Powering Generative AI and Large Language Models with Hybrid Cloud

30-Jul-2023

Introduction

Generative AI and large language models (LLMs) have taken the world by storm, revolutionizing how organizations create, process, and analyze data. I believe that Hybrid Cloud is a critical foundation for Generative AI, and I wondered if Generative AI felt the same.  So as an experiment, I asked two different publicly available gen-AI tools to write a blog on ‘why Hybrid cloud is critical to generative AI’. I took those results and merged my own thoughts and research, and the result is this blog. 

 Ten years ago the saying was “software is eating the world”. As my colleague and IBM Fellow  Ruchir Puri, recently said in a client briefing, “It’s safe to say that software has eaten the world, and now AI is eating the world. Even that AI is eating software.” Organizations are starting to leverage the capabilities of generative AI and large language models to drive innovation, enhance customer experiences, and gain a competitive edge. These AI technologies enable the creation of human-like text, images, and more, which opens up possibilities and challenges for business and society.

Harnessing the potential of these technologies requires robust infrastructure and flexible computing environments – the ability to access large volumes of data and provide significant compute power. This is where the concept of hybrid cloud comes into play. In this blog post, we will explore the significance of hybrid cloud in enabling generative AI and large language models.

Defining Key Terms

1. Hybrid Cloud: Hybrid cloud refers to a computing environment that combines private and public cloud resources. It allows organizations to store and process data on-premises or in private clouds while seamlessly integrating with public cloud services. This hybrid approach offers greater flexibility, scalability, and security.

2. Generative AI: Generative AI involves using machine learning algorithms to create new content, such as images, music, text, or video, that is not explicitly present in the training data. It enables computers to learn patterns from existing data and generate original, creative outputs.  ChatGPT, DALL-E, Bard and Watsonx are examples of generative AI applications.

3. Large Language Models (LLM): Large language models are advanced AI systems that have been trained on vast amounts of text data. These models can understand and generate human-like text, making them invaluable for tasks like natural language processing, language translation, content creation, and more.  As a form of generative AI, large language models can be used to not only assess existing text but to generate original content based on user inputs and queries.

The Need for Hybrid Cloud with Generative AI

Generative AI and large language models are resource-intensive technologies that require significant computing power and storage capabilities. Running these models efficiently necessitates a robust infrastructure that can handle the computational demands while ensuring data privacy and security. Here are some key reasons why hybrid cloud is essential in this context:

• Scalability: Hybrid cloud offers organizations the ability to scale their computing resources on-demand. . For example, training a state-of-the-art LLM like OpenAI’s GPT-3 requires hundreds of billions of parameters and can take weeks to complete on powerful GPUs. As a result, organizations must invest in scalable infrastructure that can handle the increasing demands of AI and LLM workloads. By utilizing the public cloud, organizations can dynamically allocate additional resources during peak usage, ensuring optimal performance and minimizing processing time.

• Cost Optimization: Managing costs is a critical aspect of any organization’s IT strategy, and this is especially true when dealing with resource-intensive AI workloads. Hybrid cloud enables organizations to optimize costs by leveraging the elasticity of public cloud resources. While private clouds provide enhanced control and security for sensitive data, they may lack the scalability and cost-efficiency of the public cloud. By leveraging a hybrid cloud approach, organizations can balance their workloads, allocating computationally intensive tasks to the public cloud while maintaining sensitive data on-premises or in a private cloud.

• Data Governance and Compliance: One of the primary concerns of organizations using generative AI and LLM is ensuring data privacy and security. Generative AI and large language models often work with sensitive data, such as customer information or proprietary knowledge. The value for your business enterprise comes from your data being added to the base model. This means that compliance with data governance regulations and security protocols is paramount. (It also means that quibbles about base models are less meaningful – the value only comes when you merge the base with your data. ) Hybrid cloud offers organizations the flexibility to store and process data locally in a private cloud, ensuring compliance with regulatory requirements, while still leveraging the power and scalability of public cloud services for less sensitive operations.  “AI must be explainable, fair, robust and transparent.” Effective data governance is a critical element in maintaining trust in AI systems.
• Latency and Bandwidth Optimization: In certain scenarios, low latency and high bandwidth are critical for real-time decision-making and interactive user experiences. Hybrid cloud enables organizations to process data closer to the edge, reducing latency and optimizing bandwidth usage. For example, a large language model deployed in a hybrid cloud environment could analyze customer queries locally, providing quick responses, while offloading more computationally intensive tasks to the public cloud.

