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The meeting discussed the development and application of AI in various fieldsartificial intelligence, as well as related technologies and challenges, 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 substitutionsEdge Lake: A software enabling AI at the edge, with features and potential applications.

• Graftino: A unified metadata layer to handle data silos in the AG era.

• Language models: Used in identifying drivers of political polarization and reducing it through deliberative polling.

• Interoperable chatbot: Its features, ability to participate in teams, and handling of security concerns.

• AI in data analysis, speech recognition, and intelligent assistants: Research results and experiences shared.

• Challenges and solutions in AI development: Such as data privacy, model performance, and energy consumption.

• Open source projects and initiatives: To promote the development and application of AI.

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

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03:45 Edge Lake: Enabling AI at the Edge and Addressing Challenges

This section is a meeting transcript mainly discussing issues related to Lark, including someone's inability to join, the use of AI transcription feature, and account purchase. It also covers an overview and demonstration of Edge Lake, a platform for managing data at the edge, along with questions on data protection and handling single point of failure.

33:31 Edge Lake: Enabling AI and Federated Learning with Virtualization and Security

This section discusses Edge Lake, its use of blockchain for virtualization and metadata sharing, enabling synchronized operation of nodes. It covers Edge Lake's role in AI, including data collection, model generation, and distribution, comparing traditional and federated learning. Security and challenges of federated learning are also addressed. The presentation is a work in progress, welcoming input and contributions.

55:39 Discussions on Various Technical and Operational Issues in TikTok and Related Fields

This section contains a somewhat chaotic and diverse discussion. It covers topics such as breaks, TikTok, data and security issues related to AI products, login problems, various musical mentions, and scattered remarks about different technologies and experiences. The conversation is fragmented and lacks a clear central theme.

01:14:06 Introducing Graptino: A Unified Metadata Lake for Data Management

This section focuses on the introduction of Graptino, a unified metadata lake project. It discusses the evolution of data management architectures and the challenges they pose. Graptino aims to unify metadata to solve data silo problems, support various data types and scenarios, and is presented with its architecture, features, usage scenarios, and community aspects. Different from similar projects, Graptino is an open source solution with a focus on real-time operation and a distinct scope.

01:57:02 Research on Reducing Political Polarization with AI and Deliberative Polling

This section mainly focuses on Josh Joseph's presentation on using AI tools to address political polarization. He explains the concept of deliberative polling and its challenges, presents a new model with Bert and GPT-4 components, shows its training and results, and discusses applications and future work. It also covers issues like data analysis, synthetic noise addition, and potential expansions to other domains.

02:31:40 Discussions and Thoughts on Various Topics

This section is a rather chaotic and diverse discussion involving lunch preparations, presentations, various topics such as TikTok, personal experiences, and random thoughts. Participants like Wilson Wang and others contribute sporadically with no clear central theme or concrete decisions reached.

03:11:06 Discussions on AI-related Projects and Technologies

This section contains discussions from multiple speakers on various AI-related topics. It includes talks about Grog's focus on inference part in AI, Jose's presentation on its capabilities, and alivedaru's introduction to the Open Platform for Enterprise AI project with its components, challenges, and ways to get involved like hackathons. There were also questions and exchanges on related aspects.

03:56:20 Challenges and Solutions in Real-Time Speech Recognition for Robotics and First Responders

This section is mainly about Signal Logic's participation in various projects related to speech recognition for robotics and edge computing. It discusses challenges such as sound alike errors, background noise, and the need for real-time processing without relying on the cloud. The company is working on post-processing and using reduced language models to improve accuracy, and they are exploring different hardware and algorithms to meet functional requirements.

04:33:33 Updates on ETA and Conversational Technologies in Data and AI

This section features presentations from Wilson Wang and Zebra Doll. Wilson Wang represents the ETA team, discussing their project addressing data-related challenges and the use of LM agents. Zebra Doll presents on the Open Voice Interoperability Initiative, aiming to enable seamless interaction among conversational AI systems and outlining their progress and future work. There were also technical issues and limited questions.

05:19:06 Various Topics and Experiences Shared

This section contains various remarks from different speakers. It mentions the happy hour at 4:30 and driving directions. There are also discussions about events, TikTok connections, work experiences, and the growth of a company from 29 to over 25 people. Speakers share diverse and somewhat disjointed information.