Background and Motivation:
Emad Mostaque, Stability AI’s founder and CEO, expressed his desire to use AI to improve the world, particularly through education and drug discovery. Recognizing the potential of AI during the COVID-19 pandemic, he witnessed the power of transform-based architectures like attention-based systems in extracting principles and latent spaces.

Involvement with Open Source AI:
Mostaque joined the EleutherAI community, contributing to the development of GPT-Neo and GPT-J, which have seen significant adoption among developers. Frustrated by the lack of access to cutting-edge models for his COVID-related work, he saw an opportunity to organize and extend the resources available in the open-source AI community.

Stable Diffusion and Funding Open Source AI:
Mostaque’s daughter’s experience with image generation sparked his interest in making the technology accessible to everyone. He provided funding for various image generation projects, including Mid-Journey and collaborations on Google Colab, with the aim of supporting the growth of the open-source AI community. Mostaque recognized the potential for a sustainable business model based on open-source software, where commoditized models and open datasets could serve as new programming primitives. This approach could generate revenue through scale and service, funding open-source AI initiatives and enabling the development of models for diverse languages and regions.

Latent Diffusion and Collaboration:
Stability AI’s involvement with latent diffusion, which evolved into stable diffusion, stemmed from the company’s funding efforts within the open-source AI space. Lead coder Catherine Krausen, along with other developers, made significant contributions to the project. Collaborating with the LMU team, Stability AI combined the efficiency of latent diffusion with their own advancements, leading to the development of a “good enough, fast enough, cheap enough” model. Mostaque emphasized the collaborative nature of open-source projects, which allow for the participation of a broader community and the creation of infrastructure accessible to all.

Key Improvements and Modality Fusion:
The key improvement in image generation models, including Dali and Imogen, was the fusion of a language model with an image model, allowing for faster and higher-quality results.

Open Collaboration and Community Involvement:
Stability AI fosters open collaboration and community involvement to drive innovation and breakthroughs in AI research. The company employs full-time stability employees as a core, collaborates with regular academic partners, and funds PhDs to expand its research capabilities. Aiming for full multimodality, Stability AI strives to create a psychologically safe environment where developers and researchers from diverse backgrounds can contribute and collaborate effectively.

Developer Empowerment and Revenue Sharing:
Stability AI values the contributions of its developers and offers them revenue-sharing opportunities. Every developer can open-source anything they create, and 10% of the revenue from models run by Stability AI is shared with developers. Half of the revenue share goes to a pool allocated to fund innovative research, while the other half goes to the model authors, even if they don’t work at Stability AI.

OpenBioML Community and Partnership:
Stability AI’s OpenBioML community boasts 3,000 members, fostering open collaboration and innovation in the field of bioinformatics. LibraFold, a recent project, is a testament to the power of open collaboration, bringing together academic stability and other partnerships.

Business Model and Monetization:
Stability AI is exploring various business models and commercialization strategies, recognizing the importance of monetization for sustainable growth. The company is open to different approaches, including partnerships and revenue-sharing models, and aims to find a sustainable path forward.

Stable Diffusion and Dream Studio:
Stable Diffusion and Dreambooth are being used by content providers to create custom models, resulting in significant revenue for Stability AI. Dream Studio, with its advanced features like animation and fine-tuning, is gaining popularity and has attracted 1.8 million users without advertising.

Open Source vs. Closed Source:
Open source will always lag behind closed source due to the advantage of extending and improving upon open-source models. The focus on models and talent as competitive advantages is misguided as the amount of investment and talent entering the generative media space will eliminate those advantages.

The Future of Open Source and Closed Source:
In five to 10 years, generative media will be ubiquitous, and there will be a place for both open source and closed source, similar to the relationship between Oracle and MySQL. The standardization of open frameworks and the creation of benchmark models for different countries are essential to ensure diversity and localization.

