Category: The Data Problem

In the first three posts of this series, we have seen several ways to overcome the data problem in machine learning: first we posed the problem, then discussed one-shot and zero shot learning, and in the third post presented the reinforcement learning way of using no data at all. In this final installment, we discuss the third direction that modern machine learning takes to help with the lack of labeled data: how can we use unlabeled data to help inform machine learning models?

Today, we continue our series on the data problem in machine learning. In the first post, we realized that we are already pushing the boundaries of possible labeled datasets. In the second post, we discussed one way to avoid huge labeling costs: using one-shot and zero-shot learning. Now we are in for a quick overview of the kind of machine learning that might go without data at all: reinforcement learning.

In the previous post, we posed what we consider the main problem of modern machine learning: increasing appetite for data that cannot be realistically satisfied if current trends persist. This means that current trends will not persist — but what is going to replace them? How can we build machine learning systems at ever increasing scale without increasing the need for huge hand-labeled datasets? Today, we consider one possible answer to this question: one-shot and zero-shot learning.

Today, we are kicking off the Synthesis AI blog. In these posts, we will speak mostly about our main focus, synthetic data, that is, artificially created data used to train machine learning models. But before we begin to dive into the details of synthetic data generation and use, I want to start with the problem setting. Why do we need synthetic data? What is the problem and are there other ways to solve it? This is exactly what we will discuss in the first series of posts.