We are helping to safely bring the first woman astronaut to the moon as part of NASA’s Artemis Project with our System Invariant Analysis Technology (SIAT). This milestone represents a giant leap not just for space exploration, but for the role of artificial intelligence in ensuring mission safety and success.

Working with Lockheed Martin Space’s T-Tauri AI platform, our SIAT analytics engine ingests data from 150,000 sensors and builds a model incorporating over 22 billion data relationships. The AI model is then analyzed to identify irregularities that could lead to malfunctions in any of the spacecraft’s systems. By detecting potential issues before they escalate, SIAT plays a critical role in keeping astronauts safe and missions on track. This kind of proactive, AI-driven monitoring represents the future of human spaceflight.

The collaboration between NEC Laboratories America and Lockheed Martin reflects a shared commitment to pushing the boundaries of what AI can achieve in high-stakes environments. As space missions grow more complex, the ability to process and interpret massive volumes of sensor data in real time becomes not just an advantage, but a necessity.

Read the full article

Related: Lockheed Martin and NEC Put AI to Work on Programs like NASA’s Artemis Mission (Press Release)
“`

Using AI To Safely Put The First Woman On The Moon

“I’m really proud of what the NEC team has accomplished here. AI tools like SIAT that help manage data and complexity will be key enablers for future space missions. I look forward to what we can accomplish with great partners like Lockheed Martin.”
— Haifeng Chen, Head of the Data Science and System Security Department, NEC Laboratories America

Read Our News Posts

Mix-CLAP Teaching Audio AI to Work in the Noisy Real World

Mix-CLAP: Teaching Audio AI to Work in the Noisy Real World

Mix-CLAP from NEC Laboratories America delivers near-Transformer accuracy for sound event classification at a fraction of the compute cost, using dual lightweight encoders and adaptive, noise-aware inference for real-world edge deployment.
How AI Can Transform the Way Companies Buy What They Need

How AI Can Transform the Way Companies Buy What They Need

Procurement teams lose time and money to inaccurate demand forecasts and manual supplier negotiations. A new framework from NEC Corporation and NEC Laboratories America combines automated negotiation with multimodal AI forecasting to optimize both sides of the procurement process.
Open SAT How We Taught AI to Search Satellite Images Like a Search Engine

Open SAT: How We Taught AI to Search Satellite Images Like a Search Engine

Satellite imagery is vast, high-resolution, and rich with information, but finding specific objects within it using natural language has remained a stubborn challenge. Open-SAT, developed by researchers at NEC Laboratories America and North South University, tackles this problem without retraining any models.
Training Small AI Models Without Blindly Trusting Big Teacher Models

Training Small AI Models Without Blindly Trusting Big Teacher Models

Machine learning is shifting from learning from data alone to learning from both data and teacher models. Beta-KD uses uncertainty-aware Bayesian weighting to train compact multimodal AI without blindly trusting every teacher signal.
How Rule-Driven Routing Makes Retrieval-Augmented Generation Smarter

How Rule-Driven Routing Makes Retrieval-Augmented Generation Smarter

Most retrieval-augmented generation systems stop at documents, ignoring the relational databases that power finance, healthcare, and research. Our researchers built a rule-driven framework that learns which source to query for each question, delivering better answers at lower computational cost.
Making Video AI Fast Enough for the Real World

Making Video AI Fast Enough for the Real World

State-of-the-art video models are accurate but too slow for live deployment. This work transfers their knowledge into causal streaming models that process video frames in real time, achieving 4x lower latency with competitive accuracy across action detection and pedestrian intent tasks.