Globalization of AI:
AI safety considerations should be viewed from a global lens, addressing AI alignment, offensive content, and political policies. AI alignment is crucial, and diverse AI systems that augment human capabilities are less likely to pose existential threats. Offensive content moderation should consider the cultural and societal context to avoid orthodox and narrow views.

Regulation and Control:
Emad Mostaque expresses concern that generative AI might fall into the hands of unelected private companies, leading to a monopoly and undemocratic control. Open-source AI is challenging to regulate as it continually requires improvement for safety and security, making it difficult to release under an open-source license.

Geopolitics and Deepfakes:
The potential misuse of generative AI for creating high-quality deepfakes is a concern. The arms race between bad actors and countermeasures is likened to the regulation of cryptography in the 1990s.

Government Regulation:
The European Union is at the forefront of AI regulation, focusing on user liability for model usage, even for academics. There is a push to regulate large language models due to their unknown dangers and potential for misuse.

Alignment and Safety:
Stable Diffusion is seen as a precocious kindergartner, while larger models like the 4.3 billion parameter image model would be more like a high schooler in terms of their capabilities. Regulating large language models, insisting on diverse data sets, and establishing an intergovernmental agency are proposed as alignment measures.

Infrastructure for Combating Misuse:
Building infrastructure to combat deepfakes and malicious uses of generative AI is crucial, including initiatives like contentauthenticity.org to verify the authenticity of generative content.

Open Discussion and Wider Perspectives:
Regulation is not inherently opposed, but it should involve proper democratic discussions with a wide range of experts and stakeholders, not just a few people in Silicon Valley.

Future Applications:
Generative AI has the potential to enable “what if” scenarios, such as creating hypothetical sports matches or remaking TV shows. Custom models and fine-tuning will allow individuals, companies, countries, and cultures to create their own unique AI experiences.

Key Technology Directions:
Alternatives to transformers: Emad Mostaque believes there are promising architectures that could challenge the dominance of transformer models.

Data Quality and Structure:
The “Conchilla” paper by DeepMind suggests that scaling alone is not the key to better language models. Instead, structured and high-quality data is crucial.

Edge Computing and Small Models:
Emad Mostaque emphasizes the importance of small models that can run on edge devices, enabling personalized and accessible AI for individuals.

Globalizing AI:
National models and communities are being fostered to democratize AI and make it accessible to diverse countries and languages.

Short-Form Video:
The combination of diffusion and VAE-based models, along with the chaining of various AI models, has the potential to generate high-quality short-form videos within two years.

Value Capture and Startups:
The first wave of AI primarily benefited incumbents due to the lower complexity of AI models. However, the new generation of AI, such as transform-based architectures, offers unique programming primitives that startups can leverage to capture value.

Democratization vs. AGI:
Stability AI and other companies focus on AGI as their end goal, while Emad Mostaque and his team prioritize democratizing AI technology.

AGI and Regulation:
Emad Mostaque expresses concern about the potential dangers of large AI models, particularly structured models, and the need for regulation to mitigate risks.

Mission Statement:
Their company’s mission is to build the foundation that activates humanity’s potential, especially in high-ROI areas like India and Indonesia.

Market Creation:
Emad Mostaque believes the market creation potential of disruptive AI innovations is immense and will lead to significant growth and opportunities.

Generative Search Engines:
Generative search engines will combine image, language, audio, and video elements. They will displace traditional jobs such as image search and language translation.

Short Form Video:
Short form video is the next step for generative search engines. It combines language, video, and audio elements.

Exponential Growth of ML:
The development of machine learning is growing exponentially. It is difficult to keep up with the rapid advancements.

Crypto and AI:
Crypto can play a role in AI by providing a secure store of identity. This is important in a world with infinite content. The combination of individualized identity and AI is powerful.

Standardizing Architectures:
Standardizing architectures for AI models can lead to interesting applications. This can include value exchange from a classical information theory perspective.

