Stability AI’s Mission:
Stability AI’s mission is to build a foundation to activate humanity’s potential and make people happier. They believe open-source AI models are essential for closing the digital divide and getting AI out as widely as possible.

Stability AI’s Work:
Stability AI catalyzes the building of open-source AI models and scales and customizes them for customers. They’ve helped support communities like Luther and Lyon and are now focused on commercial open-source software of scale and service. They’ve also funded the entire open-source art space.

Stable Diffusion:
Stable Diffusion is the first model that is good enough, fast enough, and cheap enough that anyone can run it. It’s a two-gigabyte file from 100,000 gigabytes of data. This has allowed it to go off massively.

Collaboration with Developers:
Stability AI works with developers in various ways, including providing computer infrastructure support, employment, benefits, and equity. They also collaborate with academia and non-academia independent researchers. They’re building an entire infrastructure to be able to scale and train AI models.

Revenue Sharing:
When Stability AI does inference on any open-source model, they put aside 10% of the revenue from that for the developers. 5% goes into a community pool that developers can vote to allocate to research, and 5% goes to the developers themselves.

Challenges:
One challenge is that Stability AI wants to manage the whole community, but that’s not how open-source works. Another challenge is that there are many different things in open-source AI, and it’s difficult to have many contributors like in traditional open-source software.

Funding for Academia:
Stability AI supports academics by providing access to compute resources and infrastructure. This is done through community membership, grant-making portals, and collaborations with universities. The goal is to fund 100 PhDs in the next year, with dedicated compute support for their labs and projects.

Infrastructure and Open Access:
Stability AI is working to improve and standardize AI infrastructure. They encourage researchers to use tools like Weights and Biases to track their runs and promote open access to research. The company is also building large cloud platforms to allow researchers to unlock resources from day one.

Application Process:
Researchers can apply for compute support by joining the Homelive, Luther, Ion, or other relevant communities. Active members of these communities are more likely to receive support, especially if they bring in team members. A grant-making portal is also being set up for researchers to apply for funding.

Open Source and Closed Source:
Stability AI encourages open source research, but also recognizes the value of closed source models. Benchmark models should be open source to ensure that everyone has access to the latest advances. Closed source models can be used for commercial applications and to fund further research.

Business Perspective:
From a business perspective, media generation is the most lucrative area for AI research. This can fund other areas of research, such as audio, video, and other content generation. Stability AI’s goal is to focus on media generation to fund everything else.

Challenges in Quantifying the Impact of AI:
Measuring the impact of AI, especially from a societal perspective, is difficult due to the complexity and multifaceted nature of the technology. The focus on business perspectives often overshadows the impact on society, leading to a lack of comprehensive understanding of the true value of AI.

Public Good and the Need for a National Research Cloud:
The potential of AI as a public good requires a broader discussion and collaboration among stakeholders, including universities and research institutions. The creation of a National Research Cloud, as proposed by Stanford and others, could provide a platform for U.S. and international universities to access and utilize AI resources for research and education.

The Significance of Image Generation Models:
Image generation models have seen a significant leap in performance, moving from 10% accuracy to 80-90% accuracy in a short period. The impact of these models is more immediate and tangible for people compared to other AI applications, leading to increased attention and excitement.

The Changing Nature of Value in AI Models:
As AI models become more versatile and capable of performing various tasks, the value proposition of these models shifts from the models themselves to other aspects. The value may lie in creating new experiences, enabling access to models, and developing tools that facilitate the use and integration of AI models.

Creating a Sustainable Business Model:
Given the changing nature of value in AI models, businesses need to explore new strategies for creating long-term sustainability. One potential approach is to focus on content creation and helping companies transform their assets into interactive experiences using AI. Developing software tools that empower users to access and leverage AI models can also be a viable business strategy.

Key Differentiators for Growth:
Emad Mostaque emphasizes the importance of the infrastructure layer, APIs, and cost reduction for the growth of generative AI.

Sustainable Edge:
A mixture of content and experience is essential for a sustainable edge. A deal with Eros in India will enable Bollywood asset conversion and Bollywood music generation using text-conditioned audio models.

Commercialization Model:
The commercial episode software model is well-established. Training and fine-tuning models are challenging and require regular retraining.

