5.1 Mind Mosaic AI big model

The Mind Mosaic ecosystem plans to use the multimodal data streams collected based on DePIN network and the powerful computing power of Cosmic Cipher devices to build a next-generation AI big model architecture in the vertical field to break through the limitations of the current general model.

5.1.1 Decentralized model training

Traditional AI model training relies on centralized data centers, which are both expensive and have data privacy risks. Mind Mosaic will use the distributed computing power and DePIN network of Cosmic Cipher devices to establish a decentralized model training framework. Through technologies such as Federated Learning and Secure Multi-Party Computation (SMPC), users' sensitive data will be retained on local devices and only shared model update parameters to ensure privacy and security. In the future, Cosmic Cipher plans to promote distributed AI model training through the following technical means:

• Federated Learning: Perform local model training on user equipment, upload only gradient parameters (through differential privacy protection, ε=0.5), avoid centralization of sensitive data, and improve privacy security.

• Parallel Processing: Using the distributed computing power of the DePIN network, the training tasks of the AI ​​model are sliced ​​on multiple devices, significantly reducing the demand for single-point computing power. For example, the computing power required to train a medium-sized NLP model can be reduced from $2.3 million in AWS to 1/5 of a distributed network.

• Edge AI (Edge AI): After testing, only with the support of the first generation of Cosmic Cipher N1 hardware equipped with Dimensity 8300 chip, fine-tuning of the Llama 3-70B level model can be achieved, supporting real-time inference tasks (such as speech sentiment analysis, 3D modeling), with a delay of less than 50ms.

Through the above technologies, Cosmic Cipher will develop a series of proprietary AI models covering natural language processing (NLP), computer vision (CV), behavior prediction and other fields, gradually build an intelligent foundation towards AGI, and launch more hardware devices equipped with higher performance chips to accelerate the realization of this goal.

5.1.2 Scene perception model

• Technical concept

It adopts the space-time fusion Transformer architecture to process real-time input of device sensors (1000Hz accelerometer/gyroscope/ambient light data) to achieve millimeter-level action prediction and scene understanding. The model parameter volume is controlled at 7 billion levels, and the end-cloud collaborative reasoning is realized through dynamic sparse technology (Dynamic Sparsity), and the response delay is reduced to less than 8ms.

Multimodal data fusion is mainly achieved through a three-layer cascade attention mechanism:

1. Spatial feature extraction layer: using quantized convolution kernel to process 1000Hz high-frequency sensor data stream, converting the accelerometer three-dimensional vector (X/Y/Z axis ±8g range) and the gyroscope angular velocity data (0.005dps resolution) into 128-dimensional space-time embedding vector.

2. Dynamic correlation layer: introduce gated cross attention module, establish the coupling relationship between ambient light sensor (0.01lux accuracy) and motion data, and predict user scene switching behavior through light intensity mutation detection (threshold Δ>200lux/ms).

3. Predictive output layer: Deploy a hybrid density network (MDN) to generate probabilistic action trajectories, and realize millimeter-level displacement prediction within the 800ms time window (Error ±1.2mm).

The model uses dynamic sparse technology to optimize computing resources, adopting the "3-5-2" elastic structure: retain 3 billion parameters on the end side to perform real-time inference, and deploy a complete 7 billion parameter model on the cloud for trajectory correction. Through the adaptive bandwidth allocation protocol, the end-cloud communication delay is controlled within 3.2ms in Wi-Fi6 (1024QAM modulation) environment, and the overall system response reaches the military-grade 8ms time limit requirement.

• Application scenarios

1. AR navigation: In addition to predicting the user's gait trajectory and loading POI information in advance, it can also adjust navigation guidelines in real time according to the user's movements and directions in a complex indoor environment, providing users with a more accurate and intuitive navigation experience.

2. Smart home: The home appliance control intention can be identified through the grip posture, and the operating status of the home appliance can be automatically adjusted according to the user's daily behavioral habits. For example, automatically adjust the air conditioner temperature and light brightness according to the user's sleep posture and time.

3. Industrial testing: Combined with vibration data to analyze the equipment failure mode, it can also monitor the movements of equipment operators. Medical rehabilitation: It can be used to monitor patients' rehabilitation training movements, providing doctors with detailed action data and analysis reports.

4. Sports training: Help athletes optimize training movements, analyze whether the athlete's movements are standard through millimeter-level action prediction, find potential problems and room for improvement, thereby improving training efficiency and competitive level.

5.1.3 Digital human-driven engine (MetaMind)

The digital human launched by Cosmic Cipher N1 cannot assume a complete auxiliary tool for AI big model training in the early stage. In the future, we will upgrade the digital human engine to make it a "AI human" with cognitive interaction capabilities.

• Technical objectives

Build a cross-modal sentiment computing framework and integrate:

① Micro-expression recognition

② Phonetic rhythm analysis

③ Biosignal fusion

④ Achieve digital emotional feedback error rate <3%, reaching the gold standard for Turing test.

At the same time, we strive to achieve breakthroughs in cognitive interaction:

① Develop a cross-modal memory association network (CM-MemNet) to enable digital people to establish up to 30 minutes of scene dialogue memory. Through knowledge graph embedding technology, 40 million entity relationship libraries are compressed to 800MB of storage space.

② The emotional feedback system adopts a reinforcement learning framework, with the technical goal: after training 500,000 groups of human dialogue data (perhaps more), the matching degree of emotional response to human expert labels exceeds 97%, and the success rate of passing the Turing test exceeded 80% (the gold standard threshold is 80%).

• Digital people economy

In the future, developers can encapsulate the trained digital human interaction mode into verifiable digital assets by minting "behavioral NFT". Each NFT contains 128-dimensional feature vectors and interactive quality proofs, supporting rights confirmation transactions on the Ethereum ZK-Rollup layer 2 network.

At the same time, the dynamic allocation mechanism of ecological vaults and the digital human IP incubation reward mechanism are enabled:

① Of the 0.5% transaction commission of digital people, 30% is used for technology research and development funds, 45% is used as developer incentives, and 25% is injected into a decentralized insurance pool to prevent the risk of model infringement.

② Creator incentive pool: Reward the incubation of high-quality digital human IP through tokens in ecological funds or DAO vaults.

The specific allocation ratio can be determined by the DAO vote.