CCNets

CCNets: Technical Overview We want to introduce our new machine learning algorithm, Causal Learning, which integrates supervised and unsupervised learning into a unified framework. Like supervised learning, this algorithm is designed to work across various data types, including tabular, images, and time-series data. Our approach leverages three neural networks—explainer, reasoner, and producer—that learn to make predictions and generate data by understanding causal relationships between X and Y in datasets. Our model training operates differently from numerous other methods based on supervised or unsupervised (generative) learning. It allows us to offer distinct advantages over current machine learning approaches. We hope many users will benefit from this innovative addition to their machine learning toolkit.

One-Click Robotics Solution: Coming Soon to the Cloud Platform. Reinforcement Learning with CCNets Designed for Compact Environments & Applications: Optimized for Gaming, Healthcare, and Robotics Using Language Models. Tuning-Free: Automatically Learns Optimal Settings Required for Training—Gamma and Lambda. No Need for Many Agent Models! Self-Managing Training Metrics: Aims for Uniform Setup Across All Environments and Agents, Ensuring Consistently Optimal Metrics Without Demanding Prior Knowledge of Robots or Environments. Leveraging Three GPT Models to Master Contextual Behaviors: In gaming, for example, it elevates experiences by not only teaching actions and values but also by teaching the reverse-engineering of agent decisions, enhancing game dynamics and strategy formulation.

CCNets has created a flower transformation clip using our open-source technology. For this demo, our model analyzed various flower images and learned the uniqueness of each flower. It captures the information from images and outputs a vector, a group of hundreds of numbers, that describes the flowers. Unlike other generative AI models that hide such information within their data processes, CCNets explains this hidden information, enabling us to blend the unique traits of different flowers. The generated images are continuously decomposed and reconstructed into their type and positional components through infinite cycles during training, learning how to blend them seamlessly over time. Instead of memorizing numerous flower details in the model, resulting in large scaling and an uncontrollable decision process, our approach reveals this information for greater control, preventing the model from behaviors that may not meet people's needs. Open-Source Repository: https://lnkd.in/ge45Fn6V

Meet CCNets on Cloud Service We are excited to announce the upcoming launch of our Causal Learning software on cloud platforms, introducing a new ML training as a service. Our software offers unique features in time-series analysis and data generation that are hard to find in the cloud service. We will ensure our training service delivers the highest model performance and customer protection through licensing in several markets. Your insights will be invaluable to our success. Thank you for your support!

CCNets Introduces Causal Learning as the Next Machine Learning, Integrating All in One. This demo video is not generated using current Gen AI technology. Instead, CCNets presents a new method for generating images exactly as desired. The animation below demonstrates how they extract the unique style of a person's handwriting to identify the writer. By inputting specific numbers, this method can manipulate someone's handwriting style. Unlike existing generative models that create images of random writers, CCNets focuses on capturing the unique handwriting style. Consequently, the generated image reflecting someone's style can be continuously decomposed and reconstructed into its style and digit components through infinite cycles, rather than generating random ones. This addresses a major issue with today's Gen AI, which often follows uncontrolled and random paths that may not meet people's needs. For more information, visit their LinkedIn page: CCNets Website: https://ccnets.org/ Follow Aman Kumarl (AI-Man) and hit the notification 🔔 bell button for more posts related to exciting new AI tools 😃 #Learning #AI #GenAi #tool

We aim to promote causal-learning as the next ML, integrating all into a one framework. We are seeking more professionals and research groups to collaborate with us. Our goal is to assist your research by saving you time and letting you focus on your own interests in this area. We can provide you with specific implementation, examples, descriptions, and our notes on variant studies, including: 1. CCNet Mechanism: Understanding how each network functions within the cooperative architecture. 2. CCNet Variants: Exploring different configurations and adaptations using the cooperative network principle, similar to the numerous variants in GANs. 3. Finance Simulation: Evaluating our time-series data prediction performance in the metrics shown below. 4. Causal Generation: Generating treatments to achieve desired outcomes in reality. Please check the tutorials in our open-source repository. 5. Pharmaceutical Modeling: Addressing challenges with expensive experiments and sparse/imbalanced data by indirectly learning predictions through recreating data from what is predicted. 6. Publication and Education: Sharing findings through publications and educational initiatives. Explore Our Work: Patents: https://lnkd.in/dTTtP9TU Example Research Papers: https://lnkd.in/gmBSn-Rm Performance Metrics: https://lnkd.in/dp47aBai Open-Source Repository: https://lnkd.in/ge45Fn6V

Can GenAI Recreate Human Body Information Now? GenAI models are capable of producing realistic images and data, even creating compelling videos. However, there's a limitation: these models introduce randomness into their outputs and do not truly understand the relationships between different parts of what they generate. We offer Causal Generative AI: A Structured Approach, which can break down complex body information into its functional parts. This approach continuously decomposes and reconstructs these components, ensuring accurate interactions between them. While current GenAI can generate visually stunning and lifelike images, their lack of physical understanding and inherent randomness limit their accuracy for mechanical tasks like body simulation. Our solution focuses on the functional aspects of body parts, generating outcomes that align with how the real world works.

Why Train and Test Scores Are Different in Machine Learning? In supervised learning, models often achieve excellent performance on the training data but poor performance on unseen data because they tend to memorize the training data rather than generalize from it. Our causal learning trains models by generating the original data from their predictions, indirectly learning what’s necessary to recreate the data. This robustly defends against overfitting and enhances performance on unseen data. This approach proves more applicable in real-world scenarios, opening up new business opportunities by leveraging limited data and experience. It thereby offers an efficient go-to-market toolkit. For examples on public datasets including images, tabular data, and time-series, check out our report demonstrating perfectly aligned train/test scores (R2 scores) during training: https://lnkd.in/gWT4bDS9 Details on causal learning are available on our Git: https://lnkd.in/ge45Fn6V

Many ML models are designed to predict patient survival (outcome Y) based on their condition data (X). In contrast, CCNets take a reverse approach: we simulate and identify the conditions (X) necessary to achieve a desired outcome, ensuring patient survival. For example, instead of applying a treatment (T) to try to improve patient survival, we can encode patient data (X) to learn latent variables (E). By starting with the desired outcome (Y), these latent variables (E) provide the additional information needed to create new conditions (X) that lead to the same desired outcome. Unlike inference methods that gradually approach the desired outcome, our generative causal model directly sets the target outcome of survival and uses the latent information (E) derived from patient data to determine the necessary conditions for achieving it. By following the data generation process, this generative model creates real-world scenarios and identifies the essential conditions that lead to the desired outcome for the patients. We provide the essential algorithms and supporting examples below. For further business opportunities and advanced design inquiries, please contact us: Git: https://lnkd.in/ge45Fn6V Algorithm Details: https://lnkd.in/g5_hA-bi

We have observed many machine learning models focused on predicting the outcome Y or assessing the impact of changing a treatment T in the current status X. However, fewer models adopt a reverse approach that simulates and identifies the specific conditions necessary to happen a desired outcome which could be more important. CCNets introduce a generative framework based on cause-and-effect relationships learned from datasets. For example, we assume a scenario where a patient survives, and generate conditions that must have been present to lead to this preferred outcome. By demonstrating the conditional independence of changing outcome Y in relation to learned latent causes E, given status X, and establishing that Y and E are necessary and sufficient causes to causally generate X, we aim to validate CCNets as a generative causal model and invite feedback on it. To further explore these concepts, we have prepared several tutorials available at https://lnkd.in/ge45Fn6V Algorithm details: https://lnkd.in/g5_hA-bi