April 15, 2024

2024 trends in data technologies: basic models and confidential computing

Perhaps the most important force shaping (if not reshaping) the contemporary datasphere is the widespread presence of fundamental models. These models, manifested most acutely in generative AI deployments, are impacting everything from external interactions with customers to internal employee interfaces with data systems.

Consequently, in 2024 new paradigms will solidify for storing and retrieving data, applying and generating value from basic models, and emphasizing the pillars of data-driven processes (such as security and data privacy). In our lives, the fundamentals of protecting data and ensuring regulatory compliance will keep pace so that the growth of one is moderated and governed by the other.

Natural language generation from intelligent robots is just the beginning. An entire ecosystem of imperatives is emerging to support these AI capabilities and drive them through 2025. According to Talentica Software principal data scientist Abhishek Gupta, these developments “will provide a more complete and immersive understanding of our world, deepening the way we “We interact with and perceive information through AI.”

Multimodal generative models

Foundation models are so adept at generating text that it’s easy to forget that, by definition, they excel at any number of tasks to which they can be applied. As such, organizations will begin to fully leverage these capabilities in the coming months, boosting their return on investment (ROI) from investments in generative AI.

“GPT-4 can seamlessly integrate images and text, and this trajectory will soon expand to additional modes, including voice, video, music and other… inputs such as sensor data,” Gupta said. Savvy organizations will begin to explore and test use cases for multimodal generative AI, which is poised to positively impact aspects of marketing, digital assets, customer service, and more.

Triumph of vector databases

Due in large part to the standardization of core enterprise models for generative AI applications involving retrieval, augmented generation, and semantic search, vector database capabilities are projected to double in value and adoption rates. These similarity search engines can best be thought of as artificial intelligence retrieval systems: the optimal means to store the wealth of unstructured data that organizations have and query that data with language models.

“Vector databases have rapidly gained prominence due to their prowess in handling high-dimensional data and facilitating complex similarity searches,” observed Ratnesh Singh Parihar, principal architect at Talentica Software. Once organizations determine how to circumvent the potential cost inhibitors of maintaining vector database indexes in memory, these repositories will enhance any number of use cases, including “recommender systems, image recognition, natural language processing, financial forecasting or other AI-powered businesses.” Parihar noted.

Generative AI prioritizes personalization

The amounts of unstructured data (previously considered dark data) regularly accessed by generative AI models in RAG and vector similarity search implementations are raising pervasive data security and regulatory compliance concerns.

According to Gupta, another dominant trend in 2024 will involve organizations looking at “generative AI focusing on the development of domain-specific chatbots, while ensuring safeguards for data privacy at the organizational level.” RAG can assist in this effort by ensuring that chatbots powered by generative AI models access data that has been vetted and includes controls for data privacy, regulatory compliance, and data security.

Confidential computing Adoption increases

Depending on how it is implemented, confidential computing construction can greatly assist in data protection enhanced by customization of generative AI models. This computing model involves sequestering sensitive data in a secure CPU enclave for processing in the cloud. Such data and its processing methods can only be accessed by a code authorized for the enclave.

“In the coming year, we can expect an increase in the integration of hardware-based confidential computing as cloud solutions employ it strategically to attract applications with higher privacy and security demands,” said Pankaj Mendki, chief technology officer. emerging from Talentica Software. Mendki’s point is reinforced by the reality that nothing but the authorized programming code will even know what is in the aforementioned enclave. “This [confidential computing] The trend will be especially prevalent in specialized domains such as machine learning, financial services and genomics,” Mendki added.

A new day

The changes brought about by basic models include, but ultimately exceed, the data landscape on which they have so much impact. In reality, they are affecting both professional and private spheres of life in small and large ways. Multimodal deployments, vector databases, personalization, and confidential computing will be some of the many ways these AI applications will be facilitated for the good of business and perhaps even society.

About the Author

Jelani Harper is an editorial consultant serving the information technology market. She specializes in data-driven applications focused on semantic technologies, governance and data analytics.

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