The global food supply is on the brink of crisis. Facing challenges like an aging workforce, labor shortages, declining crop yields, and climate change amongst others, farming is in dire need of a lifeline. AI and AgTech are emerging as the saviors of agriculture, transforming everything from crop breeding to farm-to-table traceability. These revolutionary technologies promise to enhance sustainability, improve yields, and reduce environmental impact. With the urgency for innovation greater than ever, AI is the essential lifeline the agricultural industry needs to secure a resilient and efficient future.
Using AI Driven Video Metrics to Improve Soccer Performance
Professional athletes have long had the advantage of analysis from expensive body worn sensors. AI models, when applied to readily available video, however, offer much promise to more cost conscious amateur athletes. We ask the question, can we better an individual goalie’s chances of stopping a penalty kick with advanced AI?
Using AI to Improve Sports Performance & Achieve Better Running Efficiency
Can amateur athletes improve their performance using artificial intelligence and nothing more than a smart phone? As an AI practitioner and a dedicated runner, I decided to find out leveraging body pose estimation, an advanced AI technology that automatically generates a skeleton for people in each frame of a video.
Drones to Robot Farm Hands, AI Transforms Agriculture
From detecting pests to predicting which crops will deliver the best returns, artificial intelligence can help humanity oppose one of its biggest challenges: feeding an additional 2 billion people by 2050 without harming the planet. AI is steadily emerging as an essential part of the agricultural industry’s technological evolution including self-driving machinery and flying robots that are able to automatically survey and treat crops.
Vannot - Video Annotation Tool for Object Segmentation
Transforming Radiology and Diagnostic Imaging with AI
Throughout the diagnostic related care cycle, physicians are observing and understanding patterns. Pattern recognition is the key task for understanding the results of clinical scans. Neural networks, an automated pattern recognition capability, shows strong promise in predicting cancer and segmenting specific tumors for breast cancers, rectal cancers, and other categories of cancer that affect many Americans each year.