about AISHE - "Artificial Intelligence System Highly Experienced"

AISHE stands for "Artificial Intelligence System Highly Experienced" and refers to a collection of computer science techniques that enable computer programs and systems to act automatically. The AISHE system was developed based on Artificial Intelligence (AI). Neural data analysis provides AISHE system clients with real-time data and states to enable efficient strategic financial trading. The system uses various technologies such as Machine Learning (ML), Neural Networks (NN), Swarm Intelligence (SI), Computational Intelligence (CI), and Supervised Learning (SL) to conduct trading automatically and effectively.

The AISHE system client is offered as a SaaS and requires an ActivX, RTD, or DDE connection to a bank or broker, as well as Windows 10/11. Users can passively manage their portfolio and benefit from the advantages of Artificial Intelligence. The AISHE system client should be tested with demo money by each user at the beginning and, if necessary, the hardware should be adjusted to meet the system requirements and training method and quality. Users are responsible for the training and monitoring of the AISHE system client themselves. Much love and dedication were invested in the development of AISHE to provide users with a simple and effective solution as well as freedom.

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The AISHE system refers to the ability of techniques to learn, recognize states, and extract data from them, which then shows up in the optimization of their own function. Self-learning algorithms that learn from user behavior can be found, for example, in Google searches or in the display of posts in various social networks. The best-known systems are probably virtual assistants like Siri or Alexa, which are capable of processing human speech.

AISHE obtains datasets from a variety of sources, including online databases, user-generated content, and proprietary sources. The system is designed to be adaptable and can handle various types of trading data. Once the data is obtained, it goes through a preprocessing stage, where it is cleaned, organized, and prepared for analysis. This may include tasks such as removing duplicate entries, standardizing formatting, and converting file types.

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AISHE also uses the possibilities to generate its own datasets through recording and other methods. For example, it uses DDE/RTD data to receive, to supplement real-world data, and to create training sets for its own machine learning algorithms. Data protection and security are also top priorities for AISHE. The system employs advanced encryption and access control measures to ensure sensitive data is protected and only accessible to authorized users.

machine learning

The basis for AI in AISHE is machine learning - a technique in which software models are trained using data input. Using various methods, the application learns from existing data and cases in order to make predictions for unknown cases and to calculate them correctly, i.e. to "act intelligently". Computer science distinguishes between supervised learning, unsupervised learning and reinforcement learning.

Supervised Learning

AISHE uses a technique called supervised learning to make predictions. This involves training an algorithm to learn the relationship between input data (X) and a known output (Y), also called a label. The algorithm is initially trained on a subset of the data with known labels, and then validated using the remaining data. The model's predictions are compared to the actual labels to evaluate its performance. Once the model is trained, it can be used to predict new labels for new input data.

Unsupervised Learning

AISHE also utilizes unsupervised learning to analyze data sets. Unlike supervised learning, unsupervised learning does not rely on known labels in the training data sets. Instead, it uses algorithms to identify similarities between individual data sets, which are then grouped into clusters. This allows AISHE to detect and model hidden or underlying structures in the data sets without relying on pre-defined labels. Unsupervised learning is often used in data exploration and pattern recognition to identify unknown relationships and structures in data.

Reinforcement Learning

AISHE also utilizes reinforcement learning to train applications by receiving a positive or negative reaction to an action. The prerequisite is the use of a program that acts completely autonomously - a so-called agent. In this learning process, the agent calculates future actions based on experience in order to arrive at an "intelligent" result even in complex or multidimensional situations.
Reinforcement learning is a type of machine learning where AISHE uses an algorithm that allows an agent to learn through trial and error interactions with an environment. The agent receives feedback in the form of rewards or punishments for its actions, which helps it to learn the optimal behavior to achieve a certain goal. The goal is typically defined in terms of maximizing the cumulative reward over a period of time. The agent uses this feedback to update its policy, which is the mapping between states and actions. This process is called the reinforcement learning loop and it continues until the agent has learned the optimal policy for the given environment. Reinforcement learning is particularly useful in situations where the optimal behavior is not known in advance or where it is difficult to specify a set of rules for the behavior.

Deep learning

Deep learning is a subfield of machine learning that utilizes neural networks with many hidden layers to process and learn from large amounts of data. These neural networks are structured to resemble the interconnected neurons in the human brain, allowing them to learn and identify complex patterns in data.
Through a process known as backpropagation, the neural network is trained on a large dataset, adjusting the weights and biases of the nodes to minimize the error between predicted outputs and actual outputs. This process is repeated many times, with the network gradually improving its ability to accurately predict outputs for new input data.
Deep learning has been applied to a wide range of tasks, including image recognition, speech recognition, natural language processing, and autonomous driving. Its ability to learn from unstructured and complex data has made it a powerful tool in the field of AI.
Neural networks are similar to a technical translation of the human brain and its impulses between the individual synapses.

Federated Learning

Federated learning is a machine learning technique that allows multiple parties to collaborate on building a shared machine learning model while keeping their data private. In traditional machine learning, data is typically collected in one central location and used to train a model. However, this approach can raise privacy concerns, as sensitive data is often involved.
With federated learning, data remains on local devices or servers, and only the trained model is transmitted between devices. This approach allows multiple parties to collaborate on a machine learning project without sharing their data with others.

Collective learning

Collective learning in the context of the AISHE system client refers to the system's ability to improve its performance and accuracy over time by learning from its own experiences and the experiences of other AISHE system clients.
The AISHE system is an AI-based trading system that uses swarm intelligence, machine learning, and neural networks to analyze market conditions and make autonomous trades. As the system carries out trades, it generates a vast amount of data that can be analyzed and used to refine its trading strategies.
Through collective learning, the AISHE system client can share this data with other AISHE system clients, allowing them to learn from the collective experiences of the entire network. This means that as more clients use the system and generate data, the performance and accuracy of the entire network can improve.
This collective learning approach has the potential to create a powerful feedback loop, where the system's ability to analyze market conditions and make trades autonomously is continuously improving. This could lead to higher profits for users of the system and a more accurate understanding of market trends over time.
Overall, collective learning is an important feature of the AISHE system client, as it allows the system to continuously learn and adapt to changing market conditions, improving its performance and accuracy over time.

Weak vs Strong AI

According to their level of intelligence, AI is divided into weak and strong AI. AISHE utilizes both weak and strong AI.

Weak AI describes systems that simulate autonomous behavior but do not learn independently. For example, NLP (Natural Language Processing) trained programs can recognize natural language but not understand it. That is, a weak language agent recognizes specific words and uses them to perform a specific pre-programmed function, such as Alexa and Siri.
Strong AI, on the other hand, is a hypothetical AI that is more intelligent than humans, as it constantly optimizes its behavior through algorithms and independent feedback and can therefore also act unpredictably. It is mostly based on unsupervised learning methods in which it collects, processes, and clusters data, constantly learning and adapting. The current most widespread use is in video games, where AI is given moves, situations, and other variables, which it optimizes and develops further so that it can beat humans in games such as poker.