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Summary

The meeting discussed the application of AI in various fields, the main contents included:

• Speech recognition: Wilson Wang introduced the challenges and solutions in speech recognition, including the use of proximity addressable memory for efficient processing.

• Data catalog and LM agent: Hao Xu presented the data catalog and LM agent, which can help users query data and interact with LM.

• Conversational assistance interoperability: Deborah Dahl talked about enabling voice and conversational AI systems to function like the web, and the Open Voice Interoperability Initiative.

• Sound alike errors: Wilson Wang focused on the problem of sound alike errors in speech recognition and how to fix them.

• Intermediate stage in SLM: Wilson Wang emphasized the need for an intermediate stage before the SLM to identify likely possible sound like substitutions.

Chapters

00:00 Meeting Kickoff

Meeting Kickoff

01:34 Challenges and Solutions in Real-Time Speech Recognition with AI and Energy Efficiency Considerations

This section discusses speech recognition challenges, especially sound alike errors and precision issues. It focuses on post-processing with AI, using small language models and proximity addressable memory for accuracy. It also covers hardware requirements, energy efficiency concerns, and the status of ongoing efforts and potential future developments.

24:14 Challenges and Approaches in Context Processing and Security for First Responders' Devices

This section discusses challenges and approaches in language processing for various scenarios like factories and first responders. It considers the need for context base processing, testing in different conditions, issues of security and privacy, authentication, and the use of models like Lama 2. The conversation also touches on potential solutions and the next steps for testing and implementation.

32:37 The Project of Using LM Agent to Address Data Query Issues in the Team

This section is about a presentation by Hao Xu representing the item team. They have around 4.5 people working on the project. The team is addressing the issue of numerous ad hoc data-related queries and exploring using an LM agent to assist. They avoid giving raw data to third-party vendors due to privacy concerns and are building a system with three major components for processing and answering queries.

40:52 Overview of Components and Features in Data Query and Processing Framework

This section covers various aspects including direct data query methods, use of data frame APIs and Linux projects for metadata. It discusses the core component agent controlling aspects like prompts and tools. Workflow customization, built-in tools, prompt templates, vector stores, data catalogs, and their relationships are also mentioned.

49:49 Overview of Hao Xu's Project and Call for Contributions

This section covers the use of Python and various technologies for building an application. It mentions an integrated notebook environment for query modification and interaction. Three teams are currently using ITA. The project is in an early stage, lacks cloud deployment, needs robust testing and automation. It was recently renamed and those interested can reach out. There are no questions today and a next person introduction is planned.

56:32 Interoperability of Conversational AI Systems and Ongoing Work

This section is about Deborah Dahl from Conversational Technologies discussing the Open Voice Interoperability Initiative. She explains the aim to enable collaboration among conversational AIs, reducing complexity and duplication. Different use cases are mentioned, and the focus is on developing interoperability standards. Messages and capabilities for chatbots are detailed, along with ongoing work like enabling teams, providing context, and ensuring data security. She invites feedback and participation.