How open-source & distributed models can win AI with MosaicML’s Naveen Rao | E1754 | Summary and Q&A

TL;DR

AI is rapidly evolving and has the potential to revolutionize various industries, but questions remain about inclusivity and the economic impact.

Key Insights

• ⁉️ AI has the potential to significantly impact various industries, but questions about inclusivity and economic repercussions remain.
• 🥺 The demand for AI capabilities is increasing rapidly, leading to a shortage of GPUs and a need for more efficient computing infrastructure.
• 😒 The future of AI may involve a combination of large general models and smaller specialized models to suit different use cases.
• 🌱 Education is likely to be transformed by AI, with personalized learning and adaptive lesson plans becoming the norm.
• 🤗 Open-source models and distributed capabilities can help foster innovation and competition, preventing monopoly control over AI technology.

Transcript

AI in my view is the next evolution of what humans can do you know language was a big technology that humans use to pass knowledge that that exploded what humans could do and the influence we could have on the world uh AI is going to be that next inflection point but how are we going to make sure that everyone has a place in that world how are we g... Read More

Questions & Answers

Q: How does Mosaic ML enable organizations to utilize AI capabilities?

Mosaic ML allows organizations to train and host custom models using their own data, ensuring data privacy and ownership. They provide tools for generative AI and offer flexibility in cloud deployment.

Q: What is the impact of AI on different industries like healthcare and customer support?

AI has the potential to improve efficiency in various industries. In healthcare, AI companions can enhance the capabilities of doctors and nurses. In customer support, AI models can provide quick and accurate responses to customer queries.

Q: How does fine-tuning versus pre-training affect AI models?

Fine-tuning involves conditioning a model to produce specific outputs, which can be useful for customizing the behavior of the model. Pre-training involves training a model on a large dataset to provide a foundation of knowledge, enabling it to generate intelligent responses.

Q: What are the challenges of AI democratization and regulation?

The rapid advancement of AI raises concerns about who has access to the technology and how it may impact employment. There are debates about the need for regulatory agencies and whether centralized regulation or market solutions are more appropriate.

Summary

In this video, the speaker discusses the importance of AI in the future and how it will impact various industries. They talk about the challenges of ensuring everyone has a place in the AI world and how demand and efficiency will play a role. The speaker interviews Naveen Rao, co-founder and CEO of Mosaic ML, who talks about their work in bringing large-scale machine learning capabilities to organizations and enabling them to build models that suit their needs. The conversation covers topics such as training language models, partnering with existing platforms, data privacy, the use of prompts and context windows, the cost of running models, the scarcity of GPUs, the future of specialized models, and more.

Questions & Answers

Q: What is the speaker's view on AI?

The speaker believes that AI is the next evolution of what humans can do and will have a significant influence on the world. They compare it to the impact of language on human knowledge and emphasize the importance of ensuring everyone has a place in the AI world.

Q: What is Mosaic ML's aim?

Mosaic ML aims to bring large-scale machine learning capabilities, particularly generative AI, to organizations. They want to enable more people to build models and create the desired world. Their goal is for others to have the ability to build models that are equally as good as the experts'.

Q: How does Mosaic ML's software work?

Mosaic ML offers software called Mosaic ML that allows organizations to train their own models using their data. The software simplifies the process of training and hosting the models on various clouds, offering flexibility and ownership of intellectual property. It enables the effective use of resources, respects data privacy, and allows organizations to build on top of the models they create.

Q: How does fine-tuning modify the behavior of language models?

Fine-tuning allows users to condition a language model to act in specific ways. It can be used to focus on certain outputs, such as answering specific questions or excluding certain topics. It is similar to giving instructions to a human, guiding their behavior in a call center, for example.

Q: What is the process of using a corpus of data with Mosaic ML?

For smaller data sets with fewer than a hundred thousand words, prompts and context windows can be used to modify the behavior of the model. In larger data sets, fine-tuning can be employed to condition the model to act in certain ways. When dealing with billions of words, pre-training and data mix are used to modify the model's behavior in a more profound way. The specific approach depends on the amount of data available.

Q: Can Mosaic ML help with analyzing startup data for better investment decisions?

Yes, Mosaic ML's software can be used to analyze a corpus of startup data, such as meeting notes, applications, and external data sources. By fine-tuning or pre-training the model, investors can extract valuable insights, find correlations between companies, and make data-driven investment decisions.

Q: How does the scarcity of GPUs impact Mosaic ML's operations?

The scarcity of GPUs is a challenge for Mosaic ML and other companies in the AI space. Large-scale models require a significant number of GPUs, and demand is exceeding supply. This shortage prolongs the time it takes to access GPUs and affects the ability to scale AI solutions. However, Mosaic ML has blocks of GPUs that they can provide to customers and can run their software stack within a customer's cloud tenancy.

Q: Will specialized models or general models dominate in the future?

Both general models and specialized models will coexist in the future. General models serve broad use cases, while specialized models excel in specific tasks. While general models may be large and expensive to serve, specialized models can be smaller and more cost-effective. The economics and demand for specific tasks will determine the balance between the two types of models.

Q: Will existing platforms like Reddit or Quora create their own language models?

It is likely that platforms like Reddit, Quora, and Bloomberg will create their own language models as they have unique datasets and a need for specialized models. These platforms can narrow down the focus and tailor the models to their specific requirements. In the coming years, we will see a variety of specialized models coexisting alongside general models.

Q: What are the cost implications of running language models at scale?

The cost of running language models depends on the size and complexity of the models and the scale at which they are used. For smaller datasets and fine-tuning, costs can be relatively low, ranging from around $100 to $1,000. However, building models from scratch through pre-training can be more expensive, with the example of a 7 billion parameter model costing around $200,000 to build. The scarcity of GPUs and the need for efficient compute also impact costs.

Takeaways

AI is seen as the next evolution of human capabilities, and ensuring everyone has a place in the AI world is crucial. Mosaic ML aims to bring large-scale machine learning capabilities to organizations, allowing them to train their own models and own their intellectual property. The use of prompts, fine-tuning, and pre-training can modify the behavior of language models, and the choice between specialized and general models depends on specific use cases and economic factors. The shortage of GPUs is a challenge in scaling AI solutions, but efficiency and cost-effectiveness can be achieved. Existing platforms may create their own models for specialized tasks, leading to a coexistence of general and specialized models. The cost of running language models depends on their size, complexity, and scale, with various cost options available based on the specific requirements of organizations.

Summary & Key Takeaways

• AI is the next inflection point in human capabilities, but concerns arise regarding inclusive access and economic repercussions.

• Mosaic ML aims to bring the power of generative AI to organizations, providing tools for building and hosting customized models.

• The demand for AI capabilities is increasing rapidly, leading to a shortage of GPUs and a need for more efficient computing infrastructure.