Steve Astorino, Director of the Canada Lab and VP of Development in Data and AI at IBM, discusses the challenges and misconceptions in generative AI, addressing the skills gap in utilizing emerging technologies in enterprises, challenges and governance in enterprise data and models, and the intersection of data governance and AI ethics.
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Quick takeaways
The availability of GPUs is a major challenge in driving AI capabilities, hindering organizations from effectively leveraging AI.
Selecting the appropriate tools is crucial for successful AI implementation, as inadequate tool selection can lead to market mishaps.
Deep dives
The Challenge of GPU Availability for AI Infrastructure
The biggest challenge in driving AI capabilities, particularly in large language models, is the availability of GPUs needed for faster model training and execution. Many organizations struggle to obtain the necessary infrastructure, hindering their ability to leverage AI effectively. While there are efforts to accelerate accessibility, the scarcity of GPUs is likely to persist for some time. However, this challenge also presents an opportunity to ensure responsible adoption of the rapidly evolving technology.
Choosing the Right Tools for AI Success
Selecting the appropriate tools is critical for successful AI implementation. Many businesses face the challenge of being overwhelmed and underprepared when venturing into the AI space. It is important to choose tools that align with specific use cases and business objectives. The consequences of inadequate tool selection are evident, with various market mishaps in recent months. As AI technology continues to evolve, organizations must navigate the learning curve to make informed choices and leverage the full potential of AI.
Addressing Skill Gap and Data Challenges in AI Adoption
Alongside infrastructure and tool selection challenges, there are two significant hurdles in AI adoption: skills and data. Building AI competencies and knowledge within organizations is crucial. IBM has been actively working on enabling and educating its workforce and collaborating with academia to bridge the skills gap. Additionally, clean and accessible data remains essential for AI success. Ensuring data cleanliness and privacy protection poses ongoing challenges. Governance and control measures are necessary, not only for the data itself but also for the models trained on it, aligning access privileges and role granularity to provide secure and appropriate answers.
Today’s guest is Steve Astorino, General Manager, Product Development, Data, AI & Sustainability and Canada Lab Director at IBM. Steve joins us on today’s program to talk about the biggest challenges for enterprise leaders when it comes to driving the infrastructure innovations necessary to leverage new emerging AI use cases — especially in new, data-hungry generative AI tools. If you’ve enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!
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