The most important AI trends in 2024

In 2024, AI continues to evolve with nine key trends shaping the industry. The first trend is a shift towards more realistic expectations for generative AI, with tools like ChatGPT and Dall-E being integrated into existing software rather than replacing them. Multimodal AI is expanding capabilities, allowing models to process various data inputs like text, images, and video. The third trend focuses on smaller, more efficient models that are less resource-intensive and can run locally on devices. The fourth trend discusses the rising costs associated with GPU usage and cloud services for AI. Model optimization is the fifth trend, with techniques like quantization and Low-Rank Adaptation reducing memory usage and speeding up inference. Custom local models are the sixth trend, offering tailored AI solutions while protecting sensitive data. The seventh trend involves virtual agents for task automation. Regulatory concerns, including the EU's Artificial Intelligence Act, form the eighth trend. The ninth trend is the rise of 'shadow AI,' where employees use AI unofficially at work, potentially leading to security and compliance issues. The video challenges viewers to identify the missing tenth trend for 2024.

Takeaways

• 🧐 **Reality Check**: AI expectations are becoming more realistic, with generative AI tools being integrated into existing software rather than replacing them.
• 📈 **Multimodal AI**: AI models are advancing to handle multiple data inputs, like combining natural language processing and computer vision for more comprehensive understanding.
• ⚙️ **Smaller Models**: There's a shift towards smaller, less resource-intensive models to reduce energy consumption and cost, without compromising performance.
• 💰 **Cost Reduction**: The move towards smaller models is driven by the high costs associated with GPU usage and cloud services for larger models.
• 🔍 **Model Optimization**: Techniques like quantization and Low-Rank Adaptation are being adopted to optimize model performance and reduce computational requirements.
• 🏢 **Custom Local Models**: Organizations are developing custom AI models trained on proprietary data to meet specific needs while keeping data local for security.
• 🤖 **Virtual Agents**: AI is moving beyond chatbots to automate tasks and integrate with other services, enhancing user experience and efficiency.
• 🚨 **Regulation**: As AI becomes more prevalent, regulatory measures are being put in place, such as the European Union's Artificial Intelligence Act, to address concerns like copyright and data privacy.
• 👤 **Shadow AI**: The personal, unofficial use of AI by employees without corporate oversight can lead to security and compliance issues, highlighting the need for clear policies.
• 🔑 **Missing Trend**: The audience is encouraged to consider and suggest the 10th trend for AI in 2024 that was not covered in the video.

Q & A

What is the main focus of Trend #1 mentioned in the transcript?

The main focus of Trend #1 is on developing more realistic expectations regarding the capabilities of generative AI.

How are generative AI tools being implemented differently now compared to earlier approaches?

Generative AI tools are now being implemented as integrated elements rather than standalone chatbots. They enhance and complement existing tools instead of revolutionizing or replacing them.

What is multimodal AI, and how is it extending its capabilities?

Multimodal AI refers to models that can take multiple layers of data as input, such as both natural language and visual information. It is extending its capabilities by allowing models to process more diverse data inputs, including video, which expands the information available for training and inference.

What are some drawbacks associated with massive AI models?

Massive AI models require significant resources for both training and inference. For instance, training a single GPT-3 size model can consume the yearly electricity consumption of over 1000 households.

What are some advantages of smaller AI models?

Smaller AI models are less resource-intensive, can be run at lower cost, and can run locally on many devices like personal laptops. They also contribute to reducing GPU and cloud costs.

What optimization technique is mentioned in Trend #5, and how does it work?

The optimization technique mentioned is quantization. It involves lowering the precision used to represent model data points, such as reducing from 16-bit floating point to 8-bit integer, to reduce memory usage and speed up inference.

How do custom local models contribute to AI development?

Custom local models, trained on an organization's proprietary data and fine-tuned for specific needs, offer powerful AI solutions. Keeping training and inference local helps avoid risks associated with proprietary data or sensitive information being used by third parties.

What is shadow AI, and what are the potential risks associated with it?

Shadow AI refers to the unofficial use of AI in the workplace by employees without proper approval or oversight from IT. Potential risks include issues related to security, privacy, compliance, and legal action due to the misuse of AI capabilities.

What recent regulatory development is mentioned in Trend #8?

In Trend #8, it is mentioned that the European Union reached a provisional agreement on the Artificial Intelligence Act in December of the previous year.

Why is it significant that employees use AI in the workplace without proper oversight?

The unauthorized use of AI by employees can lead to various risks for organizations, including exposure of proprietary data, legal issues regarding copyright infringement, and potential breaches of security and compliance regulations.

What is the prompt for the audience at the end of the transcript?

The prompt for the audience at the end of the transcript is to suggest the missing 10th AI trend for 2024 in the comments section.

Outlines

00:00

🚀 AI Trends in 2024: Realistic Expectations and Multimodal Advancements

The video script discusses nine anticipated AI trends for 2024, starting with a reality check on AI's capabilities. The first trend highlights a shift from standalone AI applications to integrated tools that enhance existing software, such as Microsoft Office's Copilot features or Adobe Photoshop's generative fill. Multimodal AI is emphasized as a growing area, with models like OpenAI's GPT-4v and Google Gemini processing various data inputs, including natural language and computer vision tasks. This leads to more diverse data processing and holistic learning. The script also touches on the environmental and economic implications of large AI models, suggesting a move towards smaller, more efficient models that require fewer resources and can be run locally on devices. Mistral's Mixtral is mentioned as an example of a smaller model that outperforms larger ones in benchmarks and inference speeds.

