In early 2022, Google AI began releasing details about an exciting new method for training deep neural networks: DeepCTRL. Google’s AI team found a way to control rule strength and accuracy in deep neural networks, allowing for improvements in some crucial AI applications. DeepCTRL is more versatile and nimble than previous methods for controlling rules in AI models and may be key to the future of the field.
What Does DeepCTRL Do?
Google AI’s DeepCTRL method presents a new way to train deep neural networks (DNNs) to understand and account for rules alongside training data. Deep neural networks with controllable rule representations, or “DeepCTRL”, allow developers to implement and adjust rules without retraining their DNN model. Rule strength can be tweaked at any time by adjusting the accuracy to rule verification ratio, ensuring both precision and accuracy in how the model learns rules and constraints.
A common problem DNN developers face is training a model to understand both data and the restrictions on that data. A model might be able to return consistent predictions based on data patterns but overestimate or underestimate because it does not understand the relevant rules. For example, a model might have to adjust predictions based on a certain formula, algorithm, or physical real-world constraint.
Training DNN models to understand these rules can be complicated at best and a hassle at worst. In real-world applications, rule training is key to not only success but also reliability. A DNN model for applications in industries like science, engineering, or healthcare has to be able to accurately predict results based on complex and crucial rules and constraints. DeepCTRL offers a cutting-edge approach to this process that is much more intuitive than traditional methods.
What Makes DeepCTRL Different?
DeepCTRL offers a simpler, more agile way to train models with rules. The major difference between Google AI’s new method and conventional rules training is right in the name: control. Not only can developers easily train rules into their models, but they can also actively control the rule strength. Technically this is possible with other methods, but DeepCTRL stands out in that rules can be adjusted without retraining the model.
This ease of controllability offers a few key benefits. DeepCTRL ensures close adherence to rules and a nuanced understanding of them. This improves the overall accuracy of DNN models. With DeepCTRL, developers can ensure their models are learning rules correctly, without accidentally learning any incorrect biases from them or misinterpreting them. Additionally, the ease of control with DeepCTRL allows for things like hypothesis testing with rule sets. Since it is easier to train rule sets with DeepCTRL, developers can add plenty of rules for extra info in cases where a small data set might leave gaps.
DeepCTRL might sound like a quality-of-life feature at first, but it could be instrumental to the future of the AI and DNN fields. Consumer distrust in AI is a major issue right now, with stories of data bias making headlines in everything from hiring to law enforcement. Developers need to train and build their models in a way that promotes reliability, transparency, and trust.
DeepCTRL helps accomplish this, even in black-box models. Rules help keep DNN models from putting too much weight on certain inputs and developing invisible biases. Adept control over those rules increases trust in a model because developers and users have a better understanding of what exactly is influencing a model’s results.
What Are the Use Cases for DeepCTRL?
People want to adopt AI and reap the benefits the technology has to offer. In fact, studies have found that implementing AI can result in a 30% increase in ROI for businesses. There is a significant market of people and businesses who are highly interested in adopting AI but are held back by a combination of consumer distrust and technical challenges. DeepCTRL can help resolve both of these issues. Google AI even outlined a couple of major use cases in their details about DeepCTRL.
The field of physics is riddled with countless complex formulas, equations, algorithms, natural laws, and other rules that need to be applied to accurately represent the physical world. These principles of physics are applicable in a vast array of fields and applications, such as engineering and chemistry. DeepCTRL allows developers to precisely train these principles, analyze how well they are working, and adjust accordingly, all without retraining a model.
One example of this use case that industry experts have cited is training a DNN model to understand a double pendulum. The model may be able to predict the pendulum’s total energy at a certain point in its trajectory from plain data. However, it will overestimate the pendulum’s energy consistently unless it is trained to understand energy conservation and other core principles of physics.
Another key use case for DeepCTRL is in healthcare applications. This is an excellent example of how valuable adjustable rule strength is. A DNN model used in healthcare will have to make predictions for a variety of patients. A patient who is older might need a different rule strength than a patient who is younger. A patient with pre-existing conditions may also need parameters adjusted to account for their needs.
A changeable ruleset ensures accuracy and reliability are maintained even with shifting and varied input data. Additionally, the model needs to understand the physical constraints of the human body that affect acceptable results. For example, the model cannot return predictions that would lead to any kind of harm to a patient.
The Importance of Rules in AI
DeepCTRL is a revolutionary development in deep neural networks. With easily controllable rule sets, developers can create more accurate and reliable models. Close control, over rule strength, will help build consumer and user trust in DNN models and reduce the likelihood of data biases developing. DeepCTRL will help increase trustworthiness in black-box AI models and improve the overall accuracy, precision, and versatility in a variety of applications.
Learn more about machine learning platforms and skills at ODSC East 2022
We just listed off quite a few machine learning engineering platforms, skills, and frameworks. It’s not expected to know every single thing mentioned above, but knowing a good chunk of them – and how to apply them in business settings – will help you get a job or become better at your current one.
At ODSC Europe 2022, we have an entire track devoted to machine learning and deep learning. Learn ML engineering skills and platforms like the ones listed above. Here are a few sessions scheduled so far:
- Beyond the Basics: Data Visualization in Python
- A Hands-on Guide to Machine Learning with TensorFlow
- Introduction to Machine Learning
- Rule Induction and Reasoning in Knowledge Graphs
- Digital Twins: Not All Digital Twins are Identical
- Time-Series in Python – Preprocessing and Machine Learning
- The Bayesian Revolution in Online Marketing
- How to Teach Our World Knowledge to a Neural Network?
- Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment
- Open Source Explainability – Understanding Model Decisions Using Alibi
- PyTorch 101: Building a Model Step-by-step
- GANs N’ Roses: Understanding Generative Models
- How to Write a Scikit-learn Compatible Estimator
- Explainability by Design: a Methodology to Support Explanations in Decision-making Systems
- Diffusion Models for Text-to-Image Generation
- Visually Inspecting Data Profiles for Data Distribution Shifts
- Computer Perception Challenges in Drone Applications Using Quality Data Annotation
- Next Generation Web Apps: Create a Machine Learning Powered Smart Cam in the Browser with TensorFlow.js
- Optimizing Your Analytics Life Cycle with Machine Learning and Open Source
- Machine Learning for Economics and Finance in TensorFlow 2
- Revealing the Inner Self: Automatic Differentiation (Autodiff) Clearly Explained
- Moving into the Frequency Domain with the Fourier Transform