Generative AI in 2024 w/ Aishwarya | Highlights and Annotations by Gistr.

This YouTube video provides a comprehensive overview of generative AI research trends in 2024. Key areas covered include prompt engineering (shifting from skill-based to automated optimization), foundation models (growth of open-source options), model sizes (smaller models gaining traction), multimodality (increasing capabilities but high costs), retrieval augmented generation (RAG, widely adopted in enterprises), and AI agents (growing complexity and deployment challenges). The speaker also discusses evaluation methods, particularly the rise of LLM judges, and offers resources for further learning. This segment provides a concise summary of the presenter's approach to reviewing the year's generative AI research, focusing on identifying key trends and their real-world applications rather than delving into individual papers. The presenter explains their methodology of creating a heatmap to categorize research trends across input, application, and output layers of AI models, making the information easily digestible and actionable for viewers. This segment details a structured overview of 2024's generative AI research trends, categorized by input, data & model, application, and output layers. The presenter highlights areas of intense research activity (e.g., multimodality, alignment, context length) and emerging areas, providing a valuable framework for understanding the landscape of current research and its implications. This segment details how AI models are evolving to handle complex tasks by breaking them down into smaller, self-generated prompts, showcasing a shift from simple prompt engineering to more sophisticated task or goal engineering. The explanation highlights the model's ability to plan steps, prompt itself, and generate a final report, illustrating the increasing autonomy of AI agents. This segment explores three key future trends in prompt engineering: model-agnostic prompts, multi-modal prompts, and goal/task engineering. The presenter discusses the move towards prompts that work across different models, the integration of various data types (images, video, etc.) into prompts, and the shift towards providing AI systems with higher-level goals rather than specific instructions. This provides valuable insight into the direction of prompt engineering and its implications for AI development. This segment traces the evolution of prompt engineering from skill-based prompting to automated methods and self-optimizing models. The presenter discusses the shift from requiring users to possess specialized skills to the development of automated prompt optimization layers and self-optimizing models, highlighting the changing role of prompt engineering in the future of AI interaction. This segment analyzes the significant increase in open-source foundation models in 2024, demonstrating their performance parity with closed-source models. The discussion focuses on the factors contributing to this rise, including increased data quality and quantity, and the implications for accessibility and application development. This segment discusses the future of prompt engineering, predicting a shift towards automation and the use of tools for prompt optimization and caching. It emphasizes the diminishing need for extensive manual prompt engineering skills as AI models become better at generating their own prompts, highlighting the efficiency gains and cost reductions associated with this automation. This segment offers a clear explanation of AI agents, differentiating them from LLMs and highlighting their engineering-driven nature rather than purely AI innovation. The speaker effectively clarifies common misconceptions and categorizes agents into four levels based on their capabilities, from basic routing to autonomous operation and self-evolution. This segment explores the recent advancements in small language models (SLMs), focusing on two key trends: overtraining and distillation. It explains how these techniques have significantly improved the performance of SLMs, making them viable alternatives to larger, more resource-intensive models for specific applications, and discusses the cost implications and future trends. This segment explains the significant improvement in open-source LLMs, attributing it to the substantial increase in training data rather than model size. It contrasts the data used in 2023 with that used in 2024, highlighting the impact of larger datasets and the use of synthetic data on model performance, while also acknowledging the engineering complexities and environmental impact of larger models. This segment focuses on the practical challenges of deploying AI agents in real-world settings. It discusses issues like error propagation, cost, safety concerns, and latency. The speaker emphasizes the importance of improved underlying AI models to overcome these limitations and achieve better agent performance. This segment provides a detailed breakdown of the four levels of AI agents, clarifying the capabilities and limitations of each. It distinguishes between simpler agents performing basic tasks and more advanced, autonomous agents that are still largely under development. The explanation helps viewers understand the current state of AI agent technology and its potential future.This segment uses the example of coding agents like GitHub Copilot to illustrate the functionality of level three agents. It describes a multi-agent system for software engineering, showcasing how different agents collaborate to complete complex tasks. This provides a practical understanding of how advanced agents operate in real-world scenarios. The speaker offers insightful predictions about the future of generative AI, focusing on the role of agents and the limitations imposed by current AI model capabilities. The discussion emphasizes the rapid pace of advancements in the field, highlighting the uncertainty inherent in long-term predictions while acknowledging the potential for increased automation and transformative changes in job functions. The segment also addresses the crucial relationship between AI model performance and the capabilities of AI agents. This segment highlights the speaker's new course on generative AI, emphasizing its unique problem-centric approach. Unlike other courses focusing on theoretical concepts, this course prioritizes practical application through real-world use cases and hands-on experience in production applications, bridging the gap between theory and practice by focusing on actionable strategies and decision-making processes in applying generative AI solutions. This segment addresses the challenges of evaluating generative AI models, highlighting the limitations of traditional metrics and introducing the concept of LLM judges. It explains how these AI-powered judges can assess the quality of generated text, offering a solution to the evaluation problem in the field of generative AI. This segment provides a practical strategy for effectively digesting research papers in the generative AI field. The speaker recommends building strong foundational knowledge before tackling advanced papers, suggesting a tiered approach based on difficulty levels. The segment also encourages the use of AI tools to aid comprehension and highlights the importance of focusing on specific areas of interest within the vast landscape of generative AI research.This segment addresses the challenge of managing the vast and rapidly growing body of research in generative AI. The speaker suggests two key strategies: first, focusing on specific niches within the AI ecosystem to avoid being overwhelmed by the sheer volume of information; and second, relying on reliable sources of information, such as Hugging Face's daily papers list, to filter out less relevant or lower-quality research. The segment emphasizes the importance of strategic selection and filtering to efficiently utilize available resources. This segment provides valuable guidance on when to employ agent-based solutions versus simpler approaches like RAG (Retrieval Augmented Generation). It emphasizes that agents should only be considered when decision-making capabilities are required, highlighting the importance of understanding the specific use case before selecting an appropriate methodology. The speaker clarifies the concept of an AI agent as an LLM with access to tools and memory, encouraging a more nuanced understanding of their application.This segment offers practical advice for software engineers seeking a career transition into AI systems. The speaker stresses the need for a personalized approach, emphasizing the diverse roles within the AI field and the importance of identifying individual interests and goals. The segment also highlights the increasing integration of AI into software engineering, suggesting that most software engineers will need to acquire some AI skills.