Source URL: https://simonwillison.net/2024/Aug/19/arvind-narayanan-and-sayash-kapoor/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Arvind Narayanan and Sayash Kapoor
Feedly Summary: With statistical learning based systems, perfect accuracy is intrinsically hard to achieve. If you think about the success stories of machine learning, like ad targeting or fraud detection or, more recently, weather forecasting, perfect accuracy isn’t the goal — as long as the system is better than the state of the art, it is useful. Even in medical diagnosis and other healthcare applications, we tolerate a lot of error.
But when developers put AI in consumer products, people expect it to behave like software, which means that it needs to work deterministically.— Arvind Narayanan and Sayash Kapoor
Tags: llms, ai, generative-ai
AI Summary and Description: Yes
Summary: The text discusses the challenges of achieving perfect accuracy in statistical learning-based systems, emphasizing that utility can be derived even with imperfect results. It highlights the tension between the expectations of consumers for deterministic behavior in AI applications and the inherent uncertainty in machine learning models, which is particularly relevant for professionals managing AI systems and models.
Detailed Description: The content reflects on the intrinsic challenges faced by statistical learning systems, particularly in how they’re perceived in various applications, including machine learning.
– **Key Insights:**
– **Imperfect Accuracy:** Achieving perfect accuracy is complex and often unnecessary, as models can still provide valuable insights when they outperform existing benchmarks.
– **Examples of Application:** The text references successful machine learning use cases such as:
– **Ad Targeting:** Where improving predictions is more important than absolute accuracy.
– **Fraud Detection:** Similarly relies on relative effectiveness over definitive correctness.
– **Weather Forecasting:** Shows how probabilistic results are accepted in practical applications, despite not being flawless.
– **Healthcare Context:** There’s a tolerance for errors in medical diagnosis applications, indicating the acceptance of imperfect yet useful outputs in high-stakes areas.
– **Consumer Expectations vs. Reality:** When AI is embedded in consumer products, users often expect deterministic behavior (like traditional software), which clashes with the probabilistic nature of machine learning. This creates a potential gap in user satisfaction and trust that needs to be managed.
– **Implications for Security and Compliance:**
– Professionals in security, AI, and software development should consider how consumer expectations could lead to risks regarding perceptions of trustworthiness and reliability of AI systems.
– There could be a need for better user education on the capabilities and limitations of AI models, especially regarding error tolerability, to prevent misalignments in expectations.
– Enhanced governance and regulatory frameworks may be required to address the challenges of deploying probabilistic models in consumer-facing applications, ensuring that users are aware of the uncertainties involved.