ABOUT THIS EPISODE
In this episode, we discussed that LLMs aren’t an answer to all AI-based problems. If you are trying to automate your factories, if you are trying to bring in predictive maintenance, if you want to do smarter planning, in all these automation tasks, LLMs are one part of the answer and aren’t the entire answer. And so, the breakthrough in AI in the last couple of years in neural networks and language models alone isn’t sufficient for us to get to this world. We dream of this world of AI-based automation and what it will do for us. It’s got the potential, but there is an X factor that’s still missing.
Guest started with discussing the misconception about large language models (LLMs) and their intended use. Initially designed for basic language tasks, summarizing text, recalling information, and answering basic to moderately complex questions, LLMs are much more intelligent than what was conceived.
He also talked about despite various attempts to improve the LLMs; they found that these enhanced models (LLMs) didn’t match the performance of standalone trained models.
Please feel free to check out more from Prof. Mausam.
1.Prof. Mausam Research
2.Finding the Perfect Blend: Merging Large Language Models With Classic Machine Learning Techniques
The conversation shifted to the limitations of LLMs in handling complex industry applications such as supply chain management. Guest highlighted that these tasks involve vast numerical considerations, vendor identification, object quantity determination, cost analysis, and optimization, which are beyond the capabilities of LLMs.
When further discussing the reasoning capabilities and how they fare when dealing with a mathematical problem, it emerged that as the level of complexity of such problems goes up, the performance of these models goes down.
He mentioned it’s better to use these models for writing code to solve mathematical problems rather than using them for solving such problems.
In the end, the guest shared his perspective on the future use of LLMs and traditional methods, and in his view, it will be better to help us solve our problems in the best way.
Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha
