76% of enterprises today prioritize AI, Machine Learning & Deep Learning over any other IT initiatives, according to Algorithmia. If anything, as organizations return to normalcy post the pandemic, they are moving to these technologies with a new sense of urgency for the resilience & insights they enable you with. But very few people understand the difference between various subsets of AI and which of these can help for your particular enterprise use case. 

In a recent IMechE conference that our CEO Jagadish Gattu spoke at, we realized this in earnest when, with senior engineers & manufacturing decision-makers, around 70% of the audience admitted not understanding the difference between the trio of AI, Machine Learning & Deep Learning. They were also unsure about how they can fit them with their digital transformation journey. 

To understand the pros and cons of these individually, consider the example of a task- separating bird and animal photos from a mixed stack. Let’s dive in to see how each of these techniques tackle this.

Out of all the three, Machine learning is the oldest, being in existence since the 1960s. Machine Learning involves extracting insights with human intelligence. This means that the user usually decides & sets the classifying criteria via structured data. Like in our example, we can classify the pictures with the criteria that images with wings & feathers are birds and others can be animals. Once the machine learning algorithm is trained with this data, it can then classify the data accordingly on its own.

Deep learning emerging in the 1990s, on the other hand, relies on artificial neural networks and does not need human intervention to unearth insights. The image data travels at different levels of networks through deep learning algorithms while being analyzed for specific features that can complete the tasks. Similar to the human brain’s methodology of a problem through various levels of cognition. In our picture example task, the system will run data through different levels of neural networks to determine identifying characteristics to determine whether the image is a bird or an animal. 

But both of these are not nearly as accurate as the output produced with human intelligence. And that is where Artificial Intelligence (AI) came into play in the 2000s.

AI involves a human user analyzing the system output, providing feedback to improve the algorithm, which allows the system to learn continuously and evolve to perform better every time.

For instance, in our example, a human can look at a bird pic without wings (peacock) classified as an animal and inform the system that it is a bird, which the system then undertakes and improves in its next output. 

So how can an enterprise know when to choose between AI, Machine learning & Deep learning?

1. Machine learning-based solutions are the best option, to begin with when an organization needs to automate some tasks that involve analysis of a data set based on structural data. Ex: Weather forecasting, IT risk detection, Condition Based Monitoring in Manufacturing. Classic algorithms like clustering, regression, or classification are typically used for implementing machine learning.

2. Deep learning-based solutions are apt for projects that typically require analysis of a large data set, typically with millions of data points. Ex: ​​Image recognition, speech recognition and natural language processing and research projects.

3. AI makes the best of both worlds with an ability to learn and analyze large amounts of data, making it suitable for applications that need to analyze large amounts of critical & complex data. Example: Machine data in manufacturing, patient/research data in healthcare, basically any data where scale & accuracy is needed. For example: While a machine learning algorithm can point to an anomaly in machine data, a robust AI system can even provide near accurate conjecture around the causes for the anomaly while also suggesting a way to mitigate this. Here is a link to discuss more about why AI is gaining popularity in the manufacturing sphere.

Some other subsets of AI based on the scope of its operation are Narrow AI or weak AI, Strong AI. For Example, Grammarly’s use of AI in optimizing communication is an excellent example of Narrow AI.


AI, Machine Learning & Deep Learning have all been the result of centuries of research and study. Although they sound similar, they have distinct strengths and weaknesses, making them shine in a particular kind of use cases. If you know these before you dive into your AI journey, the chances of success increase dramatically.