In the era of data-driven (data-based decision-making), the role of artificial intelligence (AI) is fundamental. From predicting stock market trends to generating personalized content for users, AI models are indispensable. However, the effectiveness of these models is closely linked to the quality and relevance of the data they use.
Outdated data and its impact on predictive results
The adage “garbage in, garbage out” is true in the field of AI. When models are built or powered using incomplete, biased or outdated information, predictive results are impacted. For example, in financial markets where conditions change in milliseconds, relying on outdated data can lead to missed opportunities and sometimes even colossal financial losses. Outdated data gives the illusion of accuracy. The models can then display great confidence in their predictions, but these are based on a reality that no longer exists.
Overview of possible consequences linked to the use of obsolete data:
- Business decisions: in finance, health or even retail, decisions based on obsolete data can lead to errors in orders or restocking, unwise placements and investments, and ultimatelysignificant financial losses.
- Safety Concerns: In some critical areas of personal safety, such as autonomous driving or medical diagnostics, outdated data can be a matter of life and death.
- Consumer experience: In the context of offering customer-centric services, such as online recommendation systems or personalized marketing, predictions based on outdated data would produce a drop in user engagement and satisfaction, or even repercussions on the reputation of the company, particularly through its image on social networks.
Compensating the reality of hallucinations in fundamental models
Large AI models are incredibly powerful, but they are also susceptible to generating meaningless or incorrect content, a phenomenon known as “hallucination” in fundamental models. These hallucinations occur because the model relies on a standard data set that does not necessarily contain the most recent or contextually relevant information.
Integrating real-time data into the algorithm of large models can drastically reduce the frequency of hallucinations. When the model has access to the most recent data, it can generate predictions or contextually relevant content. This is essential for businesses that want to leverage the power of AI to drive real-time decision-making, and access the high-value predictive power that AI can deliver.
The role of databases in real-time AI
The foundation for creating hypercontextualized, personalized experiences for AI-enriched generative applications lies in the organization’s document and control system. Real-time data is an integral part of this real-time AI application stack, and it is imperative that operational databases are tightly integrated with the AI pipeline.
In order to create these experiences, we must be able to guarantee developers access to a powerful multi-model database platform, capable of storing large volumes, and allowing them to efficiently manage and search unstructured data. They need a layer of long-term memory for large language models (LLM), to provide responses that take into account real-time data context, as well as conversational history, and have the ability to store and search data in the native LLM format — that of unstructured data, managed by high-dimensional vector bases.
The future of generative and predictive AI
Real-time data, combined with unstructured storage and search capacity, can give fundamental models their long-term memory. Such databases can hold large amounts of information and make it easily accessible to the model, thus acting as the “memory” of the model.
Integrating real-time data into generative and predictive AI models is not only a technical improvement, but also a philosophical shift. As we move towards a world where change is ever more frequent, AI’s ability to adapt and provide accurate, contextualized information will be the sine qua non for effective decision-making. By addressing the challenges posed by outdated data and hallucinations, AI’s true potential can be unlocked, making it an invaluable asset for the data-driven strategies of the future.