OrbiSky Systems

Advances in computational efficiency are critical as we scale AI models. Techniques such as quantization and pruning are reducing model sizes and computational loads, making AI more accessible and sustainable for real-world applications.

We're delighted to be part of the Cosmic Capital Programme! Thank you SETsquared Surrey, UK Space Agency, University of Surrey, and partners for this fantastic opportunity. We're excited about the tailored support and networking opportunities to accelerate our growth alongside other innovative space tech companies. Looking forward to the journey ahead! #spacetech #space #innovation 🚀 Ian James, Seraphim Space, Rymdkapital, Space Capital

Scaling Edge AI efficiently remains a significant challenge that we at OrbiSky are tackling head-on. Effective scaling allows AI applications to handle more complex tasks and larger datasets without performance bottlenecks. Engineers, what scalability issues have you encountered in your work with Edge AI, and how have you addressed them?

At OrbiSky, we prioritize privacy by integrating differential privacy techniques into our AI models. This approach ensures that our systems can learn from data without compromising individual privacy. Techniques like noise addition and decentralized data processing are employed to maintain data anonymity while still extracting valuable insights. Engineers, how have you applied differential privacy in your data-intensive projects?

Continuous monitoring is key to maintaining AI system safety at OrbiSky. Our approach includes real-time diagnostics and anomaly detection algorithms to promptly spot and address abnormal behaviors. This ongoing vigilance helps prevent errors and maintain system integrity. Engineers, what monitoring techniques do you find most effective for ensuring AI safety?

Ensuring the security of AI involves cryptographic techniques to protect data during transmission and processing. OrbiSky researches homomorphic encryption, allowing computations on encrypted data, and providing security without compromising functionality. Engineers, which cryptographic innovations are you watching or implementing in your work with AI?

To enhance AI safety, OrbiSky uses sophisticated simulation environments to model how AI systems respond under varied and unexpected conditions. This allows us to refine AI behavior safely and efficiently before real-world deployment. Engineers, what role do simulations play in your safety testing protocols?

We commit to ethical AI by integrating bias detection algorithms to identify and correct unfair decision-making patterns in our models. Ensuring our AI systems are fair and unbiased is a step towards building more trustworthy technology. How are you addressing bias in your AI developments?

Explore the potential of AI-driven predictive maintenance, a technology we're pioneering at OrbiSky. Using AI to predict equipment failures before they happen enhances the reliability of critical systems in aerospace and defense. This application of AI not only saves on costly repairs but also prevents downtime, boosting overall operational efficiency.

Building an AI that can perceive the world requires breaking down barriers between different sensors. Our team is tackling this challenge with multimodal learning techniques. How do you think we can best fuse data from cameras, LiDAR, and IMUs for robust environment understanding? Share your thoughts in the comments! #AI #MachineLearning #SensorFusion