One of the biggest machine learning trends you can expect to see in 2021 and beyond is the broader adoption of a machine learning segment called deep learning and a method it employs called weak supervision. This method is faster, more streamlined, and has many benefits. Moving forward, companies will assess how they can leverage this in their business to execute on desired automation tasks and learn and predict the best ways to accomplish task completion– all without the heavy burden of human intervention. Weak supervision is bringing us ever closer to software that can think and act on its own, and here we will break down the pitfalls of the past, the methods of the future, and what it means for your business. Further solidifying weak supervision as an entirely accepted practice in our immediate future is the added element of Explainable AI (XAI) that serves to future-proof this new approach to ramping up an AI system.
The Pitfalls of Traditional Machine Learning
Before we can understand where we’re headed, it’s essential first to know where we’ve been. Traditional Machine Learning was found to have the flaw of requiring an excess of time to train and set up an AI system. Traditional methods need massive sets of training data for preparing an AI system to do its job. The need for training data is a shortcoming because gathering and cleansing data is a big task. A considerable amount of upfront manual labor is involved, and the tasks become more time-consuming and complex as the amount of data increases. This is where we’ve been. Where we’re headed is toward a new approach called weak supervision.
The Weak Supervision Method
Weak supervision is about leveraging higher-level and/or noisier input from subject matter experts (SMEs). Many traditional approaches in ML are facing the bottleneck of getting tons of labeled training data. Weak supervision tries to avoid that.
Ground truth annotations are the foundation of knowledge that is initially used to train an AI system. Ground truth was seen as the gold standard of information or the basis upon which an automated system makes its decisions. It was mostly used in supervised learning systems. An excellent example of this is a spam filter that is fed all of the data upon which to make decisions. A spam filter is highly supervised, and trained specifically on all of the information about what is and is not spam. After this initial lengthy setup, the filter gets to work, removing emails that it deems to be spam based on what it has been told. If the filter has problems, those problems result from how it was trained and what information it was given to serve as its ground truth. By contrast, the weak supervision method is not as highly supervised. It learns as it goes instead of requiring a colossal upfront time investment for training. It builds machine learning models without relying on ground truth annotations. Instead, weak supervision generates probabilistic training labels.
And what are probabilistic training labels, you ask? Think of it like this- have you ever needed to get something done quickly, and instead of completing a task in an ideal way, you used a shortcut? Or made an estimation? Or used guesswork? A very similar process is used in the weak supervision method with probabilistic training labels. Certain elements are unknown, so the AI uses heuristics, which is a fancier, more technical term for a shortcut. The probabilistic training labels are made by estimating the accuracy of multiple sets of data containing info that is not meaningful and has much useless noise. These datasets containing labels with noise are called noisy labeling sources. The probabilistic training labels have figured out how to ignore the noise, and they don’t try to derive meaningful patterns from it or use it to automate a process. This has proven to be very beneficial in business.
The business needs for rapid digital automation are best served by solutions that are powered by weak supervision. For example, in large-scale product development initiatives that fall under the umbrella of ModelOps or MLOps, it is necessary to maintain any customized model over time and the product’s life. The manual entry and classification that existed with our spam filter example disappear with probabilistic training labels, which dramatically reduces the implementation time of the AI system and sets the stage for the needed optimization paths. Because the process of weak supervision allows for learning by example, shortcuts, and estimations, businesses see the benefit of time savings along with the many other advantages of deep learning.
The Benefits of Deep Learning
Deep learning moves the needle forward to help companies see more return on their investment in artificial intelligence and digital transformation. One of the most significant benefits of deep learning is the continuous improvement of accuracy. As a deep learning system now can operate without training, it is not limited to the ground truth annotations of a controlled and heavily supervised learning situation. The absence of these limits enables constant improvements in accurate and reliable results.
Another benefit of deep learning is the feedback effect. This can best be described as a feedback loop that enables the AI to receive and act on positive and negative feedback and make ongoing assessments of the success of each set of actions. A technique called backpropagation is what allows the deep learning system to return to unfavorable results and figure out how not to repeat the same mistake. Essentially, with these features, your AI solution becomes a system that can talk to itself!
Automated processing is another benefit of deep learning involving new abilities around processing data, particularly unstructured data. Unstructured data consists of images and videos or other formats that can’t as quickly be labeled in a database. Additionally, unstructured data is harder to categorize and bring up in search results when using traditional machine learning solutions. As a result, the automated processing of these data types has been more challenging to achieve. With deep learning, this is now possible, and its applications span areas like species identification, automatic reading of x-rays, and many others. If you would like to dig deeper on this topic, the Standford AI Lab wrote a blog article on this, which presents a good summary.
It’s easy to get excited about weak supervision and all that this new deep learning area can do. But does this new way forward have the capability to serve as a solid rung on the ladder of progress in the journey of AI advancement? Despite all of the improvements to machine learning, there is still always a chance that something could go wrong. The new development serving as protection against these potential deep learning problems is called explainable AI (XAI).
If an AI’s decision leaves you wondering about the logic behind an unexpected shortcut or why a dog gets labeled as a cat in your automated task results, there is a solution for this. Explainable AI (XAI) makes the inner workings of an AI’s output traceable and comprehensible. Explanations for why the solution generated particular labels, classifications, enrichments, and information help humans fix what goes wrong with feedback loops or when decisions go awry.
With Explainable AI, humans can make queries into why or how an AI system is reasoning. Visualizations of decision trees can be produced along with other graphs that show the AI’s train of thought. The system can then be fed additional information to interrupt an ineffective feedback loop or help mitigate areas where humans have a cognitive bias that unknowingly impacts an AI’s decisions.
AI’s Continued Advancements
Ultimately, as AI becomes a necessary component of making future predictions, acting on those predictions, and executing tasks, trust is paramount. Whether it’s something intense like using drones in a war, or a meaningful forecast impacting a business owner’s bottom line, you can expect AI to enter into every possible industry to help support decision making. Until now, artificial intelligence was slowed due to system preparation and training times when machine learning was still experiencing growing pains. Now, new deep learning methods have been thrust into the spotlight as a component of machine learning. Weak supervision has been a winning process in deep learning to achieve quicker start times and fewer human time commitments. And as advancements continue, deep learning has found a great safety net in the transparency and recourse for XAI modifications. Not only will these trends expand, but you also will more likely wind up benefitting from the determinations of our new digital intellectual partners.