Anthropic Co-founder on Claude 3, Constitutional AI, and AGI | Ask More of AI with Clara Shih

In the 'Ask More of AI' podcast, Clara Shih, CEO of Salesforce AI, interviews Jared Kaplan, Co-founder and Chief Scientist of Anthropic. Kaplan discusses the latest release of Claude 3, an AI model that aims to be more reliable and reduce hallucinations. He explains the concept of 'constitutional AI,' which uses AI to train systems to follow a set of principles, promoting transparency and rapid iteration. Kaplan also shares examples of Anthropic's success with enterprise customers across various domains such as coding, medical drug discovery, and legal AI. He touches on the challenges of AI development, emphasizing the need for safety, robustness, and reliability. The conversation explores the potential of AI in education, the future of work, and the rapid progress in AI capabilities, with Kaplan expressing optimism for AI's role in enhancing human productivity and creativity.

Takeaways

• 🚀 **AI Advancements**: Anthropic's release of Claude 3 represents a significant step forward in AI, aiming to make systems more reliable and useful for business.
• 🤖 **Multimodal Capabilities**: Claude 3's new models are designed to integrate multimodal performance, allowing for a broader range of applications in business productivity.
• 📜 **Constitutional AI**: Anthropic is pioneering a method where AI systems are trained to follow a set of principles, potentially reducing the need for extensive human labeling.
• 🔍 **AI Transparency and Iteration**: The use of constitutional AI allows for faster iteration and transparency in AI development, which is crucial for keeping pace with rapid AI advancements.
• 🧐 **Inception Challenge**: There's an inherent trust issue in AI training, where the initial model must be reliable to ensure the system's safety and efficacy.
• 🏥 **Enterprise Applications**: Anthropic's AI, Claude, is being used by major companies like GitLab, Pfizer, and LexisNexis, demonstrating its versatility across various domains.
• 🛠️ **Customization Layers**: Customers can customize Claude through prompting and data context, offering flexibility and ease of use without the need for fine-tuning.
• 🤝 **Consumer and Enterprise Focus**: While Anthropic is primarily enterprise-focused, they also aim to make AI accessible to consumers for experimentation and learning.
• 🌟 **Amazon Bedrock Partnership**: Anthropic models, including Claude 3, are being made available through Amazon Bedrock, offering customers more deployment options.
• 🔗 **Salesforce and Slack Usage**: Anthropic leverages Salesforce for sales and Slack for research team collaboration, highlighting the transformative impact of these tools on productivity.
• ⚙️ **Future AI Capabilities**: Jared Kaplan anticipates AI systems to become more capable in handling complex, multi-step tasks and integrating with real-world applications like robotics.
• 🧱 **Building Blocks for Progress**: Safety, robustness, and reliability are the main focuses for Anthropic as they continue to develop and improve AI systems.
• 🎓 **Education in the AI Era**: Kaplan discusses the potential for AI to serve as tutors and enhance the learning process, though the impact on specific skills is still uncertain.
• 🌟 **AGI Aspirations**: Anthropic maintains a level of anxiety about the development of AGI, which influences their responsible and safety-focused approach to AI research and deployment.
• 🔮 **Interpretability in AI**: A key area of focus for Anthropic is understanding the inner workings of neural networks to improve AI systems' monitorability and safety.
• ⚖️ **Ethical Considerations**: The rapid pace of AI development calls for careful consideration of ethical implications and the potential societal impact of advanced AI systems.

Q & A

What is the main driving force behind the release of Claude 3?

The main driving force behind the release of Claude 3 is the ongoing effort to make AI systems more honest, reliable, and useful by decreasing hallucinations and enhancing multimodal performance and tool use.

How does constitutional AI work and why should people trust it?

Constitutional AI allows for the transparent articulation of a set of principles guiding an AI's behavior and uses AI itself to train systems like Claude to abide by those principles. This approach promotes transparency, rapid iteration, and the ability to test new constitutions without the need for extensive human labeling of data.

What are the challenges faced when implementing constitutional AI?

An inception problem exists where the initial supervisor of the AI system needs to be trusted. Additionally, thorough testing with red teamers and various categories is required to ensure the system meets the desired safety benchmarks.

Can you provide examples of Anthropic's success with enterprise customers?

Anthropic has worked with GitLab on coding, Pfizer for medical drug discovery, and LexisNexis on legal AI, demonstrating the versatility and general applicability of Claude across different domains.

How do companies typically deploy Claude, and do they fine-tune it?

Companies can deploy Claude as is or fine-tune it based on their specific data. The first line of customization is through prompting and providing relevant data for the use case. Fine-tuning is an option for customers with good data, but it's encouraged to consider the goal before opting for fine-tuning due to its complexity.

What are the differences in serving consumers and enterprises with AI?

Anthropic is primarily focused on enterprise use, where safety, reliability, and the message of AI's potential resonate strongly. However, they also provide access to consumers to experiment with Claude and understand its capabilities.

How does deploying Anthropic models through Amazon Bedrock differ from other consumption methods?

