With the widespread acceptance of relational AI, 2023 marked a pivotal moment in technological advancement. The realm of conceptual AI is poised for rapid transformation as we approach 2024, ushering in a host of trends that could potentially reshape both technology and its practical applications.
These upcoming trends, spanning enhancements in bidirectional AI models to the emergence of compact language models, have the capacity to redefine interactions, creativity, and our understanding of AI’s capabilities, thereby influencing the contemporary landscape.
Let’s delve into the key relational AI trends to watch for in anticipation of 2024:
Emergence of Multimodal AI Models
Advancements in large-scale language models such as OpenAI’s GPT4, Meta, LLama 2, and Mistral have been notable. By leveraging bidirectional AI models, this technology transcends mere text processing, allowing users to amalgamate and adapt content across various modalities like text, audio, images, and video to generate innovative content. This approach integrates diverse data types such as visuals, text, and speech using sophisticated methodologies to make predictions and yield outcomes.
The evolution of multimodal AI in 2024 is expected to bring about a paradigm shift in conceptual AI capabilities. Through the incorporation of multiple data modalities like images, speech, and music, these models are progressing beyond traditional single-mode functionalities. This transition to multimodal models will render AI more agile and dynamic.
Already embraced by ChatGPT Plus subscribers for its bidirectional capabilities, GPT4-V is gaining popularity. The upcoming year may witness the introduction of new models like LLava or the Big Language and Vision Assistant.
Rise of Effective Small Language Models
If 2023 was characterized by the dominance of large language models, 2024 is poised to showcase the prowess of small language models. These models, trained on smaller yet high-quality datasets comprising sources like textbooks, publications, and authoritative content, are set to exhibit enhanced performance. Despite their smaller size in terms of parameters and storage requirements, small language models deliver superior quality outputs compared to some larger counterparts.
Microsoft’s PHI-2 and Mistral 7B are promising small language models that are expected to power the next generation of relational AI software.
Ascendancy of Autonomous Agents
Autonomous agents represent a cutting-edge approach to constructing conceptual AI models. These self-contained software programs are designed with specific objectives in mind, overcoming the constraints of traditional engineering by enabling the generation of content autonomously without human intervention.
These intelligent agents leverage advanced technologies and machine learning algorithms to make decisions, learn from data, adapt to new scenarios, and evolve with minimal human input. By amalgamating different AI techniques like natural language processing, computer vision, and machine learning, autonomous agents analyze diverse data forms concurrently to make predictions, take actions, and interact effectively.
Frameworks such as LangChain and LlamaIndex are instrumental in creating agents based on small language models, with new systems leveraging multimodal AI expected in 2024.
Convergence of Open Models with Commercial Versions
The evolution of open conceptual AI models in 2024 is projected to bring them on par with custom models, with some forecasts suggesting equivalence. However, comparing open and proprietary models is complex and contingent on variables such as specific use cases, development tools, and training data.
Models like Mistral AI’s Mixtral-8x7B, LLama 2 70B, and Falcon 180B gained significant traction in 2023 for their performance akin to custom designs such as GPT 3.5, Claude 2, and Jurrasic. As the distinction between open and custom models diminishes, enterprises will have compelling options for hosting conceptual AI models in hybrid or on-premises environments.
The upcoming iterations of models from Meta, Mistral, and potential new contenders in 2024 are poised to serve as viable alternatives to proprietary models offered through APIs.
Essential Role of Cloud Native in On-Premise GenAI
Kubernetes has emerged as the preferred environment for hosting conceptual AI models. Major players like Hugging Face, OpenAI, and Google are expected to leverage cloud-native infrastructure powered by Kubernetes to deliver conceptual AI systems.
Tools like vLLM, Ray Serve by AnyScale, and Text Generation Inference from Hugging Face now support running inference in containers. The upcoming year may witness a proliferation of systems, tools, and platforms utilizing Kubernetes to manage the entire lifecycle of base models, enabling users to efficiently pre-train, fine-tune, deploy, and scale conceptual models.
Key players in the cloud-native ecosystem will offer reference architectures, best practices, and optimizations for running relational AI on edge infrastructure. LLMOps may facilitate the integration of embedded cloud-native processes.
