Get clued in on popular AI trends for 2024 and how they intend to revolutionise the industry.
In November 2022, ChatGPT was launched and, by the end of 2023, caused both drastic and exciting changes in the field of artificial intelligence. This includes complex multimodal models and a thriving open-source landscape. These AI trends of 2024 are evidence of how the development of AI and deployment of AI tactics are becoming increasingly sophisticated and cautious by aligning itself with concerns regarding safety, ethics, and the ever-changing environment.
Top trends for 2024:
1. Open-Source AI
Open-source is an AI system that is free and accessible to the public. It emerged as the primary driver in the digital era, specifically during the pandemic, when digital transformation was integral for many organisations staying afloat.
This trend involves the growing availability and quality of open-source AI models and tools. While proprietary options like ChatGPT were dominant initially, 2023 saw a surge in strong open-source competitors such as Mistral AI’s Mixtral models and Meta’s Llama. This democratises access to advanced AI models, allowing researchers and organisations to build upon existing code without significant costs. Open-source AI has the potential to reshape the AI landscape by fostering collaboration and innovation in the field.
According to research, by 2028 the global Open-source Software market is estimated to reach USD 75 209 million with a CAGR of 18.09%.
2. Agentic AI
Agentic AI represents a shift towards proactive AI systems that exhibit autonomy, proactivity, and the ability to act independently.
Unlike traditional reactive AI systems, AI agents are programmed to understand their environment, set goals, and take actions without a need for direct human intervention. This trend has implications for various domains, from environmental monitoring to proactive risk management, as AI agents are trained to gather data, identify current and future trends and anticipate and respond to situations before they escalate.
The benefits of agentic AI systems are vast and wide. There is a potential to transform the standard operational framework of the industry by improving business efficiency, augmenting intelligence, creating new job categories, driving innovation and enhancing human capabilities.
3. Multimodal AI
Multimodal AI involves analysing multiple input formats such as text, graphics, and sound, enabling AI systems to process diverse sensory information similar to humans. For example, OpenAI's GPT-4 model incorporates multimodal capabilities, allowing it to respond to both audio and visual inputs. Essentially, mimicking the brain's ability to process multiple interlinked inputs to generate precise insights.
By 2028, the global market for Multimodal AI is projected to grow from the current USD 1.0 billion to USD 4.5 billion with a CAGR of 35%. An example of real-world applications include improved diagnostic precision in healthcare through the integration of medical imaging with patient history and genetic information.
4. Customised Enterprise Generative AI Models
According to Grand View Research, the global Enterprise Generative AI market size is expected to grow at a compound annual growth rate (CAGR) of 36.4% by 2030. This trend highlights the growing demand for customised AI models tailored to specific enterprise needs. While large, general-purpose models like ChatGPT are popular, smaller, more focused models are gaining traction for commercial applications due to their ability to address specialised requirements effectively.
Organisations often modify existing models rather than building from scratch, reducing costs and development time while meeting niche market demands across various sectors like document analysis, supply chain management, and customer service.
5. Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is an AI framework intended to improve the quality of large language model generated responses by combining text generation with information retrieval to enhance the precision and relevance of AI-generated content. By providing AI models access to external data, RAG improves response accuracy and contextual sensitivity while reducing model size and resource requirements. This technique is particularly useful for business applications where up-to-date factual knowledge is crucial, such as developing more effective virtual assistants and chatbots.
6. Shadow AI
Shadow AI refers to the adoption of AI tools and technologies by employees without official approval or oversight from IT departments. While it demonstrates innovation and a desire for quick solutions, it also poses risks related to data privacy, security, and compliance. In 2024, organisations will need to implement governance frameworks to manage shadow AI effectively, balancing innovation with security and compliance requirements.
Conclusion
These trends underscore the continued evolution and diversification of AI and machine learning applications across various industries. Staying abreast of these developments and adapting to emerging trends will be essential for organisations and professionals to remain competitive in 2024 and beyond.
This article was inspired by an article authored by Jennifer Wales. Please find the link to this article here.
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