Playing with Claude from Anthropic

I have been playing with Claude, ChatGPT's biggest competitor.

Claude represents the culmination of more than a year of research and engineering by Anthropic to develop an AI assistant focused on safety, transparency and ethical principles. Its launch and ongoing improvements mark a significant milestone in responsible AI.

Anthropic began working on self-supervised learning techniques that would allow AI systems like Claude to learn general skills from a diverse dataset without human labeling. Their technique, Constitutional AI, constrains the training process to minimize potential risks.

In early 2022, Anthropic began testing preliminary versions of Claude in restricted settings to gauge its capabilities and safety. The initial results were promising, demonstrating Claude's ability to have harmless conversations. Throughout 2022, Anthropic focused on scaling up Claude's training using large datasets of anonymous internet text. The training process utilized privacy-preserving techniques like federated learning to ensure user data protections.

In early 2023, Claude's research preview was opened up to allow tens of thousands of users to have conversations with the bot and provide input on its capabilities and safety. Anthropic plans to continue expanding Claude's conversational abilities and make it widely available as a beneficial AI assistant for business uses and consumers. Safety remains the top priority throughout future developments.

While ChatGPT is wide and more general purpose, Claude has a narrow focus on "responsible and harmless AI". It calls itself an "AI Assistant". I asked how we transition from AI Hype to Practical AI. Here is the answer by Claude.

- Focus on solving real-world problems: The industry needs to shift from touting hypothetical AI capabilities to developing AI solutions for practical business and social needs. This will ground AI progress in reality.

- Move beyond proof-of-concepts: Many AI applications today are narrow proof-of-concepts that work under constrained conditions. More investment is needed to turn these into robust, reliable systems that work in the complexity of the real world.?

- Improve data quality and availability: Much of AI's potential is unrealized due to lack of large, high-quality training datasets in many domains. Better data curation and policies that enable responsible data sharing will help.

- Invest in core technologies: Advances in core areas like natural language processing, computer vision and reasoning are needed to make AI systems more capable and useful. The hype should translate to more research in these areas.

- Address trust and ethics: For widespread adoption, AI systems must be trustworthy and ethical. Self-regulation and government policies are needed to ensure AI is developed and used responsibly.

- Support multidisciplinary teams: Combining technical and domain expertise by bringing together engineers, subject-matter experts and other stakeholders will lead to more practical applications.

- Set realistic expectations: Overinflated expectations around AI lead to disillusionment. Communicating what AI can and cannot do currently will set more realistic expectations.

- Education and skills development: Knowledge-sharing about AI best practices and expanding technical skills are needed to translate hype into reality.

The path from hype to practical AI will require sustained long-term efforts on these fronts by all stakeholders. But achieving more democratized and beneficial real-world AI is well worth the effort.