HOW TO DESIGN THE ARCHITECTURE OF AN AI ENGINE
We support companies on their Digital Transformation journey providing our expertise to specify and design AI solutions. Our objective is to bring cognition to your solutions and make them aware.
After over 20 years doing IT for Fortune 500 companies, we have acquired a lot of experience on delivery methodologies. And we just thought that this was the good way forward. We already had success with this and we should reuse and adapt these to what we do.
It was a good idea and it is still, if and only if we talk about delivery. You have to specify and design first, do the functional work and then the technical, build, test and deploy.
But here it is all about getting to the architecture of an artificial intelligent engine which is executing cognitive and psychological tasks. All these tasks are linked together and are using data. We just thought that the correct way to have this peace of the work done is to follow the same way that information is following when we humans are processing something: perception is first, then encoding, acquisition & learning or when something is already known, recognition. The result is stored in memory for further processing… And we just went on like this. At the end, we where wrong! This is not working.
STRUCTURE YOUR VIRTUALBRAIN
The only way to structure your VirtualBrain (or for IT fellows, get your database design or physical data model) is to follow a different road. We do this in four steps.
Step 1 (cognition) is to get information about the context and the objects that are involved, what kind of cognitive processes are implicated at this level to draft a first version of the VirtualBrain structure.
Step 2 (psychology) is about measuring satisfaction, understand what reasoning has to be done to take decisions to change the attitude of the engine and define tasks to be executed. At this step, we know all the data we need to have the AI engine working.
Step 3 (mental activities) is about defining how to collect the required data and how these will be processed by the machine to be acquired, in case the data is new, or recognized, or forgotten. Once this is done, the learning process can be specified so that all the needed data are collected by the AI engine. The VirtualBrain structure can be updated and considered as final.
Step 4 (understanding) is defining how knowledge should be represented and how collected data are impacting this representation (cognitive level) in order to understand (psychological level, thinking) what is happening and adapt the actions of the AI engines.
OUR KEY DOMAINS OF EXPERTISE
Psychology / Mental Processes: Association, Judgement, Attitudes, Communication, Interaction, Needs, Motivation, Satisfaction, Perception, Memory, Recognition, Acquisition, Classification, Learning, Categorization, Semantic Network.
Cognition: Representation, Understanding, Reasoning, Decision Making, Problem Solving.
Design of AI Solutions: Ethics, Ergonomics, Psychophysiology, Neurobiology, Nervous system, Endocrine system, Physiology, Sensorial Treatment, Experimentation, Observation, Collect Verbal answers, Investigation, Questionnaire (Open-ended, Closed-ended), Interview, Test, Psychophysical Methods, Conception of AI algorithms and engines (Technical Part).
Supporting Businesses: Context assessment, maturity model for AI, support to define business cases, business process rationalization and modernization, cognitive roadmap and trainings on human cognition.