The widespread use of generative AI in 2023 was a turning point in technological history. The field of gen-AI is predicted to swiftly evolve as we approach 2024, introducing a host of innovations that promise to alter tech and its applications.
These developments, which range from advances in “multimodal AI models” to the development of “tiny language models,” will impact the technological landscape and reshape interactions, creativity, and awareness of AI’s potential.
5 Generative AI Trends for 2024:
Advancements in Multimodal AI Models:
GPT4 from OpenAI, LLama 2 from Meta, and Mistral from Mistral were all examples of significant language model improvements. Thanks to multimodal AI models, the system can do more than just process text; it can generate new material by letting users combine text, audio, images, and videos. This method combines data, such as photos, text, and audio, with powerful algorithms to forecast and generate results.
Anticipated multimodal AI advancements in 2024 will bring about a change in gen-AI capacities. These models are evolving beyond standard single-mode operations, embracing a wide range of data kinds such as images, text, and audio. AI will grow more user-friendly and creative due to the shift to multimodal models.
Small Language Models that are Capable and Powerful:
While big language models were all the rage in 2023, little language models will be king in 2024. LLMs are taught using large datasets like “Common Crawl” and “The Pile.”
These datasets’ gigabytes of data were taken from millions of openly accessible websites. Although the data is useful for teaching LLMs to generate meaningful text and anticipate the next word, it is noisy because of its foundation in common Internet material.
In contrast, small language models learn from smaller datasets that nonetheless include high-quality sources like scholarly articles, textbooks, and other authoritative materials. These models include fewer parameters and require less storage and memory, allowing them to function on less-powered and more affordable hardware.
SLMs, despite being a quarter of the size of LLMs, produce content of similar caliber as some of their bigger counterparts.The gen-AI applications of the future will be powered by two promising SLMs developed by Microsoft: PHI-2 and Mistral 7B.
The Rising of Autonomous Agents:
Autonomous agents are an innovative approach to building gen-AI models. These agents are self-contained software programs designed for achieving a certain goal. When discussing gen-AI, the capacity of autonomous agents to develop content without human intervention overcomes the limits related to traditional prompt engineering.
In the construction of autonomous agents, advanced algorithms and machine learning approaches are applied. By analyzing data, these agents can learn, adjust to different environments, and make judgments independently. OpenAI, for example, has developed tools for making good use of autonomous agents, demonstrating substantial development in the area of artificial intelligence.
The creation of autonomous agents relies heavily on multimodal AI, which integrates different AI techniques like computer vision, machine learning, and natural language processing. By assessing many data kinds at the same time and using the present context, it can develop forecasts, perform actions, and engage more properly. Frameworks like LangChain and LlamaIndex are popular tools for creating agents based on LLMs. New frameworks that leverage multimodal AI will emerge in 2024.
Open Models will be comparable to proprietary models:
The evolution of open, gen-AI models is predicted to be considerable by 2024, with some projections indicating that they will be similar to proprietary models. The comparison of open and proprietary models, however, is complicated and depends on a number of aspects, such as the individual use cases, resources for development, and data utilized to train the models.
In 2023, Meta’s Llama 2 70B, Falcon 180B, and Mistral AI’s Mixtral-8x7B were immensely popular, with performance equivalent to proprietary models like GPT 3.5, Claude 2, and Jurrasic.
The divide between open models and proprietary models will close in the future, giving organizations a viable choice for deploying generative AI models in hybrid or on-premises systems. The next edition of Meta, Mistral, and new entrants’ models will be released in 2024 as an alternative to proprietary models provided as APIs.
Cloud-Native becomes critical to on-premises GenAI:
When it comes to hosting gen-I models, Kubernetes is already the choice. Hugging Face, OpenAI, and Google are likely to use Kubernetes-powered cloud-native architecture to create gen-AI platforms.
Tools like Hugging Face’s Text Generation Inference, AnyScale’s Ray Serve, and vLLM already support performing model inference in containers. In 2024, Kubernetes-based frameworks, tools, and platforms will have matured enough to handle foundation model lifecycles. Generative models may be pre-trained, fine-tuned, deployed, and easily scaled.
Reference designs, best practices, and optimizations for operating gen-AI on cloud-native infrastructure will be provided by key cloud-native ecosystem stakeholders. LLMOps will be expanded to include cloud-native process integration.
