Witnessing the ChatGPT hype cycle, it’s reasonable to pause and consider new ways to integrate Artificial Intelligence into your business. However, this integration raises significant concerns regarding data privacy and security, as sensitive information is often processed by these systems in unexpected ways. Rower is here to help you navigate this new information landscape and help you make the right choices for your company’s goals.
The reluctance of businesses to allow use of commercial chat products highlights the need for a secure approach to harnessing the power of AI. Running private transformer models ‘in house’ offers a solution. This approach ensures that sensitive information remains within the organizational boundaries, mitigating the dangers associated with data proliferation.
Who’s Mining The Value of Your Knowledge?
When interacting with large language models, it’s crucial to understand that while they don’t directly “consume” your data in a traditional sense, they temporarily hold it within a “context window”. This context window is an essential part of the model’s operation by storing requests and responses, informing the model’s decision-making process.
However, the concern arises when this “chat history” for a given session is persisted beyond the immediate interaction. In commercial chat applications, this data could be stored long term, raising serious data security and privacy issues.
Moreover, these companies providing commercial chatbot services reserve the right to use customer interactions as feedback for their models. This style of learning reinforcement is an important element of the model’s refinement and improves the commercial viability of future model versions. Your interactions with their systems are in effect a continuous resource that could potentially be mined by these companies to evolve their product.
This business approach, while generally beneficial for improving that company’s model accuracy, poses a risk as it could lead to the inadvertent incorporation of sensitive information into the model’s training set. Such data, once part of the training corpus, can influence the model’s future responses and decision-making, potentially leading to unintended data leaks or misuse.
Protecting Your Data: Options Worth Considering
A common mistake we’ve encountered is the mistaken view that deploying the most extensive “foundation” models, some of which have over one hundred billion nodes, as the only option to gain the benefits of intelligent chat. And it doesn’t help that the cheerleaders of the latest hype cycle are not dissuading anyone from that misaprehension. Even if it were true, it would not be feasible for most IT budgets. But more importantly it’s also not a significant value add to an organization. Not many businesses need to generate both the best dessert recipe and the best workout routine.
A more practical and secure approach resides with customized open-source models. These models can efficiently run on commodity hardware, offering business-focused capabilities and manageable resource requirements. This shift offers lower running costs and refined control over data security. The key points to consider include:
- Customizability: A wide variety of open-source models allow for tailoring to specific business needs and data types.
- Cost-efficiency: Running transformer models on commodity hardware reduces financial barriers to broad organizational adoption.
- Data Sovereignty: On-premise and “walled garden” hosting ensures data remains completely under company control, crucial for sensitive information.
- Scalability and Flexibility: In house and cloud provider offerings like Kubernetes can scale to varying workloads while maintaining an actively managed security posture.
Self-deployed models not only offer enhanced data security but “sparse” LLM models also benefit from faster round-trip execution times, a critical factor in retrieval augmentation scenarios. This speed advantage allows for more efficient data processing, enhancing overall operational efficiency, particularly in real-time applications. As your familiarity with this domain grows, you’ll also be considering data enrichment strategies that get value from your current data investments:
- Utilizing your company’s existing information resources, smaller models can be fine-tuned to deliver faster and more precise responses, capitalizing on the wealth of internal data and company knowledge.
- Alignment and Grounding: Smaller models can be designed to more effectively “align” with a company’s specific needs and its responses “grounded” in the comapany’s unique information environment. This alignment ensures more accurate context driven responses, and avoids the potential for innacuracies or “hallucinations” that the current generation of generative models display with what seems to be inifinte confidence.
It’s a wise choice to consider the use of small, well-tuned transformer models to enable a more secure semantic solution. This approach also fosters deeper integration with your current resources and provides options to realize greater value across your organization.
Continue the Conversation With Rower Consulting
Here at Rower Consulting, we equip our clients with the tools they need to get the most out of their systems. So if you’re ready to take the next step in the journey, contact us to learn more about how we can help you leverage both the latest and time-tested tools to move your business forward!
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