Challenges: Common Pitfalls
Learn about the challenges and pitfalls associated with AI/ML products.
Challenges
We’ve spent a considerable amount of time talking about how to build AI/ML products and use models in a way that empowers our products. We’ve also discussed the hype and commercial excitement about AI. In this lesson, we’ll temper this hype by understanding why certain AI/ML products fail. We’ll be looking at a few real-world examples that highlight some of the common reasons why AI developments have received controversy.
We will also look into some of the underlying themes within that controversy for new AI products and their creators to try to avoid. Here, we will focus on challenges associated with ethics, performance, and safety and their accompanying examples.
Ethics
Companies have long struggled with maintaining the quality and ethics of consumer-facing conversational AIs. If we recall back in 2016, when Microsoft unleashed its AI named Tay onto the Twitter-sphere, it took less than 24 hours for Tay to post highly discriminatory rhetoric against Twitter users.
ChatGPT biases
This phenomenon seems to be happening again with the latest craze ChatGPT, which is made by OpenAI. ChatGPT is derived from OpenAI’s massive language model GPT-3 and, according to SamBiddle, “It is staggeringly impressive, uncannily impersonating an intelligent person (or at least someone trying their hardest to sound intelligent) using generative AI, software that studies massive ...