AI technical trends to watch for (and not just in healthcare)

Many gen AI end users are finding that large language models (LLMs) defy easy infrastructure setup and affordable management costs. One budding option may be to go with small language models (SMLs) instead. 

A lot of people are likely to do exactly that over the next 12 months, according to the latest InfoQ Trends report. “Companies like Microsoft have released Phi-3 and other SLMs that [people] can start trying out immediately to compare the cost and benefits of using an SLM versus an LLM,” the report’s authors write. “This new type of language model is also perfect for edge computing-related use cases to run on small devices.”

InfoQ reaches something like 1.5 million readers around the world. Its content is written by and for software engineers and developers, but much of it—like the Trends report—is accessible by, and of interest to, general technology watchers. 

Here are five more trends to anticipate, as enumerated by software architect Srini Penchikala and co-authors at InfoQ. 

1. The future of AI is open and accessible. 

“We’re in the age of large language models and foundation models,” the authors write. “Most of the models available are closed source, but companies like Meta are trying to shift the trend toward open-source models.”

‘Even though most currently available models are closed source, companies are trying to shift the trend toward open-source models.’ 

2. Retrieval Augmented Generation (RAG) will become more important.

RAG techniques, which combine LLMs with external knowledge bases to optimize outputs, “will become crucial for [organizations] that want to use LLMs without sending them to cloud-based LLM providers,” Penchikala and co-authors explain.  

‘RAG will also be useful for applicable use cases of LLMs at scale.’

3. AI-powered hardware will get much more attention with AI-enabled GPU infrastructure and AI-powered PCs.

AI-integrated hardware is “leveraging the power of AI technologies to revolutionize the overall performance of every task,” the authors observe. “AI-enabled GPU infrastructure like Nvidia’s GeForce RTX and AI-powered PCs like Apple M4, mobile phones and edge computing devices can all help with faster AI model training and fine-tuning as well as faster content creation and image generation.”

‘This is going to see significant development in the next 12 months.’

4. AI agents, like coding assistants, will also see more adoption, especially in corporate application development settings.

Autonomous agents and GenAI-enabled virtual assistants are “coming up in different places to help software developers become more productive,” the authors remark, noting that examples of AI agents include Gihub’s Copilot, Microsoft Teams’ Copilot, DevinAI, Mistral’s Codestral and JetBrains’ local code completion. 

‘AI-assisted programs can enable individual team members to increase productivity or collaborate with each other.’

5. AI safety and security will continue to be important in the overall management lifecycle of language models. 

Tip: Train your employees to have proper data privacy security practices, and “make the secure path the path of least resistance for them so everyone within your organization easily adopts it.”

‘Self-hosted models and open-source LLM solutions can help improve the AI security posture.’

The article draws from a podcast hosted by the InfoQ editorial team. Read the piece or listen to the podcast.