As we move through 2024, the rapid evolution of AI technology continues to reshape industries. From advancements in model efficiency to growing concerns over AI regulation, this year will be pivotal for the development and application of artificial intelligence. Below, we break down the top 9 AI trends shaping 2024.
1. 2024: The Year of Realistic Expectations
The hype around AI is finally settling, and this year is all about a reality check. Generative AI, once seen as a revolutionary standalone tool, is now being integrated into everyday workflows. Tools like Microsoft Office’s Copilot and Adobe Photoshop’s generative fill are enhancing existing platforms rather than replacing them, helping businesses better understand the realistic capabilities of AI.
2. The Rise of Multimodal AI
2024 will witness significant advancements in multimodal AI, where models can process multiple types of data simultaneously, such as text, images, and video. Leading the charge are OpenAI’s GPT-4v and Google Gemini, allowing for seamless transitions between tasks like natural language processing and computer vision. This opens up new possibilities, such as providing step-by-step visual aids for complex tasks or answering questions about images in real-time.
3. Smaller, More Efficient AI Models
The era of massive AI models may be coming to an end as smaller, more efficient models rise in prominence. Large models like GPT-4 consume enormous energy—comparable to the yearly electricity use of 1,000 households during training. In contrast, smaller models are more resource-efficient and can be run on personal devices like laptops. Models like Mistral’s Mixtral, with just 7 billion parameters, are outperforming larger models like GPT-3.5, making AI more accessible and sustainable.
4. Rising GPU and Cloud Costs
The demand for AI power is putting upward pressure on cloud and GPU costs. Larger models require more computing power, which increases infrastructure costs. Most businesses don’t have their own AI infrastructure, which means they rely on cloud providers to meet the needs of generative AI. This growing cost is driving the need for more optimized AI solutions that can perform effectively without excessive compute power.
5. Model Optimization for Greater Efficiency
Model optimization techniques are essential in reducing the resource requirements of AI. In 2024, expect to see more adoption of optimization methods like quantization, which reduces the precision of data representation to speed up inference. Techniques like Low-Rank Adaptation (LoRA), which freeze pre-trained model weights and add trainable layers, are also making it easier and faster to fine-tune models without requiring massive computational resources.
6. Custom Local AI Models on the Rise
Organizations are increasingly developing custom AI models tailored to their specific needs, using proprietary data for training. By keeping training and inference local, companies can avoid risks associated with third-party data handling and proprietary information being fed into public AI models. Techniques like Retrieval Augmented Generation (RAG) are also helping to reduce model size while maintaining access to relevant information.
7. Virtual Agents: Beyond Chatbots
Virtual agents are becoming more advanced, moving beyond simple customer service chatbots. In 2024, virtual agents will be capable of automating more complex tasks like making reservations, managing checklists, and connecting to multiple services to complete assignments. This evolution is expanding the scope of AI-driven automation across industries.
8. Increased Focus on AI Regulation
AI regulation will be a hot topic this year, especially following the European Union’s Artificial Intelligence Act. AI’s role in content generation and the use of copyrighted material for model training is a contentious issue that is far from resolved. Businesses need to stay informed about evolving regulations to ensure compliance and avoid legal challenges related to their AI practices.
9. Shadow AI: The Unofficial Use of AI in the Workplace
The rise of shadow AI, or the unofficial use of AI by employees without IT oversight, is creating significant security and compliance risks for businesses. In a recent study, 90% of respondents admitted to using AI at work without formal approval. This lack of oversight can lead to sensitive data being exposed or inadvertently used to train public AI models, creating potential vulnerabilities for companies.
Conclusion: 2024 will be a transformative year for AI, as companies and developers navigate these key trends. From optimizing models for efficiency to managing regulatory challenges, the landscape of artificial intelligence is rapidly changing. Staying ahead of these trends will be essential for businesses looking to leverage AI effectively and responsibly.