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

"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

Join us as we explore the impact of AI on product innovation and marketing with Set Godin, Bestselling author.

In this episode of Unpacked, marketing guru Seth Godin delves into the profound changes and opportunities AI brings to marketing and product development. The conversation emphasizes the necessity to adapt and harness AI's potential to build remarkable products.

Seth begins by discussing the evolution of marketing, highlighting the transition from traditional methods to more innovative approaches driven by technology. He underscores that the essence of marketing remains the same: understanding and meeting customer needs. However, the tools and strategies have significantly evolved, with AI playing a pivotal role.

AI's impact on marketing is multifaceted. Seth explains that AI enables more precise targeting and personalization, allowing marketers to deliver tailored experiences to consumers. This not only enhances customer satisfaction but also drives engagement and loyalty. He stresses that marketers must embrace AI to stay competitive, as it offers insights and capabilities that were previously unattainable.

Building on this, Seth explores how AI can aid in creating remarkable products. He asserts that AI can streamline product development processes, from ideation to execution. By analyzing vast amounts of data, AI can identify trends, predict consumer preferences, and optimize designs. This accelerates innovation and helps companies bring products to market faster and more efficiently.

A key theme throughout the podcast is the importance of being remarkable. Seth emphasizes that in a world saturated with options, standing out is crucial. Remarkable products are those that elicit conversations and inspire loyalty. AI can help identify what makes a product remarkable by analyzing consumer feedback and market trends, enabling companies to refine and enhance their offerings continuously.

Seth also touches on the ethical considerations of using AI in marketing. He advocates for transparency and responsible use of AI, ensuring that consumer trust is maintained. Marketers should be mindful of privacy concerns and strive to use AI in ways that benefit consumers without compromising their rights.

The conversation then shifts to practical advice for marketers looking to leverage AI. Seth suggests starting with small, manageable projects to gain familiarity with AI tools and build internal expertise. He also recommends collaborating with AI specialists and investing in training to bridge any knowledge gaps.

Seth concludes with a call to action for marketers. He urges them to embrace AI not just as a tool but as a transformative force that can elevate their marketing efforts and product development. By being proactive and innovative, marketers can create remarkable products that resonate with consumers and drive business success.

In summary, the podcast with Seth Godin provides valuable insights into the intersection of AI and marketing. It highlights the potential of AI to revolutionize product development and underscores the importance of creating remarkable products in today’s competitive landscape. Seth’s practical advice and call to action inspire marketers to embrace AI and leverage its capabilities to stay ahead of the curve.