Bedrock aims to maintain parity with Anthropic's first-party API, allowing customers to use Claude on AWS in the same way they would if working directly with Anthropic. All Claude models, including Claude 3 Sonnet, are available on Bedrock, with more models to be added soon.

How has Slack transformed research team productivity at Anthropic?

Slack has been transformative for medium to large research teams at Anthropic by allowing them to retain all content, share ideas, data, plots, and discussions at any time, which significantly contributes to their research productivity.

What are the two main directions of improvement Jared envisions for AI models in the near future?

Jared envisions improvements in the ability of AI systems to operate across different modalities, bringing AI more flexibly to business applications, and enhancing the time horizon on which AI can operate, making them more robust and reliable for tasks that require planning and multiple actions.

What are the key building blocks that need to be created or perfected for the advancement of AI?

The main building blocks for AI advancement are safety, robustness, and reliability. Current systems like Claude 3 can provide correct answers to certain questions but struggle with fixing mistakes, which is a critical area of focus for improvement.

How does Jared's background in theoretical physics influence his approach to AI research?

Jared's background in theoretical physics brings a focus on simplicity and generality to AI research, which has been beneficial in developing concepts like scaling laws. His physics perspective helps in asking the right questions and understanding the big picture in the rapidly evolving field of AI.

Outlines

00:00

🚀 Introduction to AI's Impact and Challenges

The first paragraph introduces the potential of AI systems to assist in scientific research and address social issues like poverty and inequality. However, it also acknowledges the challenges that come with these advancements. The speaker, Clara Shih, CEO of Salesforce AI, welcomes the audience to the podcast 'Ask More of AI' and expresses excitement about the event, Trailblazer DX. She is about to converse with Jared Kaplan, Co-founder, and Chief Scientist of Anthropic, and they share a history of attending school together. The discussion is set to cover the latest release of Claude 3, its development, and its unique features, including multimodal performance and tool use.

05:00

🤖 Trust and AI Safety with Constitutional AI

In this segment, the conversation delves into trust and safety in AI, particularly with Anthropic's focus on AI safety and research. Clara asks about the concept of 'constitutional AI,' which Jared explains as a method to guide AI behavior with a set of principles, using AI to train systems like Claude to adhere to these principles. This approach aims to increase transparency and allows for rapid iteration without the need for extensive human labeling of data. However, Jared acknowledges the 'inception problem' of trusting the initial AI supervisor and emphasizes the importance of human interaction and evaluation to ensure safety.

10:00

📈 Anthropic's Success Stories and Customizing Claude

Jared shares examples of Anthropic's successful collaborations with enterprise customers, such as GitLab, Pfizer, and LexisNexis, across various domains. He discusses the flexibility of Claude's deployment, highlighting that while some companies use Claude as-is, others fine-tune the model with their data. Jared advises considering the goals of AI before opting for fine-tuning and suggests that often, valuable objectives can be achieved without it.

15:03

🌐 AI's Future and the Role of Anthropic

The discussion turns to the future of AI and how Anthropic serves both consumers and enterprises. Jared emphasizes the company's focus on enterprise use while maintaining accessibility for consumers. He also talks about the deployment options for Anthropic models through Amazon Bedrock, aiming to provide parity with Anthropic's first-party API. The conversation includes the benefits of using Salesforce and Slack within Anthropic and Jared's predictions for AI's capabilities in the coming years, focusing on generality, multimodal capabilities, and real-world applications.

20:06

🔍 The Evolution of AI Research and Development

Jared reflects on his transition from theoretical physics to AI, highlighting the rapid pace of progress in AI and the challenges of safety, robustness, and reliability. He discusses the importance of research in interpretability to understand the inner workings of neural networks and improve AI systems' monitoring and safety. The conversation also touches on the impact of AI on education and the potential for AI systems to act as tutors.

25:07

🌟 From Physics to AI: Jared's Journey and Vision

Jared shares his personal journey from being a theoretical physicist to becoming deeply involved in AI research. He talks about the influence of his friend and Anthropic's CEO, Dario Amodei, and how living together and sharing conversations led to his interest in AI. Jared describes his experience volunteering at OpenAI and eventually co-founding Anthropic. He emphasizes the importance of dreaming big and being prepared for the rapid advancements in AI, advising trailblazers to be ambitious with AI applications.

🎓 The Intersection of Physics and AI

The final paragraph covers the intersection of physics and AI, with Jared discussing his academic background and how it influenced his approach to AI research. He talks about the value of a physics perspective in focusing on simplicity and generality, which helped in developing concepts like scaling laws. Jared also addresses the goals shaping Anthropic's work, emphasizing the company's commitment to making AI as capable, safe, and useful as possible.

Keywords

Anthropic

Anthropic is an AI safety and research company focused on developing reliable and trustworthy AI systems. In the video, Jared Kaplan, Co-founder and Chief Scientist of Anthropic, discusses the company's latest release, Claude 3, which aims to improve upon previous models by making them more honest and decreasing hallucinations. The company's approach to AI is guided by a set of principles, known as 'constitutional AI,' which helps ensure that AI systems behave in a predictable and safe manner.