Surprises in the Space:
The ability of people to contribute in an open manner has been surprising. The excitement and activation energy of developers has been unexpected. The potential for breakthrough innovations from unexpected sources is high.

New Content and Social Networks:
It is uncertain whether this technology will lead to new content or social networks. It is possible that the content will reside on existing platforms.

Generative AI’s Disruption of Social Networks:
Generative AI will disrupt social networks by moving intelligence from centralized advertising models to personalized AI models at the edge. Apple’s shift towards becoming an AI company is a strategic move to control the flow of information and provide personalized experiences. The decentralization of AI will eliminate the need for centralized platforms and enable direct communication between AI agents.

Generative AI for a More Direct and Digital Democracy:
Generative AI can help break filter bubbles and promote more direct communication by allowing individuals to create their own AIs. Standardized AI systems can enable translation between different realities and facilitate communication across diverse perspectives. Filter bubbles are not entirely accurate; there is also a lack of bridges between different groups of people.

The Challenge of Fragmentation vs. Cohesion:
Personalized and fragmented media can lead to further division and divergence among individuals. The design and implementation of generative AI systems will determine whether they promote freedom and personalization or create a panopticon-like surveillance state. Organizations and governments, as slow and dumb AI systems, tend to optimize based on their own interests rather than the needs of individuals.

The Need for a Collective Effort and Optimism:
The development of generative AI presents a fork in the road, with the potential for either positive or negative outcomes. A collective effort is required to ensure that generative AI is used for the benefit of society and to prevent the enhancement of existing problems. A forcing function may be necessary to promote openness and democracy in the face of powerful AI systems.

B Corp Status:
EleutherAI is a private company with full control. Applying for B Corp status to adjust to a mission-based structure. Eleuther and other research organizations are spinning off into foundations with independent governance. B Corp is the closest to a mission-based organization structure they could adopt, serving as a formal designation.

Investment Round:
EleutherAI did an investment round in August. No independence was given up, as money often comes with strings. Investors are open-source, AI-aligned, and founder-aligned, ensuring continued freedom to pursue projects.

Potential Industry Disruptions:
Call center workers may be the first to be disrupted. Language modalities and retrieval-augmented models are as good as call center workers. Artists may not be disrupted, as new forms of art will emerge. PowerPointing and visual communication may be disrupted in the next couple of years. Tedious make work will be reduced, requiring fewer entities. Legal and other fields are also seeing disruptions.

Unleashing Potential in Developing Countries:
EleutherAI’s technology has great potential for high-potential young people in developing countries like India and Indonesia. Governments should endorse this technology to: Create new job opportunities. Balance out job losses caused by AI disruptions. Promote economic development and innovation.

Accessibility of AI Tools for Everyone:
Emad Mostaque emphasizes the importance of providing access to AI technology to everyone, especially in developing countries. He believes that the current paradigm restricts access to AI via APIs, limiting customization to local cultures and contexts.

Educational Initiatives:
Repl.it’s charitable arm is deploying education tablets in refugee camps, teaching literacy and numeracy in a short time frame. Such initiatives lay the foundation for integrating applications like Repl.it, which empower young people to create interactive experiences. Once AI becomes a fundamental primitive, it will benefit even the most disadvantaged people, providing them with agency.

Global Participation and Economic Opportunities:
Elad Gil highlights the global participation observed in talent marketplaces, with contributions from unexpected places like Pakistan. AI tools can augment the capabilities of those already participating in the global economy, expanding their range of possibilities. Unleashing creativity and innovation can lead to diverse applications, from SME shop labels to copywriting and legal support.

Trust in AI-Powered Learning Systems:
Emad Mostaque acknowledges the lack of access to interactive teachers in certain regions. He cites positive results from basic AI feedback loops in low-income environments and refugee camps. He envisions a future where humans and AI form positive friendships, providing personalized education optimized for individual objectives.

