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

"Exploring what should organization considering when choosing to adopt LLMs" with guest Nick Brady, Senior Program Manager at Microsoft Azure Open AI Service

AI has been at the forefront of transformation for more than a decade now. Still, the Open AI launch of chat GPT in November 2022 will be noted as a historical moment – the scale of which even Open AI did not expect – in the history of technological innovations. Most people don't realize or fully appreciate the magnitude of the shift that we're in. Now, we're able to directly express to a machine a problem that we need to have solved; equipping these technologies with the right reasoning engines and the right connectivity could bring the biggest technology leapfrog not just for enterprises but even in everyday lives.

The onset of leapfrog does bring out a few questions for enterprises looking to adopt GenAI as a part of their strategy, operations and way ahead, like:

What use cases are best suited to adopt the models?

While most customers are looking for how this could reduce business costs in their organizations, the true value is when it is used to maximize business value productivity and downstream that could lead to employee satisfaction and customer satisfaction. Any place where there's language – programming or natural language – is a good use case for generative AI, and that probably would be the most profound shift. So, if you have language, if you have a document, if you have big data where you're trying to sort of synthesize, understand what that content and what the content is, generative AI models can do this ad nauseam without any delay.

The most common metric used across the world to describe LLMs is the number of parameters; in the case of GPT 3, it is trained on 175 billion parameters, but what does this mean?

Parameter size refers to essentially the number of values that the model can change independently as it learns from data and stores all information in the vast associative ray of memory as its model weights. What's perhaps more important for these models, and it speaks to more of their capability, is their vocabulary size.

How does one decide and evaluate which would be the best-suited model for the selected use cases?

The best practice really is to start with the most powerful and advanced language model like GPT 4.0 to test, if it's even possible, with your use case. Post confirming the possibility of use case trickle down to simpler models to find its efficacy and efficiency. If the simpler model can probably achieve 90% of the way, with just a little bit of prompt engineering, then you could optimize for costs.

Organizations would have to define what quality means to them. It could be the model's output, its core response, or performance in terms of latency, where the quality of the output may not be as important as how quickly we can respond back to the user.

The key for leaders is to pay close attention to the potential use cases, test them with the best model and then optimize the model to balance the cost, efficacy and efficiency factors.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha