Introduction to Compositional AI
The rise of AI originally took place so machines could be able to mimic the actions and the intelligence of humans, which became the reason for the birth of Machine Learning and Deep learning, in which we use ANN's (Artificial Neural Networks), which also developed to mimic the functions of its biological counterparts. But here is the catch, the Human brain works compositionally, which means there are separate parts for every function.
The resultant action is the output of combining the results of all different parts. On the other hand, Machine learning and Deep learning models work on the Single task Single Model principle. To counter this catch, compositional AI comes into existence. This article is all about understanding compositional AI. What is compositional AI? How does it work? Why is it needed?
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What is Compositional AI?
Take an example of modern-day machine learning, if anyone wants to develop a wind power forecasting machine learning model which should take different weather forecasting parameters from different weather sources (there are different types of weather forecasting sources available according to regions, areas, and number of days of forecasting horizon) and can predict the power forecasting. In that case, that person will develop a single model using regression analysis or other time series forecasting algorithms.
Compositionality is different from the above idea. In simple words, compositionality is all about developing a new combined module using existing or new sub-modules that will perform a specific part of the combined module or solution.
The basic idea of compositional AI is that a single model can not be used for tasks with different sub-tasks. One model can not do it all, especially in the cases where machine learning and deep learning have been used. In Deep learning, a single neural network does all the work to solve a problem. Example of these problems is Object Detection, Face Recognition, and Chatbot.
What is the role of Compositional Machine Learning?
Developing an Artificial Intelligence System is not all about developing a machine learning model. It is much more than that. It includes Data, Design, and continuous integration between all different technologies. Although Machine learning plays a vital role in developing an AI system and in the case of compositional AI, compositional machine learning becomes an essential technique for implementing that kind of AI. There are already state-of-the-art machine learning techniques concentrating on training separately on different kinds of data. Let us understand how they are different from Compositional Machine Learning.
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Federated Learning vs. Compositional Machine Learning
Federated Learning mainly focuses on training different machine learning models on different types of data and sharing the trained weights that further collaborate to become one global machine learning. That is why it is also known as collaborative learning (because results get collaborated in a single global machine learning model) or privacy-preserving learning (privacy got preserved because in the case of federated learning, data does not get shared. Only weights get shared).
The main difference between these two techniques is that although federated learning focuses on training models separately on the different types of data, the focus is on making a global model which can solve one single problem. On the other hand, Compositional Machine learning concentrates on different models on different data sets but focuses on the different types of tasks.
Ensemble Machine Learning vs. Compositional Machine Learning
Ensemble Machine learning also focuses on the distribution of learning but caters to only one single target problem. Bagging, Boosting, and Stacking are standard techniques used under ensemble machine learning.
Compositional Machine learning always focuses on different tasks, different model approaches. However, it is a kind of distribution in learning that concentrates on different parts of the same problem.
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What is best approach for Compositional AI?
The approach is already mentioned in distributing the problem into different sub-problems and operating them individually using different machine learning models and algorithms. Still, before that, the most important before approaching a business problem is to define it correctly. So the first step is “Define the Problem.”
Using different models (or even different machine learning algorithms) for different sub-problem makes compositional AI a little bit of a complex technique in operationalizing and maintaining it at the production level. Let us discuss the approach of compositional AI briefly. In compositional AI, the models are trained and served separately, which also means that they require a different pipeline of training and serving. The delivery of a machine learning solution involves the integration between three types of pipeline.
- A pipeline that ensures the delivery of data.
- The pipeline ensures the delivery of Machine learning models.
- The pipeline ensures the training of Machine learning models.
According to the compositional machine learning approach, all three pipelines should be there separately as the approach requires different models for different tasks in a solution of a given problem.
What are the benefits of Compositional AI?
- If you want to make any complex things simple, it is simple, divide it
The biggest value of compositional AI is that every problem can be treated after dividing it into a simpler sub-problem. Problems like wind power forecasting, which eventually also requires weather forecasting, can also be solved by compositional AI.
- Reducing the headache of choosing the best algorithm
While using compositional AI, you don’t need to use the best algorithm for the whole business problem. Every sub-problem can be solved using different algorithms.
- Increment in the Accuracy
One of the main reasons behind the introduction of compositional AI is the accuracy of complex problems, as it becomes hard to treat such problems using a single ML algorithm. Compositional AI treats it well and gives an increment inaccuracy.
- Decision Making just like Human Intelligence
It uses compositional AI, how human intelligence can be mimicked in Artificial Intelligence too, as in the brain every part has its function to perform, which is also the core idea behind compositional AI.
Conclusion
Compositional AI can be a solution to avoiding the selection process of correct algorithms, mimicking human intelligence, and increasing the accuracy of the whole solution. But still, it is in an immature state, and the implementation is still a subject of the trial. The scalability is another issue that should be experimented with within the case of the implementation of Compositional AI.
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