Is There Potential for Unintended Consequences in Large Language Models?

The term artificial intelligence, AI, is having a bit of a boom, with the explosion in popularity of tools like ChatGPT, Lensa, DALL•E 2, and many others. The praises of AI have been equally met with skepticism and criticism, with cautionary tales about AI information quality, plagiarism, and other risks. Susan Farrell, the Principal UX Researcher at mmhmm, shares a bit about her experiences in researching chatbots and AI driven tools, and defines what AI is, what it isn’t, and what teams should consider when implementing AI systems.

Susan Farrell's social media: LinkedIn; Mastodon
What Susan is working on: mmhmm.app

NN/g courses referenced in this episode: 

• Design Tradeoffs & UX Decision Making (full-day and 2-day course) 
• Emerging Patterns in Interface Design (full-day and 2-day course) 

Recommended Reading to deep dive into artificial intelligence & machine learning: 

• Age of Invisible Machines - Robb Wilson 
• The Promise and Terror of Artificial Intelligence - Os Keyes 
• Becoming a chatbot: my life as a real estate AI’s human backup
• The Invisible Workforce that Makes AI Possible
• For Humans Learning Machine Learning 
• What are large language models (LLMs), why have they become controversial? 
• On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 

Interested in working for us? Check out our job posting and apply by Jan. 30, 2023.