Episode 10  |  61 Min  |  April 04

What you should know about LLM’s with Anupam Datta, Co-founder TruEra, and ex-CMU

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Engaging topics at a glance

  • 00:09:15
    Introduction
  • 00:13:40
    What is a Large Language Model (LLM)?
  • 00:18:40
    Is LLM a form of intelligence?
  • 00:20:25
    Comparing how LLMs learn than human learning.
  • 00:22:50
    How LLMs differ from one another?
  • 00:27:56
    What to consider when choosing LLMs?
  • 00:44:05
    Can LLMs retrieve past human knowledge?
  • 00:51:45
    How can companies harness power of statistical models?
  • 00:53:05
    Key things to keep in Mind when integrating LLM into the business.
  • 00:56:10
    Conclusion

Join us in this episode featuring Anupam Datta, Co-founder and Chief Scientist, TruEra, as we dive into the evolution of LLMs and what they hold for the future!

This world of generative AI has caught us by storm. And as enterprise leaders in your companies, understanding the technology behind generative AI will give you a competitive advantage as you plan your companies and businesses. And to help you do this, we will unpack a technology, large language models (LLMs), that powers AI today and represent a paradigm shift in this field of Artificial Intelligence.

LLMs can craft meaningful responses across many domains. Their performance has notably improved recently thanks to the substantial increase in model size and data volume.

Is it like human? No, far from it. Generative AI is still a machine that is learning statistical patterns and generalizing from there remarkably well, but these are machines.

– Anupam Datta

With the increasing acceptance of this technology, numerous companies are unveiling various Large Language Models (LLMs). It’s important to recognize that opting for the largest or highest-performing LLM isn’t always the most suitable approach. Instead, one might prefer LLMs that excel in specific tasks relevant to their application. As a leader in the enterprise, it’s crucial to integrate this understanding into your company’s strategy, aiding in identifying the appropriate LLMs to match and adapt for your applications. Achieving equilibrium between LLM selection, cost considerations, and latency considerations stands as a pivotal concern for enterprises. Equally essential is the thorough validation and assessment of generative outputs, serving as a safeguard prior to embarking on consequential choices. Hence, the undertaking of reliability testing at this current juncture is paramount.

Furthermore, enterprises need to consider a few other key aspects in this evolving landscape of LLMs as they build out LLMs. Starting with a well-defined business use case that offers real value is crucial. As LLMs move from development to production, it’s important to establish thorough evaluations and observability throughout their lifecycle. Education across the organization is vital to implement LLMs effectively. Companies should train their workforce to adapt to this changing technology stack. Fostering a community around responsible AI development and evaluation can contribute to a better understanding and management of LLMs. With these steps, enterprises can navigate the complexities of LLMs and harness their potential for positive impact.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

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Trailer  |  01 Min  |  April 04

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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Episode 9  |  56 Min  |  April 04

Building prototypes and pilots using generative AI with Mark Donavon, Nestlé Purina

Building prototypes and pilots using generative AI with Mark Donavon, Nestlé Purina

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Engaging topics at a glance

  • 00:11:20
    Introduction
  • 00:16:30
    How does the market mindset help in conceptualizing ideas?
  • 00:19:00
    Consumer research, design, and prototype for AI-based products
  • 00:22:40
    Data sources and models used in early product development
  • 00:25:35
    When to feed data into AI model?
  • 00:28:32
    When to take the prototype to production?
  • 00:37:35
    ML models used during prototyping
  • 00:40:46
    Generative AI in your products
  • 00:43:05
    Testing early models
  • 00:45:25
    Grounding models
  • 00:47:20
    Key insights

Join us in this episode where our guest Mark Donavon, Head of Digital Strategy and Ecosystem Development at Nestle Purina PetCare shares his real-life experiences and insights to explore what it takes to understand and build prototypes and pilots using AI.

This podcast gives insights into how a pet care organization harnesses the power of AI and IoT technologies to enhance pet welfare. The discussion centers on innovative problem-solving and the considerable potential for AI applications in the pet care domain.

The podcast opens by highlighting the importance of allowing technology to be driven by problems and needs rather than dictating solutions. The emphasis is on understanding specific user groups and comprehending the challenges faced by pet owners. Instead of beginning with existing technology and searching for problems to solve, their approach revolves around understanding the needs of end users and subsequently exploring how technology can address these issues. This user-centric approach is a cornerstone of their organization, reinforcing their commitment to developing products tailored to pet owners' requirements.

The conversation then pivots to the process of understanding user needs. The organization conducts consumer research, with variations across regional divisions. Each division maintains its own consumer insight team working closely with external agency partners to gather research data. Their digital team collaborates with these divisions, allowing them to access consumer insights that might not be uncovered through traditional research methods. This highlights the adaptability of their company and the synergistic relationship between divisions.

The podcast proceeds to discuss the practical application of AI and IoT technologies. An example is presented: a smart litter box equipped with IoT capabilities that utilizes AI to provide valuable insights. The aim is to detect early signs of kidney disease in cats, a common yet often undiagnosed ailment. The organization saw an opportunity to intervene earlier by identifying changes in a cat's bathroom behavior that correlate with an increased risk of the disease. This innovative device provides pet owners and veterinarians with early warning indicators, effectively transforming the approach to cat health.

The speaker underscores how the smart litter box is revolutionizing pet care. Traditional practices often involve diagnosing the disease at advanced stages, making it challenging for veterinarians to do more than manage symptoms. However, this device alerts pet owners to subtle behavioral changes, enabling early intervention and potentially life-saving treatments.

The journey toward developing this ground-breaking device is then explored. It began with a low-fidelity prototype, using a simple mechanical device to record data when a cat entered the litter box. This provided initial insights into behavioral patterns. Subsequently, more sensors and technologies were integrated, resulting in the current iteration of the smart litter box. The speaker stresses the importance of combining various sensors to collect comprehensive data for diagnosing specific behaviors and patterns in cats, thus facilitating early detection of health issues.

The podcast also delves into AI models, which are employed to gain a deeper understanding of pet behavior. Early prototypes collected data on behavioral patterns but could not interpret the cat's actions within the litter box. To address this limitation, machine learning models were incorporated. These models were trained to distinguish between various behaviors, such as urination, defecation, and digging. This enhanced the system's ability to provide meaningful insights, enabling the early detection of potential health issues by interpreting the pet's actions within the litter box.

In response, a point is made regarding the flexibility and adaptability of AI models. It's crucial to allow machine learning models to evolve and adapt since pets may exhibit diverse behaviors. This flexibility aligns with the organization's commitment to accumulating extensive data and generating high-quality training data to enhance their systems.

The discussion then touches upon the challenges of introducing innovative technologies within an established company. The speaker describes the initial hurdles they faced when convincing management to invest in these new technological directions. Skepticism and questions about the impact on pet food sales were common concerns. Yet, by presenting real-world data, success stories, and tangible outcomes, they were able to build a compelling case and garner support for their projects over time.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

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