Research Series: AI Need To Know
← Back to Research-
The Architecture of Trust: Merging Traditional Approaches with the Latest in Data Security
Summary
Data security and privacy have long been essential in technology. Artificial Intelligence (AI) introduces new, often overlooked risks that require an understanding of new security concepts and a modern approach. Vendors must recognize the limitations of traditional security practices in addressing AI-specific challenges such as adversarial attacks, new data extraction techniques, and regulatory compliance under new laws like the EU AI Act. This note helps highlight the nuances of these issues and provides actionable strategies for AI solution providers to mitigate risks and maintain client trust.
Key Take: AI introduces new security risks that traditional approaches may overlook. Vendors need to implement AI-specific security measures to stay compliant and secure.
Don’t Drift Away from Reality — Ensure Vendors Offer AI Model Longevity
Summary
In the ever-evolving landscape of machine learning (ML), winning the battle against model drift and decay is pivotal to maintaining a competitive edge and achieving sustainable success. Model drift and decay refer to the degradation in the performance of machine learning models that happens over time. This research delves into the critical importance of addressing these issues head-on and offers actionable insights to support verifying investment in the right Artificial Intelligence (AI) vendor solutions. Continuous monitoring, implementing automated retraining pipelines, and ensuring robust data management safeguard the integrity of AI systems and unlocks their full potential. Discover how proactive measures can ensure long-term value for your organization and what to be asking AI solution providers.
Key Take: Effective management of model drift and decay is essential for sustaining the value of ML systems. Without these measures, models can become obsolete, leading to inaccurate predictions and reduced ROI.
Maximize AI With Clear Understanding of Fine-Tuning vs. Retrieval-Augmented Generation (RAG)
Summary
Fine-Tuning and RAG (Retrieval-Augmented Generation) are essential AI model enhancement methods, each offering distinct advantages and challenges. Implementing these approaches strategically, based on specific AI application needs, can significantly enhance the performance and outcomes of pre-trained models. Conversely, choosing the wrong approach can undermine the benefits of an AI solution. This research explores the benefits of both Fine-Tuning and RAG, emphasizing the importance of selecting and combining these methods appropriately to achieve optimal results.
Key Take: Companies that fail to align Fine-Tuning and RAG with the appropriate use cases will encounter inefficiencies, diminished performance, and missed opportunities to fully leverage AI’s potential.