Forecasting the development of data technology in 2024: basic models and confidential computing

Perhaps the greatest strength of the contemporary data field lies in the widespread application of underlying models. These models play an important role in the deployment of artificial intelligence, with a clear impact on everything from external customer interactions to internal employee interfaces with data systems.

Thus, by 2024, new paradigms for storing and retrieving data, applying and generating value from underlying models will be solidified. At the same time, the importance of data-driven processes will be emphasized, including data security and data privacy. As advanced machine learning deployments continue to evolve, our lives will become richer, while also requiring data protection and regulatory compliance. The development of these two aspects will complement and promote each other.

The generation of natural language by intelligent robots is just the beginning. In order to support these artificial intelligence functions and advance to 2025, a complete ecosystem is gradually forming.

Multi-modal generative model

The base model is so good at generating text that it even makes it easy for people to ignore its actual definition. Its ability to handle an unlimited number of tasks allows organizations to take full advantage of these capabilities in the coming months, thereby increasing the return on investment in generative AI.

GPT-4’s integration capabilities are not limited to images and text, but will soon be extended to other modes such as voice, video, music, and sensor data inputs. This will have a positive impact on various areas, including marketing, digital assets and customer service. Smart organizations will start exploring and piloting use cases for multimodal generative AI to better serve different needs.

Victory of vector databases

It is expected that standardization of enterprise base models for generative AI applications involving retrieval-enhanced generation and semantic search will significantly increase the value and adoption of vector databases. These similarity search engines can be thought of as artificial intelligence retrieval systems that are able to store and organize large amounts of unstructured data and utilize language models to figure out the best way to query that data.

Vector databases have attracted attention for their ability to handle high-dimensional data and facilitate complex similarity searches. Once organizations address the potential costs that may result from maintaining vector database indexes in memory, these repositories will play a greater role in many use cases, such as recommender systems, image recognition, natural language processing, financial forecasting, or other AI-driven enterprise.

Generative AI Prioritizes Personalization

Generative AI Models Frequent access to large amounts of unstructured data in RAG implementations and vector similarity searches raises concerns about data security and regulations Compliance is a widespread concern. Previously this data was called dark data.

Another major trend in 2024 is that enterprises will focus on developing generative AI chatbots to meet domain-specific needs while ensuring data privacy protection at the organizational level. To achieve this, RAG technology can provide support by ensuring that chatbots powered by generative AI models only have access to vetted data and provide controls for data privacy, regulatory compliance and data security. This way, businesses can develop chatbots while keeping user data secure and private.

Confidential Computing Adoption Increases

Depending on how it is implemented, confidential computing structures can greatly help enhance data protection through the personalization of generated AI models. This computing model involves isolating confidential data in secure CPU enclaves for processing in the cloud. These data and their processing methods can only be accessed by code authorized by Enclave.

In the coming year, the integration of hardware-based confidential computing is expected to increase as cloud solutions strategically leverage it to attract applications with higher privacy and security requirements. And this (confidential computing) trend will be especially prevalent in specialized fields such as machine learning, financial services, and genomics.

Looking ahead

The changes brought about by the underlying model include, but ultimately extend beyond, the data environment in which it has such an impact. In fact, it affects the professional and private spheres of life in large and small ways. Multimodal deployments, vector databases, personalization, and confidential computing will be some of the many ways in which these AI applications can bring greater benefits to businesses and even society.

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