As we are heading towards the end of 2024, artificial intelligence (AI) continues to be a transformative force across industries. From healthcare to retail, AI is reshaping how businesses operate and how consumers interact with technology. Nowadays, Chief Information Officers (CIOs), Chief Technology Officers (CTOs) and other C-level executives must understand the latest artificial intelligence trends in order to fully leverage the evolution of the technology in the implementation of the business strategy.
Trend #1: Large Language Models (LLMs)
During the last couple of years LLMs have been at the forefront of AI advancements. These models, capable of understanding and generating human-like text, have revolutionized natural language processing (NLP) applications. In 2024, LLMs have become more sophisticated, based on the advent of models with trillions of parameters like GPT-4. Moreover, there are models (e.g., OpenAI’s o1 preview) that provide advanced reasoning capabilities leveraging techniques like Chain of Thought (CoT) and Tree of Thought (ToT), which can nowadays be used to solve complex problems. Also, there are LLM-based applications that revolutionize human-AI interaction based on voice modalities. In 2025, these capabilities will continue to evolve to enable increased automation and conversational capabilities based on agents. Future LLM applications will increasingly comprise agents that operate independently towards making decisions and executing tasks based on their training and the instructions they receive. These LLM agents will be able to handle complex tasks and offer personalized experiences without constant user guidance.
Trend #2: Multimodal AI
Many large language models (LLMs) process only text data. During the last couple of months there is a however a surge of interest in multimodal models that can grasp information from different data types, like audio, video, and images, in addition to text. These models enable search and content creation tools to become more seamless and intuitive and integrate more easily into other applications. Soon we will witness the use of multimodal LLMs in different use cases. For instance, in marketing and advertising multimodal LLMs will be used to create dynamic marketing campaigns based on the integration of audio, images, video, and text. This will provide personalized content creation that will enhance customer engagement and improve the efficiency of marketing teams. As another example, we expect to see multimodal AI models improving customer support interactions by simultaneously analyzing text, images, and voice data. This will lead to more personalized and context-aware responses, which will enhance the overall customer experience
Trend #3: Energy Efficient AI
The demand for AI models that consume less energy is increasing as sustainability becomes a priority. Energy-efficient AI focuses on reducing the carbon footprint of AI systems by optimizing computational processes and utilizing hardware accelerators like FPGAs (Field-Programmable Gate Arrays). In this direction many AI systems are nowadays deployed within edge computing infrastructures, which leads to the emergence of a wave of edge AI systems. For instance, EdgeLLM system use advanced FPGA architectures to significantly enhance energy efficiency when compared to traditional GPU (Graphic Processing Unit) setups. This trend is set to make AI more sustainable and accessible in resource-constrained environments.
Trend #4: Data Efficient AI
Data-efficient AI is another emerging trend that addresses the challenge of training models with limited data. This is very important given that there are many use cases where AI systems must be deployed and used despite the lack of large volume of quality data for training algorithms. Techniques such as transfer learning and synthetic data generation are therefore being used to reduce the dependency on large datasets. This trend accelerates the model development process and makes AI more inclusive by enabling applications in areas where there is data scarcity.
Trend #5: New Chip Architectures
The rise of AI models and applications is creating unprecedented demand for novel chip architectures that can offer both energy efficiency and exceptional performance. For instance, NVIDIA’s Blackwell chips represent a significant leap forward in AI hardware. They are designed to enhance the performance and efficiency of AI computations. The Blackwell chips are part of the company’s continuous efforts to advance AI processing capabilities, which build on the success of their previous architectures like Ampere and Hopper. These new chips are anticipated to deliver significant enhancements in processing power and energy efficiency towards addressing the increasing demands of AI workloads.
A standout feature of novel chip architectures is their emphasis on optimizing parallel processing capabilities, which enables efficient management of complex AI models and large datasets. This makes them well-suited for deep learning and high-performance computing applications. Furthermore, novel chip architectures are being designed to support advanced AI algorithms, including support for both training and inference tasks.
Trend #6: Distributed Learning Paradigms
Distributed learning has become a significant trend in AI due to its ability to handle the increasing complexity and scale of modern AI models. As AI applications demand larger datasets and more computational power, distributed learning offers a solution by dividing tasks across multiple processors or nodes, which enhances scalability and efficiency. This approach allows for parallel processing that significantly speeds up training times and enables the handling of vast amounts of data that can be hardly managed within a single machine alone. As a prominent example, Federated Learning (FL) is a distributed learning paradigm that allows multiple devices to collaboratively train a model without sharing their raw data. This approach enhances privacy and reduces communication overheads. In 2024, FL is being integrated with other technologies like model parallelism to improve scalability and efficiency in deep learning applications. Moreover, innovations such as the Forward-Forward algorithm are being explored to overcome computational challenges associated with FL, especially in cases where resource-limited devices are involved.
Trend #7: Regulation and Ethics
The proliferation of AI systems and applications leads to a trend for establishing regulatory frameworks that can mitigate AI risks. Government agencies and other institutional stakeholders are striving to ensure that AI is used and deployed responsibly and ethically. For instance, the European Union has recently debated a landmark comprehensive AI bill, which is designed to regulate AI and address concerns for consumers. This European Regulatory framework, known as the AI Act, will drive AI legislation across the member states of the European Union.
If AI is not regulated, data manipulation, misinformation, bias, and privacy risks can arise and pose greater societal risks. For example, tools can be susceptible to discrimination or legal risk if AI doesn’t collect data representative of a population. Also, Generative AI tools like ChatGPT pull information from internet searches worldwide, but companies and publications have sued AI vendors for copyright infringement.
Overall, the landscape of AI trends in 2024 is characterized by rapid advancements across various domains. Large Language Models will continue to evolve and are expected to offer enhanced capabilities for natural language processing tasks, as well as for reasoning over complex problems. Meanwhile, energy-efficient and data-efficient AI models are addressing sustainability concerns. Edge AI is enabling real-time decision-making at the device level, while federated learning offers privacy-preserving solutions for distributed model training. As these trends unfold, it is essential for businesses and policymakers to navigate the ethical implications associated with AI deployment. Modern enterprises must embrace these AI trends responsibly and become prepared to harness the full potential of AI.
