Summary

This article discusses the race to build an operating system for generative AI and its impact on enterprises. It highlights the challenges faced by enterprises in adopting generative AI and the need for a new infrastructure to support the intelligence and autonomy of generative AI applications. The article also explores the four main layers that enterprises need to consider when building an operating system for generative AI. Additionally, it mentions the emergence of open software frameworks and platforms that enable developers to create more intelligent and autonomous generative AI applications. The article concludes by emphasizing the strategic advantage that enterprises can gain by mastering generative AI and the opportunity to learn more about it at the VB Transform event.

Highlights

Generative AI is reshaping the business world and has the potential to add $4.4 trillion to the global economy.
Enterprises face challenges in transforming their processes, systems, and cultures to embrace generative AI.
Orchestration of complex interactions between generative AI applications and other enterprise assets is a major hurdle.
An operating system for generative AI is needed to support the intelligence and autonomy of these applications.
Four main layers to consider when building an operating system for generative AI: data layer, development layer, runtime layer, and user experience layer.
There is a vibrant ecosystem of open software frameworks and platforms advancing the state of the art of generative AI.
Developers can leverage foundational large language models (LLMs) and customize them for specific needs and goals.
Enterprises like Intuit are experimenting with self-guided, automated LLM “agents” to enhance intelligence and autonomy.
The race to build an operating system for generative AI is both a technical and strategic challenge for enterprises.
The VB Transform event provides an opportunity for enterprise tech executives to learn from industry experts and leaders in generative AI.

FAQ

Q: What is generative AI? Generative AI is a technology that can auto-generate various forms of content, such as text, images, and application code.

Q: How much value can generative AI add to the global economy? According to a recent report by McKinsey, generative AI has the potential to add $4.4 trillion to the global economy.

Q: What are the challenges faced by enterprises in adopting generative AI? Enterprises face challenges in transforming their processes, systems, and cultures to embrace generative AI. They also need to orchestrate complex interactions between generative AI applications and other enterprise assets.

Q: What are the four main layers to consider when building an operating system for generative AI? The four main layers are the data layer, development layer, runtime layer, and user experience layer.

Q: How can developers leverage foundational large language models (LLMs)? Developers can access foundational LLMs through APIs and integrate them into their existing infrastructure. They can also customize these models for specific needs and goals using techniques like fine-tuning, domain adaptation, and data augmentation.

Conclusion

The race to build an operating system for generative AI is a significant challenge and opportunity for enterprises. By mastering generative AI and building a robust infrastructure, enterprises can gain a strategic advantage over their competitors and deliver more value and innovation to their customers. The VB Transform event provides a platform for enterprise tech executives to learn from industry experts and leaders in generative AI, furthering their understanding and adoption of this transformative technology.

Source

Inside the race to build an operating system for generative AI

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