The most important AI trends in 2024
TLDRThe video script outlines nine emerging AI trends for 2024, emphasizing the shift towards realistic expectations, integration of generative AI into existing tools, advancement in multimodal AI, development of smaller and more efficient models, rising GPU and cloud costs, model optimization techniques, custom local models, virtual agents, increasing regulation, and the phenomenon of shadow AI. It highlights the growing importance of responsible AI usage and invites viewers to contribute their thoughts on a potential 10th trend.
Takeaways
- 📉 **Realistic Expectations**: 2024 is the year for a reality check in AI, with a shift towards more refined understanding and practical implementation of generative AI tools.
- 🌐 **Integration Over Revolution**: AI tools are increasingly becoming integrated elements of existing software, like Microsoft Office's Copilot and Adobe Photoshop's generative features, rather than standalone solutions.
- 🧠 **Multimodal AI Advancements**: AI models are expanding their capabilities to process multiple data types, such as images and text, providing richer interactions and more diverse data for training.
- 📈 **Smaller Models, Bigger Impact**: Innovations in LLMs focus on achieving greater output with fewer parameters, addressing the resource-intensive nature of larger models.
- 💡 **Efficiency in Model Design**: Companies like Mistral are developing models like Mixtral, which combine multiple neural networks to match or outperform larger models with faster inference speeds and lower resource requirements.
- 🚀 **Reducing GPU and Cloud Costs**: The trend towards smaller models is driven by the need to reduce the high costs associated with GPU usage and cloud services for AI training and inference.
- 🔍 **Model Optimization Techniques**: Techniques like quantization and LoRA are being adopted to optimize models, reducing memory usage, speeding up inference, and cutting down on the need for extensive computational resources.
- 🏢 **Custom Local Models**: Organizations are developing custom AI models trained on proprietary data and fine-tuned for specific needs, keeping sensitive information secure and reducing reliance on cloud services.
- 🤖 **Virtual Agents for Task Automation**: Virtual agents are evolving beyond chatbots to automate tasks, interact with other services, and enhance overall productivity.
- 📜 **Increasing Regulation**: With the European Union's Artificial Intelligence Act and ongoing debates on copyright material use, regulatory developments in AI are expected to gain momentum.
- 🕵️♂️ **Shadow AI Concerns**: The unofficial use of AI by employees without corporate oversight can lead to security, privacy, and compliance issues, highlighting the need for clear AI policies within organizations.
Q & A
What is the overarching theme for AI trends in 2024?
-The overarching theme for AI trends in 2024 is the shift towards more realistic expectations, with a focus on integrating AI tools into existing workflows and developing smaller, more efficient models.
What does the 'reality check' in AI refer to?
-The 'reality check' in AI refers to the industry's move towards a more refined understanding of the capabilities and limitations of AI-powered solutions, with a focus on their implementation as integrated elements rather than standalone applications.
How are multimodal AI models enhancing AI capabilities?
-Multimodal AI models are enhancing capabilities by processing diverse data inputs, such as text, images, and video, which allows for a more comprehensive understanding and response to user queries.
What is the significance of smaller AI models in 2024?
-Smaller AI models are significant because they are less resource-intensive, allowing for faster inference speeds and the possibility of running AI on local devices like personal laptops, which reduces costs and increases accessibility.
How does the development of smaller models impact GPU and cloud costs?
-The development of smaller models reduces the demand for high-end GPUs and lowers cloud infrastructure costs, as they require less computational power for both training and inference.
What is model optimization and why is it important?
-Model optimization techniques, such as quantization and Low-Rank Adaptation (LoRA), are important for improving the efficiency of AI models by reducing memory usage, speeding up inference, and minimizing the number of parameters that need to be updated.
Why is the custom local model trend emerging?
-The custom local model trend is emerging because it allows organizations to train AI models on their proprietary data and fine-tune them for specific needs, while also maintaining control over sensitive data and avoiding third-party exposure.
What role do virtual agents play in the AI trend?
-Virtual agents go beyond traditional chatbots by automating tasks, such as making reservations or completing checklists, and connecting to other services, thereby improving overall efficiency and user experience.
How is the European Union addressing AI regulation?
-The European Union has reached a provisional agreement on the Artificial Intelligence Act, which aims to regulate the use of AI and address issues related to security, privacy, and compliance.
What is 'shadow AI' and why is it a concern?
-Shadow AI refers to the unofficial, personal use of AI in the workplace by employees without IT approval or oversight. It can lead to security, privacy, and compliance issues, such as the inadvertent sharing of trade secrets or the use of copyrighted material.
What is the missing 10th trend that the video challenges viewers to identify?
-The video does not specify the 10th trend, leaving it to the viewers to contribute their insights and ideas on what additional AI trend for 2024 has not been covered in the discussion.
Outlines
🚀 AI Trends in 2024: Reality Check and Multimodal Advancements
This paragraph discusses the anticipated AI trends for 2024, emphasizing the shift towards realistic expectations and the integration of generative AI into existing tools. It highlights the transition from standalone chatbots to AI features embedded in everyday software like Microsoft Office and Adobe Photoshop. The paragraph also delves into the potential of multimodal AI, which can process various data inputs and provide richer, more comprehensive responses. It mentions the development of smaller AI models that are more energy-efficient and cost-effective, setting the stage for a more sustainable and accessible AI future.
🌐 Model Optimization and the Rise of Custom Local Models
The second paragraph focuses on model optimization techniques such as quantization and Low-Rank Adaptation (LoRA), which aim to reduce memory usage and speed up inference by lowering the precision of model data points and injecting trainable layers into pre-trained models. It also discusses the emergence of custom local models that are trained on proprietary data and fine-tuned for specific organizational needs, enhancing data security and privacy. The paragraph further explores the use of Retrieval Augmented Generation (RAG) to access information without storing it directly within the model, contributing to smaller and more efficient AI systems.
Mindmap
Keywords
💡AI trends
💡Reality check
💡Multimodal AI
💡Smaller models
💡Model optimization
💡Custom local models
💡Virtual agents
💡Regulation
💡Shadow AI
💡Generative AI
Highlights
The pace of AI in 2024 shows no signs of slowing down, with 9 key trends expected to emerge.
Trend #1: 2024 is the year of the reality check, with more realistic expectations for AI capabilities.
Generative AI tools are being integrated into existing software, like Microsoft Office and Adobe Photoshop, rather than being standalone.
Multimodal AI is extending capabilities by processing diverse data inputs like images, text, and video.
Smaller AI models are gaining attention due to their lower resource intensity and faster inference speeds.
GPT-4 is rumored to have 1.76 trillion parameters, but many successful open source models operate with 3 to 17 billion parameters.
Mistral's Mixtral model demonstrates that smaller models can match or outperform larger models on standard benchmarks.
Trend #4 highlights the increasing GPU and cloud costs associated with training and running larger AI models.
Model optimization techniques like quantization and LoRA are becoming more prevalent to reduce computational needs.
Custom local models allow organizations to train AI on proprietary data without risking sensitive information leakage.
Trend #7 focuses on virtual agents that automate tasks, going beyond simple customer service chatbots.
The European Union's Artificial Intelligence Act represents a growing trend of AI regulation.
Shadow AI refers to the unofficial use of AI by employees without corporate oversight, raising concerns about security and compliance.
The dangers of generative AI increase with its capabilities, emphasizing the need for responsibility.
The transcript challenges viewers to identify the missing 10th AI trend for 2024.