C3W1L08 WhyHumanLevelPerformance

TL;DR

Machine learning teams are interested in comparing their systems to human level performance due to advances in deep learning algorithms and the more efficient workflow when working on tasks that humans can do.

Transcript

in the last few years a lot more machine learning teams have been talking about comparing the machine learning systems to human level performance why is this I think there are too many reasons first is that because of advances in deep learning machine learning algorithms are suddenly working much better and so it's become much more feasible a lot o... Read More

Key Insights

• 🎰 Advances in deep learning algorithms have made it feasible for machine learning algorithms to compete with human level performance.
• 🐢 Progress tends to be rapid until algorithms surpass human level performance, after which it slows down.
• 🎚️ Comparing to human level performance helps in improving algorithms and understanding bias and variance.
• 👋 Humans are good at tasks like image recognition, audio transcriptions, and language comprehension, which makes them useful for improving machine learning algorithms.
• 🎭 Knowing how well humans can perform on a task can help in determining how much bias and variance need to be reduced.

Questions & Answers

Q: Why are machine learning teams interested in comparing their systems to human level performance?

There are multiple reasons for this. One is that advances in deep learning algorithms have made it more feasible for machine learning algorithms to be competitive with humans. Additionally, the workflow of designing and building machine learning systems is more efficient when trying to mimic human level performance.

Q: How does progress typically behave as algorithms approach human level performance?

Progress tends to be relatively rapid as algorithms approach human level performance. However, after surpassing it, the progress and accuracy slows down. While improvements can still be made, the rate of improvement slows down.

Q: What is Bayes optimal error?

Bayes optimal error is the theoretical best possible error that can never be surpassed. It represents the highest level of accuracy that any function mapping from X to Y can achieve. It serves as a limit that algorithms cannot surpass.

Q: Why does progress often slow down after surpassing human level performance?

One reason is that human level performance is often not far from Bayes optimal error. Therefore, once algorithms surpass human level performance, there may not be much room for further improvement. Another reason is that certain tactics for improving performance, such as getting label data from humans or conducting nano error analysis, are harder to apply once algorithms are better than humans.

Summary & Key Takeaways

• Machine learning algorithms are becoming competitive with human level performance due to advances in deep learning.

• Progress tends to be rapid as algorithms approach human level performance, but slows down after surpassing it.

• Comparing to human level performance is helpful for improving algorithms and understanding bias and variance.