Guest blog: democratising AI - how open source unlocks value while balancing innovation and responsibility
As AI continues to advance, open source is playing a critical role in standardising and democratising the language models, tools, and platforms needed to help enterprises achieve business value from AI. Open Source means community participation. Contributors can support regular builds of an enhanced version of a Large Language Model. This approach is designed to lower costs, remove barriers to testing and experimentation, and improve alignment—that is, ensuring the model's answers are accurate, unbiased, and consistent with the values and goals of its users and creators using Open Practices.
Open source projects like InstructLab have recently been launched to lower the barrier of entry for Generative AI. These projects facilitate an open approach to building more capable and domain-specific models, including participation from those with minimal machine learning experience. Therefore, they help progress the state of AI for organisations regardless of size and resources.
While adopting AI solutions, organisations want to create a balance between innovation and responsibility. While AI has significant potential benefits, there are also risks to consider. Prioritising openness, transparency, and community participation, we can define AI systems that drive innovation while building safe and trustworthy applications.
Lowering the barrier of entry
Another aspect to evaluate to lower the barrier of entry for organisations to adopt Generative AI is the language model size.
Large Language Models can generalise well to different domains but they are also associated with higher financial and computational resources to train them.
Smaller language models specialise in domain-specific tasks and can improve performance and efficiency for targeted applications compared to using a more general model.
AI's success hinges on trust, particularly in industries with stringent regulations. Smaller open-source models enhance privacy and security and are more suitable for handling sensitive, customer-specific data. Open source plays a central role in the security aspect, providing transparency throughout the AI lifecycle, from data pipelines to model development and deployment. This transparency extends beyond the models to encompass the data used to train them. Proprietary data from legacy systems is especially valuable for enterprise use cases. By training models on this curated data - on-premises or within private clouds- organisations can satisfy compliance requirements whilst instilling confidence that AI outputs are derived from data unique to their operations.
Agile and sustainable architecture for AI
As the AI community evaluates the benefits of small language models, there is also a focus on agile architectures for AI, with faster and more efficient development cycles,tailored to put the AI models in production quicker to capitalise on the benefits.
Many AI projects don't scale beyond the lab environment, and enterprises often face challenges in standardising the model building, training, deployment, and monitoring processes. To address this, it is essential to automate AI lifecycles with MLOps, streamlining processes across teams. Companies need to make sure they have the foundations in place that are going to enable scalability, efficiency and sustainability for AI-infused applications. A hybrid cloud infrastructure allows AI models and applications to be consumed in the cloud, an existing data centre, or a hybrid landscape, including at the edge, as needed.
Deploying AI at the edge opens up new possibilities for real-time and personalised applications in various sectors.
Working with AI models and applications in a hybrid cloud environment reduces latency, improves responsiveness, and allows enterprises to balance cost-efficiency with technical capabilities, supporting the efficient development and deployment of AI models.
With increasing AI adoption, one of the most notable challenges is the significant energy usage associated with training and running AI systems. A hybrid architecture allows workloads to be seamlessly migrated between on-premises, edge and cloud environments to optimise costs over compute, storage and network resources.
A hybrid cloud infrastructure also enhances data management by positioning data pipelines across on-premises, edge, and cloud environments as needed.
This integration and cross collaboration across teams in an organisation is more than just a technological solution – it is a strategic imperative that enables enterprises to innovate and adapt in an interconnected and AI landscape. By blending hybrid cloud resources and bringing teams together, organisations can fully harness AI's potential, in a sustainable, agile and scalable manner.
Find out more! Speak to Erica or any of our AI experts at our upcoming Red Hat Summit: Connect London 2024.
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