Valuation


Artificial Intelligence is difficult and complex, because if it would be easy, then everybody could do it, ;-)

Here are the "treasures" of our company as soft IP - Intellectual Property

 1. AI knowledge - status ready

  a. AI theory, taking the biological brain as a template
   b. AI model,  creating a digital biomimetic model of a brain, based on ANS classification, e.g. templates
   c. AI structure, quantifying the different brain tissues into different types of neural nets
   d. AI framework by AI levels, which imply a digital brain also develops itself in time
   e. AI levels, comparison with human brain development at different ages, e.g. at the end of year
       toddler
      - 1 month, 6 months, 1 year
      - 3 years, walk + talk + play
      kid
      - 6 years, kindergarden
      - 10 years, primary school
     teenager
      - 14 years, secondary school
      - 18 years, high school
      young
      - 23 years, degree
      - 28 years, job experience
      adult
      - 30 years - dedicated social experience

2. AI semiconductor technology in development, beta release expected in 2020

  a. AI opsys - proof of concept
           simulate brain functionality as a network of neural nets connected to
           sensors, actors and internal organs, forming an ANS for an embedded system

      - written entirely in VHDL enabling massive parallel processing
      - external interfaces - Ethernet only
      - internal interfaces - HBM2 and DDR4
      - applications - neural nets only
      - dedicated networks on chip - connecting all neural nets including the i/o neural drivers
   b. AI prototype as ANS - brain functionality with specialized regions, as ANS template + time development
            for a human with the age of 1 month, evtl. 6 months - 1 year
      - neural driver input             : sensor cam as eye, e.g. cerebral cortex for visual system
      - neural driver output           : actor screen as face, e.g. cerebellum for body reaction
      - neural net applications     : reflexes as pre-defined connectivity, e.g. brainstem
      - self-learning mechanism : at different levels, e.g. hippocampus
      - accumulated knowledge  : depending on sensors and actors, e.g. neocortex

3. AI semiconductor implementation

  a. FPGA SiP with integrated HBM2 : XCVU37P in development, beta release expected in 2020
         - different FPGA boards  

      FPGA software    
       
  - encrypted FPGA bitstream with VHDL operating system

     ANS software    
         - encrypted data as ANS with corresponding applications, according to species, see ANS classification
         - encrypted data as instincts and pre-defined knowledge, according to species
         - encrypted data as knowledge accumulated via the self-learning mechanism
         - encrypted functionalities according to training program and environment

b. 3DSoC - in evaluation
         - 3DSoC - three Dimensional monolithic System-on-a-Chip, with 3D Carbon nanotubes logic and RRAM
         - this revolutionary technology is currently transferred into production exclusively to Skywater foundry
         - we are already in contact with Skywater, in order to check a conversion from FPGA SiP to 3DSoC

For further and detailed information on 3DSoC, kindly visit Skywater corporate site, see link below:
https://www.skywatertechnology.com/

Regarding the 2019 AI hardware summit, see online article page 2 with link below
Groq: No Show at AI Hardware Summit

under the topic "Building a new brain", there are very some valuable information for startups,
concerning the big issues to build a general purpose AI and how to attract possible investors,
therefore we have higlighted these topics in bold, as we find them very useful, see text below: