Real time applications and insights: Fabio Gerosa, Sales Director Italy of Couchbasehighlights the main technological trends that will characterize the next year
The Retrieval-Augmented Generation (RAG) technique will be fundamental to obtain well-founded and contextual results with AI
Enthusiasm for large language models and their generative capabilities will continue to be limited by the problem of hallucinations. These are cases where models produce outputs that, while consistent, may be distant from the factual reality or context of the input. As companies progress, it will be important to demystify AI hallucinations and implement an emerging, defined technique Retrieval-Augmented Generation (RAG), which, combined with real-time contextual data, can reduce these hallucinations, improving the accuracy and value of the model. RAG introduces company or user context, reducing hallucinations and increasing veracity and usefulness.
Real-time data and insights will become the standard for powering experiences Generative AI; data will need to support transactional and real-time analytics
The dramatic growth of generative AI will continue strongly in 2024. More companies will integrate it to power real-time data applications and create solutions dynamic and adaptive based on AI. Becoming fundamental to business, companies will have to ensure that the data on which AI models are based is based on truth and reality, exploiting information that is as “fresh” as possible.
Just like food, gift cards and medicine, data also has an expiration date. For generative AI to be truly effective, accurate and deliver contextually relevant results, it must rely on continuously updated real-time data. The growing demand for real-time insights will drive the adoption of technologies that enable the processing and analysis of such data. In 2024 and beyond, companies will increasingly leverage a level of information that supports both transactional and real-time analytics to make timely decisions and respond instantly to market dynamics.
The AI paradigm shifts from model-centric to data-centric
Data is critical in modern machine learning, but it needs to be addressed and managed properly in AI projects. Because this technology takes a model-focused approach, hundreds of hours are wasted fine-tuning models based on low-quality data.
As AI models mature, evolve and scale up, efforts will be made to bring models closer to data. L’AI data-centric It will enable companies to provide both generative and predictive experiences, based on the latest data, significantly improving model performance while reducing hallucinations.
Businesses will rely on AI co-pilots to gain insights faster
The integration of AI and machine learning in data management processes and analytics tools will continue to evolve. With the emergence of generative AI, businesses need a way to interact with AI and the data it produces on a contextual level. Taking advantage of the increase in information and analytics, companies will begin to integrate AI co-pilots – whose ability to understand and process large amounts of data act as assistants for AI models, ordering them and generating best practices and recommendations – into their products to gain insights more quickly.
Augmented information is a powerful tool that will change the way we build infrastructure and applications in the coming years, as augmented data management will automate routine quality and integration tasks, while augmented analytics will provide advanced insights and automate the process decision-making.
LLM and multimodal databases will enable a new frontier of AI applications across all industrial sectors
One of the most interesting trends for 2024 will be the rise of LLM multimodal. With the emergence of this phenomenon, the need for multimodal databases capable of efficiently storing, managing and querying different types of data has grown. However, the size and complexity of multimodal datasets pose a challenge to traditional databases, which are typically designed to store and query a single type of data, such as text or images.
Multimodal databases, on the other hand, are much more versatile and powerful and represent a natural progression in the evolution of LLMs to combine different aspects of information processing and understanding, using multiple modalities such as text, images, audio and video. There are many use cases and industries that will directly benefit from an approach multimodalamong which healthcare, robotics, e-commerce, instruction, retail e gaming. Multimodal databases will see significant growth and investment in 2024 and beyond.
The success of Edge AI will depend on progress in lightweight AI models
The innovation that comes with AI is exciting, and edge computing is one way to enable new applications. However, to make edge AI a viable option, models must be lightweight and capable of running on resource-constrained embedded devices and edge servers while still providing results with acceptable levels of accuracy.
While much progress has already been made in model compression, innovation in this area will continue further, and combined with advances in AI processors for the edge, will make EdgeAI ubiquitous.
The only way to scale AI will be to distribute it, with the help of edge computing
The convergence of edge and cloud AI will be the way to deliver AI at scale, cloud and edge, with compute offloading if necessary. For example, the edge can handle model inferences and the cloud its training or the edge can offload queries to the cloud depending on the length of a request, and so on.
When it comes to a successful AI strategy, it is not practical to have a cloud-only approach. Companies must consider an edge computing strategy – coupled with the cloud – to enable low-latency, real-time AI predictions in a cost-effective manner, without compromising data privacy and sovereignty.
Federated learning will be a key element in the future of AI, especially in a world where privacy is under strain
While it’s true that we may have archived traditional learning when it comes to AI, there are still many untapped data sources. Everything we do on our devices is synced to a server to train large language models (LLMs) or fine-tune LLMs for a specific use.
This is where federated learning comes in. With the recent popularity of generative AI, the idea of taking a decentralized approach to training AI models – federated learning – has become popular.
With its ability to secure training models and support privacy-sensitive applications, federated learning will be a crucial element in unlocking the future of AI while addressing critical concerns around data privacy and security.