Bollywood Application:
The initial focus is on basic TikTok and Snapchat-type applications. Bollywood assets will be used internally to save production costs. Future applications will include the creation of entire audio and movies using generative AI.

Research and Commercialization Split:
Open source researchers have the freedom to publish their work and experiment with different approaches. Product and deployment teams focus on commercialization and customer solutions. Research and product teams collaborate without forcing functions to ensure proper research.

Open Source Models and Competitors:
Emad Mostaque welcomes the release of more open source models to grow the community and support development. Stability Diffusion’s success is seen as a positive development, and Emad Mostaque has offered assistance to address issues. The potential displacement point lies in another company replicating Stability’s approach and decentralized structure.

Future Role of Stability AI:
Stability AI will transition from a catalyst for open source initiatives to a services company. Indian and Filipino models and large content providers will be a focus. The ultimate goal is to create an AI platform that makes AI easy and accessible for everyone.

Models in the Cloud:
Cloud-based models will continue to grow in size. Smaller versions of models will be available for ease of deployment and cost. Data quality will become essential for model development. The use of multiple models with specific strengths will be key.

Scaling and Data Quality:
Emad Mostaque believes that various models for various tasks and personalized models are better than a single all-purpose model. Data quality and model usage for instruction will become increasingly important. Scale is crucial, and compute availability will allow for extensive scaling.

Model Size and Performance:
Contrary to current trends, Emad Mostaque predicts that AI models may become smaller. Examples of smaller yet performant models like struct GPT and T5 Flan are cited. The effectiveness of big neurons and the necessity of extensive data are questioned.

Image and Text Model Comparison:
The significant difference in size between image and text models is puzzling. Stable diffusion, with just 890 million parameters, outperforms larger language models. The minimum number of images and texts required for stable diffusion-quality output is unknown.

Optimizing Data and Model Scaling:
Optimizing data may lead to smaller and more efficient models. Stable diffusion was trained on a large but unfiltered dataset. The relationship between data size, model size, and output quality needs further exploration.

Resource Allocation and Compute Predictions:
Lukas Biewald expresses surprise at the predicted decrease in model size. Emad Mostaque’s earlier insight was that massive compute is necessary for broad accessibility. The potential impact of smaller models on resource allocation and compute requirements is unclear.

Data Quality and Compute:
Emad Mostaque emphasizes the importance of data quality for training models, arguing that it has been underestimated compared to compute. Compute is no longer a significant bottleneck, as clusters with significantly more compute power than the one used to train GPT-3 are being built.

Optimal Models:
The focus is shifting from large models to optimal models tailored to specific tasks and data. Standardized models will be customized to create a variety of optimal models.

Public Datasets:
The acquisition and creation of public datasets are crucial for open source research. Collaborations with national broadcasters and other public institutions are pursued to obtain high-quality, structured data. The goal is to make these public datasets available to researchers and eventually to a broader audience.

Media and BioML Datasets:
Acquiring and creating datasets in media, bioinformatics, and other domains is a priority. Examples include national broadcaster data, protein folding data, and image datasets like Lion. The aim is to replace lower-quality scraped data with high-quality public datasets.

Time Series and Structured Data:
Research in time series analysis and structured data is not a primary focus for the foundation models being developed. Foundation models are viewed as more suitable for principle-based analysis, similar to the brain’s processing.

Foundation Models and the AI Index Report:
Foundation models are the focus of 80% of AI research, according to the AI index report from Stanford. There is extensive funding for foundation models, particularly in the areas of media and language.

Transformers and Their Dominance:
Transformers are currently the primary architecture for many AI applications due to their momentum, talent, and commonality. They have demonstrated success in various tasks such as image generation and text-to-image synthesis.

Intentional Free Transformer Research:
There are promising developments in architectures beyond Transformers, such as intentional free transformers. Research in this area is ongoing, with projects like RWKB exploring language model possibilities.

Innovative Approaches to Image Generation:
Recent breakthroughs in image generation have combined generative models, language models, and diffusion-generated modeling. These methods have enabled the creation of more coherent and realistic images.

Exponential Growth in ML Research:
The number of papers on ML is doubling every 24 months, indicating exponential growth in the field. This rapid progress suggests that new and innovative approaches are likely to emerge in the near future.

Unpredictable Applications of AI:
The usefulness and value of AI applications are still largely unknown. Despite the focus on media generation, it is uncertain where the most significant applications will emerge.

