Name: Machine Learning TO (MLTO) Monthly Meetup
Start: 2024-07-23T17:30:00-04:00
End: 2024-07-23T20:30:00-04:00
Location: OneEleven

After a great turn out for our inaugural meetup in June, we are excited to keep the momentum going with our next meetup for July!

This month we once will again be meeting at OneEleven, and are pleased to have two tech talks on large language models (LLMs) and generative AI around the theme of *open source*.

**Agenda**
5:30-6:30 PM Arrivals and Networking
6:30-7:00 PM Tech Talk #1
7:00-7:30 PM Tech Talk #2
7:30-8:30 PM Networking & Wrap-up
8:30 PM Tear-down and Departures

**Tech Talks**
Talk #1 - *Leveraging Open Source AI for Audio and Visuals* \- Arsham Eslami\, Co\-Founder & CTO @ Aida

This session delves into the exciting world of generative AI, showcasing how open source tools can revolutionize creative fields. We will explore foundational concepts of generative models, including GANs, VAEs, and Transformers, and demonstrate their application in generating audio, images, and videos.

Through live demonstrations, we'll illustrate the capabilities of cutting-edge open source models like Stable Audio from Stability AI for music, OpenJourney for image creation, and OpenSora for video synthesis. Attendees will witness the seamless integration of these modalities, culminating in a multimedia presentation that combines AI-generated audio and visuals.

The talk will also address the ethical implications of technologies like deepfakes, promoting responsible use of AI. Designed for enthusiasts, professionals, and anyone curious about the creative potential of AI, this talk will inspire and equip you with the knowledge to explore and experiment with generative AI tools on your own.

Talk #2 - *Open Source and AI: Exploring the Future with Retrieval-Augmented Generation* \- Ezequiel Lanza\, Open Source AI evangelist at Intel

Open source has long been a mandate for software release, ensuring transparency, collaboration, and innovation. The impact of open-source principles on AI development is significant, as it fosters community-driven improvements and accessibility. This talk will explore the implications of open source in AI, particularly as AI models continually evolve.

One of the critical areas of focus will be the Retrieval-Augmented Generation (RAG) approach. RAG combines LLMs with external data retrieval mechanisms to enhance the accuracy and relevance of AI responses. This hybrid method leverages the strengths of both pre-trained models and dynamic data sources.

In this talk, we will delve deeper into the technical aspects of RAG, showcase practical implementations, and discuss the future potential and challenges of integrating open-source principles with AI advancements.

**About the Speakers**
[Arsham Elami](https://www.linkedin.com/in/arsham-eslami/) is a serial entrepreneur with experience in software development, XR, AI, signal processing, and mechanical design. As CTO at Skatescribe Corporation, he fostered a product-first culture, steering the company to find product market fit with the NHL; at Aiva Labs, he led a team of developers, implementing cutting-edge machine learning algorithms and a multi-cloud strategy for seamless scaling; and recently built out an AI assistant to help data teams automate their data requests.

[Ezequiel Lanza](https://www.linkedin.com/in/ezelanza/) is an AI open source evangelist at Intel. He holds an M.S. in Data Science, and he’s passionate about helping people discover the exciting world of artificial intelligence; Ezequiel is a frequent AI conference presenter and the creator of use cases, tutorials, and guides that help developers adopt open-source AI tools.

The space was for this event has been generously facilitated by [OneEleven](https://oneeleven.com/) and [Boast.ai](http://boast.ai). Refreshments provided by [Covalent](https://www.covalent.xyz/).

Myles

Mario Gibney

MLTO: Machine Learning Toronto

Technology

Artificial Intelligence

Big Data

Machine Learning

Data Analytics

Data Visualization

Predictive Analytics

Data Mining

Internet of Things (IOT)

M2M Machine-to-Machine

Data Science

Machine Learning TO (MLTO) Monthly Meetup

AI and Society

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MLTO: Machine Learning Toronto

Machine Learning TO (MLTO) Monthly Meetup

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