Topics

This session provides a comprehensive overview of artificial intelligence (AI), focusing on the evolution, concepts, and applications of Generative AI. The session explores topics such as the history of AI, different types of machine learning, neural networks, deep learning, large language models, and AI agents. It examines various generative models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The session emphasizes prompt engineering and transfer learning techniques for optimizing the performance of these models.

Prompt engineering works for large language models (LLMs) by leveraging their underlying architecture, training data, and contextual learning capabilities to guide their outputs toward desired results. LLMs, like GPT-4, are based on transformer architectures that use self-attention mechanisms to process vast amounts of text data and generate human-like responses. These models are pretrained on diverse datasets and rely on tokenization to interpret input prompts. Prompt engineering exploits this pretraining by crafting precise, contextually relevant instructions that align with the model’s learned patterns.

This session explores the intricacies of prompt engineering for large language models (LLMs), emphasizing its importance in optimizing LLM performance for specific tasks. Unlike traditional machine learning models, evaluating LLMs involves subjective metrics like context relevance, answer faithfulness, and prompt relevance.

LLM finetuning is a powerful technique that tailors pre-trained large language models to specific tasks or domains, enhancing their performance and applicability. Unlike prompt engineering, which works within the constraints of a model’s existing knowledge, finetuning involves additional training on a curated dataset to modify the model’s parameters. This process allows the model to learn new patterns, adapt to specific vocabularies, and refine its understanding of particular contexts or tasks.

Finetuning frameworks like LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation) revolutionize the way large language models (LLMs) are adapted for specific tasks, offering efficiency and scalability. These methods modify only a fraction of a model’s parameters during fine-tuning, reducing resource requirements while maintaining or enhancing model performance. LoRA introduces low-rank matrices to efficiently adjust large models without retraining their entire architecture, while QLoRA combines this approach with quantized precision to further optimize memory and computation overhead. These frameworks make high-quality fine-tuning accessible for resource-constrained environments, broadening the adoption of LLMs.

Retrieval-Augmented Generation (RAG) enhances generative AI by integrating external information retrieval, enabling models to produce accurate, contextually informed responses. It combines indexing, retrieval, augmentation, and generation, with semantic search playing a key role in retrieving relevant data based on meaning rather than exact keywords. By leveraging vector embeddings and similarity measures, RAG reduces hallucinations and dynamically updates knowledge without retraining the model. This approach is widely applied across industries, such as customer service, education, legal research, and content creation, to address knowledge-intensive tasks. Its implementation relies on vector databases and advancements in embedding models to efficiently connect AI systems with external, authoritative information sources.

Vector databases have emerged as a crucial tool for enhancing the efficiency and capabilities of large language models (LLMs), offering improved information retrieval, knowledge management, and computational efficiency. By storing and managing high-dimensional data representations, these databases enable LLMs to access vast amounts of information quickly and accurately, leading to more contextually relevant and up-to-date responses in various applications.

Agentic AI represents a significant advancement in artificial intelligence, characterized by autonomous, goal-oriented behavior and adaptability across diverse environments. Unlike traditional AI systems that operate within predefined parameters, Agentic AI demonstrates the ability to set and pursue complex objectives independently, adapting to changing conditions with minimal human intervention.

Agentic AI frameworks are transforming the development of intelligent systems by integrating Large Language Models (LLMs) with traditional programming languages to create autonomous agents capable of complex decision-making and task execution. These systems are built on foundational concepts such as skills, which are modular components enabling agents to perform specific actions or make decisions. Skill selection, a critical aspect of agentic AI, involves choosing the most appropriate skill for a given context through methods like generative and semantic skill selection. Additionally, orchestration, which coordinates multiple skills to accomplish complex tasks, plays a key role in ensuring agents can plan, execute, and adapt effectively to changing circumstances.

Symbolic AI and Neuro-symbolic AI represent two significant paradigms in artificial intelligence, with the former focusing on rule-based reasoning and the latter combining logical inference with neural network capabilities. This comprehensive overview explores their principles, applications, and the emerging role of knowledge graphs in enhancing large language models.

Knowledge graphs are powerful data structures that represent information as a network of interconnected entities and relationships. Unlike traditional databases, knowledge graphs store data in a way that mimics human-like understanding of concepts and their associations. At their core, knowledge graphs consist of nodes (representing entities) and edges (representing relationships between entities), forming a flexible and intuitive representation of complex information.

Multimodal models, such as vision-enhanced large language models (LLMs) and diffusion models, are designed to process and integrate different types of data, including text, images, and audio. Vision LLMs combine visual and textual information to perform tasks like image captioning and visual question answering. These models use cross-attention mechanisms to align visual features with textual representations, allowing them to generate text based on images or answer questions about visual content.

‘Safeguarding LLMs, Detecting BIAS, Privacy, Federated Learning’

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