Claude 3

Claude 3 is a new AI model developed by Anthropic. It represents an advancement over its predecessor, Claude 2, by incorporating multimodal performance and tool use, which allows for a wider range of applications in business. The model is designed to be more reliable and to reduce 'hallucinations'—errors or inaccuracies in the AI's responses. It is a central topic in the discussion, highlighting Anthropic's commitment to improving AI systems.

Constitutional AI

Constitutional AI is a concept developed by Anthropic that involves setting a clear set of principles to guide the behavior of their AI systems, like Claude. It allows for the AI to be trained to follow these principles autonomously, without the need for extensive human labeling of data. This approach is intended to increase transparency and enable rapid iteration and improvement of the AI model. It is a key part of Anthropic's strategy to build trustworthy AI.

AI Safety

AI safety is a critical theme in the conversation, emphasizing the importance of creating AI systems that are not only effective but also secure and reliable. Anthropic, as an AI safety research company, is dedicated to ensuring that their AI models, such as Claude, are used responsibly and do not pose risks. The concept is integral to the development of Constitutional AI and the overall mission of Anthropic.

Multimodal Performance

Multimodal performance refers to an AI system's ability to process and understand multiple types of data inputs, such as text, images, and video. In the context of Claude 3, multimodal capabilities allow the AI to be more versatile and applicable in a broader range of business and research scenarios. It is a significant upgrade from previous models and is a key feature of Anthropic's latest AI system.

Enterprise Customers

Enterprise customers are businesses that use Anthropic's AI models for various applications, such as coding with GitLab, medical drug discovery with Pfizer, and legal AI with LexisNexis. The discussion highlights the versatility of Claude's application across different domains and how these companies are leveraging AI to enhance their operations.

Fine-tuning AI Models

Fine-tuning AI models involves adjusting a pre-trained AI model to perform better on a specific task or dataset. While Anthropic's customers have the option to fine-tune Claude on their data, Jared Kaplan suggests that in many cases, the benefits of using Claude without fine-tuning are significant. He advises customers to consider their goals with AI before opting for fine-tuning to avoid unnecessary complexity.

Amazon Bedrock

Amazon Bedrock is a platform that allows for the deployment of Anthropic models, including Claude 3, on Amazon Web Services (AWS). The discussion mentions that Anthropic aims to keep the experience of using Claude on Bedrock at parity with their first-party API, ensuring customers can use the AI model in a familiar and consistent manner.

AGI (Artificial General Intelligence)

AGI, or Artificial General Intelligence, refers to AI systems that possess the ability to understand or learn any intellectual task that a human being can do. The conversation touches on the potential future development of AGI and the profound societal changes it could bring. It is presented as a long-term goal and challenge for AI research and development.

Interpretability

Interpretability in AI is the ability to explain or understand the inner workings of an AI model, particularly large language models. Anthropic has a team dedicated to this field, aiming to decode how neural networks make decisions. This is crucial for making AI systems more transparent, reliable, and safe to use.

Scaling Laws

Scaling laws in AI research refer to the patterns observed in how the performance of AI models improves as they are scaled up in terms of data, compute power, and model size. Jared Kaplan's background in physics influenced his contribution to the concept of scaling laws, which are essential in understanding and predicting the progress of AI capabilities.

Highlights

Anthropic Co-founder Jared Kaplan discusses the release of Claude 3, an AI system designed to be more honest and reliable.

Claude 3 introduces multimodal performance and tool use, enhancing its productivity for business applications.

Constitutional AI is a method that uses AI to train systems like Claude to follow a set of principles, increasing transparency and iteration speed.

Anthropic's success with enterprise customers includes collaborations with GitLab, Pfizer, and LexisNexis, showcasing Claude's versatility.

Customizing Claude involves prompting and providing relevant data, allowing for flexibility and ease of use.

Jared Kaplan emphasizes the importance of safety, reliability, and the rapid pace of AI development when considering future advancements.

Anthropic models can be deployed through Amazon Bedrock, offering parity with first-party APIs for customers.

Slack has been transformative for research teams, facilitating idea sharing and collaboration.

AI systems like Claude are expected to improve in multimodal capabilities and agentic tasks, such as robotics.

The main challenge in advancing AI systems is ensuring their safety, robustness, and reliability.

AI has the potential to act as a tutor, enhancing learning and engagement in education.

Jared Kaplan's transition from theoretical physics to AI was influenced by the rapid progress and potential impact of AI on society.

Core concepts like scaling laws and reinforcement learning from human feedback have been shaped by Kaplan's background in physics.

Anthropic's research is focused on making AI systems as capable, safe, and useful as possible, driven by a sense of responsibility.

The field of AI is moving so quickly that even leading researchers do not fully understand how large language models work.

Anthropic is working with healthcare and pharmaceutical companies to accelerate drug development through AI.

Kaplan advises trailblazers to dream big and be prepared to act on ambitious ideas as AI capabilities advance rapidly.

Orchestrating AI systems like Claude can lead to better performance by generating and refining multiple possibilities.