Communication Revolution:
Generative AI, particularly language and image models, is transforming human communication. Visual communication, traditionally challenging for our species, is becoming more accessible, enabling new forms of expression and creativity. The ease of creating visual content, like memes, empowers individuals to communicate more effectively and creatively.

Impact on Textual Communication:
Generative AI, such as Copy.ai and PseudoWrites, has simplified text-based communication. Language models offer suggestions and enhance writing quality, making it more efficient and effective.

From Consumption to Creation:
Generative AI shifts the focus from AI-driven targeted advertising to creation and expression. This change creates opportunities for individuals to engage in creative pursuits, such as visual communication.

Structured and Unstructured Data Integration:
Generative AI allows for seamless switching between structured and unstructured data, providing human-level understanding. Retrieval augmentation and factual accuracy verification enhance the reliability of AI-generated content.

Broad Applications and Value:
The versatility of generative AI has the potential to revolutionize various fields, including report writing, legal processes, and bureaucracy reduction. Its general applicability makes it difficult to predict where the most significant value will be realized.

Open Access to Models:
Emad Mostaque’s decision to make all his models openly accessible has democratized access to generative AI. This contrasts with the more restrictive approach of OpenAI and others, which created a sense of exclusivity and limited access.

Ethical Concerns and Model Openness:
Arguments against releasing large language models due to ethical concerns are similar to those of orthodox and ultra-orthodox religions, where anything leading to a sin is considered sinful. Regret minimax approach minimizes the maximum regret if something goes wrong due to model release. Bad actors already possess the technology, and regulation against misuse exists.

Small vs. Large Models:
Small models being widely used and community-regulated are less problematic than large models. Large models, due to their power and lack of oversight, pose potential risks that warrant more scrutiny.

Openness and Regulation:
Open models allow for public scrutiny and discussion, facilitating regulation against misuse. Closed models lack control and regulation, making it difficult to address potential issues.

Community Norms and Attribution:
Attribution mechanisms need to be developed to ensure proper credit and avoid co-opting of others’ work. Ethical, moral, and legal aspects need to be considered when addressing the use of copyrighted or individual-specific content.

Community Governance:
Deep disagreements within the community occur due to diverse moral values and differing lines of acceptability. Governance structures are being explored to address these disagreements, such as Luther AI, which aims to create an independent community. Adaptive governance structures are necessary given the evolving nature of this technology and its applications.

Control over Technology:
No single entity should have control over the technology of large language models, which should be considered a common good.

Stability.ai’s Approach to Open Source AI Models:
Stability.ai aims to contribute to open source generative AI models rather than control them. They believe that disagreements in the community will lead to forks of the models. Fine-tuning models is less computationally expensive than training them from scratch. Stability.ai is working to create a more diverse and decentralized ecosystem for AI models.

Challenges in Coordinating a Large Community of AI Model Users:
Coordinating a large community of AI model users can be challenging, especially as the number of users grows. Stability.ai believes that an independent entity should be responsible for coordinating the community and ensuring that all voices are heard.

Stability.ai’s Vision for the Future of AI Models:
Stability.ai envisions a world where a vast majority of the world’s population uses AI models. They believe that thousands of developers will create models adhering to standards set by various communities. These models will be regularly released and maintained by an independent entity. Stability.ai sees a future with AI at the edge, in the cloud, and a hybrid interaction between the two. They emphasize the importance of a multimodal approach to AI, as learnings and value can come from various modalities.

Education in the Context of Stability.ai:
Education is a passion for Stability.ai, and they believe it plays a crucial role in the development of AI models. Stability.ai aims to make AI accessible to everyone, regardless of their background or location. They are working to create educational resources and programs to help people learn about and use AI models.

Technology for Children’s Education:
Emad Mostaque prioritizes children’s rights and well-being, emphasizing the need for ethical considerations when developing AI technology. He envisions a future where children have access to AI tools that empower them to create, code, communicate, and learn effectively. Mostaque highlights the potential of AI-powered educational models that can adapt to individual learning styles and provide personalized instruction at scale.

AI in Education:
By leveraging AI, educational experiences can be standardized and improved globally, ensuring that all children have access to quality learning resources. AI models trained on diverse datasets can capture cultural contexts and provide culturally relevant learning experiences. Mostaque proposes the concept of “one AI per child,” a personalized AI assistant that guides students through their educational journey.

