Uncovering whether LLMs are the one part of the answer or the entire answer to your problem with our guest, Prof. Mausam, with our guest, Prof. Mausam, a distinguished figure in Computer Science at IIT Delhi with over 2 decades of experience in Artificial Intelligence.

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.

They are not large maintenance models, they are not predictive maintenance models, they are not large doctors, they are not, they’re not large anything else but language.

– Mausam

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.

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.

If you ask it to do reasoning, it doesn’t do a good job. But if you ask it to write code to do reasoning, it does a better job.

– Mausam

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

Join us in this episode featuring Shikhar Ghosh, Professor, Harvard Business School, as we explore how AI can fundamentally impact business and society!

In the ever-evolving landscape of technology, artificial intelligence stands as a true disruptor, poised to reshape not only our businesses but also the very fabric of society. In a captivating podcast discussion with Shikhar Ghosh, Harvard Business School professor, we delve deep into the riveting world of AI, exploring why its impact is so seismic, how enterprise leaders should navigate this new frontier, the question of human relevance in the age of AI, and whether we are truly prepared for this transformative journey.

We will uncover the essence of AI's disruptive power and provide compelling insights into the sheer transformation that AI can herald.

Be prepared to be guided through the stormy seas of AI influence on businesses. Our expert highlights the critical importance of a well-defined AI approach. Enterprise leaders must be agile and proactive, recognizing that AI is not merely a tool but a transformational force. We will discuss how to approach AI with an open mindset, viewing it as a catalyst for innovation rather than just a threat.

We will also see why leaders should maximize the upside of AI. This underscores the value of human-machine collaboration, emphasizing that AI augments human capabilities rather than replacing them entirely. It's a matter of harnessing AI's analytical prowess to inform decision-making and free up human resources for more creative and strategic pursuits.

One of the most intriguing segments of the podcast explores the question that lingers in the minds of many: Will humans remain relevant in the age of AI? This is discussed with nuances that business leaders can take a leaf from and be proactive in embracing AI wisely and effectively.

In a world teetering on the precipice of AI-driven transformation, this podcast offers a compelling exploration of why AI is the disruptive force of our era. It presents an alluring narrative that transcends the technical jargon, making the topic accessible and engaging for both the tech-savvy and those new to the AI landscape. As we listen to Professor Shikhar’s captivating insights, we are left with a resounding question: Will we embrace AI as a catalyst for positive change, or will we be swept aside by its inexorable tide of disruption? The answer may very well determine the fate of businesses and society as we know it. Find out more, tune in to the full podcast and embark on a journey into the future of AI, business, and our shared human experience.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

“Develop AI strategy for your organization” with Dr. Kavita Ganesan, where she discusses things to consider when implementing AI.

Many programmes, specifically AI-based programmes, start with the right intentions but often fail when they go into production. And, to explore this topic, we had an insightful discussion with our guest in this episode to understand why this happens and how it can be solved.

Most of the AI initiatives today fail to make it into production because people are not solving the right problems with AI, and there is a lack of understanding of what AI is at the leadership level.

The perception that Gen AI can solve every problem is inaccurate, and understanding this is crucial for enterprise leaders. There are many other AI techniques that can solve business problems and it's important to have a general understanding of what AI is and what types of problems it can solve. As implementing AI is not only cost intensive, but it also comes with many risks.

After the emergence of Gen AI, contrary to what many people think today, data collection is still a very integral part of AI initiatives in order to fine-tune the models for company-specific problems.

When deciding on the application of AI, it is advisable to use it for intricate issues that require numerous narrow prediction tasks. In such cases, a large amount of data points needs to be evaluated for making decisions, which could be challenging for human minds to process.

It's important for companies to have a strategic approach while implementing AI. Instead of just focusing on the latest trends (like implementing Gen AI for all the problems), companies should identify the problems that need to be solved in their business in order to have a huge business impact.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha