As an avid science fiction reader, I have long been intrigued by Isaac Asimov’s books on robots. Many of his short stories describe how robots become integrated into society – at times resented, and at other times embraced. One robot strives to become more human, portraying the intricacies and challenges of doing so. Asimov’s Three Laws of Robotics also come into play – that robots can cause no harm to humans, must obey orders, and must protect themselves unless it conflicts with the first two laws.

Lately I’ve been exploring generative AI tools like ChatGPT and Claude. Claude is a large language model chatbot, but what makes it unique is its ability to analyze uploaded documents. For example, you can upload a report and ask Claude to summarize it

Earlier this month, I was asked by a Midwest university to propose a process for inventorying faculty knowledge. The goal was to enable the school to share expertise with the business community through consulting engagements. I had Claude review a PDF listing university faculty and departments. I asked it to develop a taxonomy to catalog areas of expertise. Claude complied, but noted it needed more information about each faculty member. So, I had Claude draft a faculty survey to gather additional details. Claude then outlined a proposal to conduct the survey, compile the data, and organize it into a searchable system.

Upon reflection, I realized we didn’t need a complex database. Instead, we could upload faculty CVs and consulting histories into a cloud-based file, and have Claude directly answer questions about expertise matching. This streamlined system can now identify faculty who align with specific business needs – all powered by Claude’s natural language capabilities.

This exercise showed me how capturing organizational knowledge could enable valuable AI-driven services. Companies could extract and harness internal talent and skills more efficiently.

As more of these AI systems emerge, organizing their capabilities will also be crucial. A framework for tracking the strengths of different tools would help users select the right solutions. I see openings here for enterprising individuals or companies.

Months ago, I wrote about whether AI is a “friend or foe.” That key question remains unanswered. For now, I’m focused on learning to prompt these systems productively. Determining the right prompts feels like the most constructive thing I can do.

Overall, AI promises to boost productivity enormously across sectors. But it also raises pressing issues around human-machine relationships. Asimov’s laws highlight the challenges of control, safety, and ethics that society must grapple with. Striking the appropriate balance between augmenting humans and replacing them will be essential. With the right policies and norms, AI can hopefully act more as a “friend,” accelerating progress and economic growth. But active governance and thoughtful oversight will be needed to steer these powerful technologies toward positive impacts.

The future promises to be exciting – and we must approach it with care, creativity, and wisdom.