1. Deep data analysis:
It took 15 years of tons of gaming and scientific experiments to achieve these results. USA, CANADA, FRANCE, RF, CHINA countries that helped train AI on open data sets for game strategies.
Today, LMM AI is able to process data as an eight-dimensional computational model, revealing hidden correlations and patterns, a fundamental difference from 2-dimensional neural networks that rely on massive computing power and past patterns.
The use of new computational technology helps to signal potential risks to players and businessmen.
This stage is passed and ready for application.
2. Modeling complex scenarios:
LMM AI enables the creation of inconsistent and multidimensional models that process entropy and game strategies in different business situations and track their evolution over time. This approach to risk assessment makes it possible to calculate the likely consequences of various events and develop effective risk defense strategies.
This stage is passed and ready for application.
Complex Scenario Modeling:
LMM AI enables the creation of controversial and multidimensional models that process entropy and game strategies in different business situations and track their development over time. This approach to risk assessment makes it possible to calculate the likely consequences of various events and develop effective risk defense strategies.
This stage is passed and ready for application.
3. Machine learning for prediction:
Machine learning algorithms are able to learn from historical data to is a silly pattern from the past.
In order to predict the probability of certain events happening in the future, completely new algorithms and creative individuals are needed that work together to create an unusual and very effective solution. This is how the symbiosis of man and machine in the LMM algorithm was born.
For example, an LMM trained on complex data sets can predict the probability of traffic accidents, failures in game strategy, detect anomalies in crowd wisdom behavior, or predict demand for products and services.
This phase is ready for funding.
4. Automating monitoring and management:
With proper funding and deployment of computing power, AI will be able to automate routine risk monitoring tasks, allowing experts to focus on analyzing more complex and strategic business survival issues.
This greatly accelerates decision-making and improves responsiveness to emerging threats.
This phase is ready for funding.
5. Early Warning and Preventive Measures:
By subscribing, the AI system user will be able to receive timely warnings of potential risks, enabling organizations to take preventative measures and minimize damage.
This stage is ready for notification protocols to be established.
6. Ethical and social aspects:
This is a topic for the distant future, but the application of AI in risk management presents a number of ethical and social challenges. It's important to ensure AI algorithms are objective, protect data privacy, and minimize negative social impacts. BUT for now, lawmakers are delving into the 2 words artificial intelligence instead of the art of intelligence
This stage is not ready for the dialog of artificial intelligence with human intelligence
7. The real benefits of using AI for risk forecasting:
Improved accuracy of game and risk forecasts: AI enables the identification of complex relationships between different facts and ulterior motives, which improves forecasting accuracy.
When investor funding is deployed, it will allow the creation of mechanisms Accelerating decision-making: Automation of routine tasks speeds up decision-making and allows for rapid response to changes in the situation.
8. Optimize resources: AI enables efficient resource allocation and minimizes risk management costs.
Increase competitiveness:
Organizations that use AI to manage risk gain a competitive advantage by operating more efficiently and reducing risk.
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