Tipalo® - Time based pattern logic
Autonomy + AI = Cognitive AI
The body is the decisive argument for autonomy, as it enables to do everything on its own.
Cognitive AI requires a body hardware with sensors, actors and internal organs, like any living entity.
Next level of autonomy are the exclusive use of spiking neural nets, organized in knowledge areas.
This implies that only some neurons are needed for a specific purpose, allowing explainable AI.
Next advantage of autonomy is the software, as synapses between neurons, which implies no code.
Synapses are used as universal format for any purpose and enable the accumulation of knowledge.
Tipalo GmbH is a Swiss LLC near Zurich, a fabless AI startup with own software to pioneer logic applications.
The Tipalo approach to AI is very simple: we take biological intelligence as a template, means the living brain.
It needs a different mindset to comprehend and represent parts of the real world and replicate the human mind.
Our understanding of intelligence is based on logic, our AI concept contains objects reacting in time and space.
Tipalo AI enables the simulation of different brain regions in real-time, all the time and stand alone,
while being connected to a certain body hardware, which is equipped with sensors, actors and organs.
The VHDL implementation of our AI model allows massive parallel execution, without any processors.
This implies we do not use any math/statistics/algorithms, while no bias/training/inference is required.
We use 3 intelligence levels, which have a template in nature: insects, fishes/mammals/birds and primates.
They reflect the amount of neurons and their synapses, from 1M with 16, via 1G with 256, to 10G with 1K.
Each level has pre-defined knowledge, to manage the body and the mind, together with some genetic memory.
The AI explores autonomously the environment and stores its own experience as accumulated knowledge.
AI PLATFORM FOR DIGITAL BRAINS
Tipalo develops strong AI with cognitive features creating hereby digital brains with:
1. Real-time operating system in VHDL, enables simulation of brain tissue via Programmable Neural Nets (PNN)
2. Self-Learning Mechanism (SLM) enables knowledge accumulation using Self-Associative Memory (SAM)
3. Artificial Nervous System (ANS), contains the brain landscape as map of all synapses, see connectome
AI LIBRARIES FOR NEURAL COMPONENTS
Tipalo develops AI libraries with configurable reference designs based on PNNs for different ANS levels as:
1. Neural drivers for interfacing sensors, actors and organs of any kind, e.g. visual, limbs, rechargeable battery
2. Neural applications for various knowledge areas using SLM, e.g. identification, locomotion, task workflows
3. Neural storage for different memory types based on SAM, e.g. short-, mid- and long-term memory
Tipalo AIs can connect with each other, via cable or wireless, to communicate for several purposes:
1. positive identification, to enable the coordination of their actions for a possible common task
2. certain interaction, to enable the extension of knowledge by teaching each other new things
3. direct exchange, to enable the aggregation of information for a certain area of knowledge
EDGE AI FOR EMBEDDED SYSTEMS
Tipalo brains are linked via a single interface to the corresponding body and perform intelligence for:
Level 1, as managers for smart building automation, e.g. indoor manufacturing, outdoor surveillance
Level 2, as pilots for autonomous vehicles of any kind, e.g. terrestrial, naval, aeronautics, space
Level 3. as robotic workers for outer space activities, e.g. in space stations, on extraterrestrial planets
CLOUD AI FOR TELEPRESENCE
Tipalo brains can be connected via Internet to any embedded system or robot body,
by using a connection with high bandwidth and low latency, like 5G or fiber glass,
Furthermore, the accumulated experience of many individual brains can be aggregated,
enabling the collective knowledge for a certain body type, e.g. autonomous vehicles.
COLONY AI FOR VARIOUS ENVIRONMENTS
Tipalo brains can be connected with each other in order to build a colony AI in a certain environment,
this implies the spatial distribution of many connected AIs in a given landscape, both edge and cloud AI.
While the edge AIs is performing its tasks within the adjacent neighborhood and stores its experience,
the cloud AIs aggregate the new information by areas of knowledge, making it accessible to everyone else.