Smart Talks with IBM: The power of Granite in business
Sep 24, 2024
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Maryam Ashoori, Director of Product Management for IBM’s watsonx.ai, dives into the evolution of AI technologies. She discusses the transition to smaller, customizable AI models that enhance business efficiency and customer care. The importance of transparency in AI, especially for regulated industries like finance and legal, is emphasized, highlighting Granite's advantages. Additionally, they explore how generative AI is reshaping enterprise strategies and the need for data quality to ensure effective AI integration. It's a revealing look at the future of AI in business.
The shift towards smaller, customizable AI models like Granite enhances efficiency and addresses challenges related to larger models in business applications.
Fostering openness in AI encourages collaboration and innovation, enabling businesses to develop transparent and ethical AI solutions tailored to specific needs.
Deep dives
The Concept of Openness in AI
Openness in artificial intelligence extends beyond just open source code or data; it involves creating an environment where diverse ideas and perspectives thrive, leading to enhanced innovation and transparency. The exploration of openness reshapes industries by enabling businesses to implement AI solutions that are more effective and accessible. By fostering collaboration and leveraging a variety of models, organizations can harness the power of AI while ensuring ethical considerations and transparency are prioritized. This multifaceted approach to openness empowers enterprises to navigate the complexities of AI with greater agility and responsibility.
Granite: A Shift Towards Targeted Models
Granite represents a significant shift in the AI landscape, moving away from large, general-purpose models to more specialized and efficient smaller models that can be customized for specific business needs. This trend is partially driven by the challenges associated with larger models, including increased latency, costs, and energy consumption, which can be detrimental in high-volume enterprise scenarios. By utilizing proprietary data, businesses are finding that these smaller models deliver high performance while remaining cost-effective, making them more viable for real-world applications. The integration of models like Granite allows enterprises to streamline operations and enhance customer service, particularly in areas like customer care through rapid and accurate responses based on internal data.
Challenges and Future of AI Implementation
Enterprises face significant challenges when moving from the experimentation phase with generative AI to full production, as they need to balance innovation with practicality. Successful implementation requires organizations to consider quality data access, trusted partnerships, and the regulatory landscape surrounding AI applications. Moreover, there's a pressing need for transparency in how AI models are trained, especially in regulated industries, to mitigate risks associated with unknown data sources. Looking to the future, generative AI is expected to evolve into systems that can reason, plan, and act autonomously, emphasizing the importance of continuous learning and agility in adopting emerging technologies.
As the scale of artificial intelligence continues to evolve, open technology like many of IBM’s Granite models are helping enhance transparency in AI and improve efficiency across businesses. In this episode of Smart Talks with IBM, Jacob Goldstein sat down with Maryam Ashoori, the Director of Product Management and Head of Product for IBM’s watsonx.ai, where she spearheads the product strategy and delivery of IBM’s watsonx Foundation Models. Together, they explored the shift from large general-purpose AI models to smaller, customizable models tailored to specific needs.
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