Expert AI Project Management Community Session

Table of Contents

• Introduction
• Evaluating AI Models as a Product Manager
• Implementing Responsible and Ethical AI
• Hands-on Lab with Large Language Models
• Conclusion and Next Steps

Introduction to Large Language Models and Anthropic's New Cloud Platform

This blog post provides an introduction to large language models and discusses a new platform called Cloud launched by Anthropic. Large language models like GPT-3 and Claude have shown impressive capabilities in generating human-like text. However, there are still challenges around safety, ethics, and technical limitations.

Anthropic is a new startup trying to address some of these challenges. Their flagship product Claude focuses on safety through a technique called constitutional AI. The model strives to be harmless, helpful, and honest. Claude also supports much longer input context than predecessor models, allowing more precise and detailed responses.

Welcome and Agenda

We will be discussing Claude and other large language models from a product management perspective. Key topics will include:

• Comparing capabilities and benchmarking
• Pricing and business model
• Responsible and ethical AI practices
• Hands-on experiments with models

Anthropic's Claude Model

Claude is trained on more recent data than GPT-3, though still likely outdated compared to a human's knowledge. The model supports up to 200,000 tokens of context, 3X more than GPT's 55,000 token limit. This allows Claude to digest full documents and papers to provide more nuanced and contextual responses. We will demonstrate these capabilities later in the post.

Evaluating AI Models as a Product Manager

As a product manager evaluating Claude versus GPT-3, some key aspects we would analyze include:

• Pricing: Claude appears over 5X more expensive per 1,000 tokens

• Accuracy and capabilities: Claude has stronger document understanding, but may lag in areas like common sense

• Responsible and ethical AI: Claude aims for more safety via constitutional AI, but has less transparency so far

• Benchmarking: We would run experiments across criteria like quality, safety, and more

• Infrastructure: GPT-3 benefits from Azure optimization and Pinecone integration

Implementing Responsible AI

Anthropic focuses closely on responsible AI practices in areas like transparency, benchmarking, and setting constitutional constraints:

• Model cards publicly detail Claude's training process, benchmarks, limitations and more

• The model strives to be harmless, helpful and honest during training iterations

• Work-in-progress frameworks steer away from harmful model behaviors using human feedback

Hands-on Lab with Large Language Models

We can get hands-on with Claude and GPT-3 to compare capabilities. For example, when summarizing a sample legal contract:

• GPT-3 struggles with the long document context

• Claude accurately extracts and summarizes key details from the full contract text

This demonstrates stronger document understanding, though prompts would need tuning for production. We share more labs and resources for readers to experiment further.

Conclusion and Next Steps

Anthropic's Claude offers impressive improvements, but still has limitations in areas GPT excels at. The choice depends heavily on the product use case and existing infrastructure.

As with any fast-moving space, we will continue to evaluate new model releases through responsible practices as product managers. Please stay tuned for more AI updates and feel free to enroll in our ongoing community program.

FAQ

Q: What is the Anthropic Cloud model?
A: Anthropic Cloud is a new large language model released by AI company Anthropic in late 2022. It is designed to be more helpful, harmless, and honest than previous models.

Q: How does Cloud compare to GPT-3?
A: Cloud has a much larger context length, allowing it to incorporate more information when generating text. However, GPT-3 likely still has more training data overall currently.

Q: What metrics should I use to evaluate AI models?
A: Key metrics to evaluate include accuracy, safety, bias, pricing, benchmark results, ability to fine-tune, and infrastructure requirements.