Artificial intelligence is opening up new areas of application across many industries. Groundbreaking AI trends are already emerging that can open new doors in data management. Couchbase, provider of a cloud database platform, presents the five most important ones:

1. real-time data as a game changer

The integration of real-time data into generative AI models will increasingly become a new standard for companies because the precision and effectiveness of results depend on an up-to-date flow of information. In the face of rapidly changing market dynamics, contextual and accurate results will make a difference when it comes to making data-based decisions. Real-time data processing and analysis is the decisive factor.

2. increase in multimodal solutions

While conventional databases are designed to process a specific type of data and current AI tools specialize in one format, such as text, image, audio or video, the future is multi-track. Multimodal AI models that generate different data types require powerful database all-rounders that also support all these formats.

3. expansion of edge AI

The fusion of AI and edge computing will bring about a new level of real-time analysis and model optimization in 2024, which will lead to faster decisions wherever companies collect and process data. The consequences are reduced latency and improved data protection because information does not have to be sent to cloud systems for processing. The trend is moving towards local data processing structures, which can also be used to train AI models on devices and servers directly on site.

4. solutions for AI hallucinations

Generative AI tools are not flawless; the results are often distorted by incorrect data, for example, or the AI invents its own results. In 2024, approaches to solving these problems will make further great progress and make the models much more precise - above all the concept of Retrieval Augmented Generation (RAG), which improves AIs with context-related real-time data and mitigates hallucinations.

5. data-centric AI models

In the future, companies will increasingly turn to a data-centric approach for the training of AIs, which brings models closer to the data and not the other way around. The focus here is on the quality of the data and its careful collection - because only with the right input can companies train their AI models precisely for the subsequent task, expect exact results and minimize erroneous results.

"Artificial intelligence and data management go hand in hand. We are just at the beginning of a development from which we can still expect many valuable synergy effects," explains Paul Salazar, Senior Director Central Europe at Couchbase. "Above all, the focus on data-centric models is an important step towards AI 2.0, which can deliver the right results even better and faster."