Sunghee Yun (He/Him)

Co-founder & CTO @ Erudio Bio, Inc., Chief Business Development Officer @ WeStory.ai, Advisor & Evangelist @ CryptoLab, Inc., KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, Adjunct Professor @ Sogang University, Advisory Professor @ DGIST, Global Advisory Board Member @ Innovative Future Brain-Inspired Intelligence System Semiconductor of Sogang University, Network Expert @ Gerson Lehrman Group

Abstract

The contemporary landscape of artificial intelligence (AI) has undergone a profound transformation, driven by the emergence and widespread application of large language models (LLMs), with OpenAI’s ChatGPT based on GPT-x models serving as a pivotal catalyst. This seminar, tailored for university faculty and students, delves into the dynamic intersection of LLMs and generative AI (genAI), engaging both major industry players and agile startups in a comprehensive exploration.

The first part of the seminar initiates with a concise overview of LLMs, followed by a nuanced technical analysis focusing on the attention mechanism, a linchpin within the Transformer architecture. This examination aims to unveil the intricacies underlying the training and inference processes of LLMs having Transformer components, offering valuable insights into their remarkable capabilities.

Expanding the discourse to genAI, encompassing both technical and business dimensions, the seminar illustrates how LLMs and genAI collectively herald a new era. Numerous examples showcase how these technologies stimulate the creation of innovative applications and transformative business models. The first part of the seminar culminates with an exploration of current trends in genAI, providing strategic insights to navigate market dynamics and foster the development of impactful products.

The second half of the seminar shifts focus to critical non-technical topics surrounding the rapid progress of AI development. Engaging in meaningful discussions, we explore the rationale behind pursuing human-level AI, potential biases, ethical and legal challenges, and the elusive concept of AI consciousness. The speaker encourages active participation from the audience, fostering debates on these intriguing topics. The seminar concludes with the speaker’s contemplation on the knowledge, reasoning, and belief systems of AI, particularly those inherent to LLMs, offering a thought-provoking finale to this exploration of the frontiers of AI.

Claude’s version

This seminar provides a comprehensive exploration of Large Language Models (LLMs) and Generative AI, examining their technological foundations, industry impact, and broader societal implications. The presentation delves into the architectural breakthrough of the Transformer model, particularly the attention mechanism that revolutionized natural language processing by enabling models to evaluate dependencies between arbitrarily distant words through parallelizable computations. Key technical concepts covered include the evolution from RNN-based sequence-to-sequence models to modern LLMs like GPT, BERT, and multimodal systems, along with the scaling laws that demonstrate how performance improves with increased model size and training data.

The seminar addresses the rapidly evolving AI market landscape, highlighting the unprecedented pace of research developments where new breakthroughs are announced daily, with industry now leading academia in AI advancement. Market analysis reveals explosive growth projections, with global AI markets expected to reach $0.5T by 2024 and generative AI software sales potentially surging 18,647% by 2032. The presentation examines the competitive dynamics among major tech companies, the opportunities for smaller players in niche markets, and the hardware implications including the semiconductor industry’s response to AI demands, particularly benefiting companies like NVIDIA, Samsung, and SK Hynix.

Perhaps most importantly, the seminar tackles critical philosophical and societal questions surrounding AI development. It challenges the assumption that human-level performance should be the benchmark for AI systems, explores cognitive biases present in both humans and AI models, and examines ethical considerations including potential misuse, legal liability, and fairness concerns. The presentation concludes with a nuanced discussion of whether LLMs truly possess knowledge, belief, and reasoning capabilities from cognitive scientific perspectives, arguing that while these systems demonstrate remarkable utility and can mimic intelligent behavior, they fundamentally operate through statistical pattern recognition rather than genuine understanding, emphasizing the importance of informed decision-making about AI trustworthiness and safety.