In the past In the last six months, we’ve witnessed an incredible growth in AI. The release of Stable Diffusion changed the art world forever. ChatGPT-3 rocked the internet with its ability to write songs, mimic research papers, and provide complete, seemingly intelligent answers to commonly-searched questions on Google.
These advances in generative AI are further proof that we are on the brink of an AI revolution.
However, most of these generative AI models are basic models. In other words, high-capacity unsupervised learning systems that require millions of dollars of processing power to train on huge amounts of data. Currently, only well-funded institutions with access to vast amounts of GPU power can build these models.
The majority of companies developing application-layer AI, which is driving the technology’s pervasiveness, still rely on supervised learning with large amounts of labeled training data. Despite the impressive achievements of our foundational models, we are still in the early days of the AI revolution and many bottlenecks prevent the adoption of application-layer AI.
Downstream of the well-known data labeling problem, there are additional data bottlenecks that impede later-stage AI development and its deployment into production.
Due to these issues, despite the flood of initial promise and investment, technologies like self-driving cars have been around for just over a year since 2014.
These exciting proof-of-concept models perform well on datasets benchmarked in research environments, but are difficult to predict exactly when they will be released in the real world. , struggles to meet the higher performance thresholds required in high-risk production environments, and falls short of key benchmarks for robustness, reliability, and maintainability.
For example, these models often fail to handle outliers and special cases, so self-driving cars mistake the bicycle reflection for the bicycle itself. Lacking reliability and robustness, the robot barista makes two of his five perfect cappuccinos, but spills the other three.
As a result, the AI production gap—the gap between “this is great” and “this is useful”—is much bigger and more daunting than ML engineers first expected.
Counterintuitively, the best systems also have the most human interaction.
Fortunately, as more and more ML engineers adopt a data-centric approach to AI development, the implementation of active learning strategies is on the rise. The most sophisticated companies leverage this technology to leapfrog his AI production gap and build models that can go into production faster.
What is active learning?
Active learning makes training a supervised model an iterative process. The model is trained on an initial subset of labeled data from a large dataset. It then tries to make predictions on the remaining unlabeled data based on what it has learned. The ML engineer evaluates how confident the model’s predictions are and uses different acquisition functions to see the added performance benefits of annotating one of the unlabeled examples. Quantifiable.
By representing the uncertainty of the prediction, the model determines which additional data is most useful for training. Doing so asks the annotator to provide more examples of only that particular type of data so that we can train more intensively on that subset in the next training round. Think of it like quizzing your students to figure out where their knowledge gaps are. Once you know which issues you are missing, we can provide you with textbooks, presentations, and other materials to help you focus your studies to better understand specific aspects of your subject matter.
With active learning, model training moves from a linear process to a circular process with strong feedback loops.
Why Sophisticated Enterprises Must Be Ready to Leverage Active Learning
Active learning is fundamental to bridging the gap between prototype and production and increasing model reliability.
It’s a common mistake to think of AI systems as static pieces of software, but these systems must be constantly learning and evolving. Otherwise, they either make the same mistakes over and over again, or encounter new scenarios when released into the wild, make new mistakes and have no chance to learn from them.
