The effectiveness of AI models for risk prediction is still a matter of debate among academics and investors, and there is still no reliable method of predicting risk using artificial intelligence.
So at the behest of the boss, any programmer can download from GitHub a free program or framework for risk prediction, in any programming language:
Python libraries TensorFlow, Keras, PyTorch, and Theano;
C++: C++ Libraries, TensorFlow and Caffe;
Java: these are the Deeplearning4j, DL4J frameworks;
MATLAB: MATLAB library Neural Network Toolbox;
Julia: Julia is Flux and Knet frameworks.
For a Data Scientist, choosing a neural network for training is a complex task, to select the best solutions from a sea of codes and data, you also need to consider many indirect features of the event and understand the specifics of the risk assessment segment.
It's simple when you know the scale of the problem that will allow AI with long-term memory to look into the future. It takes 15 years to develop sophisticated prediction models to save lives and increase money and capital.
Dry figures of 87.5% recognition at a brute force of 0.1% is an opportunity to save millions of lives and billions of money, and investors will gain trillions in stock market capitalization. Where these predictions come from - to see the FUTURE is to understand the REAL times and events.
Competitors in 2019 were only able to solve the risk prediction problem by 98%.
This is with their tremendous capabilities and level of AI scientists and the availability of huge amounts of money. The problems were in the brute force and the computations required. But we went the other way and got the minimum brute force in 2025.
The reason seems trivial, but it's still global:
Neural networks typically run flat 2-dimensional computations to assess 3-dimensional risks. If we add the factor of time and money, the problem is solved to the fifth power of event variants. Our long-term memory model LMM AI solves the problem to the 8th degree of understanding risks in insurance driving autopilots and gaming strategies.
Predicting outcomes remains a challenge due to the pseudo randomness of events in capital and service markets.
Lack of open proven methods to create reliable risk predictions that can guarantee an AI startup can win BIG money in the insurance market, driving autopilots protecting people from risk.
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