Ernest Chan – Generative AI for Asset Managers Workshop Recording
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Ernest Chan – Generative AI for Asset Managers Workshop Recording

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About Course

Workshop Overview

Day 1:

Exploring Generative AI and Large Language Models (LLMs) in Asset Management

  • In depth look, into the applications of LLMs such as BARD, ChatGPT in the industry.
  • Utilizing LLMs to develop trading approaches.
  • Practical session; Converting data into signals for high frequency trading.

Day 2:

Advanced Strategies and Real World Uses

  • Informal Conversation with Lisa Huang
  • Discussion, on design and methods to manage risks associated with LLMs.
  • Enhancing trading tactics with detailed sentiment analysis through LLMs.
  • Hands on activity; Testing trading strategies and exploring how LLMs could transform asset management practices.

 

Workshop Outline

01 Exploring Big Language Models (BLMs) & Pre trained Generative Transformers (PGT)

  • Getting familiar, with BLMs like BARD, ChatGPT and other advanced language models
  • Common Uses of BLMs
  • Understanding the functionality of BLMs
  • Accessing BARD/PaLM online using their API

02 Developing Software

  • Introduction to Prompt Design
  • Creating software, for tasks like writing text summarizing content and more.
  • Exploring few shot learning, with BARD
  • Introduction to embeddings and their significance
  • An overview of the BARD embeddings API. How it is utilized

03 Risks Linked with Language Models (LLMs)

  • Recognizing risks associated with LLMs, including hallucinations, bias, consent and security.
  • Strategies, for mitigating the risk of hallucinations, such as retrieval enhancement, prompt manipulation and self analysis.
  • Techniques for identifying and managing hallucinations, including reinforcement learning based on feedback (RLHF) and model driven approaches.

04 Utilizing Language Models for Analyzing Federal Reserve Chairs Speeches

  • Reasons for selecting the BARD family over LLMs.
  • Assessment of BARDs performance.
  • Enhancing performance through embeddings.
  • Practical demonstration; evaluating sentiment scores on companies using embeddings.
  • Test data; Video recordings of the Federal Reserve Chairs press conferences.
  • Conducting an analysis of a trading strategy based on the sentiment analysis provided by an LLM.

05 Implementation of Language Models in Real world Scenarios

  • Practices for deploying LLMs in production environments.
  • Overview of models, like ChatGPT, BART, Cohere, Alpaca, etc.

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