Predictions for AI in 2024

Tuesday - December 19 2023, 19:30 UTC - 11 months ago

2023 was a huge year for AI and Large Language Models. This article discusses predictions of AI in 2024, including customization for enterprises, open source AI, RAG, quantum computing, and telecom AI.

2023 was a huge year for AI and Large Language Models. What are the predictions for AI in 2024? .

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.

Retrieval Augmented Generation (RAG) is an AI-based technology that uses language modeling and information retrieval techniques to create precise responses to user queries. Retrieval augmented generation (RAG) is a natural language processing (NLP) technique that combines the strengths of both retrieval- and generative-based artificial intelligence (AI) models.

RAG can: .

* Access real-time data .

RAG can access data in real-time, without the need to retrain the core LLM. This saves time and reduces operational costs.

* Improve contextualization .

RAG can create context-aware answers, instructions, or explanations in human-like language.

* Provide source citations .

RAG can provide source citations or references to sources. This increases transparency and trust in the content.

* Reduce AI hallucinations .

RAG can reduce AI hallucinations.

Other capabilities of RAG include: .

Updatable memory .

Increasing user trust .

Reducing data leakage .

Saving time .

RAG is considered the most cost-effective, easy to implement, and lowest-risk path to higher performance for GenAI applications.

Open-source AI will get more important. Open-source pretrained models makes 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.

Application programming interface (API) endpoints empower developers to build complex applications.

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. Exascale AI will be the minimum for relevant large AI centers.

Quantum Computing Will Become Mainstream as necessary research and then as a major enhancement in 2025-2028. Nvidia talks about hybrid classical simulations of quantum but Nextbigfuture has talked about the error corrected systems on real hardware (in particular neutral atom systems by QuEra.) .

Telecom companies are using generative AI for various operational improvements in analytics, application analysis, messaging, monitoring, speech-to-text, fraud detection, robot process automation, customer care and more.