One AI per Child Initiative:
Mostaque proposes the “One AI per Child” initiative, aiming to provide personalized education tailored to each child’s needs. He believes this approach can address the challenges of limited teachers and resources in many educational settings.

Licensing for Stability Models:
Stability generally uses Apache or MIT licenses for their models, except for Stable Diffusion 1.4 and 1.5 which were released under the CreativeML OpenRail license. The CreativeML OpenRail license requires ethical use and includes a safety filter.

Releasing Safer Work Models:
Stability plans to release only safer work models in the future. This decision was made after discussions within Stability’s developers.

Stable Diffusion Foundation:
Stable Diffusion 1.4 and 1.5 were released as a collaboration. The Stable Diffusion Foundation will be set up soon to handle these models.

Stability’s Role in Open Source AI:
Stability believes that open source AI should not be controlled by any single company. The foundation model will accelerate progress in this area and incorporate input from the community.

Democratech and Open Source:
Stability supports the idea of Democratech, where people have a say in the development of open source technologies. The company encourages people to make their voices heard in shaping the future of open source AI.

Open Source Text-to-Text Generation Models:
No information was provided regarding Stability’s plans for releasing an open source text-to-text generation model.

Alutha AI Releases GPT-Neo J and Neo X Models:
Alutha AI, supported by Google, released the GPT-Neo J and Neo X models, which are being used by developers. These models can be used for various tasks, including language generation, translation, and summarization.

Copper AI Lab Releases Instruct Model Framework:
Copper AI Lab, a Google research lab, released the Instruct model framework, allowing users to reduce the size of large language models to 20 million parameters. This enables the use of large language models on smaller devices and for applications with limited computational resources.

Google’s Support for Language Model Development:
Google provides thousands of GPUs to support the development of language models. The company has released language models for various languages, including Korean and English. Google believes that the combination of these advancements will lead to powerful applications.

Concerns about Copyright Law and AI-Generated Art:
The use of AI-generated art has raised concerns among artists about the potential infringement of their copyrights. Emad Mostaque, a Google researcher, acknowledges these concerns and the complexity of copyright law in this context. He highlights that only a small percentage of the data used to train AI models is from artists, and that the models cannot generate art in a specific artist’s style unless explicitly instructed to do so. Google is exploring mechanisms to attribute AI-generated art to the artists whose work was used in training the models.

Compute Centralization and Google’s Approach:
The compute landscape for deep learning is highly centralized, with a few companies dominating the market. Google is evaluating various architectures and partnerships to address this centralization. The company is also working on optimizations at the hardware kernel level to improve efficiency. Google supports alternative approaches to centralized compute, such as distributed training, to provide more options for users.

Google’s Focus on Training AI Models:
Google’s focus is on training AI models and making them accessible to users, rather than creating multiple versions of the same model. This approach allows users to leverage the pre-trained models without having to expend additional energy and resources on training.

Paradigm Shift in Research Computing:
Emad Mostaque highlights the paradigm shift in research computing, moving from individual researchers using their own computers to a national research cluster that can be accessed by any university.

Filling the Gap:
Mostaque emphasizes the need to fill the gap in research computing resources for academics, enabling them to conduct large-scale research projects that were previously infeasible.

Building a National Research Cluster:
Mostaque describes the successful establishment of a national research cluster that eliminates the gap in resources for academics, allowing them to conduct advanced research.

Scaling Up:
Mostaque shares plans to scale up the national research cluster by five to ten times in the next year, further enhancing its capabilities and capacity to support even more research projects.

Packed Room and Appreciation:
Mostaque expresses gratitude for the packed room of attendees, demonstrating the strong interest in the national research cluster initiative.

Stability Story:
Elad Gil shares the stability story of the national research cluster, both in terms of its past performance and future prospects, showcasing its reliability and resilience.

Thank You:
Mostaque extends his thanks to Notion for hosting the event, Ahmad for sharing the stability story, and the Ocean Team for their contributions to the national research cluster project.