Latest innovations in the field of Artificial Intelligence have made it possible to describe intelligent systems with a better and more eloquent understanding of language than ever before. With the increasing popularity and usage of Large Language Models, many tasks like text generation, automatic code generation, and text summarization have become easily achievable. When combined with the power of Symbolic Artificial Intelligence, these large language models hold a lot of potential in solving complex problems. Such a framework called SymbolicAI has been developed by Marius-Constantin Dinu, a current Ph.D. student and an ML researcher who used the strengths of LLMs to build software applications.
Symbolic AI simply means implanting human thoughts, reasoning, and behavior into a computer program. Symbols and rules are the foundation of human intellect and continuously encapsulate knowledge. Symbolic AI copies this methodology to express human knowledge through user-friendly rules and symbols. In the recently developed framework SymbolicAI, the team has used the Large Language model to introduce everyone to a Neuro-Symbolic outlook on LLMs.
Large Language Models are generally trained on massive amounts of textual data and produce meaningful text like humans. SymbolicAI uses the capabilities of these LLMs to develop software applications and bridge the gap between classic and data-dependent programming. These LLMs are shown to be the primary component for various multi-modal operations. By adopting a divide-and-conquer approach for dividing a large and complex problem into smaller pieces, the framework uses LLMs to find solutions to the subproblems and then recombine them to solve the actual complex problem.
The Neuro-symbolic programming used by SymbolicAI uses the qualities of both a neural network and symbolic reasoning to develop an efficient AI system. The neural network gathers and extracts meaningful information from the given data. Since it lacks proper reasoning, symbolic reasoning is used for making observations, evaluations, and inferences.
For the neuro-symbolic computation of data, the team uses OpenAI’s neural engines, such as GPT-3 Davinci-003, DALL·E 2, and Embedding Ada-002. The framework also uses search engines to process text, speech, and images. The neuro-symbolic programming provides a clear viewpoint on the LLMs, their ability to understand, and their areas of failure. It helps validate the processes by debugging model predictions.
Comparing SymbolicAI to LangChain, a library with similar properties, LangChain develops applications with the help of LLMs through composability. The library uses the robustness and the power of LLMs with different sources of knowledge and computation to create applications like chatbots, agents, and question-answering systems. It provides users with solutions to tasks such as prompt management, data augmentation generation, prompt optimization, and so on.
SymbolicAI mainly involves application development, quick facts-based text generation, flow control, and more. Considering how AI is flourishing in every sector and specifically how LLMs are the talk of the town, SymbolicAI is undoubtedly a great development for the current modernized software development. Checkout SymbolicAI, The Powerful Framework That Combines The Strengths Of Symbolic Artificial Intelligence And Large Language Models
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.