05:05

🔍 Model Optimization and the Rise of Custom Local Models

The second paragraph delves into model optimization techniques that are becoming increasingly important as AI evolves. It mentions quantization, which reduces memory usage and speeds up inference by lowering the precision of model data points. Another technique, LoRA or Low-Rank Adaptation, allows for the fine-tuning of pre-trained models by injecting trainable layers into transformer blocks, thus reducing the number of parameters that need updating. The paragraph also discusses the benefits of custom local models developed from open-source models, which can be trained on proprietary data and fine-tuned for specific organizational needs. This approach helps to maintain privacy and security by keeping AI training and inference local, avoiding the risk of sensitive data exposure. Additionally, the use of Retrieval Augmented Generation (RAG) is highlighted as a method to access information without significantly increasing model size.

Keywords

💡AI trends

AI trends refer to the evolving patterns and developments in the field of artificial intelligence that are expected to gain prominence in the coming year. In the context of the video, these trends shape the future of technology and its integration into various sectors, influencing how AI is perceived and utilized.

💡Reality check

In the video, 'reality check' is used to describe the shift from initial hype to a more grounded understanding of AI's capabilities. It signifies a move towards realistic expectations of what AI can achieve, particularly in the wake of generative AI's initial impact on mass awareness.

💡Generative AI

Generative AI is a type of artificial intelligence that can create new content, such as text, images, or music. The video discusses how generative AI tools are being integrated into existing software to enhance functionalities rather than replace them, exemplified by features like Copilot in Microsoft Office.

💡Multimodal AI

Multimodal AI refers to AI systems that can process and understand multiple types of data inputs, such as text, images, and video. The video highlights the growing capabilities of multimodal AI in handling diverse data inputs, which expands the scope of AI applications.

💡Smaller models

Smaller models in AI are a focus because they are more resource-efficient compared to their larger counterparts. The video points out that smaller models, with fewer parameters, can achieve similar performance to larger models while consuming less energy and computational resources.

💡GPU and cloud costs

GPU (Graphics Processing Unit) and cloud costs are significant factors in AI development due to the high computational demands of training and running AI models. The video discusses how the trend towards smaller models can help reduce these costs, making AI more accessible.

💡Model optimization

Model optimization involves techniques to improve the efficiency and performance of AI models. The video mentions methods like quantization and Low-Rank Adaptation (LoRA) that reduce memory usage and speed up inference, which are crucial for the practical deployment of AI.

💡Custom local models

Custom local models are AI models that organizations develop using their proprietary data, tailored to their specific needs. The video emphasizes the benefits of keeping AI training and inference local to protect sensitive data and align the model closely with the organization's objectives.

💡Virtual agents

Virtual agents are AI-driven systems that can perform tasks on behalf of users, such as making reservations or completing checklist tasks. The video suggests that virtual agents will become more advanced, offering more autonomous and integrated services.

💡Regulation

Regulation in the context of AI refers to the legal and policy frameworks that govern the use and development of AI technologies. The video discusses the importance of regulation, citing the European Union's Artificial Intelligence Act, to address issues like copyright and ethical use.

💡Shadow AI

Shadow AI is the unauthorized or unofficial use of AI tools within an organization by employees, without formal approval or oversight. The video warns of the potential risks of shadow AI, such as security breaches and legal complications, due to unregulated use.

Highlights

The year of the reality check: AI-powered solutions are now being implemented as integrated elements, enhancing existing tools rather than replacing them.

Multimodal AI is extending its capabilities, allowing models to process diverse data inputs and expand information available for training and inference.

The trend towards smaller AI models is driven by the high resource consumption of massive models for both training and inference.

Model optimization techniques like quantization and Low-Rank Adaptation are emerging to reduce memory usage and speed up inference.

Custom local models trained on proprietary data offer powerful AI solutions tailored to specific organizational needs while mitigating privacy risks.

Virtual agents go beyond traditional chatbots to automate tasks like reservations and checklist completion, promising increased efficiency.

Regulation, exemplified by the EU's Artificial Intelligence Act, is becoming increasingly important to govern AI use and protect rights.

Shadow AI, the unofficial use of AI in the workplace, poses risks to security, privacy, and compliance without proper oversight and policies.

The missing 10th trend in AI for 2024 is solicited from viewers, encouraging engagement and participation.

Generative AI tools like ChatGPT and Dall-E are now integrated as elements within existing workflows, rather than standalone applications.

Multimodal AI models like OpenAI's GPT-4v and Google Gemini can seamlessly switch between natural language processing and computer vision tasks.

Smaller AI models, such as Mistral's Mixtral, offer competitive performance to larger counterparts like OpenAI's GPT-3.5 while being more efficient.

Optimization techniques like quantization and Low-Rank Adaptation aim to reduce resource consumption and improve efficiency in AI models.

Utilizing open source models for custom local solutions enables organizations to leverage proprietary data for tailored AI applications.

Virtual agents promise to streamline task automation beyond simple customer service interactions, improving productivity.

The EU's Artificial Intelligence Act exemplifies the growing importance of regulatory frameworks to govern AI development and deployment.