AI’s next big leap - Neuro-symbolic AI

 

AI’s next big leap - Neuro-symbolic AI

 

                Artificial intelligence research has made great achievements in solving specific applications, but we’re still far from the kind of general-purpose AI systems that scientists have been dreaming of for decades.

                Among the solutions being explored to overcome the barriers of AI is the idea of neuro-symbolic systems that bring together the best of different branches of computer science.

                What Is Neuro-Symbolic AI? A fancier version of AI that we have known till now, it uses deep learning neural network architectures and combines them with symbolic reasoning techniques. For instance, we have been using neural networks to identify what kind of a shape or color a particular object has. Applying symbolic reasoning to it can take it a step further to tell more exciting properties about the object such as the area of the object, volume and so on.

                It’s also taking baby steps toward reasoning like humans and might one day take the wheel in self-driving cars, meaning that neuro-symbolic AI brings us closer to machines with common sense. What exactly does that mean?

                Our minds are built not just to see patterns in pixels and soundwaves but to understand the world through models. As humans, we start developing these models as early as three months of age, by observing and acting in the world.

                For example, people (and sometimes animals) can learn to use a new tool to solve a problem or figure out how to repurpose a known object for a new goal (e.g., use a rock instead of a hammer to drive in a nail).

                These capabilities are often referred to as “intuitive physics” and “intuitive psychology” or “theory of mind,” and they are at the heart of common sense.

                These cognitive systems are the bridge between all the other parts of intelligence such as the targets of perception, the substrate of action-planning, reasoning, and even language.

                AI agents should be able to reason and plan their actions based on mental representations they develop of the world and other agents through intuitive physics and theory of mind.

 

                Overcoming The Shortfalls Of Neural Networks And Symbolic AI 

                If we look at human thoughts and reasoning processes, humans use symbols as an essential part of communication, making them intelligent. To make machines work like humans, researchers tried to simulate symbols into them. This symbolic AI was rule-based and involved explicit embedding of human knowledge and behavioral rules into computer programs, making the process cumbersome. It also made systems expensive and became less accurate as more rules were incorporated.

                To deal with these challenges, researchers explored a more data-driven approach, which led to the popularity of neural networks. While symbolic AI needed to be fed with every bit of information, neural networks could learn on its own if provided with large datasets. While this was working just fine, as mentioned earlier, the lack of model interpretability and a large amount of data that it needs to keep learning calls for a better system.

                To understand it more in-depth, while deep learning is suitable for large-scale pattern recognition, it struggles at capturing compositional and causal structure from data. Whereas symbolic models are good at capturing compositional and causal structure, but they strive to achieve complex correlations. 

                The shortfall in these two techniques has led to the merging of these two technologies into neuro-symbolic AI, which is more efficient than these two alone. The idea is to merge learning and logic hence making systems smarter. Researchers believe that symbolic AI algorithms will help incorporate common sense reasoning and domain knowledge into deep learning. For instance, while detecting a shape, a neuro-symbolic system would use a neural network’s pattern recognition capabilities to identify objects and symbolic AI’s logic to understand it better. 

                A neuro-symbolic system, therefore, uses both logic and language processing to answer the question, which is similar to how a human would respond. It is not only more efficient but requires very little training data, unlike neural networks. 

 

In conclusion, the primary goals of NS are to demonstrate the capability to:

1. Solve much harder problems
2. Learn with dramatically less data, ultimately for a large number of tasks rather than one narrow task)
3. Provide inherently understandable and controllable decisions and actions
4. Demonstrate common sense
5. Solve the AI black box problem
   

Bibliografie:

                   1.ttps://knowablemagazine.org/article/technology/2020/what-is-neurosymbolic-ai?fbclid=IwAR0gX5x1FetwXmpnW6KJA_nBiO5YKUi1EgLi8dye62Qp3OeuH6wUtXUArfQ

         2.          https://bdtechtalks.com/2022/03/14/neuro-symbolic-ai-common-sense/?fbclid=IwAR1bZM4LtRDm9EdwvNnixnHoQpy5KZN4U6qNQIV1ifRAqgqeFoG9k70R78U

         3.          https://analyticsindiamag.com/what-is-neuro-symbolic-ai-and-why-

are-researchers-gushing-over-it/

         4.          https://researcher.watson.ibm.com/researcher/view_group.php?id=10518