Tipalo® - Time based pattern logic

Tipalo GmbH is a Swiss LLC, we are an AI startup, with own hardware + software to pioneer logic applications.
The Tipalo approach to AI is very simple: we take biological intelligence as a template, means the living brain.
The levels of intelligence have a template in nature, from insects via fishes / mammals / birds to primates.
This reflects the amount of neurons and their synapses, from 1M with 16, via 1G with 256, to 10 G with 1K.

The biological nervous system, BNS, has some complex features, which are the basis for living organisms:
1. interfaces to the different body components, means sensors, actors and internal organs
2. specialized neural nets, capable to adapt for a specific purpose, depending on the body and its intelligence
3. an own self-learning mechanism, enabling knowledge accumulation via own experience

Tipalo develops software with biological features similar to those found in biological brains:
1. A real-time operating system, which enables parallel execution of neural nets, capable of self-programming
2. Artificial Nervous System(ANS), as the sum of all connections within the neural nets, known as connectome
3. A self-learning mechanism(SLM) enables knowledge accumulation, using SAM, Self-associative memory

This enables living machines with body and self-learning brain, capable of coordination of all body parts.
For this we use FPGAs to prove our biological approach in conjunction with a certain body hardware;
afterwards combine the FD-SOI process with new persistent memory technologies to create a digital brain,
which will be placed in a cubesat, allowing its use on Earth, in harsh environments and even in deep space.

The digital brains can be used according to their levels of intelligence:
Level 1, for surveillance, intruder detection and autonomous delivery
Level 2, as pilots for autonomous vehicles of any kind, terrestrial, naval, aerospace
Level 3. as robotic workers, with different body hardware

What do we consider intelligence and why can we do this?




Our company has a holistic approach, integrating many areas, such as philosophy, physics, chemistry, cytology, histology, genetics, embryology, biology, anatomy, physiology, neurology, psychology, sociology, business management, economics, ethics, history,  mythology, linguistics, pedagogy, semiconductor technologies, databases, programming languages, operating systems, software development and especially logic.

The Tipalo AI model is very clear and each element has a fixed scope, it allows hereby the interactive tracking of each active neuron. This approach allows quantifying and hereby explaining the architecture of each neural network, so that an extension is possible at any time, within the predefined amount of neurons that an ANS allows, which on its side, depends on the level of intelligence as well as of the corresponding body structure with the hereby connected devices, means sensors, actors and internal organs.

Furthermore, a predefined genetic knowledge of the corresponding ANS structure, in the form of predefined connections between the individual cells, is both given and desirable. Thus, further knowledge can be easily added on the basis of personal experience, including the current context, so that different areas of knowledge are updated independently of each other. This is enabled by an own embedded SLM, Self-Learning Mechanism, using many and scalable SAM, Self-Associative Memory.

By remembering the accumulated knowledge, the ANS can easily explain why a certain decision or action was made, at any given time. The black-box problem of all current/conventional neural networks is thus solved, by the exact localization of each neuron and also the meaning of each connection to other neurons, although Tipalo neural networks are spiking and thus neuromorphic in design, do not use biases, but at the same time are fully digitally implemented.


  • Tipalo - SLM for ANS-L1 using SAM
  • Tipalo_ANS
  • Tipalo AI perspectives
  • Tipalo - explaining AI
  • Tipalo AI - explanation and impact
  • Tipalo AI - Rework Summit