab available nearby your pickup spot. In calculating the time taken to reach your pickup spot via a route, the AI takes the traffic, one-way paths as well into account to arrive at the final numbers.

During the ride, if the driver deviates from the suggested route, you may have noticed the route getting updated accordingly to guide the driver to your desired destination. This is because the route optimization is done by an AI solution, based on the real-world scenario.

AI Application in Amazon

In the case of Amazon, when you see a product in the app, you can also see the estimated time for the product delivery to your location. This time estimation, if preprogrammed, can only be based on the distance between the product’s geographic location and your location. 

AI use case in Amazon eCommerce

However, the AI estimates this time (with x % accuracy rate, we think the x should be more than 90) based on several real-world factors that may include:

  • The shortest possible route from the zip code of the product location to the zip code of your location
  • The different Amazon hubs through which the product will travel on its way to you
  • The predicted availability of manpower for picking up the package and delivering in different hubs
  • The physical size of the package
  • Space availability for carrying the product in the truck between each hub etc

The AI optimizes many other several real-world factors and arrives at the estimated date for the delivery.

That pretty much explains the difference between a preprogrammed app and an AI-driven app. Now take a quick look at Machine Learning.

What’s Machine Learning (ML)

Machine Learning (ML), as the term reflects, refers to programming a machine for automatically learning and improving the algorithms (that lead to decisions) using statistical methods. Therefore, Machine Learning (ML) is a branch of Artificial Intelligence (AI).

Example of Machine Learning

A perfect example of Machine Learning is automated stock trading systems, where the machine reads the historic stock data for identifying predictable patterns of the highly fluctuating stock prices. The ML solution is designed to suggest (or execute, in some cases) an automated investment decision in the real-world based on the price movement patterns it identified (while further improving and fine-tuning the algorithm by itself, based on the results).

Now let’s take a quick look at Deep Learning (DL), which is a subset of Machine Learning!

Deep Learning (DL)

Deep Learning (DL) is a subset of Machine Learning that mimics human intelligence in using logic, if-then analysis etc, to improve the algorithm. 

DL deals with data formats such as images, videos, voice, shapes, colors, etc., for helping a machine improve its algorithm on its own!

A quick example of a Deep Learning (DL) application is the square boxes that mark the faces when the camera app is opened on a smartphone. 

face recognition in camera apps

The camera app works based on a DL solution that’s trained to recognize human faces or other objects.

An example of a DL powered AI solution is self-driving cars, where the car automatically recognizes the roads, other vehicles, pedestrians, traffic signals and signs, and other relevant inputs that humans require to drive the car. 

Artificial Intelligence (AI) and Machine Learning (ML) – Quick Comparison

As Machine Learning (ML) is a subset of Artificial Intelligence (AI), it is not right to say AI is a direct alternative to ML, or vice-versa. Hence, a comparison between AI and ML isn’t possible with a neutral scale.

Therefore, when we compare AI and ML, we cannot conclude one is better than the other in an aspect. However, we can get a better understanding of both!

An AI application, like any other software application, runs based on a set of algorithms created by humans. These algorithms enable the AI solution to make decisions automatically without human intervention by handling highly dynamic inputs from the real-world.

An ML solution, on the contrary, is focused on improving the accuracy of the algorithm that powers the decisions of the AI solution. In other words, ML automates the fine-tuning of the algorithm without human intervention.

In short, an AI application that’s able to fine-tune and improve its algorithm on its own is technically an ML-driven AI solution.

Role of AI & ML in BI & Analytics

Artificial Intelligence and Machine Learning applications in Business Intelligence and Analytics

The AI and ML fire is catching up fast in the world of Business Intelligence and Analytics as well. 

Natural Language Processing (NLP), is used in all the market-leading BI tools, especially Power BI and Tableau.

NLP enables the users to simply ask questions (like below): 

  1. What is the profit% compared to the previous year?
  2. Which product clocked the highest sales in North America?
  3. Compare the profitability of segment x to segment y
  4. Show the customer retention rate in Asia

The feature enables users to get answers directly without having to navigate manually to a report or dashboard representing the data.

On the other hand, ML plays a vital role in predictive analytics across all industries, as it is capable of uncovering data patterns that human intelligence struggles to uncover even in hindsight. 

Here’s a single drop of water (provided as an example) from the mighty ocean of ML applications:

  1. In the Finance world, asset managers rely on ML for stock price movement prediction, while banks and insurance companies rely heavily on AI and ML for fraud detection.
  2. In the case of manufacturing companies, ML helps in predicting the supply/demand for products and inventories. 
  3. For eCommerce companies, ML helps understand customers better by analyzing their buying/spending behaviors, and advertise products in a personalized way. 
  4. The governments use ML for weather forecasting, and the list goes on and on!

Some of the common use-cases of ML in Predictive Analytics are Supply/Demand forecasting, weather predictions, stock price movements projection, etc. 

AI and ML together can solve a lot of real-world problems when put to the right use!