Gain valuable perspectives on integrating responsible AI into business strategies from Dr. Rachel Adams, CEO and founder of the Global Center on AI Governance.

In this insightful podcast, we embark on a journey to understand the intricate landscape of responsible AI practices, especially tailored for business leaders. Dr. Rachel Adams, one of the top global voices on responsible AI and the founder and CEO of the Global Center on AI Governance, serves as our guide through this complex terrain.

The conversation begins by framing the discussion around the concept of “thinking with care” when it comes to AI development and deployment. Dr. Adams emphasizes the importance of inclusivity and diversity in AI development, particularly in addressing the unique needs of different regions and communities worldwide. She stresses the significance of aligning technological advancements with the real needs of people, advocating for a user-centric approach driven by community engagement and feedback.

Both developers and deployers of AI need to be responsive to the negative impacts AI is having on the ground.

– Dr. Rachel Adams

As the dialogue progresses, the focus shifts towards the role of business leaders in navigating the multifaceted dimensions of responsible AI. Dr. Adams elucidates the critical considerations that business leaders must keep in mind during both the development and deployment phases of AI initiatives. From addressing inherent biases in AI models to safeguarding user privacy and data protection, she outlines a comprehensive framework for ethical AI governance within organizations.

Moreover, Dr. Adams sheds light on emerging policy developments in the field of AI regulation, highlighting the European AI Act as a pioneering effort in this space. She underscores the need for nuanced, sector-specific regulations tailored to the diverse contexts and challenges faced by different industries and regions.

Throughout the conversation, Dr. Adams emphasizes the importance of collaboration and cross-disciplinary dialogue in advancing responsible AI practices. She underscores the need for closer collaboration between technologists, policymakers, and communities to navigate the evolving landscape of AI governance effectively.

Thinking with care means getting as many people thinking as possible. So creating kind of diverse groups of people that are thinking with care allows that thinking to be bigger.

– Dr. Rachel Adams

As the podcast draws to a close, we reflect on the fundamental principles of responsible AI adoption, emphasizing the imperative of “thinking with care” in every aspect of AI development and deployment. Dr. Adams reiterates the need for a collective effort to ensure that AI technologies are developed and deployed in a manner that prioritizes human values, equity, and societal well-being.

In summary, this podcast provides invaluable insights into the complex challenges and opportunities presented by AI technology for business leaders. Through engaging dialogue and expert analysis, Dr. Rachel Adams offers a roadmap for ethical AI adoption, empowering business leaders to navigate the ethical complexities of AI with confidence and integrity.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

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

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.

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