Join us in exploring the evolving space of GenAI tools and infrastructure, featuring Rajat Monga, Co-Founder of TensorFlow and Google Brain.

A good power tool can make the difference of easily six to seven hours of work when you’re doing woodworking. And the world of AI is no different. The types of tools and infrastructure that are developed to help you as enterprise leaders build artificial intelligence products and features are very, very important. In this episode, with our guest’s help, we will unpack the infrastructure that surrounds you and the tooling that will help you as enterprise leaders build AI products and services.

However, you can also tell it, you know what? I don’t want you to do the math instead generate code that will do the math for me or use this certain calculator to do the math for me, and it can do that really, and you’ll get the right answers. And that’s basically what prompted generating has been about.

– Rajat Monga

We will look at how tooling and infrastructure needs are changing in the world of AI with the increasing adoption of GenAI. One key change that emerged from the talk is that now things have evolved to the extent that we don’t need to train the models from scratch. We already have foundation models available that know our world up to some level, reducing the burden of training the model with tonnes of data. However, models today have become so large that they sometimes don’t fit in a single machine.

As connecting your database with these models is very important, we also discussed the trade-offs between the open-source and private libraries. Should companies manage data on their own or outsource it? When you are not training your model, the easiest and fastest way is to use API, and if you want your data on-prem, then it will mostly cost you more. In the end, it boils down to what core to you is, and often, not all part of the infrastructure is core to companies. So, if your data is not your core strength, then better outsource it.

This episode also uncovered the current tools and infrastructure available for GenAI. The current tools and infrastructure available for large-level deployments are going through a rapid evolution; they are not very hard to rebuild or replace, and new companies are emerging in the tooling and infrastructure for GenAI space.

Now what’s going away in some sense is perhaps the lead you might have where your beta model was ready two years ago and you were doing something interesting. Now you know what? Unless that model had some proprietary data and you can continue to leverage that, that’s gone.

– Rajat Monga

When looking at the talent and skills needed for GenAI implementation in your organization, it is important to have technically sound people with domain expertise in the organization’s particular area.

For the differentiation in the market domain knowledge in your area, relationship with the customers, distribution channel, your execution, etc., today plays an even bigger role. However, in this data-driven world, having proprietary data and knowing how to leverage it can be an added advantage. To find out more, tune in to the full podcast.

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

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