The Rise and Future of Generative AI

An AI Ecosystem for All

The fast rise of generative AI has been both fascinating and exciting for companies and consumers worldwide. The growing number of apps and frameworks from companies like NVIDIA, Hugging Face, and Anyscale are paving the way for more democratic use of AI and machine learning.

“The potential gains are immense. McKinsey estimates generative AI could add up to $4.4 trillion annually to the global economy,” says Abhay Kumar, Vice President, Hyperscalers & Technology Partners at VMware.

To support customers’ AI journeys, enterprise leaders need to engage in committed collaborations and build and leverage new AI and ML platforms. This requires cultivating an open and interconnected ecosystem.

“Despite the explosive growth of generative AI over the past year, we’re still in the early days. Responsible, safe, and controlled use of AI and ML can lead to better outcomes for customers and sustainable growth for organizations,” Kumar explains.

Cultivating an Open AI Ecosystem

There are several key steps for CIOs and other stakeholders looking to cultivate an open AI ecosystem fueled by new collaborative efforts:

  • Accelerate use of AI and ML responsibly using private AI to balance business gains with privacy and compliance needs.
  • Enable generative AI use cases by bringing the IBM Watson AI and data platform to VMware Private AI.
  • Simplify building and deploying AI models by utilizing existing general-purpose infrastructure and open-source software.
  • Develop clear ethical principles to ensure fairness, privacy, accountability, intellectual property protection, and transparency of training data.
  • Share data and coding techniques to collectively reach greater heights in AI innovation.

“Generative AI tools, including large language models and computer vision, can empower companies to increase innovation and output while delivering better products and services,” says Kumar. “But there are challenges that enterprises must confront head-on.”

Challenges to Overcome

Enterprises face several challenges when it comes to generative AI:

  • The cost and complexity of training AI models.
  • The shortage of specialized talent to build successful AI models.
  • The notable risks associated with generative AI, such as security breaches and IP violations.

“To tackle these challenges, enterprises need to use open-source software to build smaller AI models optimized for specific tasks,” suggests Kumar. “They also need to simplify the creation and training of AI models by utilizing reference architectures.”

Building Trust and Collaboration

As generative AI continues to evolve, it’s essential for industry stakeholders to address concerns over privacy, data integrity, bias, and other flags. Collective efforts within the open-source community can help build greater trust around the use of generative AI for business growth.

“New rules and regulations around generative AI will take shape over time,” says Kumar. “To ensure a stable groundwork, industry stakeholders across sectors need to take greater ownership over the disruptions brought on by AI through collaboration and teamwork.”

VMware, working closely with CIOs and decision makers, is dedicated to optimizing digital infrastructure for AI and ML integration. By fostering collaboration and teamwork with generative AI, VMware aims to contribute to a thriving open ecosystem that remains interconnected and democratic.

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