How Geopolitics is Shaping the Future of AI
The world of technology is constantly evolving, and one area that’s seeing significant changes is the AI market. As geopolitical events unfold, they’re having a profound impact on the development, methodology, and use of AI in the enterprise. In this article, we’ll explore the current state of AI, the challenges it faces, and how Red Hat is working to make AI more accessible, transparent, and responsible.
The Current State of AI
The expectations surrounding AI are high, but they’re also balanced with real-world realities. There’s a growing suspicion about the technology, and many are questioning its use and development. The closed-loop nature of large language models (LLMs) is being challenged by new instances like Llama, DeepSeek, and Baidu’s Ernie X1. In contrast, open-source development provides transparency and the ability to contribute back, which is more in tune with the desire for "responsible AI."
The Importance of Responsible AI
Responsible AI encompasses a range of issues, including the environmental impact of large models, how AIs are used, what comprises their learning corpora, and issues around data sovereignty, language, and politics. Red Hat, a company that’s demonstrated the viability of an economically sustainable open-source development model, wants to extend its open, collaborative, and community-driven approach to AI. We spoke with Julio Guijarro, the CTO for EMEA at Red Hat, about the organization’s efforts to unlock the power of generative AI models in ways that bring value to the enterprise.
The Challenges Facing AI
One of the significant challenges facing AI is the lack of transparency. The complex science and mathematics behind AI make it a "black box" for many. This lack of transparency is compounded when AI is developed in largely inaccessible, closed environments. There are also issues with language, data sovereignty, and trust. European and Middle-Eastern languages are under-served, and businesses need to ensure they’re aware of the risks of exposing sensitive data to public platforms with varying privacy policies.
Red Hat’s Response
Red Hat’s response to the global demand for AI is to pursue what it feels will bring the most benefit to end-users while removing many of the doubts and caveats associated with traditional AI services. One answer is small language models (SLMs) that run locally or in hybrid clouds, on non-specialist hardware, and access local business information. SLMs are compact, efficient alternatives to LLMs, designed to deliver strong performance for specific tasks while requiring significantly fewer computational resources.
The Benefits of Small Language Models
Using SLMs means that organizations can keep their business-critical information in-house, close to the model, if desired. This is important because information in an organization changes rapidly. SLMs also provide greater control over costs, as the scope is limited by the cost of the organization’s own infrastructure, not by the cost of each query. Additionally, Red Hat is optimizing models to run on more standard hardware, making it possible for organizations to use AI without having to procure specialized equipment.
Keeping it Small and Local
Using and referencing local data pertinent to the user means that the outcomes can be crafted according to need. Red Hat is focused on supporting and improving vLLM (the inference engine project) to make sure people can interact with all these models in an efficient and standardized way, wherever they want: locally, at the edge, or in the cloud. This approach also addresses issues with latency and trust, as organizations can have more control over their data and the models they use.
Democratizing AI
Red Hat advocates for open platforms, tools, and models to move towards greater transparency, understanding, and the ability for as many people as possible to contribute. The company recently acquired Neural Magic to help enterprises more easily scale AI and improve performance. Red Hat also released InstructLab to open the door to would-be AI builders who aren’t data scientists but have the right business knowledge.
Conclusion
The future of AI is uncertain, but one thing is clear: it needs to be more transparent, responsible, and accessible. Red Hat believes that AI has a future in a use case-specific and inherently open-source form, a technology that will make business sense and be available to all. As the company’s CEO, Matt Hicks, said, "The future of AI is open."
FAQs
- What is responsible AI?
Responsible AI encompasses a range of issues, including the environmental impact of large models, how AIs are used, what comprises their learning corpora, and issues around data sovereignty, language, and politics. - What are small language models (SLMs)?
SLMs are compact, efficient alternatives to LLMs, designed to deliver strong performance for specific tasks while requiring significantly fewer computational resources. - Why is transparency important in AI?
Transparency is essential in AI because it allows organizations to understand how the technology works, what data is being used, and how it’s being used. This helps to build trust and ensure that AI is used responsibly. - What is Red Hat’s approach to AI?
Red Hat’s approach to AI is to pursue open-source development, transparency, and community-driven collaboration. The company believes that AI should be accessible, responsible, and available to all. - What is the future of AI?
The future of AI is uncertain, but Red Hat believes that it will be shaped by open-source development, transparency, and community-driven collaboration. The company is working to make AI more accessible, responsible, and available to all.
