The Advent of Neuro-symbolic Modeling to Bring Better AI
Imagine you enter your neighbor’s kitchen room for the first time. As soon as you step your foot inside, you can figure out where the essential food items are present if the refrigerator is having two doors or a single door, what do their plates look like, etc. We humans barely have any trouble identifying things. This is because we have evolved ourselves through reasoning, learning, and cognitive thinking. Even toddlers can do the same when left in a playroom full of colorful balls. However, this is impossible for today’s state-of-the-art neural networks.
Since the rollout of AI, there have been some impressive advances in data processing. Yet it still far behind replicating human behavior and intelligence in its totality. This has given scientists many restless nights until now. A collaborating team of researchers from the MIT-IBM Watson AI Lab, MIT’s Computer Science and Artificial Intelligence Laboratory, Alphabet’s DeepMind, and Harvard University have found a solution to this. Introducing CoLlision Events for Video REpresentation and Reasoning (CLEVRER), which is a new, large-scale video reasoning data set, is developed using principles of neural networks and symbolic AI, commonly termed as neuro-symbolic modeling.
The formation of such a system was primarily based on the need for an AI that can multi-task in a variety of domains, and can read data from a variety of sources (text, video, audio), whether the data is structured or unstructured. The neural network or deep learning allows large-scale pattern recognition and capturing complex correlations in massive data sets as inputs and hence interprets it using the natural language of various questions and answers. While, symbolic AI is good at capturing compositional and causal structure. Also, later can filter out irrelevant data too. Combining these two can help overcome each other’s limitations. A neural network is a data-driven approach that is contrary to the rule-based approach of symbolic AI. So without a doubt combining these will help us reap the better of the two.
