The lack of labeled data limits the current state of AI. This is a problem because most data is unstructured and not labeled. We need to find new and creative ways to label data to solve this problem.
One way to label data is through active learning. Active learning is a process where the user labels data points. This is usually done by having the user select which points to label. The advantage of this approach is that it is very efficient. The downside is that it requires expert knowledge to label the data correctly.
Another way to label data is through semi-supervised learning. This is a process where the machine labels data points. This is usually done by having the machine learn from a small amount of labeled data and then applying that knowledge to a larger data set. The advantage of this approach is that it is much more scalable than active learning. The downside is that it can be less accurate than active learning.
A third way to label data is through transfer learning. This is a process where the machine learns from one domain and then applies that knowledge to a different domain. The advantage of this approach is that it can be used to label data in domains where there is no labeled data available. The downside is that it can be less accurate than semi-supervised learning.
No matter what method is used to label data, it is essential to have a large amount of data to train the machine learning algorithm. This is because the more data the algorithm has, the better it will be able to learn and generalize to new data.
References:
https://en.wikipedia.org/wiki/Active_learning_(machine_learning)
https://en.wikipedia.org/wiki/Semi-supervised_learning
https://en.wikipedia.org/wiki/Transfer_learning