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+1 857 242 8359
hello@weareainatives.com
Examples of projects developed with AI Natives mentors
Weighs inherited versus environmental risk factors for disease onset, with a focus on modifiable exposures such as early-life diet and infections.
Tests whether balancing training data by skin tone makes dermatology AI more accurate for patients with darker skin.
Documents designing, printing and fitting a low-cost open-source prosthetic hand, comparing materials and joint designs.
Predicts heart disease risk from easy-to-report information like lifestyle and family history, as a low-cost screening aid.
Adds socioeconomic and population-diversity factors to clinical data to build a fairer, more representative risk model.
Combines imaging, clinical and molecular data to group patients by risk and subtype for more personalized care.
Trains a computer-vision model to detect and grade osteoarthritis on knee X-rays to support clinical judgment.
Compares machine learning classifiers on diagnostic data against clinical benchmarks for sensitivity and specificity.
Uses walking force data to detect and anticipate freezing episodes, aiming at earlier fall-prevention warnings.
Learns early warning signs from trade and government data to flag drugs at risk before they run out.
Estimates where undiagnosed cases are likely, ranks districts for intervention, and models inheritance risk for couples.
Predicts how cancer cell lines respond to glucose restriction combined with thousands of candidate drugs.
Predicts how long operations will take from surgical records to improve operating-room scheduling.
Flags high-risk patients from medical history and demographic data to support earlier intervention and bed planning.
Examples of projects developed with AI Natives mentors
Examines how Mars rover locomotion and autonomy could be adapted for earthquake, wildfire and flood zones.
Combines multiple camera views and object detection to predict where a robot should grip unfamiliar objects.
Builds a real-time vision system that locates game pieces from a competition robot's camera.
Fine-tunes an object detector to spot cracks, corrosion and wear in engine inspection images.
Trains a model to tell satellites apart from debris in real time to support collision avoidance.
Uses machine learning to correct positioning errors in a robotic arm beyond standard calibration.
Explores exoskeleton configurations with machine learning to balance support, efficiency and comfort.
Examples of projects developed with AI Natives mentors
Predicts the best laser power, speed and frequency settings and flags errors before they produce defective parts.
Searches structural configurations to recommend lower-cost designs that still meet safety constraints.
Forecasts production-line output so managers can plan staffing before slowdowns happen.
Pairs physics simulation with machine learning to recommend heat-shield materials and thicknesses.
Compares classic models with a neural network on 40,000 images; the neural network reached near-perfect accuracy.
Identifies what drives fuel burn and models flight parameters that cut both cost and emissions.
Examples of projects developed with AI Natives mentors
Predicts a vehicle's top speed from engine specifications as a quick alternative to physical testing.
Uses sensor and diagnostic data to flag electric vehicles at risk of failure, for predictive maintenance.
Trains a model to interpret officers' gestures so self-driving cars can follow manual traffic direction.
Identifies the environmental and behavioral conditions most linked to severe accidents.
Predicts whether a driver is ready to retake control from eye gaze, head movement and reaction time.
Examples of projects developed with AI Natives mentors
Identifies the factors that best explain future spending behavior, rather than a black-box forecast.
Models loan repayment behavior, including for borrowers with limited credit history.
Compares models on two decades of macroeconomic and commodity data; random forests performed best.
Predicts which discontinued sets gain value based on piece count, theme and release year.
Builds a pricing benchmark to spot over- and under-priced games across online marketplaces.
Examples of projects developed with AI Natives mentors
Builds a holistic valuation from advanced metrics, team fit and contracts, tested against real trades.
Measures how much weather and track data improve race predictions beyond driver and team form.
Finds which physical and in-match statistics best predict winning outcomes.
Benchmarks salaries against performance across six leagues and flags underpaid young attackers.
Examples of projects developed with AI Natives mentors
Calculates dynamic evacuation paths based on the type and location of an emergency.
Maps sidewalk gaps across US cities and the communities most affected by them.
Designs games around the sensory needs of autistic children, guided by research on autism-friendly design.
Tests whether an ordinary webcam can replace costly eye-tracking hardware for patient communication.
Examples of projects developed with AI Natives mentors
Detects phishing from domain age, redirects and URL structure, catching attacks content filters can miss.
Combines URL and webpage features and measures how much each contributes to detection accuracy.
Examples of projects developed with AI Natives mentors
Predicts superconducting temperatures from chemical composition to prioritize which materials to test in the lab.
Tests whether a quantum model can outperform classical methods at classifying collider events.
Examples of projects developed with AI Natives mentors
Forecasts air quality and identifies the weather factors that drive pollution in a Georgia city.
Uses NASA JPL data to estimate asteroids' mineral content and value for future space mining.
Examples of projects developed with AI Natives mentors
Applies math and machine learning to timing, harmony and dynamics in piano music.
Examples of projects developed with AI Natives mentors
Compares how the US and EU could tackle AI-generated counterfeits, and who should be held responsible.