Adept CEO David Luan and Stanford's Percy Liang | Progress from Large Language Models to AGI | Summary and Q&A

TL;DR

Large-scale models are still in the early stages of deployment, but their potential for creating new applications and collaborations between humans and machines is vast.

Key Insights

• 🌥️ The potential applications of large-scale models go beyond language models and include various modalities and creative endeavors.
• 👋 Openness, transparency, and community collaboration are important for advancing the field and establishing best practices.
• 👨‍🔬 Developing benchmarks and metrics that align with human needs and ensuring the reliability of models are ongoing research goals.

Transcript

Read and summarize the transcript of this video on Glasp Reader (beta).

Questions & Answers

Q: What are the current limitations of large-scale models?

Large-scale models often generate false information and biases. Ensuring reliability and reducing falsehoods is a major research challenge. Adversarial attacks and misuse are also concerns, requiring robust security measures.

Q: How can developers navigate the decision of building on open APIs, open source models, or their own large models?

The decision depends on the desired level of control, reliability, and differentiation. APIs offer quick prototyping and access to powerful models, but building custom models provides more control and durability.

Q: What advancements are needed in human-computer interaction and user interface design for large-scale models?

Researchers are exploring how humans can interact with models more effectively and how to improve the interfaces. Developing better benchmarks and metrics for evaluating human-machine interactions is an ongoing challenge.

Q: How can companies ensure the durability and long-term viability of their products built on large-scale models?

Transitioning away from APIs and experimenting with different models is a gradual process. Building a strong connection between users and machines, understanding customer use cases, and addressing limitations are important steps.

Q: What are the current limitations of large-scale models?

Large-scale models often generate false information and biases. Ensuring reliability and reducing falsehoods is a major research challenge. Adversarial attacks and misuse are also concerns, requiring robust security measures.

More Insights

• The potential applications of large-scale models go beyond language models and include various modalities and creative endeavors.

• Openness, transparency, and community collaboration are important for advancing the field and establishing best practices.

• Developing benchmarks and metrics that align with human needs and ensuring the reliability of models are ongoing research goals.

• Addressing limitations and risks, both in terms of falsehoods and adversarial attacks, requires continuous improvement and proper safety measures.

Summary

In this video, David Percy and Percy David discuss the current state of large-scale models and their deployment. They highlight the power and potential of these models, but also acknowledge their limitations and the need for further research. They discuss the potential for new applications, such as creative tasks and multimodal interactions. They also address the challenges of building products and companies around these models, including the decision of whether to use open AI APIs, open source models, or build custom models. The speakers also emphasize the importance of openness, transparency, and community standards in the development and deployment of these models.

Questions & Answers

Q: What is the current state of large-scale models and their deployment today?

The speakers believe that large-scale models are incredibly powerful, but there is still a lot of untapped potential. While these models have already shown significant improvements in existing machine learning tasks and the creation of new AI products, there is still much more to explore and discover.

Q: What are some potential net new applications that become possible with large-scale models?

The speakers suggest that large-scale models can enable new creative applications in various domains, including code, text, proteins, videos, PowerPoint slides, and more. They highlight the potential of multimodal models that combine different types of knowledge, such as language and images, to create new types of content, like illustrated books or films.

Q: What are some key research areas needed to make large-scale models more effective?

The speakers mention the need to push the capabilities of large-scale models, particularly in areas like scaling to handle video sequences. They also emphasize the importance of making the models more reliable, interpretable, and safer. They mention the need for research in developing effective surrogate metrics to evaluate model interactions with humans and finding ways to address falsehoods and biases in the models.

Q: How should founders navigate the decision to build on top of open AI APIs, open source models, or build their own large models?

The speakers suggest that founders should consider their long-term goals and differentiation as a business. They recommend thinking about the compounding loop for their company and the level of reliability and affordances they require. While it may be easier to start with open AI APIs for quick prototyping and experimentation, over time, building durable products may require transitioning away from APIs to gain more control and differentiation.

Q: How can durability be built into products that start with open AI APIs?

The speakers mention the importance of transitioning from API usage to building your own models or customizing existing models. They suggest using human Wizard of Oz experiments to understand the interface issues and gradually replacing human involvement with APIs. They also discuss the need for better documentation and benchmarking practices to enable downstream developers to understand the models' capabilities and limitations.

Q: What are some of the limitations of large-scale models?

The speakers highlight falsehoods as a key limitation, with models being prone to generating inaccurate or misleading information. They discuss the challenge of encoding all world facts into the models and the need for more efficient methods. They also mention risks related to adversaries and misuse of the models, such as data poisoning and security concerns.

Q: How should developers navigate the risks and limitations of large-scale models?

The speakers recommend designing effective interfaces and user experiences that mitigate the risks of falsehoods and other limitations. They emphasize the iterative nature of working with large-scale models and finding the right balance between capabilities and limitations. They also mention the importance of developing community norms, benchmarks, and standards to guide developers in choosing appropriate models for their applications.

Q: What are some future possibilities for interacting with large-scale models that excite the speakers?

The speakers discuss the potential for multimodal models that encompass various domains of human knowledge and enable interactions beyond language. They envision models for robots that can learn from human demonstrators and perform complex tasks. They also mention the possibilities of generating images, videos, immersive experiences, and even entire environments using large-scale models.

Q: What academic contributions would the speakers like to see in relation to large-scale models?

The speakers emphasize the need for openness, transparency, and community standards in the development and deployment of large-scale models. They call for more accessible toolkits, tutorials, and datasets. They also mention the importance of developing a better language for understanding model capabilities, contracts, and best practices. Additionally, they stress the need for research in addressing risks, including security concerns and adversarial attacks.

Q: How should developers think about the risks and limitations of large-scale models as they build new applications?

The speakers suggest considering the specific application and its requirements for capabilities and reliability. They recommend starting with the available tools and APIs for quick prototyping and iteration. They also advise developers to be aware of security risks, adversarial threats, and ethical considerations. The speakers emphasize the importance of using effective surrogate metrics to evaluate models and developing adaptable frameworks to address evolving risks.

Q: What are some challenges and opportunities in the current landscape of large-scale models and their deployment?

The speakers note the challenges of addressing limitations, ensuring reliability, and mitigating risks associated with large-scale models. They mention the need for standardized toolkits, benchmarking practices, and documentation to make informed decisions about model usage. They also highlight the opportunities for new creative applications, multimodal interactions, and collaboration between humans and machines. The speakers encourage open dialogue and collaboration within the research and industry communities to foster responsible development and deployment of large-scale models.

Takeaways

The speakers emphasize the enormous potential of large-scale models but also acknowledge the need for continued research and development. They discuss the importance of addressing limitations, such as falsehoods and biases, and ensuring reliability and safety. They emphasize the need for openness, transparency, and community standards in the development and deployment of these models. The speakers also highlight the opportunities for new creative applications and multimodal interactions. They encourage developers to navigate the risks and limitations of large-scale models by considering the specific application requirements and iteratively improving and customizing the models. The speakers call for collaboration and dialogue within the academic and industry communities to drive responsible and effective utilization of large-scale models.

Summary & Key Takeaways

• Large-scale models, like GPT-3, have already shown great potential in improving existing machine learning models and creating new AI products.

• The field is still in its early stages, with much more to explore in terms of different modalities and sources of knowledge.

• The ability to generate creative content, such as code, text, proteins, videos, and more, is a promising area of development.

• Bridging the gap between models and real-world actions is a challenge, but crucial for enabling effective collaboration between humans and machines.