• Enhanced Collaboration. Generative AI and LLM projects often involve collaboration between various teams, including data scientists, engineers, and business analysts. A well-constructed Hybrid cloud environment facilitates seamless collaboration by providing a unified environment where team members can access and share data, tools, and resources, to contribute to the development and deployment of AI and LLM solutions.

What’s next:

In what might be the understatement of the year, the possibilities with generative AI are evolving and expanding rapidly.   We already see AI in Chatbots, automating simple to medium interactions.  Coming soon are assistants to make software developers more efficient and that will extend to other knowledge-based roles.  And according to a recent IBM Institute for Business Value (IBV) study, two out of three C-suite executives feel pressure from investors to accelerate use of generative AI.  So we can expect the pace of implementation and change to continue to accelerate.  As business and individuals become use to and trust generative AI, decision making speed will increase and it will be a major contributor to the increasing pace of society.  We can expect regulations legally governing generative AI to lag the use cases that are implemented.

And of course, as with any new powerful technology, there is a risk of both accidental or deliberate misuse.  For responsible organizations, strong data governance and ongoing work to manage model drift and implementing mechanisms to trace outputs back to the foundation model, dataset, and originating prompt are necessary.  And of course, dealing with AI bad actors is a major new focus of cybersecurity tools and process.

There is always the danger with an exciting new technology that we forget the value that other tools can provide.  In the long-term, AI will be driven with a combination of Machine Learning models and Foundation models and no doubt, other models, that drive real value.  Like hybrid cloud, the long-term future of AI is also a hybrid.

Longer term, according to the World Economic Forum, in November 2022, Metacalculus, a professional forecasting organization, believe “that there is a 50/50-chance for an ‘Artificial General Intelligence’ to be ‘devised, tested, and publicly announced’ by the year 2040, less than 20 years from now.” An Artificial General Intelligence being one where  unaided machines are able to accomplish every task better and more cheaply than human workers. The impact of that would be profound on every business, industry, government and society generally.

Conclusion

Hybrid cloud plays a vital role in enabling generative AI and large language models to achieve their full potential. It provides the necessary scalability, cost optimization, data governance, compliance, and optimization of latency and bandwidth. By embracing hybrid cloud, businesses can unlock the true power of generative AI and large language models, driving innovation and staying ahead in the age of digital transformation. As generative AI and LLM continue to evolve and become more prevalent, organizations that embrace hybrid cloud will be better positioned to stay ahead of the curve and reap the benefits of these transformative technologies.

Note

The initial drafts of this blog came from two different Generative AI models:  Anthropic and ChatGPT3.0.  The results were combined, edited together with my own thoughts and experiences, and of course, any errors are mine.

Additional Links Used in Preparing this Blog:

“Hybrid Cloud: The Path to Enterprise Cloud Computing” – Forbes: https://www.forbes.com/sites/forbestechcouncil/2021/08/26/hybrid-cloud-the-path-to-enterprise-cloud-computing/?sh=57b5ea4c6842

“The Impact of Hybrid Cloud on AI and Machine Learning” – TechRadar: https://www.techradar.com/news/the-impact-of-hybrid-cloud-on-ai-and-machine-learning]

“Hybrid Cloud: A Comprehensive Overview” – IBM: https://www.ibm.com/cloud/learn/hybrid-cloud

 “The Power of Generative AI in Transforming Industries” – OpenAI Blog: https://openai.com/blog/generative-ai-transforming-industries/

“What Is a Large Language Model?” – EWeek: https://www.eweek.com/artificial-intelligence/large-language-model/

 “Understanding Large Language Models” – Medium:  https://medium.com/ai%C2%B3-theory-practice-business/understanding-large-language-models-5a5838017aed

“Enterprise Generative AI: State of the market” – IBM: https://www.ibm.com/downloads/cas/3YZ1N2PB

“What’s the Future for A.I.?”. The New York Times: https://www.nytimes.com/2023/03/31/technology/ai-chatbots-benefits-dangers.html

“The Future of AI: What to expect in the next five years” – TechTarget: https://www.techtarget.com/searchenterpriseai/tip/The-future-of-AI-What-to-expect-in-the-next-5-years

“The Future of AI: 5 Things to Expect in the Next 10 Years” – Forbes https://www.forbes.com/sites/forbesbusinesscouncil/2022/05/05/the-future-of-ai-5-things-to-expect-in-the-next-10-years/?sh=502955cd7422

“Forward Thinking:  Experts reveal what’s next for AI” – IBM: https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence.html