Since conventional AI technologies were not suitable for our purposes, we decided to start from scratch and develop our own technology. It was important for us to overcome the following limitations:

  1. Neural nets always need highly structured input data: These are generally prepared by a very homogeneous group and so it often happens that biases arise already before the training.
  2. The interaction with AI is reserved for only a few: Mostly software developers and data scientists. This also leads to biases that occur already in the development phase and continue throughout the entire training process (training of neural networks) until the output of the AI.
  3. All AI systems are based on a probabilistic inductive system (stimulus-response scheme). This means that independent reasoning (thinking and creativity) is not possible with it.
  4. Lack of transparency: Neural networks are still a black box.
  5. Compatibility with quantum computers: As of yet, there are no future-proof AI algorithms that run on quantum computers
  6. Existing AI systems require high computing power, hence consume huge amounts of electricity

These were the reasons why we have created a completely new AI-technology which is the first Artificial General Intelligence (AGI) in the world.  This has paid off, because we have created a system that has the following advantages:

  1. Our AGI can work with any kind of input data. These do not need to be specifically prepared by humans.
  2. Interaction with our system is in natural language (any possible language) including the training as well as the verification of the results. Thus, anyone who can read and write can work with our system.
  3. Our system is based on empirical deductivism. Thus, thinking[1] is also possible.
  4. In our neural networks there is transparency inside and outside the neural networks.[2]
  5. We are the first company in the world to develop quantum neural networks[3] running on quantum computers (IBM).
  6. Our AGI requires 80% less computing power, hence consuming less electricity than standard AI-solutions.[4]

Xephor Quantum Neural Networks:

In the last years there has been tremendous progress in the field of Artificial Intelligence, in particular regarding Artificial Neuronal Networks (ANNs) which are structures that can be thought of as consisting of stacked layers of computational nodes called artificial neurons. Each layer receives input from the previous layer and sends data to the following layer in the following way: Every artificial neuron of a layer receives input signals from a plurality of artificial neurons from the preceding layer and uses these input signals to compute a single output signal which is then sent together with the output signals of the other artificial neurons of this layer to the artificial neurons of the next layer. It is important to know that signals coming from different artificial neurons of the preceding layers are given different weights when calculating the output signal.

An ANN must be trained using training data to work properly. The training data is used, for each artificial neuron, to find the “correct” weights that should be applied to the input signals such that the ANN gives correct results.

Conventional ANNs need:

  • a lot of highly structured training data needed
  • can have the problem of becoming “unstable” such that training must be restarted
  • need a lot of computational power

Result: The entire effort is very tedious: data must be available in a certain quantity and must be prepared in a complex way so that it can be processed at all. This also means that there is no flexibility to quickly run tests with other data. In addition, the entire process is a real energy guzzler: enormous hardware resources have to be used to keep the process running.

It’s much simpler and less complicated with Quantum Neuronal Networks (QNNs):

QNNs use one of Quantum Mechanics most striking features, “Entanglement”. In a sense, two quantum systems are called “entangled” when it is possible to change one quantum system by influencing the other quantum system, no matter how far the two quantum systems are away from each other.

In QNNs the weights of the artificial neurons are not “corrected” independently from each other but in such a way that they show features best called “entangled”, i.e. a correction of one of the entangled weights changes all of the other entangled ways.

Result: QNNs have a variety of advantages compared to conventional ANNs:

  • they need far less training data (about 10 % of the amount needed for ANNs)
  • they run stable, therefore there is no need to restart training
  • it is possible to parallelly correct a lot of weights which reduces the amount of computation power by 80%

And the highlight: QNNs can also be adapted to run on quantum computers!

For those who want to know exactly how QNNs are working:

[1] Machine thinking, not human thinking. This means that the system is able to generate new ideas and react to not trained and unforeseen situation. Pending patents: Category Theory (Machine Thinking), EP20193672, Random Number Generator, EP21179252

[2] For more information please see : Outline of a trustworthy AI, CSR and Artificial Intelligence, Reinhard Altenburger,Springer Verlag, 2021

[3] Pending patent: Parallel Stochastic Quantum Neuronal Networks, EP21173761.4, US17/319708

[4] Patent: US 2019243795 A1


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