In 2022, generative artificial intelligence (AI) gained widespread attention, and in 2023, it started to make its mark in the business world. As we enter 2024, it becomes a crucial year for the future of AI as researchers and enterprises strive to integrate this technology into our daily lives. The evolution of generative AI has followed a similar path as computers, but at a much faster pace. Initially, large mainframe computers were operated by a few players, but over time, smaller and more efficient machines became accessible to enterprises and research institutions. Eventually, home computers with user-friendly interfaces became commonplace. Generative AI has already reached the “hobbyist” stage, and further advancements aim to achieve better performance in smaller packages.
In 2023, there was a surge in the development of highly efficient foundation models with open licenses. Meta’s LlaMa family of large language models (LLMs) was the first to launch, followed by other models like StableLM, Falcon, Mistral, and Llama 2. Models such as DeepFloyd and Stable Diffusion have reached a similar level of performance as leading proprietary models. With the help of fine-tuning techniques and datasets developed by the open-source community, many open models now outperform most closed-source models on various benchmarks, despite having fewer parameters.
While the focus is often on the progress of state-of-the-art models, the most significant developments may lie in governance, middleware, training techniques, and data pipelines that make generative AI more trustworthy, sustainable, and accessible to enterprises and end-users. Here are some important AI trends to watch out for in 2024:
1. Realistic expectations: As the initial hype around generative AI settles, businesses are gaining a better understanding of its capabilities. Expectations are becoming more realistic, with leaders recognizing that generative AI offers unique opportunities but is not a one-size-fits-all solution.
2. Multimodal AI: The next wave of advancements will focus on multimodal models that can process multiple types of data as input. These models can operate across different data modalities, such as natural language processing and computer vision, enabling more intuitive and versatile AI applications.
3. Small(er) language models and open source advancements: Larger parameter counts may not necessarily lead to better performance. Smaller models trained on more data can often outperform larger models trained on less data. Smaller models are also more resource-efficient and can be run on less powerful hardware, democratizing AI and enabling its use in edge computing and IoT scenarios.
4. GPU shortages and cloud costs: The demand for AI technologies has led to shortages of GPUs and increased cloud costs. Addressing these challenges will be crucial for the widespread adoption of AI.
5. Model optimization: Making model optimization more accessible will be a focus in 2024. This includes developing techniques to improve the performance of AI models without significantly increasing their size or resource requirements.
6. Customized local models and data pipelines: Tailoring AI models to specific local needs and optimizing data pipelines will play a crucial role in maximizing the effectiveness of generative AI applications.
7. More powerful virtual agents: Virtual agents that can understand and respond to natural language and visual inputs will become more powerful and useful in various industries.
8. Regulation, copyright, and ethical AI concerns: As generative AI becomes more prevalent, concerns about regulation, copyright infringement, and ethical implications will need to be addressed to ensure responsible and fair use of the technology.
9. Shadow AI and corporate AI policies: The use of AI technologies within organizations may give rise to “shadow AI” practices, where employees use AI tools without proper oversight or controls. Developing corporate AI policies will be essential to manage and regulate AI usage.
In conclusion, 2024 will be a crucial year for the integration of generative AI into everyday life. While advancements in state-of-the-art models are exciting, the focus should also be on governance, middleware, training techniques, and data pipelines to ensure the trustworthiness, sustainability, and accessibility of generative AI.
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