• On-Chain

Distributed training approach optimizes data processing

Skinner AI presents a revolutionary solution for the challenges associated with training, testing, and validating computer vision models for drones, robotics, and autonomous vehicles. Its unique Ecosystem fosters a vibrant and active community, encouraging users to contribute computational resources, and easily generate high-quality datasets. This, in combination with its scalable, efficient, and flexible framework, positions Skinner to drive significant advancements in the deployment of trustworthy machine intelligence in these fields

The role of Skinner AI in training machine intelligence, that should have multiple global implications

SKINNER, as a Synthetic Knowledge Integrator for Neural Network Enhanced Robotics, offers an innovative distributed deep learning training framework focused on Сomputer vision applications for drones, robotics, and autonomous vehicles. Beyond its distributed training capabilities, Skinner features a unique Synthetic data generator that enables users to create, purchase, and sell high-quality datasets derived from Photogrammetric, Generative, or custom 3D models. Utilizing these technologies, Skinner ensures the integrity and reliability of its training processes and addresses the critical challenge of retraining neural networks. This has numerous global implications, including advancements in health, education, climate science, and more.

Alexander Pak, Founder,
Lead Technician

Ultimately, our Mission today is to make the new world a better place for the next generation tomorrow, the morality upon which our future is based should prioritize human well-being, fairness, and the protection of fundamental rights.

Ann Pak, MEcon,
LLM Engineer

By considering the ethical implications of AI training and development, we can work towards creating systems that are beneficial, respectful, and aligned with our shared values.

We Are Committed to continuous improvement of the platform and interaction with the community

The roadmap envisions Skinner AI as a transformative player across multiple sectors. Through its continuous platform refinement, community engagement, multi-system integration, and global expansion, Skinner AI is set to redefine the way machine intelligence is trained, tested, and deployed across various domains

Frequently Asked Questions

Synthetic data refers to artificially generated data that mimics the statistical properties and characteristics of real-world data. It is created using algorithms and models to closely resemble the patterns and structure of the original data without containing any personally identifiable information (PII) or sensitive details.
Synthetic data is generated using various techniques such as generative models, simulation algorithms, or statistical methods. These methods analyze the patterns and correlations present in real data and use that information to generate new datasets that closely resemble the original data.
Synthetic data offers several benefits such as privacy preservation, data augmentation, and scenario simulation. It can be used when the original data is sensitive or restricted, when the dataset needs to be expanded, or when creating realistic scenarios for testing or training purposes.
The advantages of using Synthetic data include the ability to protect privacy by replacing sensitive information, reducing the risk of data breaches, enabling wider data sharing, creating larger and more diverse datasets, and facilitating controlled experiments and simulations without real-world consequences.
Synthetic data is artificially generated and does not originate from real-world observations. While it closely mimics the statistical properties of real data, it does not capture the nuances, specific context, or biases present in the original data. However, it can still be valuable for many use cases and provide reliable insights.
Synthetic data is a valuable resource for training and fine-tuning machine learning models. It can be used to supplement existing datasets, balance class distributions, mitigate bias, enhance model generalization, and augment rare or hard-to-obtain samples. It enables model training in situations where real data may be limited or expensive to acquire.
Yes, Synthetic data can be used for both training and testing machine learning models. Synthetic data can serve as a valuable training set, especially when real data is scarce or protected. It can also be used in conjunction with real data for model evaluation and performance testing in controlled scenarios.
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