17 Predictions for 2024: From RAG to Riches to Beatlemania and National Treasures

Move over, Merriam-Webster: Enterprises this year found plenty of candidates to add for word of the year. Generative AI and generative pretrained transformer were followed by terms such as large language models and retrieval-augmented generation (RAG) as whole industries turned their attention to transformative new technologies.

Generative AI started the year as a blip on the radar but ended with a splash. Many companies are sprinting to harness its ability to ingest text, voice and video to churn out new content that can revolutionize productivity, innovation and creativity.

Enterprises are riding the trend. Deep learning algorithms like OpenAI's ChatGPT, further trained with corporate data, could add the equivalent of $2.6 trillion to $4.4 trillion annually across 63 business use cases, according to McKinsey & Company.

Yet managing massive amounts of internal data often has been cited as the biggest obstacle to scaling AI. Some NVIDIA experts in AI predict that 2024 will be all about phoning a friend - creating partnerships and collaborations with cloud service providers, data storage and analytical companies, and others with the know-how to handle, fine-tune and deploy big data efficiently.

Large language models are at the center of it all. NVIDIA experts say advancements in LLM research will increasingly be applied in business and enterprise applications. AI capabilities like RAG, autonomous intelligent agents and multimodal interactions will become more accessible and more easily deployed via virtually any platform.

Hear from NVIDIA experts on what to expect in the year ahead:

MANUVIR DAS

Vice President of Enterprise Computing

One size doesn't fit all: Customization is coming to enterprises. Companies won't have one or two generative AI applications - many will have hundreds of customized applications using proprietary data that is suited to various parts of their business.

Once running in production, these custom LLMs will feature RAG capabilities to connect data sources to generative AI models for more accurate, informed responses. Leading companies like Amdocs, Dropbox, Genentech, SAP, ServiceNow and Snowflake are already building new generative AI services built using RAG and LLMs.

Open-source software leads the charge: Thanks to open-source pretrained models, generative AI applications that solve specific domain challenges will become part of businesses' operational strategies.

Once companies combine these headstart models with private or real-time data, they can begin to see accelerated productivity and cost benefits across the organization. AI computing and software are set to become more accessible on virtually any platform, from cloud-based computing and AI model foundry services to the data center, edge and desktop.

Off-the-shelf AI and microservices: Generative AI has spurred the adoption of application programming interface (API) endpoints, which make it easier for developers to build complex applications.

In 2024, software development kits and APIs will level up as developers customize off-the-shelf AI models using AI microservices such as RAG as a service. This will help enterprises harness the full potential of AI-driven productivity with intelligent assistants and summarization tools that can access up-to-date business information.

Developers will be able to embed these API endpoints directly into their applications without having to worry about maintaining the necessary infrastructure to support the models and frameworks. End users can in turn experience more intuitive, responsive and tailored applications that adapt to their needs.

IAN BUCK

Vice President of Hyperscale and HPC

National treasure: AI is set to become the new space race, with every country looking to create its own center of excellence for driving significant advances in research and science and improving GDP.

With just a few hundred nodes of accelerated computing, countries will be able to quickly build highly efficient, massively performant, exascale AI supercomputers. Government-funded generative AI centers of excellence will boost countries' economic growth by creating new jobs and building stronger university programs to create the next generation of scientists, researchers and engineers.

Quantum leaps and bounds: Enterprise leaders will launch quantum computing research initiatives based on two key drivers: the ability to use traditional AI supercomputers to simulate quantum processors and the availability of an open, unified development platform for hybrid-classical quantum computing. This enables developers to use standard programming languages instead of needing custom, specialized knowledge to build quantum algorithms.

Once considered an obscure niche in computer science, quantum computing exploration will become more mainstream as enterprises join academia and national labs in pursuing rapid advances in materials science, pharmaceutical research, subatomic physics and logistics.

KARI BRISKI

Vice President of AI Software

From RAG to riches: Expect to hear a lot more about retrieval-augmented generation as enterprises embrace these AI frameworks in 2024.

As companies train LLMs to build generative AI applications and services, RAG is widely seen as an answer to the inaccuracies or nonsensical replies that sometimes occur when the models don't have access to enough accurate, relevant information for a given use case.

Using semantic retrieval, enterprises will take open-source foundation models, ingest their own data so that a user query can retrieve the relevant data from the index and then pass it to the model at run time.

The upshot is that enterprises can use fewer resources to achieve more accurate generative AI applications in sectors such as healthcare, finance, retail and manufacturing. End users should expect to see more sophistic

LINK:	https://blogs.nvidia.com/blog/2024-ai-predictions/...
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