CALI, COLOMBIA – APRIL 17: A Colombian urban shaman plays cards to predict the future … [+]
AI is real. But even though artificial intelligence (AI) and machine learning (ML) are definitely here, a certain level of AI agnosticism is still necessary. Specifically, we need to take an agnostic stance toward the components that come together to build our AI intelligence. We are of course talking about the arrival of new and updated extended language models (LLMs), which underpin AI knowledge in its various shapes, sizes and appearances.
Although organizations now have the ability to deploy enterprise AI applications that have the potential to change their business operations, it is essential for any company evaluating AI solutions to understand how they work with the different LLMs available and which ones will offer the greatest advantages.
As such, AI agnosticism will be vital.
What is AI agnosticism?
Not exactly a formal practice, industry standard approach, or defined methodology as such, AI agnosticism is like any form of computational agnosticism in as it advocates the adoption of widespread interoperable technologies, processes, practices, tools and components. Applicable to both hardware and software, computer agnosticism and AI agnosticism in particular means keeping an open mind (figuratively and literally) about the data knowledge resources we use to create AI models for a number of business use cases.
BlueFlame AI, provider of a generative AI platform for alternative investment managers, believes now is the time for firms to embrace LLM agnosticism and seek LLM model-agnostic AI solutions (Large Language Model) and AI providers. BlueFlame deploys its LLM agnostic platform to help companies select the best LLM for specific tasks that can reduce risk and optimize performance and efficiency.
“The facts are simple: we can say that optimization, personalization and resilience are three essential benefits that LLM-agnostic AI solutions can deliver, with optimization being key as it allows users to select the right model for each specific task based on the strengths and weaknesses of the model. », says James Tedman, Europe manager of BlueFlame AI. “Companies using LLM-agnostic AI solutions will have the greatest impact, as they will reduce model bias and decrease reliance on a single LLM. In the event of an LLM breakdown, businesses will want diversified LLMs to move to an alternative without significant disruption. Independent approaches to LLM ensure that your business does not have to deal with service interruptions or performance issues.
A volatile time in AI
An agnostic approach to LLM requires software developers to understand the capabilities, limitations, and complexities of each LLM. The volatility of certain AI-related events (job changes, rapidly evolving platforms, acquisitions, etc.) during the events of 2023 has arguably shown us how risky relying on a single LLM could be. It is suggested that an agnostic approach (or at least a more agnostic approach) can ensure reduced disruption from product or price changes, service interruptions, etc. It’s this type of adaptability that is essential in today’s evolving AI landscape.
“Different models may require different data management or processing strategies. Developers will need robust application programming interfaces (APIs) and middleware solutions due to the increased complexity of multiple LLM integrations,” Tedman said. “Since everyone has their own learning behaviors, it is important to ensure that LLMs provide consistent and reliable results. »
Enterprise AI Guardrails
Staying secure and compliant will be another key priority for any company building enterprise AI applications. Regulators are already preparing to review this space, particularly following the EU AI law. So, what best practices can businesses follow to ensure compliance? Tedman explains that regularly updating security protocols to protect against sensitive information handled by various LLMs will support data security.
“Ensuring you have commercial agreements in place to prevent data from being used for training models and that compliance with privacy laws such as GDPR or CCPA will meet privacy requirements. It will also be essential to implement strict access controls and authentication mechanisms to prevent unauthorized access to AI systems or sensitive data,” he noted.
The BlueFlame team reiterated that regular auditing of AI systems for security vulnerabilities and monitoring for malicious activity will keep organizations vigilant. Businesses also need to ensure their AI solutions comply with industry-specific standards and regulations, particularly in industries such as financial services, healthcare, and legal.
“It is essential that companies maintain transparency about how their AI systems use and process data, informing stakeholders about the AI models used and their data processing practices. Companies that can leverage LLM agnostic AI solutions to optimize performance and mitigate risk will have the greatest impact in the next AI revolution,” concluded Tedman.
Greek for knowledge: gnosis
Perhaps it’s no coincidence that – as Gavin Wright said TechTarget explains here – the word agnostic comes from the Greek a-, meaning without and gnōsis, meaning knowledge. “In computing, this translates to the ability of an object to operate without “knowing” or requiring anything about the underlying details of the system in which it works. »
We might evolve the term AI agnosticism to become open-mindedness, AI rationalism or maybe even unfaithful AI in the future, who knows. What we can say is that it is important to believe in AI, but believing in AI that is non-secular and open to all sources of information might well be more important.
Leave the AI to us.