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Local Models and Human AI Art Co-creation | Shahbaz Mansahia
In this episode, we dive deep into the world of local AI models and their creative applications with Shahbaz Mansahia, an ML engineer, an IEEE author, researcher and advocate for democratizing AI technology. Shahbaz shares his unique perspective on running AI models locally, making AI accessible to art students, and using technology to address representation gaps in art.
TOPICS DISCUSSED:
1. Local Models
Shahbaz explains how running AI models locally offers freedom from service providers while maintaining similar capabilities through quantization. He discusses the trade-offs between model size and performance, sharing insights about the future of 4-bit quantization and its potential for mobile AI deployment.
2. AI in Education
Making AI technology accessible to students presents unique challenges. Shahbaz discusses how open-source alternatives to commercial AI services can democratize access while maintaining quality, emphasizing the practical applications in academic environments.
3. AI and Artistic Creation
Rather than viewing AI as a threat to creativity, Shahbaz presents it as a tool for enhancing artistic workflows and democratizing expression. He shares his experience working with art students and using AI to address historical representation gaps in art.
4. Technical Implementation
The conversation covers practical aspects of local model deployment, including multimodality challenges and the evolution of hardware requirements. Shahbaz provides insights into the future of CPU vs. GPU computing for AI and the development of inference-optimized hardware.
INSIGHTS:
1. One can break dependency from any model provider with local models.
2. Foundation models work like the internet in your pocket.
3. Local deployment enables better privacy control for sensitive data.
4. We can amplify bias to extend the perceptions of artworks.
TOOLS AND TECHNOLOGIES MENTIONED:
- LM Studio
- Hunyuan Text-to-Video Model
- Comfy UI
- Dreambooth
CONTACT INFO:
- LinkedIn: Shahbaz Mansahia
- Email: [email protected]
CHAPTERS
00:00 Intro into Shahbaz's Background
02:40 Local Models and Their Advantages
05:57 Quantization and Model Performance
10:01 Practical Applications of Local Models
14:53 Multimodality and Future Developments
18:07 The Role of CPUs and GPUs in AI
20:57 AI in Art: Creativity vs. Automation
25:56 Bias in AI and Art Representation
32:02 Ethics of AI in Art and Representation
Follow us at AI Ketchup for bi-weekly stories of AI builders and founders turning ideas into successful tech products.
View all episodes
By Elina LesykIn this episode, we dive deep into the world of local AI models and their creative applications with Shahbaz Mansahia, an ML engineer, an IEEE author, researcher and advocate for democratizing AI technology. Shahbaz shares his unique perspective on running AI models locally, making AI accessible to art students, and using technology to address representation gaps in art.
TOPICS DISCUSSED:
1. Local Models
Shahbaz explains how running AI models locally offers freedom from service providers while maintaining similar capabilities through quantization. He discusses the trade-offs between model size and performance, sharing insights about the future of 4-bit quantization and its potential for mobile AI deployment.
2. AI in Education
Making AI technology accessible to students presents unique challenges. Shahbaz discusses how open-source alternatives to commercial AI services can democratize access while maintaining quality, emphasizing the practical applications in academic environments.
3. AI and Artistic Creation
Rather than viewing AI as a threat to creativity, Shahbaz presents it as a tool for enhancing artistic workflows and democratizing expression. He shares his experience working with art students and using AI to address historical representation gaps in art.
4. Technical Implementation
The conversation covers practical aspects of local model deployment, including multimodality challenges and the evolution of hardware requirements. Shahbaz provides insights into the future of CPU vs. GPU computing for AI and the development of inference-optimized hardware.
INSIGHTS:
1. One can break dependency from any model provider with local models.
2. Foundation models work like the internet in your pocket.
3. Local deployment enables better privacy control for sensitive data.
4. We can amplify bias to extend the perceptions of artworks.
TOOLS AND TECHNOLOGIES MENTIONED:
- LM Studio
- Hunyuan Text-to-Video Model
- Comfy UI
- Dreambooth
CONTACT INFO:
- LinkedIn: Shahbaz Mansahia
- Email: [email protected]
CHAPTERS
00:00 Intro into Shahbaz's Background
02:40 Local Models and Their Advantages
05:57 Quantization and Model Performance
10:01 Practical Applications of Local Models
14:53 Multimodality and Future Developments
18:07 The Role of CPUs and GPUs in AI
20:57 AI in Art: Creativity vs. Automation
25:56 Bias in AI and Art Representation
32:02 Ethics of AI in Art and Representation
Follow us at AI Ketchup for bi-weekly stories of AI builders and founders turning ideas into successful tech products.