Notes AI is expected to complete the upgrade of model parameters from the current 175 billion to 1.2 trillion by 2026, based on the optimization of the Pathways architecture of Google DeepMind, its multi-modal processing capacity will be tripled. Allow simultaneous parsing of text, image, video and sensor data (e.g., vibration frequency of industrial equipment ≥200Hz). It is shown from OpenAI’s 2024 technical white paper that Notes AI’s semantic understanding error rate can be reduced by 18% for every 10 times parameter size increase, e.g., medical image diagnosis target location accuracy can be enhanced from 89% to 96% with the processing time still within 0.3 seconds/task. A multination automaker has inked a deal with Notes AI and will use it in an autonomous driving system in 2027 for real-time processing of in-vehicle cameras (8K@60fps) and lidar point clouds (1200 points/m2 density). The goal is to enhance traffic sign recognition from 98.7% to 99.999% accuracy (meeting ASIL-D safety standards).
In the vertical direction, Notes AI will dive deep via industry-specific models (vertical parameters account for more than 40%). For example, the legal industry’s Knowledge Graph will contain 5 million case data, which will improve the accuracy of contract clause risk prediction from 91% to 97%, estimated by one top law firm to reduce the time for human review by 75% and the cost per case by 42%. In education, its adaptive learning engine can analyze in real time students’ answer patterns (e.g., math error type distribution: 63% algebraic mistakes, 28% geometric proof logic gaps), generate personalized learning pathways, and increase the average rate of grade level improvement from 15% to 27%. Gartner estimates that between now and 2028, AI-driven enterprise solutions for Notes will cover 73% of Fortune 500 companies worldwide with an average yearly cost saving of $38 billion.
Notes AI will also incorporate quantum computing in hardware collaboration for advances in computing capability. IBM Quantum Cloud platform trial showed that using 1121 qubit processors, Notes AI’s complex decision-making processes (such as supply chain network optimization) were completed faster than before, from 22 minutes to 0.8 seconds, and energy consumption was reduced by 99%. In the meantime, edge computing versions (device size ≤10cm³) will be deployed to iot terminals, i.e., a smart factory company that will embed Notes AI in 100,000 sets of industrial robots for the application of fault prediction (vibration amplitude alarm threshold 0.05mm) and self-healing decision-making (accuracy rate 92%), with the effect of reducing equipment downtime by 65%. But delays in commercialising quantum computing can make it too expensive by 2027 – at $850 per qubit hour today, it needs to fall below $5 for general adoption.
Evolution of ethics and compliance will be paramount. The European Union has proposed requiring Notes AI-like systems to be “full link interpretable” by 2025, with black-box decision transparency increasing from 48% today to 90%. A bank test found that by utilizing a causal reasoning model (counterfactual analysis accuracy of 83%), Notes AI’s credit approval discrimination rate was reduced from 1.2% to 0.3%, meeting the EU AI Act Level A compliance threshold. In the meantime, some governments are promoting the use of Notes AI to public administration – e.g., Singapore used its model to simulate epidemic spread (R0 prediction error ±0.15) to reduce the policy-making process from 14 days to six hours. However, Deepfake defense remains challenging: the 89% detection rate of AI-generated fake videos in the current version needs to rise to 99.9% by 2026 to thwart information warfare attacks.