Jin Kim

Jin Kim

Co-Founder and Business Lead at Linq

    Master of Financial Engineering from UC Berkeley. Associate of Actuaries. As an ex-founder with a background ranging from years of deep learning research, crypto quant trading, military cyber-intelligence, to venture capitalist, Jin's experience ranges from deep tech research to business and entrepreneurship. He is an avid fan of in-depth discussions regarding futuristic technologies such as artificial general intelligence and blockchain along with their real world applications.

    All Sessions by Jin Kim

    Day 3 04/25/2024
    12:10 pm - 12:20 pm

    The Evolution of Professional Assistance: From AI Assistant to AI Agents

    <span class="etn-schedule-location"> <span class="firstfocus">Generative AI</span> </span>

    Generative Artificial Intelligence (GenAI) is set to transform knowledge work worldwide. In this talk, Jin from Linq aims to share insights from deploying GenAI productivity services globally, from the Americas to the Asia-Pacific (APAC) region, offering lessons for integrating GenAI into diverse business processes by discussing: 1. AI Assistants vs. AI Agents AI Assistants, like ChatGPT, enhance worker capabilities by performing tasks or providing information. They represent the current AI support tier. AI Agents advance this by autonomously planning and optimizing workflows. These agents proactively manage complex work processes. 2. The Impact of AI Agents on Workflow Planning AI Agents transform workflow management by not only assisting but adapting and actively participating in decision-making, serving as strategic partners. 3. Current Landscape: AI Assistants Despite their potential, AI Assistants have limitations, primarily in strategic areas. They serve as precursors to the more autonomous AI Agents and the concept of AI multi-agents, mirroring a team structure. 4. Transitioning to AI Agents Moving from Assistants to Agents is crucial, addressing the gaps in handling complex workflows and offering strategic benefits, especially in fields like consulting and legal services. 5. Data Requirements for the AI Evolution from Assistant to Agent Key data include user-customized service data, understanding and execution tasks, adapting to changes, complex decision-making, and managing interactions. 6. The Future of AI Agents: Collaborative Multi-Agent Decision-Making Envision AI Agents working as a team, leveraging varied expertise for superior problem-solving. This includes assembling the right mix of agents for a task, collaborative strategy formulation, and refining approaches based on feedback. This session aims to elucidate the path from current AI Assistants to future collaborative AI Agent ecosystems, highlighting the strategic adoption of GenAI in professional settings.

    Open Data Science

     

     

     

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