The year 2023 marked a watershed moment in the evolution of technology, with generative AI becoming mainstream. As we approach 2024, the landscape of generative AI is expected to rapidly evolve, introducing a slew of trends that promise to transform technology and its applications.
These trends, ranging from advancements in multimodal AI models to the rise of small language models, will not only shape the technological landscape but also redefine interactions, creativity, and understanding of AI’s potential.
As we look forward to 2024, let’s explore the top generative AI trends:
Emergence of Multimodal AI Models
OpenAI’s GPT4, Meta’s LLama 2, and Mistral all served as examples of the advancements in large language models. The technology goes beyond text with multimodal AI models, allowing users to mix and match content based on text, audio, image, and video for prompting and generating new content. This approach involves combining data, such as images, text, and speech, with advanced algorithms to make predictions and generate outcomes.
In 2024, multimodal AI is expected to evolve significantly, ushering in a shift in generative AI capabilities. These models are progressing beyond traditional single-mode functions, incorporating diverse data types such as images, language, and audio. As a result of this transition to multimodal models, AI will become more intuitive and dynamic.
GPT4-V is already popular among ChatGPT Plus subscribers for its multimodal capabilities. In 2024, we can expect the rise of open models such as the Large Language and Vision Assistant or LLava.
Capable and Powerful Small Language Models
If 2023 was the year of large language models, 2024 will witness the power of small language models. LLMs are trained on massive datasets such as Common Crawl and The Pile. The terabytes of data comprising these datasets were extracted from billions of publicly accessible websites. Although the data is indeed beneficial in teaching LLMs to generate meaningful content and predict the next word, its noisy nature stems from its foundation based on general Internet content.
Small language models, on the other hand, are trained on more limited datasets that are nonetheless comprised of high-quality sources such as textbooks, journals, and authoritative content. These models are smaller in terms of parameter count as well as storage and memory requirements, allowing them to run on less powerful and less expensive hardware. SLMs produce content of comparable quality to some of their larger counterparts despite being a fraction of the size of LLMs.
Microsoft’s PHI-2 and Mistral 7B are two promising SLMs that will power the next generation of generative AI applications.
The Rise of Autonomous Agents
Autonomous agents represent an innovative strategy for constructing generative AI models. These agents are autonomous software programs designed to accomplish a specific objective. When considering generative AI, the ability of autonomous agents to produce content devoid of human intervention surmounts the constraints associated with conventional prompt engineering.
Advanced algorithms and machine learning techniques are used in the development of autonomous agents. These agents use data to learn, adapt to new situations, and make decisions with little human intervention. For example, OpenAI has created tools that make effective use of autonomous agents, indicating significant progress in the field of artificial intelligence.
Multimodal AI, which combines various AI techniques such as natural language processing, computer vision, and machine learning, is critical in the development of autonomous agents. It can make predictions, take actions, and interact more appropriately by analyzing different data types at the same time and applying the current context.
Frameworks such as LangChain and LlamaIndex are some of the popular tools used to build agents based on the LLMs. In 2024, we will see new frameworks that take advantage of multimodal AI.
Open Models will become comparable with proprietary models
In 2024, open, generative AI models are expected to evolve significantly, with some predictions suggesting that they will be comparable to proprietary models. The comparison between open and proprietary models, on the other hand, is complex and depends on a variety of factors, including the specific use cases, development resources, and data used to train the models.
Meta’s Llama 2 70B, Falcon 180B and Mistral AI’s Mixtral-8x7B became extremely popular in 2023, with comparable performance to proprietary models such as GPT 3.5, Claude 2, and Jurrasic.
In the future, the gap between open models and proprietary models will be narrowed, providing enterprises with a great option for hosting generative AI models in hybrid or on-premises environments.
In 2024, the next iteration of models from Meta, Mistral, and possibly new entrants will be released as viable alternatives to proprietary models available as APIs.
Cloud Native becomes key to on-prem GenAI
Kubernetes is already the preferred environment for hosting generative AI models. Key players such as Hugging Face, OpenAI, and Google are expected to leverage cloud native infrastructure powered by Kubernetes to deliver generative AI platforms.
Tools such as Text Generation Inference from Hugging Face, Ray Serve from AnyScale, and vLLM already support running model inference in containers. In 2024, we will see the maturity of frameworks, tools, and platforms running on Kubernetes to manage the entire lifecycle of foundation models. Users will be able to pre-train, fine-tune, deploy, and scale generative models efficiently.
Key cloud native ecosystem players will provide reference architectures, best practices, and optimizations for running generative AI on cloud native infrastructure. LLMOps will be extended to support integrated cloud native workflows.
