📢Introduction

Sentient is a research lab focused on synthetic dataset generation for robotic systems. Embodied AI. Our token HELP stands for Human Enhanced Learning Process. We are using human coordination capabilities to advance robotics through the creation of hyper specific synthetic datasets for multimodal systems. Unlocking the ability to generate high-quality synthetic datasets at scale is a billion-dollar opportunity. HELP's mission is the construction of sophisticated datasets that simulate real-world scenarios and interactions. These synthetic datasets are meticulously designed to capture the complexity and nuances of various sensory inputs that robots may encounter in their operational environments. AI rapid advancement led to an unprecedented demand for high-quality, diverse datasets to train increasingly sophisticated models. AI systems becoming more complex and specialised, requires exponentially more training data. The availability of suitable real-world data is becoming a significant bottleneck. We are hitting a data wall.

Synthetic data, artificially generated to mimic the statistical properties of real-world data, offers solutions to the data scarcity problem. Unlocking the ability to generate high-quality synthetic datasets at scale is a billion-dollar opportunity.

Synthetic data in AI training has the ability to address data imbalances and fill gaps in existing datasets. In many real-world scenarios, certain data points or categories may be underrepresented, leading to biased or poorly performing models. Synthetic data generation techniques can be employed to create additional samples for these underrepresented classes, effectively balancing the dataset and improving the model's overall performance and generalization capabilities.

Synthetic data enables us to explore edge cases and rare scenarios that may be difficult or impossible to capture in real-world data collection. This is particularly valuable in fields such as autonomous driving, where simulating dangerous or uncommon situations is crucial for training robust AI systems. By generating synthetic data for these scenarios, we can expose our models to a wider range of possibilities, enhancing their ability to handle unexpected situations in the real world. Sentient's 1st project aims to capture human reasoning by using AI and humans in tandem.