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What is high-precision AI?

High-precision AI refers to an AI system whose output results can meet high-precision requirements, and sometimes also refers to some or all of the technologies required by high-precision AI systems.

Why general AI cannot achieve high precision?

There are two main general AI models, one is the language models. The language models inherit the fuzziness of language symbol operations, and their output is usually selected from the first K words according to the relative probabilities of these words. They basically pursue applicability rather than high precision, so they are generally impossible to perform high-precision reasoning.

Another general model is the visual models. The visual models process visual signals, which are essentially the same as language signals; but due to its lower complexity, visual signal has lower dimensionality.

It is difficult for any signal processing model to achieve high precision, which is determined by the characteristics of symbolic operations.

While traditional machine learning models are too simple to capture the deep patterns of scientific data, and in most cases cannot achieve high accuracy.

Symbolic operation and continuous operation

Symbolic operation is a discrete operation. Symbols are essentially clouds of meanings, so symbols have fuzzy boundaries and volumes. Symbolic operation is an operation on meaning clouds, and the calculation result only needs to fall within a target meaning cloud. In high-dimensional space, the meaning clouds are often huge in volume, which essentially determines that the results of symbolic operations are fuzzy results and cannot be accurate results.

Continuous operation in the field of artificial intelligence is a vector operation in the mathematical sense. The vector points to a unique point in space and has no volume. The result of continuous operation is also a point without volume, which is accurate.

At present, all language AI models and visual AI models perform symbolic operations under the disguise of continuous operations.

Dataset purification

First, test the purity of the dataset and determine its noise level. In scientific datasets, the general noise level is usually lower than that of symbolic datasets such as languages and images. The big difference is that the noise of symbolic datasets is easier to identify and remove. The noise in scientific datasets is generally deep noise. There data and noise have very similar distribution patterns and are difficult to distinguish and separate. It is often necessary to develop dedicated test models and noise reduction models based on the characteristics of the dataset. In addition to statistical interpolation technology and population testing technology, its underlying technology also requires the use of deep neural networks, reinforcement networks, dual AI generation-discrimination systems, model-free systems and sometimes their hybrid models.

The purification of datasets often cannot reach 100%, and it is necessary to remove the impure parts during model designing and training.

Data Regularization

Before model designing, during model training, and after model training, the probabilistic nature of AI requires the data, intermediate output, and final output must be periodically regularized to eliminate noise. These techniques mainly include batch normalization, layer normalization, regularization, scaling, etc.

Training mechanism

The design of the training mechanism mainly includes loss function and reward function, batch size, epochs of training, learning rate, weight decay rate, etc. The focus is on designing dedicated loss function and reward function, and setting parameters related to learning rate. These need to be set according to the results of the characteristic analysis of the dataset and confirmed through small-scale experiments.

Data synthesis

Synthetic data must be carefully checked to confirm that it belongs to the same population as the original data before they can be mixed. There will also be noise in the synthetic data, and sometimes the noise level might be relatively high, which is related to the purity of the original data and the data synthesis model. Therefore, synthetic data requires a special noise reduction and reconfirmation process. This process is often overlooked, resulting in poor performance of the prediction model trained using these synthetic data.

Uses of high-precision AI

On the one hand, high-precision AI systems can be used to synthesize high-precision data, saving a lot of experimental costs. High-precision AI synthetic data could even remove experimental errors and is more accurate than experimental data.

On the other hand, high-precision AI systems can be used for reverse engineering predictions to predict the initial state or condition data from the result data.

In fact, high-precision AI can be used for modeling, data synthesis, and data prediction in most fields and any directions.

What is high precision?

The output data accuracy of high-precision AI reaches more than 95%, and often reaches more than 99%.

Symbolic data and precise data

Languages and images are both symbolic data because these data have a large degree of freedom to change. For example, the embedding in the language model, even if the dimension value changes by 1/3, the semantics of this embedding will not change. Changing the value of some pixels in the image will have minuscule effect on the result of image recognition.

However, scientific data is much more precise. If the value of scientific data is modified by the same magnitude, the data will completely lose its scientific value and become unusable noise.

General models and special models

Most pre-trained models are general models in a sense. AI scientists pre-train these models with the focus on improving the generalization ability of the models to widely adapt to the different needs of users.

On the other hand, special models are developed for a specific task and can best match the characteristics of the target system and its data structure.

General models cannot naturally achieve high accuracy because they cannot take into account the particularities of specific usage scenarios. Only special models can take into account the particularities of the target system and achieve high accuracy.

Therefore, the models used to process scientific datasets need to be built from scratch according to the characteristics of the datasets, and every detail needs to be fine-tuned to achieve high accuracy requirements.

Pre-trained models cannot be used directly to process scientific datasets unless the accuracy requirements are greatly reduced.

Three foundations of high-precision AI

High purity of datasets

The structure and complexity of AI match the structure and complexity of the data itself

Appropriate training mechanisms

Model Design

In the design of high-precision AI models, we must first plan the denoising in the model information flow according to the noise level of the data. In the design of activation functions, the setting of the number of layers, the design of algorithms, the connection of functional modules, the use of forward layers, etc.

The design of high-precision AI models requires evaluating the complexity of the data and matching the complexity of the model with the complexity of the data. If the complexity of the AI system exceeds the complexity of the data, it will overfit, amplify the noise, and quickly reduce the accuracy of the model. If the complexity of the AI system is lower than the complexity of the data, it will not be able to discover all useful patterns in the data, and the model's predictions will certainly not achieve a high accuracy. An important aspect of data complexity analysis is to analyze the real system that generates the data. The complexity of the data is directly related to the complexity of the real system that generates the data. The complexity of the AI model is mainly related to the number of parameters of the model, and there are some other factors. The matching of complexity requires experience and intuition, as well as repetitive experiments.

A more advanced model design is to achieve consistency between the internal structure of the AI model and the data structure. For example, nested structures are widely present in natural languages, and language models with nested structures can better adapt to language tasks. The internal structures of data in different scientific research fields are different, and case by case analysis and design are required to achieve the best results.

Theoretical accuracy

High-precision AI systems designed specifically for processing a scientific dataset can theoretically achieve 100% accuracy, that is, meet all requirements in terms of precision according to the object systems.

Data Regularization

Before model designing, during model training, and after model training, the probabilistic nature of AI requires the data, intermediate output, and final output must be periodically regularized to eliminate noise. These techniques mainly include batch normalization, layer normalization, regularization, scaling, etc.

Application fields of high-precision AI

High-precision AI can be widely used in many scientific research areas such as physics, chemistry, biology, medicine, and economics for single-step or multi-step forward and reverse engineering data synthesis and data prediction. Even with a small initial dataset, the entire research system can be constructed by expanding the dataset and reverse engineering, greatly accelerating the project progress.

High-precision AI is particularly suitable for such research projects where:

1) The experimental cost is high, including equipment cost and time cost.

2) The relationship between the input data and output data of the system is complex and difficult to solve with existing formula or formula group.

High-precision AI is suitable for scientific research areas with high precision requirements and high complexity.