Episode 8  |  51 Min  |  March 28

Are LLMs the Answer to Everything with Prof. Mausam, IIT Delhi

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Engaging topics at a glance

  • 00:32:28
    Introduction
  • 00:38:00
    Intended use of LLMs
  • 00:41:30
    Performance of smaller model trained for specific task vs LLMs.
  • 00:45:00
    How LLMs fare when dealing with mathematical and reasoning problems
  • 00:52:40
    How small models are able to perform better than LLMs?
  • 00:55:45
    Future of LLMs and Traditional AI Models

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, Rinat Sergeev, Chandan Jha, Nikhil Sood, Dipika Prasad

Latest podcasts

Episode 5  |  57 Min  |  March 28

Exploring reinforcement learning with MIT Professor Vivek Farias

Exploring reinforcement learning with MIT Professor Vivek Farias

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Engaging topics at a glance

  • 00:16:50
    What is Reinforcement Learning
  • 00:20:10
    Reinforcement Learning for LLMs
  • 00:24:00
    How do you reward your model?
  • 00:33:00
    Revealed preferences v/s just a few individuals doing that
  • 00:36:00
    AI model training AI in the future?
  • 00:40:18
    Methodologies other than Reinforcement Learning
  • 00:43:10
    Considerations when in the Reinforcement Learning with Human Feedback (RLHF) Phases
  • 00:48:10
    About Cimulate

“Exploring Reinforcement Learning” with guest Vivek Farias, Professor, MIT, discusses what role reinforcement learning has to play in this world of Artificial Intelligence.

Learning systems with humans date back to almost 5,000 years ago. And these learning systems have what allowed us to progress as a society. Being able to teach other people what we know and share knowledge has been the foundational pillars of our evolution and civilization. And interestingly, these learning systems are not unique to just humans. Animals also have these learning systems. When you look at orcas, dolphins, the higher-order intelligent animals spend time training and teaching their young ones. In the last 50 to 60 years, we have not just been teaching humans how to learn, but we have been teaching machines how to learn. And this artificial intelligence area has benefited from our understanding of these learning systems.

The guest started with highlighting the importance of acknowledging uncertainty and balancing between exploiting what is known and exploring to learn more about the environment. This problem is referred to as a "multi-arm bandit problem" and is considered fundamental in reinforcement learning, where the goal is to optimize actions in an environment.

When looking at it specifically for Large Language Models (LLMs) the role of Reinforcement Learning. RL has played the central role in building general purpose chatbots that are based on LLMs. Because the resulting model that has been trained on data might not give you the refined output that you are expecting from it.

When discussing about rewards and losses in reinforcement learning phase, it came out that the way we structure rewards and penalties for AI models greatly influences their reliability, how they interact with public and the accountability.

Overall deploying AI involves a balance. Backend deployment offers some level of predictability, while front-end deployment is uncertain. Successful business must experiment and capitalize in both aspects.

Production Team
Arvind Ravishunkar, Ankit Pandey, Rinat Sergeev, Chandan Jha, Nikhil Sood, Dipika Prasad

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Trailer  |  01 Min  |  March 28

Unpacked with Arvind Ravishunkar

Unpacked with Arvind Ravishunkar

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In this series, Unpacked, I explore and unpack the most important concepts that business leaders need to know about emerging technologies. I connect with reputed scholars, industry experts and leaders through conversations. I also do short 5 min episodes on key concepts. I am your host Arvind Ravishunkar and this is Season 1 : Generative AI

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