It’s time to believe in AI agnosticism

AI is real. But despite the fact that artificial intelligence (AI) and machine learning (ML) are definitely among us, a degree of AI agnosticism is still necessary. More specifically, we need to adopt an agnostic stance regarding the components that come together to build AI smartness. We are, of course, talking about the arrival of new and updated large-scale language models (LLMs) that underpin AI knowledge in all shapes, sizes, and guises.

While organizations have the opportunity to deploy enterprise AI applications that have the potential to transform business operations, companies evaluating AI solutions are wondering how to work with the various LLMs available and which ones. It’s important to understand which one will bring you the most benefit.

Therefore, it is important to be AI agnostic.

What is AI agnosticism?

AI agnosticism is not a formal practice, industry standard approach, or defined methodology per se, but in that it encourages the adoption of generalized and interoperable technologies, processes, practices, tools, and components. As with any form of IT agnosticism. While this applies to both hardware and software, IT agnosticism and AI agnosticism are particularly important when it comes to what data-knowledge resources are used to build AI models for any business use case (metaphorically as well). It means keeping an open mind (literally too).

BlueFlame AI, a generative AI platform for alternative investment managers, believes it’s time for companies to embrace LLM agnosticism and look for AI solutions that don’t rely on large-scale language models (LLMs) or AI providers. BlueFlame deploys an LLM-agnostic platform that allows companies to choose the best LLM for a specific task that can reduce risk and optimize performance and efficiency.

“The fact is simple: optimization, customization, and resiliency are the three key benefits that LLM-independent AI solutions can provide. ”, asserts James Tedman, European Head of BlueFlame AI. “Companies that use LLM-agnostic AI solutions will have the greatest impact as they reduce model bias and reduce dependence on a single LLM. In the event of an LLM outage, enterprises wants to migrate their diversified LLMs to alternatives without significant disruption. With an LLM-agnostic approach, businesses won’t have to deal with service outages or performance issues.”

The volatile era of AI

LLM-agnostic approaches require software developers to understand the capabilities, limitations, and complexities of each LLM. The volatility of some AI events in 2023 (job changes, fast-moving platforms, acquisitions, etc.) perhaps showed how dangerous reliance on one LLM can be. It has been suggested that an agnostic approach (or at least a more agnostic approach) can ensure that disruptions due to product or price changes, service interruptions, etc. can be reduced. This kind of adaptability is key in today’s evolving AI environment.

“Different models may require different strategies for managing or processing data. Multiple LLM integrations add complexity, so developers need robust application programming interfaces (APIs) and middleware solutions. We will,” Tedman said. “Each has its own unique learning behavior, so it is important to ensure that the LLM is providing consistent and reliable results.”

Enterprise AI guardrails

Maintaining safety and compliance is also a key priority for any business building enterprise AI applications. Regulators are already gearing up for a review of this area, especially in line with his EU AI law. So, what are the best practices that companies should follow to ensure compliance? Tedman says that by regularly updating his security protocols to protect against sensitive information handled by various LLMs, , explains that data security is supported.

“Entering commercial agreements to prevent data from being used to train models and complying with privacy laws such as GDPR and CCPA will support privacy requirements. It is also important to implement strict access controls and authentication mechanisms to prevent unauthorized access to the

The BlueFlame team reiterates that organizations can stay vigilant by regularly auditing AI systems for security vulnerabilities and monitoring for malicious activity. Companies also need to ensure that their AI solutions comply with industry-specific standards and regulations, especially in areas such as financial services, healthcare, and legal.

“It is important for companies to be transparent about how their AI systems use and process data, and to inform stakeholders about the AI ​​models used and data processing practices. AI independent of LLM Companies that can leverage solutions to optimize performance and reduce risk will have the greatest impact on the upcoming AI revolution,” Tedman concluded.

Greek word meaning knowledge: gnosis

As TechTarget’s Gavin Wright explains here, the word agnostic probably comes from the Greek “a-” meaning “without” and “gnōsis” meaning knowledge. It’s no coincidence. “In IT, it means the ability for something to work without ‘knowing’ or requiring any of the underlying details of the systems running inside.”

In the future, the term AI agnosticism may evolve to become AI tolerance, AI rationalism, or perhaps even AI without faith. What we can say is that while it is important to believe in AI, it may be more important to believe in a secular AI that is open to all sources of information.

Let’s make it AI.