Our biggest innovation is what we call machine thinking. For the first time, it is possible for an AI to generate independent ideas and to react to untrained content. To accomplish this, we took the human brain as role model because it has an astonishing ability to deal with complex information in real-time.

For many years, research in Artificial Intelligence has tried to mimic that ability with results that begin to transform our everyday life. It is now possible for an Artificial Neuronal Network (ANN) to recognize objects and persons in a live video stream paving the way to autonomous driving. For this to be possible, the different parameters of the ANN must be finetuned which is done by supervised training, i.e., a supervisor provides the ANN with training data and checks whether the ANN provides correct results.

To the researcher’s astonishment, tasks that seem very easy to a person are quite difficult for an ANN. For example, imagine a logistic distribution center where packages of different shape and size must be reliably recognized and sorted, even if they have been damaged in transport and do no longer have nice geometric shapes. No problem for a person but next to impossible for an ANN without extensive supervised training.

Xephor Solutions has succeeded in creating an Artificial General Intelligence (AGI) system which can successfully perform any intellectual task which a human being can do after minimal supervised training so that it can be viewed as an Artificial Brain.

Xephor Solutions’ Artificial Brain comprises thousands of ANN organized in neuronal columns. Each neuronal column is entrusted with a specific task. When the Artificial Brain is provided with a data stream, the neuronal columns continuously check whether there is something in the data stream which they are supposed to work on. To facilitate these tasks a separate computational process divides the data stream into small data chunks which are tagged so that the neuronal columns can easily recognize those data chunks which they are supposed to work on.

Astonishingly, by organizing the neuronal columns into larger work units using concepts from the mathematical field of category theory, XephorSolutions’ Artificial Brain is able to learn in an unsupervised way. Coming back to the logistic distribution center given as an example above, once a work unit of neuronal columns has been trained to recognize a box-shaped package irrespective of size, color and transportation damages, other neuronal columns which represent the categorical concept of a “functor”[1] send these results to a different work unit which is supposed to recognize packages of irregular shape so that this work unit does not need separate supervised training.

This leads to a thinking circuit[2]:

1. Special neurons in the thalamo-neural networks generate structured random data. These random data are sent to the surrounding neural networks.
2. The neural networks receive them and start sending them to the different cortical columns according to their structure. The data are sent from the thalamo neural networks to the corresponding cortical columns as if through a gateway/hub and further classified. This happens mostly in a circuit between the thalamo neural networks and the cortical columns. After the data or parts of the data have been classified as meaningful, they are sent back to the thalamo neural networks. These now try to locate the cortical columns available for these data and send them there.

3. The data are appropriately structured by the category-theoretically modeled networks of neural networks, e.g., into data that the system can understand and data that makes sense but that the system do not know if it is correct in context. In case of uncertainty, the system queries the environment (trainer, compiler, sensor, etc.).

Once this classification is done, output data are given in the desired form (e.g., chart, graph, number series, natural language). The machine thinking model affects the nature of learning: As new content is learned the AGI uses reasoning to try to make and understand connections from the information in the sentence. In the process, this content is compared to previously learned data. This is especially important for learning from context, as it allows the system to constantly check what knowledge is already available on a given topic and what conclusions can be drawn from it. This means that new and existing content is

placed in meaningful contexts. Information should have a similar structure in order to be defined as meaningful. This allows the AGI to interpret new content.

Which advantages does this circuit of thinking provide?

• No cleaning or structuring of data is needed before the AI-system (“artificial brain”) is trained
• 20 times fewer input data needed (patented)
• 80% less computing power required. Therefore, also substantial energy savings (patented)
• 99.9% less programming as it is a general-purpose system
• Evolved problem solving: Ability to provide new ideas and react to new and unforeseen situations (patented)
• 1,000,000,000 times faster on Quantum computers

More technical details can be found here:
https://at.espacenet.com/publicationDetails/originalDocument?CC=EP&NR=3961508A1&KC=A1&FT=D&ND=3&date=20220302&DB=EPODOC&locale=de_AT# 

[1] Machine thinking can be seen as an extremely advanced and biologically oriented variant of GNN (Graph Neural Nets)

[2] Analog to the thalamic dynamic core theory