Self-Supervised Learning (SSL) models have a lot of potential, but their downsides deserve attention, too.
Machine learning is a fast-moving field, with new, exciting innovations emerging all the time. This rapid advancement quickly produces solutions to pressing issues, but it also makes it easy for technologies to gain serious momentum before people fully understand them. Self-supervised learning (SSL) is one such technology.
In SSL, models train themselves by using one part of their data to predict and label others. It works similarly to unsupervised learning but focuses on drawing conclusions from information rather than clustering or grouping.
With 59% of global executives accelerating their investments in ML, it’s important to take a step back to understand these technologies before fully embracing them. Here’s a closer look at the pros and cons of self-supervised learning.
Pros of Self-Supervised Learning
The world’s rising interest in SSL is understandable. This technology offers several significant advantages over supervised and semi-supervised approaches to ML. Here are a few of the most substantial.
One of the biggest benefits of SSL is how it streamlines the machine learning process. Conventional supervised learning approaches require data scientists to go through each data point and label it manually. In Google’s Inception V3 model, that would mean tagging 1.2 million data points individually. That takes a considerable amount of time.
SSL provides a way around that issue by automating this part of the process. There’s far less manual involvement because the model learns to label and classify its own information. That frees data scientists to focus on other tasks while streamlining the labeling process through ML’s noted efficiency over manual work.
This efficiency makes complex ML models more accessible to teams with tighter schedule restraints. Getting a model up and running earlier will also help adjust and deploy it faster, helping achieve a faster return on investment.
Similarly, self-supervised machine learning can lower the costs of training and deploying ML models. One of AI’s primary draws for businesses is that it’s more cost-effective than human employees, but extensive training expenses can hinder that.
Tagging ML datasets can cost anywhere between a few cents to multiple dollars per label. When dealing with more than a million data points, that will incur incredible total costs. SSL reduces these expenses by automating much of the process, minimizing labor-related expenses for the project.
SSL also applies to future learning and model adjustments, so these cost savings extend across the model’s entire life cycle. Creating and optimizing advanced ML models could become far more affordable and accessible as a result.
These speed and cost benefits make self-supervised models a more scalable solution. A business’s machine learning needs will change as it grows and adapts. Models are rarely perfect after their initial programming, even apart from these dynamic needs. ML models need ongoing tweaking and improvement, which can be expensive with manual approaches.
Scaling an ML model or refining it often involves introducing larger datasets. Managing this increasingly sizable and complex information manually will quickly become unfeasible. With persistent tech talent shortages limiting teams’ manual resources, automation is an essential tool for this expansion.
SSL reduces the workforce burden of retraining, refining or scaling ML models. Businesses investing in these technologies can ensure they’re getting the most out of their investments.
Understanding Human Learning
SSL could also help researchers understand human learning. It seems counterintuitive, but minimizing humans’ role in training could make ML models more human-like.
Conventional supervised learning involves frequent human input and correction, but those interactions don’t always exist in the real world. Removing them forces ML models to learn without others correcting them on how to do so. The insight we gain from these projects could lead to developing more human-like models.
Use cases like customer service automation could improve when machine learning tools more closely resemble humans. Communicating with machines would feel more natural.
Cons of Self-Supervised Learning
SSL has many benefits, but it also has its fair share of downsides. It’s important to understand these to put its advantages in context and enable more informed decisions about which ML approach is best for your needs.
High Computing Power Needs
One of the most significant limitations to self-supervised learning is its computing demands. As you might expect, automatically scanning and labeling vast amounts of data is a resource-intensive task. Consequently, SSL requires computing power that could make it inaccessible for some teams.
Overall IT spending is rising, and experts expect device budgets to shrink in the coming years, likely due to increasing IT infrastructure costs. This trend could make scaling computing power to support the workloads SSL requires unviable. SSL’s time and labor cost savings could help make up for this, but it may remain inaccessible in some contexts.
SSL models also introduce reliability issues by minimizing humans’ role in the process. Automation typically reduces human error, but supervised learning exists as a concept because models often need some input. SSL models could easily mislabel data, leading to larger issues if no one catches these errors before long.
Poor-quality data already costs businesses $9.7 million annually, and workers spend up to 50% of their time searching for and correcting errors. Employees may not catch labeling errors in a self-supervised learning process until after the model has already taught itself from the erroneous information. That could lead to costly remediation and limited ROIs.
Using SSL to Its Highest Potential
These downsides don’t make SSL useless, but they do put its potential into perspective. You should consider this technology’s pros and cons if you want to make the most of it.
Effective SSL implementation comes from using it for the right applications and with the correct data. SSL may not be the best way forward if it’s easy to make labeling errors in a dataset. Alternatively, if you have an easily distinguishable dataset that would incur substantial costs to label manually, SSL could be helpful.
Finally, remember that people should always be the ones who make the final decisions, especially with SSL. These models can produce helpful insights with minimal time and investment, but they can still introduce errors, so a human touch is necessary for all mission-critical decisions.
Understand SSL’s Pros and Cons to Use It Effectively
Self-supervised learning is exciting, and it’ll become increasingly accessible and reliable as the technology advances. However, for now, businesses should consider its strengths in light of its weaknesses to ensure it’s their best option. You can make more informed decisions when you know how SSL can benefit you and how it could fall short.