Uncertainty distributions that are typically included with probabilistic models are a fantastic advantage

Probabilistic programming is a paradigm or technique that combines programming tools with bayesian statistical simulation, inference methods, and machine learning components. You may argue that a deep learning model is typically one big compiled structure that is black-boxed from beginning to end compared to standard machine learning and deep learning.

What are the Benefits?

Less Data is Required

You may include your domain knowledge into the model using probabilistic programming, and the model will continue to learn as it processes the data. That cannot be done by a deep neural network. As a result, you can begin with far less data than you would in a standard machine learning setting.

Imagine you want to develop an AI that can forecast client attrition. For standard machine learning to provide a really meaningful model of your customer attrition, you might not have access to sufficient data. However, if you are aware that market churn is about 10% every year, you may incorporate this information into your model and watch as the data gradually changes it. We refer to the 10% as a "prior." After looking at the statistics, you have a 12% chance of churn. We refer to that as your backside. You can even decide whether you need a lot of data or very little data to change the previous. Strong and weak priors are what we refer to as, respectively.

Your uncertainty is well known

The fact that uncertainty distributions are typically included with probabilistic models is another fantastic advantage. Therefore, instead of getting a probability of anything like in classical learning, you now get a probability distribution. And why does that matter? Consider the case of a self-driving vehicle. Our AI predicts a green light to be present 99% of the time. How confident are we that the 99% estimate is accurate? Usually, we simply don't know. We obtain a distribution using probabilistic programming. That indicates that you are aware and certain that the 99% represents the true probability. That is quite helpful while operating an automated vehicle.

You could describe your algorithm

Although in high demand, explainability in AI is frequently a relatively limited resource. As was already said, many machine learning models are end-to-end "black boxes," so you won't know why the model made a particular choice. That's a problem in many instances. Legislation may, for instance, grant loan applicants the right to know the reasons behind their denial. The explainability of probabilistic programming is substantially simpler.