The Importance of Open Source Standards:
Mostaque emphasizes the importance of creating open-source educational standards that can be scaled and adapted to different regions and cultures. He believes that open-source platforms can ensure that AI-powered educational tools are accessible to all, especially in underserved communities.

Balancing Public Good and Private Interests:
Mostaque advocates for a balance between public goods and private interests in the development of AI technology. He suggests that tools that empower creativity, education, and other fundamental aspects of human development should be considered public goods. He cautions against allowing private companies to control these tools, while also recognizing the limitations of bureaucratic systems.

Focus on Emerging Markets:
Mostaque believes that the greatest impact of AI-powered educational technology can be achieved in emerging markets, where the need for quality education is most pressing. He emphasizes the importance of tailoring AI models to the specific needs and contexts of these regions.

Building a Stable Infrastructure for the Future:
Mostaque envisions a stable infrastructure built around AI technology that can provide a foundation for future generations. He believes that investing in AI-powered education now can create a better system that benefits everyone in the long run.

AI for Everyone:
Emad Mostaque emphasizes the importance of accessible AI, focusing on emerging markets and creativity. He aims to close the digital divide and improve lives, rather than pursuing generalized AI.

Specific Intelligences:
Mostaque believes in developing specific intelligences widely available to bridge the gap between general-purpose AI and specific applications.

Edge Intelligence:
The trend is shifting towards edge intelligence, with reinforcement learning and deep learning combining to create more intelligent models.

Open Source Models:
Open-source models will always be inferior to closed-source models, leading to a paradigm shift in optimization.

Model Optimization:
Optimization points lie in models used by large user bases, as they uncover innovative tricks and techniques.

Community Development:
Community involvement drives rapid model development and improvement through collaboration and iteration.

Autism and Machine Learning:
Mostaque and Lukas Biewald discuss the connection between autism and machine learning, noting its frequent mention in interviews.

Transformer-Based Architecture and Its Similarity to Human Language Rebuilding:
Emad Mostaque finds parallels between transformer-based architecture and the process of rebuilding language capabilities in individuals with autism. Transformer models pay attention to the important parts, reducing noise and imbalance, similar to how applied behavioral analysis helps individuals with autism learn and rebuild their language skills.

Personalized Medicine and Education Through AI:
Emad Mostaque emphasizes the potential of AI for personalized medicine and education. He envisions a future where AI can help every child, regardless of their needs, by providing individualized support and adapting to their unique characteristics.

Addressing Systematic Diseases and Conditions with AI:
Emad Mostaque highlights the importance of AI in addressing systematic diseases and conditions like autism and COVID. He stresses the need for first principles analysis and personalized treatment approaches to effectively manage these complex conditions.

The Role of Data in Machine Learning:
Emad Mostaque believes that data is an underrated aspect of machine learning that deserves more attention. He sees potential for significantly improving model outcomes by optimizing data usage and understanding its impact on latency and other factors.

Emad Mostaque’s Advantages from Asperger’s in Building Stability:
Emad Mostaque attributes his skill in mechanism design and his ability to position Stability at the right time to his Asperger’s and ADHD. He focuses on his strengths, absorbs criticism, and allows the developers and community to drive the project forward.

The Right Thing to Do:
Emad Mostaque’s approach to Stability is guided by a moral and mechanism design perspective. He believes that capitalizing on the project and making it widely accessible is the right thing to do.

Data Cleaning and Optimization:
Data cleaning is crucial for improving model performance, yet often overlooked. Lukas Biewald emphasizes the importance of data cleaning, which he found to be the most valuable aspect in his previous company focused on data collection. Emad Mostaque acknowledges that data cleaning is a significant challenge, requiring extensive optimization to achieve sub-one-second inference time for large models.

Fine-tuning and Custom Models:
Fine-tuning and creating custom models pose unique challenges. Determining the appropriate storage and management of custom models and user data is a complex issue.

Snappy Consumer Feedback Loops:
Ensuring responsive user experience is a key challenge, especially for large models that generate animations. Balancing user expectations for real-time results with the inherent latency of large models is a critical factor.

Additional Information:
Show notes with links to papers, supplemental material, and transcription are available in the description.