The most important AI trends in 2024

Summary

In 2024, AI is set to evolve with nine emerging trends, from realistic expectations and multimodal capabilities to smaller, more efficient models. The industry is shifting towards integrating AI into existing workflows and optimizing models for less resource consumption. Innovations in model optimization, custom local models, virtual agents, and regulatory developments will shape AI's trajectory. The rise of 'shadow AI', personal AI use in workplaces, highlights the growing need for corporate policies to manage AI responsibly.

Takeaways

• 🚀 **Reality Check**: 2024 is the year for more realistic expectations in AI, with a shift from standalone AI tools to integrated elements enhancing existing ones.
• 🧠 **Multimodal AI**: AI is advancing in processing multiple data inputs, such as combining natural language with computer vision, and even incorporating video for more holistic learning.
• 🔍 **Smaller Models**: There's a trend towards smaller, less resource-intensive AI models, which are more efficient and can be run locally on many devices, reducing reliance on cloud computing and GPU resources.
• 💡 **Model Optimization**: Innovations like quantization and Low-Rank Adaptation are being adopted to optimize model performance, reducing memory usage and speeding up inference with fewer parameters.
• 💼 **Custom Local Models**: Organizations are developing custom AI models trained on their proprietary data to meet specific needs while keeping AI operations local to avoid data privacy risks.
• 🤖 **Virtual Agents**: The evolution of AI is moving beyond chatbots to virtual agents capable of task automation, such as making reservations or completing checklists.
• 📜 **Regulation**: With the European Union's Artificial Intelligence Act and ongoing discussions about copyright in AI training, regulation of AI is becoming increasingly important.
• 🕵️‍♂️ **Shadow AI**: The informal use of AI by employees without corporate oversight, known as 'shadow AI', poses risks to security, privacy, and compliance.
• 🌐 **AI and Infrastructure**: The demand for AI is driving the need for optimized models to reduce the pressure on GPU resources and cloud costs, which are escalating due to the size of AI models.
• 🔮 **Future of AI**: The script invites viewers to consider and contribute their thoughts on the 10th trend for AI in 2024 that was not covered in the video.

Q & A

What is the main focus of the video?

The video discusses nine AI trends expected to emerge in 2024, providing insights into the current and future state of AI technology.

What is meant by 'the year of the reality check' in AI?

It refers to 2024 being a year of developing more realistic expectations about AI, understanding its capabilities and limitations more clearly as it becomes more integrated into existing tools and workflows.

How are multimodal AI models extending the capabilities of AI?

Multimodal AI models can process multiple layers of data, allowing them to move between tasks like natural language processing and computer vision, and even incorporate video data for more comprehensive learning and inference.

Why is there a trend towards smaller AI models?

Smaller models are more resource-efficient, requiring less energy and computational power for both training and inference, making them more accessible and practical for a wider range of applications.

What challenges are associated with large AI models?

Large AI models demand significant computational resources, resulting in high energy consumption and increased costs for GPUs and cloud infrastructure.

What is model optimization, and why is it important?

Model optimization involves techniques like quantization and Low-Rank Adaptation (LoRA) to reduce the memory usage and computational requirements of AI models, making them more efficient and faster to run.

What are custom local models, and what benefits do they offer?

Custom local models are AI models trained on an organization's proprietary data and fine-tuned for specific needs, keeping data secure and reducing reliance on third-party infrastructure.

How do virtual agents differ from traditional chatbots?

Virtual agents go beyond customer service interactions by automating tasks such as making reservations, completing checklists, and connecting to other services, thereby enhancing productivity and efficiency.

What is the significance of AI regulation in 2024?

AI regulation is becoming more prominent, with agreements like the EU's Artificial Intelligence Act addressing issues such as the use of copyrighted material in AI training and ensuring compliance with privacy and security standards.

What is 'shadow AI' and what risks does it pose?

Shadow AI refers to the unauthorized use of AI by employees without IT approval, which can lead to security, privacy, and compliance issues, such as exposing trade secrets or using copyrighted material improperly.