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The year 2023 marked a watershed moment in the evolution of technology, as generative AI became mainstream. As we approach 2024, the landscape of generative AI is expected to evolve rapidly, presenting a plethora of trends that promise to transform the technology and its applications.
These trends, ranging from advances in multimodal AI models to the emergence of microlanguage models, will not only shape the technology landscape, but will also redefine interactions, creativity, and understanding of the potential of AI.
As we look to 2024, let's explore the most important generative AI trends:
The emergence of multimedia artificial intelligence models
OpenAI's GPT4, Meta's LLama 2, and Mistral have all been examples of advances in large language models. This technology goes beyond text to multimedia AI models, allowing users to mix and match content based on text, audio, image and video to stimulate and create new content. This approach involves combining data, such as images, text, and speech, with advanced algorithms to make predictions and generate results.
In 2024, multimodal AI is expected to evolve dramatically, heralding a shift in generative AI capabilities. These models advance beyond traditional single-mode functionality, incorporating diverse types of data such as images, language, and audio. As a result of this shift to multimodal models, AI will become more accessible and dynamic.
GPT4-V is very popular among ChatGPT Plus subscribers due to its multimedia capabilities. In 2024, we can expect 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, then 2024 will witness the power of small language models. LLMs are trained on large datasets such as Common Crawl and The Pile. The terabytes of data that make up these datasets were extracted from billions of publicly available websites. Although the data is already useful in MBA teaching for generating meaningful content and predicting the next word, its noisy nature stems from its foundation in public Internet content.
On the other hand, small language models are trained on more limited datasets that nonetheless consist of high-quality sources such as textbooks, journals, and official content. These models are smaller in terms of number of parameters as well as storage and memory requirements, allowing them to run on less powerful and less expensive hardware. SLMs produce content of similar 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 SLM systems that will power the next generation of generative AI applications.
The rise of independent agents
Autonomous agents represent an innovative strategy for building generative AI models. These agents are independent software programs designed to achieve a specific goal. When considering generative AI, the ability of autonomous agents to produce content free of human intervention overcomes the limitations associated with traditional agile engineering.
Advanced algorithms and machine learning techniques are used in developing autonomous agents. These agents use data to learn and adapt to new situations and make decisions with little human intervention. For example, OpenAI has created tools that effectively use autonomous agents, which indicates significant progress in the field of artificial intelligence.
Multimodal AI, which combines different AI techniques such as natural language processing, computer vision, and machine learning, is crucial in the development of autonomous agents. It can make predictions, take actions, and interact more appropriately by analyzing different types of data at the same time and applying current context.
Frameworks like LangChain and LlamaIndex are some of the popular tools used to build agents based on LLMs. In 2024, we will see new frameworks leveraging multimodal AI.
Open models will become comparable to private models
In 2024, open and generative AI models are expected to evolve dramatically, with some predictions suggesting they will be comparable to proprietary models. On the other hand, comparing open and proprietary models is complex and depends on a variety of factors, including specific use cases, development resources, and the data used to train the models.
Meta's Llama 2 70B, Falcon 180B and Mistral AI's Mixtral-8x7B have become very popular in 2023, with performance similar to proprietary models such as GPT 3.5, Claude 2 and Jurasic.
In the future, the gap between open models and private models will narrow, providing organizations with a great option for hosting generative AI models in hybrid or on-premises environments.
In 2024, the next version of models from Meta, Mistral, and possibly new entrants will be released as viable alternatives to the proprietary models available as APIs.
Cloud Native is becoming a key component of native 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 Kubernetes-powered cloud-native infrastructure to deliver productive AI platforms.
Tools like Hugging Face's Text Generation Inference, AnyScale's Ray Service, and vLLM already support running model inference in containers. In 2024, we will see the maturity of frameworks, tools, and platforms that run on Kubernetes to manage the full lifecycle of the underlying models. Users will be able to efficiently pre-train, tune, deploy and scale generative models.
Key players in the cloud-native ecosystem will provide reference architectures, best practices, and optimizations for running generative AI on cloud-native infrastructure. LLMOps will be expanded to support integrated cloud-native workflows.
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