newer AI techniques and trends that have emerged or gained significant traction after 2023:

 Here are some of the newer AI techniques and trends that have emerged or gained significant traction after 2023:

## Multimodal AI

Multimodal AI combines multiple modalities such as text, images, audio, and video to create more versatile and effective AI models. This approach allows models like ChatGPT-4 to generate text from various inputs, including images and audio, and to convert between different modalities seamlessly. This trend is expected to enhance applications in fields like financial services, customer analytics, and marketing[1][5][7].

## Small Language Models (SLMs)

SLMs are smaller versions of large language models (LLMs) that can operate efficiently on fewer computing resources, making them accessible on devices like smartphones. These models, such as Microsoft's Phi and Orca, offer similar or sometimes better performance than LLMs in certain areas, democratizing AI use and reducing the need for significant financial investments[1].

## Customizable Generative AI

Customizable AI models are designed to cater to specific industries and user needs, offering more personalization and control over data. This is particularly beneficial in sectors like healthcare, legal, and financial services, where specialized terminology and practices are crucial. Customizable models also enhance privacy and security by reducing reliance on third-party data processing[1].

## Decoupled Contrastive Learning (DCL)

DCL is a new approach to contrastive learning that improves learning efficiency by removing the negative-positive-coupling (NPC) effect present in traditional contrastive learning methods like InfoNCE. This method requires fewer computational resources, smaller batch sizes, and shorter training epochs, yet achieves competitive performance with state-of-the-art models[2][4].

## Explainable AI (XAI)

XAI focuses on making AI models more transparent and interpretable by providing insights into how the models arrive at their decisions. Techniques such as decision trees, linear models, and rule-based systems are used to ensure that AI-driven decisions align with human values and expectations. This trend is gaining popularity as it builds trust and understanding in AI-generated outcomes[3].

## Agentic AI

Agentic AI represents a shift from reactive to proactive AI systems. These AI agents exhibit autonomy, proactivity, and the ability to act independently, setting goals and taking actions without direct human intervention. Applications include environmental monitoring, financial portfolio management, and other areas where autonomous decision-making is beneficial[7].

## Multi-view Graph Contrastive Learning

This approach adapts contrastive learning to recommendation systems by incorporating multiple views of user data. Techniques such as node dropout, edge dropout, and random walks are used to generate diverse views, enhancing the model's ability to capture underlying preferences and behaviors[6].

## Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning (CLEFT)

CLEFT is a novel method that combines efficient large language models with prompt fine-tuning for language-image contrastive learning. This approach reduces the need for extensive GPU resources and prolonged training times, making it suitable for applications with limited datasets, such as medical imaging[9].

## Retrieval-Augmented Generation

This trend involves combining generative AI models with retrieval systems to enhance the accuracy and relevance of generated content. By retrieving relevant information from a database and integrating it into the generation process, models can produce more informed and contextually accurate outputs[7].

These techniques and trends highlight the rapid evolution and diversification of AI, enabling more efficient, versatile, and interpretable AI applications across various domains.

Citations:

[1] https://khoros.com/blog/ai-trends

[2] https://ai.meta.com/research/publications/decoupled-contrastive-learning/

[3] https://devabit.com/blog/top-11-new-technologies-in-ai-exploring-the-latest-trends/

[4] https://www.amazon.science/blog/new-contrastive-learning-methods-for-better-data-representation

[5] https://www.ibm.com/think/insights/artificial-intelligence-trends

[6] https://www.nature.com/articles/s41598-024-73336-5

[7] https://www.techtarget.com/searchenterpriseai/tip/9-top-AI-and-machine-learning-trends

[8] https://gram-blogposts.github.io/blog/2024/contrast-learning/

[9] https://arxiv.org/abs/2407.21011