The InfoQ Trends Reports offer InfoQ readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s podcast with external guests to discuss the trends in AI and ML and what to look out for in the next 12 months. In conjunction with the report and trends graph, an accompanying podcast features insightful discussions of these trends.
The key takeaways from this year’s report included:
- The future of AI is open and accessible. We’re in the age of LLM and foundation models. Most of the models available are closed-source, but companies like Meta are trying to shift the trend toward open-source models.
- Retrieval Augmented Generation (RAG) will become more important especially for applicable use cases of LLMs at scale.
- AI-powered hardware will get much more attention with AI-enabled GPU infrastructure and AI-powered PCs.
- Due to the constraints in infrastructure setup and management costs of LLMs, small language models (SLMs) will see more exploration and adoption.
- Small language models are also excellent for edge computing-related use cases that run on small devices.
- AI Agents, like coding assistants, will also see more adoption, especially in corporate application development settings.
- AI safety and security will continue to be important in the overall management lifecycle of language models. Self-hosted models and open-source LLM solutions can help improve the AI security posture.
- Another important aspect of the LLM lifecycle is LangOps or LLMOps, which help support the models after deploying them to production.
This content is an excerpt from a recent InfoQ article by Srini Penchikala et al., "InfoQ AI, ML, and Data Engineering Trends Report - September 2024".
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