Mackenzie is the Global Startup Evangelist at AWS. His days are spent traveling the globe to meet startups, share their stories, and connect engineering teams together. Every day there are a large number of startups launching on AWS across every imaginable industry. It’s Mackenzie’s mission to find stories of startups that are helping to improve the world and share these stories with a wide audience.
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Prior to joining AWS, Mackenzie was the Head of Technical Operations at Betterment, the world’s largest independent robo-advisor based in NYC which manages over $8B in assets. Mackenzie was a founding engineer and Head of Technical Operations at Oscar Health, an insurance startup also based in NYC, helping to grow the company to over 400+ employees.
Join us for an exciting evening of learning and networking focused on Machine Learning and Generative AI (GenAI). This event is designed to bring together machine learning enthusiasts, professionals and experts in the field to share their knowledge, insights and real-world experience.
During this meetup, we will dive deep into Responsible AI in the Era of Generative AI, Multi-turn Emotional Support through Cognitive Principle of Relevance, and Unlocking AWS GPUs for Machine Learning. Our lineup of industry experts will deliver insightful talks, demos, and case studies, showcasing the latest advancement in GenAI and the formidable compute power that empower them.
Large Language Models (LLMs) have showcased remarkable proficiency in tackling Natural Language Processing (NLP) tasks efficiently, significantly reducing time-to-market compared to traditional NLP pipelines. However, upon deployment, LLM applications encounter challenges concerning hallucinations, safety, security, and interpretability. With many countries recently introducing guidelines on responsible AI application usage, it becomes imperative to comprehend the principles of constructing and deploying LLM applications responsibly. This hands-on session aims to delve into these critical concepts, offering insights into developing and deploying LLM models alongside implementing essential guardrails for their responsible usage.
Be helpful but don’t Talk too much: Improving Multi-turn Emotional Support through Cognitive Principle of Relevance
Cooprerative conversation is underpinned by multiple linguistic-pragmatic principles. The improvement demonstrates cognitive relevance as a rewarding goal for language models to achieve optimal relevance during communication, through maximizing cognitive effect while minimizing the processing effort imposed on the listener. To achieve and maximize user-preferred cognitive effect during interaction, Reinforcement Learning from Human Feedback (RLHF) has been widely adopted to empower LM-based conversation agents with the capability of producing positive cognitive effect. However, the minimization of user’s processing load, which is equally essential to cooperative conversation, has never been given sufficient attention. This study proposes a theory-driven reinforcement learning, Optimal Relevance Learning (ORL), to improve the performance of language models in multi-turn emotional support conversation. The improvement demonstrates cognitive relevance as a rewarding goal for language models to acquire human-like communication ability.
Bhaskarjit applies his machine learning skills and domain knowledge to build innovative solutions for the world's largest asset manager. He has have over 10 years of experience in data science, spanning multiple industries and domains such as retail, airlines, media, entertainment, and BFSI. At BlackRock, he is responsible for developing and deploying machine learning algorithms to enhance the liquidity risk analytics framework, identify price-making opportunities in the securities lending market, and create an early warning system using network science to detect regime change in markets. He also leverages his expertise in natural language processing and computer vision to extract insights from unstructured data sources and generate actionable reports. His mission is to use data and technology to empower investors and drive better financial outcomes.
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