It seems like everyone today is talking about the latest generative AI trends, from the rise of new small language models to multimodal solutions. It’s easy to see why. Ever since the launch of ChatGPT in November 2022, Gen AI has taken the world by storm.
This innovative technology is impacting every business, industry, and consumer. With Gen AI, companies can enhance productivity, improve customer service, transform data analysis, and more. It’s little wonder that McKinsey research suggests generative AI will add up to $4.4 trillion to the global economy each year.
As adoption of generative AI grows, the technology itself is evolving too, creating new opportunities for businesses to explore. Here are the top generative AI trends worth watching this year.
1. Multimodal Shapes Generative AI Trends
Back when ChatGPT was first introduced, generative AI was primarily seen as a tool for analyzing, understanding, and creating text. Now, model frameworks are evolving, allowing users to process and create a wider range of content, from images and videos, to code, and audio.
Multimodal solutions, like GPT-4o, and Google’s Gemini aren’t just influencing generative AI trends in content creation, marketing, or advertising either. Companies are creating tools to understand video feed images to provide more intimate, personalized customer service.
We’re also seeing the rise of solutions that can improve security by identifying biometric data in a person’s face, eyes, or voice or pinpointing suspicious factors in an image. The rise of multimodal models will lead to endless new use cases for generative AI in every business.
2. Small Language Models Make a Big Impact
2023 was the year when large language models stepped into the spotlight. They offered valuable insights into how generative AI solutions worked and how powerful they could be. However, many companies struggled to take advantage of LLMs due to the computing power and data required.
In 2024, small language models, like Microsoft’s Phi-3, began gaining more attention. These models are smaller in parameter count, storage, and memory requirements, which means they can run on less expensive and powerful hardware. However, they still produce content of comparable quality to some of their larger counterparts.
Plus, many vendors offering access to “SLMs” are allowing enterprises to fine-tune their SLMs to specific tasks and functions. This could help companies create customized applications and tools more rapidly while keeping costs low.
3. Autonomous Agents Grow More Advanced
Autonomous agents are another key factor shaping generative AI trends. Not to be confused with Gen AI chatbots for the contact center, autonomous agents offer companies a way to build AI solutions that can accomplish specific objectives.
These software programs use AI to perform a range of tasks without human input. For instance, they can use tools, like information stores or knowledge bases to surface information, and plan and execute tasks. With advanced algorithms and machine learning, the agents can adapt to new situations and evolve over time, becoming more efficient.
Already, various frameworks have emerged for developing autonomous agents, such as LangChain and LlamaIndex. In the years ahead, we’ll likely see new frameworks that can take advantage of multimodal AI capabilities, and new algorithms. Gartner even predicts that by 2028, one third of interactions with Gen AI services will involve the use of autonomous agents.
4. Demand for Flexibility Drives Generative AI Trends
The rise of small language models and autonomous agents are just two generative AI trends that indicate a growing demand for customization and flexibility in the industry. There are plenty of other examples of vendors looking for unique ways to democratize access to AI, and enable companies to tailor their tools to their needs.
For instance, open-source generative AI models have become increasingly popular this year, such as Falcon 180B, and Claude 2. These models offer endless flexibility to enterprises, allowing them to build the tools best-suited to specific tasks, and host them either in the cloud, or on-premises.
