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, advancements in multimodal AI, and the development of smaller, more efficient models. It also highlights the growing importance of model optimization, custom local models, virtual agents, and increasing regulatory focus. The script concludes by discussing the phenomenon of 'shadow AI' and invites viewers to consider additional trends that may arise.
Takeaways
- π§ The year 2024 marks a reality check for AI, with more realistic expectations and integration into existing tools.
- π Generative AI tools are moving from standalone applications to integrated features within everyday software like Microsoft Office and Adobe Photoshop.
- π€ Multimodal AI is expanding, with models like OpenAI's GPT-4v and Google Gemini bridging natural language processing and computer vision.
- π Smaller AI models are gaining attention due to their lower resource requirements, offering similar performance with fewer parameters.
- π‘ Model optimization techniques such as quantization and Low-Rank Adaptation (LoRA) are becoming more prevalent to reduce computational needs.
- π’ Custom local models allow organizations to train AI on proprietary data, enhancing security and privacy by avoiding third-party exposure.
- π The use of Retrieval Augmented Generation (RAG) helps reduce model size by accessing information without direct storage.
- π€ Virtual agents are evolving beyond chatbots to automate tasks and interact with other services, improving efficiency and user experience.
- π Regulatory developments in AI are expected to continue, with the European Union's Artificial Intelligence Act being a notable example.
- π΅οΈββοΈ Shadow AI, the unofficial use of AI by employees, raises concerns about security, privacy, and compliance within organizations.
Q & A
What is the main theme of AI development in 2024 according to the transcript?
-The main theme of AI development in 2024 is the shift towards more realistic expectations, with a focus on integrating AI tools into existing workflows and the emergence of smaller, more efficient models.
How has the perception of generative AI changed since its initial mass awareness?
-Initially, generative AI was met with a lot of excitement and breathless news coverage. However, as time has passed, the industry has developed a more refined understanding of the capabilities of AI-powered solutions, recognizing that they enhance and complement existing tools rather than completely replacing them.
What is multimodal AI and how does it extend the capabilities of generative AI?
-Multimodal AI refers to AI models that can process and understand multiple types of data inputs, such as text, images, and video. This capability allows for a more comprehensive understanding of data and enhances the information available for training and inference, thereby expanding the potential applications of generative AI.
What are the drawbacks of massive AI models?
-Massive AI models, while powerful, require significant amounts of electricity for both training and inference. This high energy consumption can lead to increased costs and environmental concerns, as well as challenges in maintaining the necessary infrastructure.
How are smaller models addressing the resource intensity of AI?
-Smaller models are being developed to yield greater output with fewer parameters, thus reducing the computational resources required. They can be run at lower costs and on local devices like personal laptops, making AI more accessible and cost-effective.
What is model optimization and why is it important?
-Model optimization involves techniques that improve the efficiency of AI models without sacrificing performance. Techniques like quantization and Low-Rank Adaptation (LoRA) reduce memory usage, speed up inference, and decrease the number of parameters that need to be updated, making models more practical for widespread use.
What is the significance of custom local models in AI development?
-Custom local models allow organizations to train AI on their proprietary data and fine-tune it for specific needs. Keeping AI training and inference local helps protect sensitive data and personal information, avoiding the risks associated with using third-party models.
How do virtual agents differ from traditional chatbots?
-Virtual agents go beyond the basic interaction capabilities of chatbots by automating tasks. They can perform actions like making reservations, completing checklists, or connecting to other services, providing a more interactive and useful experience for users.
What are some key regulatory developments in AI mentioned in the transcript?
-The European Union reached a provisional agreement on the Artificial Intelligence Act in December of the previous year. Additionally, the use of copyrighted material in AI training for content generation is a contentious issue that is likely to see further regulatory developments.
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. This can lead to security, privacy, and compliance issues, as employees might unknowingly expose sensitive information or use copyrighted material in ways that could legally implicate the company.
What is the 'missing 10th trend' mentioned in the transcript and how can viewers contribute to it?
-The 'missing 10th trend' is an open-ended question posed by the video creators to encourage viewers to think about and contribute their own ideas on AI trends for 2024 that were not covered in the video. Viewers are invited to share their thoughts in the comments section.
Outlines
π AI Trends in 2024: Realistic Expectations and Multimodal Advancements
The paragraph discusses the anticipated trends in AI for the year 2024. It begins with the notion of a 'reality check', emphasizing the shift towards more realistic expectations for AI capabilities. The initial excitement around generative AI tools like ChatGPT and Dall-E has evolved into a better understanding of their role as integrated elements rather than standalone solutions. The focus is now on enhancing existing tools, such as Microsoft Office's Copilot features and Adobe Photoshop's generative fill. The paragraph also highlights the growth in multimodal AI, which can process various types of data inputs, like natural language and images, and provide more comprehensive responses. This advancement allows for more diverse data to be used in training and inference, thus improving the models' capabilities.
π Optimizing AI: Smaller Models, Costs, and Customization
This paragraph delves into the trend of optimizing AI models in terms of size and cost. It addresses the environmental and financial impact of training large AI models, citing the electricity consumption required for models like GPT-3. The discussion then shifts towards smaller models that are less resource-intensive and can be run on local devices, such as personal laptops. The paragraph also touches on the innovation in Low-Rank Models (LLMs), which aim to achieve greater output with fewer parameters. Techniques like quantization and LoRA (Low-Rank Adaptation) are highlighted as methods to optimize and fine-tune models more efficiently. Additionally, the paragraph mentions the importance of custom local models, which are trained on an organization's specific data and needs, enhancing privacy and security. It also introduces the concept of virtual agents that can automate tasks and the increasing focus on AI regulation, exemplified by the European Union's Artificial Intelligence Act. Lastly, it warns about the risks associated with 'shadow AI', the unauthorized use of AI in the workplace, which can lead to security and legal issues.
Mindmap
Keywords
π‘AI trends
π‘Reality check
π‘Multimodal AI
π‘Smaller models
π‘Model optimization
π‘Custom local models
π‘Virtual agents
π‘Regulation
π‘Shadow AI
π‘Generative AI
Highlights
The pace of AI in 2024 shows no signs of slowing down, with 9 key trends expected to emerge.
Trend #1: The year of the reality check, with more realistic expectations for AI capabilities.
Generative AI tools are being integrated into existing tools like Microsoft Office and Adobe Photoshop, rather than replacing them.
Multimodal AI is extending capabilities by processing diverse data inputs like images and text.
Smaller AI models are gaining attention due to their lower resource intensity compared to massive models.
GPT-4 is rumored to have around 1.76 trillion parameters, but smaller models have seen success with 3 to 17 billion parameters.
Mistral's Mixtral model demonstrates that smaller models can match or outperform larger models in performance and inference speed.
Trend #4 highlights the impact of GPU and cloud costs on AI adoption and the push towards more optimized models.
Model optimization techniques like quantization and Low-Rank Adaptation (LoRA) are becoming more prevalent.
Custom local models trained on proprietary data can enhance security and privacy by avoiding third-party data exposure.
Retrieval Augmented Generation (RAG) helps reduce model size by accessing relevant information without storing it directly.
Virtual agents are evolving beyond chatbots to automate tasks and interact with other services.
The European Union's Artificial Intelligence Act represents a growing focus on AI regulation.
Shadow AI refers to the unofficial use of AI in the workplace, which can lead to security and compliance issues.
As AI capabilities grow, so does the responsibility for its ethical and secure use.
The transcript challenges viewers to identify a potential 10th trend for AI in 2024 that has not been covered.