Can enable

A NEW BALANCE 

between 

"Experience" and "Science"

Can concentrate  

Data = Examples = Experience 

into  

Example-Based Models, 

using techniques available 

from

"Machine Learning",

"Data Science",

"Deep Learning",

Neural Networks, 

"Predictive Analytics", 

Data Engineering,

"Data Modeling",

"Data Mining",

"Big Data",

Object-Oriented Modeling,

"Data Architecture",

"Explainable AI"

etc.

Can construct  

High-Dimensional, Inter-disciplinary, Nonlinear Models 

of arbitrary complexity, 

including 

"local explanation features" 

to meet restricted low-dimensional human understanding capabilities.

Can do 

Assumption-Free = Prejudice-Free Modeling 

with intrinsic Accuracy-Control.

Can include and integrate 

any Scientific, Rule-based, 

and Heuristic Know-How 

into intermodal Example-Based Models. 

Can develope 

from Discipline-Specific to

 Interdisciplinary up to Holistic Models, 

using "mixed" (= numeric and categorical) Parameter Sets.

Makes today's Computer Power useful, 

not only for Science-Based, but 

also for Example-Based Modeling.

Can be used for High-Dimensional Nonlinear 

Parameter Identification, 

to find most relevant Parameter Sets 

for any given Problem.

Can be used especially for 

Dynamic Problems

(in time or any other parameter !), 

using our special 

Quasi-Partial-Differential-Equation-Modeling

  Technique.

Can be used 

for any application pattern,

like

 Pattern Recognition,

Pattern Extrapolation,

Time Series Prediction,

Series Extrapolation,

Data Driven Processes,

Data Driven Businesses,

Data Driven Government,

Data Driven Anything,

etc pp.

Can enable

A NEW BALANCE 

between 

"Experience" = "Examples" and "Science",

when it comes to understanding and guiding today's and tomorrow's world.

The key for

successful and innovative 

CAEBM projects

is not the straight-forward deployment of today's Buzzword Technologies (like eg Big Data, Machine Learning, Neural Nets etc) 

but 

to identify, refine, and deploy problem-relevant, intermodal, interdisciplinary Parameter Sets,

and 

to collect & maintain (continuously) the appropriate Examples = Experience!

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> Computer-Aided Example-Based Modeling (= CAEBM) can analyze and explain arbitrary, interdisciplinary, high-dimendinal (also dynamic) problems from any domain,  employing models with numeric and no-nnumeric parameters.

> The associative modeling is done based exclusively on examples= experience, using "Genetic Neural Nets" (GNN). No theories, no assumptions = prejudices, no additional verifications are required: The problem-examples alone, collected from any source, define the models, with built-in analysis and representation of the required model complexity and intrinsic accuracy control.

Alternatively, especially for models with mostly non-numeric parameters, "Boosted Decision Tree Forests" (BDTF) techniques are deployed, for reduction of computing power and for more efficient explanation capabilities.

> The resulting highly portable models can "explain" the contribution of any Influence Parameter, in addition to any relationship details between the participating parameters in 1 to 3 of many dimensions, making the models  "understandable" by and plausible for "1-3 dimensional thinking" human users.

> As practically anything on earth can be seen as (a sequence of) examples, CAEBM can use the "reality" = myriads of examples available and happening day by day, to identify the know-how contained, and to make it re-usable for humans and computers.

> In general, CAEBM can expand considerably the Example- = Experience-based problem understanding capabilities of humans, restricted normally to max 1-3 dimensions, the same way like Computer Aid (CA) has done it so impressively for science-based problem understanding over several decades. 

> A new fruitful balance between "Experience" = "Examples" and "Science" becomes possible, leading to understanding and controlling today's and tomorrow's problems, even if "unsolvable" up to now.

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> Know-how is the most important resource in today's businesses and organizations.

> Know-how is distributed in people's heads, in science-based models, in test results, in rules, in example-collections etc.

> Any know-how from any source, and anything (happening) in this world can be understood and represented by (a sequence of) examples, with numeric and nonnumeric parameters, if appropriate.

> Our Example- = Experience-Based Modeling (EBM) technology constructs high- dimensional parametric models, from examples only. Neural Nets are used as modeling kernel and Genetic Algorithms setup the models and train them.

> We use Computer Aid (CA) for example preparation, iterative model setup, generalization & quality assurance, and parameter set optimization, resulting in our CAEBM technology: EBM+CA=CAEBM.

> Our results are handy computer models, containing the example-based know-how from any source and complexity in arbitrary problem domains, defined by the parameter set in use, error measures & control included.

> Our CAEBM technology can be used to collect, consolidate, continuously refine, and systematically RE-USE any know-how in any problem domain.

> Our CAEBM technology can construct  new solutions for high-dimendional, interdisciplinary, even scientifically unresolvable problems, to make them understandable at the same time for human brains, normally restricted to max 2-3 dimensional problem understanding.

> Our CAEBM technology makes know-how becoming a portable, tradable product, independent from the know-how sources. This allows for totally new business opportunities, eg concentrating on know-how collection and refinement in their special problem domains. And until now  "impossible" problem solutions become feasible.***

*** Design of predictive local, regional, national etc  Covid-19 strategies, based on worldwide examples of infections and contra-measures taken, minimizing the damage done to people's health, to personal freedom, and to economy, while avoiding "wavy" pandemic developments.

*** Exploring and quantification of the important influence factors on "global warming", to identify the relevant parameters and to avoid blind & helpless activities, especially if they ruin economics and people's standard of living.

*** Design of a low-cost, self-learning, local, regional, national etc weather prediction network, based on the weather itself as example source, making short to long-time predictions as needed at low budgets.

*** Design of a self-learning, inter-modal, local, regional, national etc traffic control network, based on the traffic itself as example source, minimizing traffic burden + maximizing transport performance for a given infrastructure, and identifying cost-minimal bottle-neck eliminations + most cost-efficient infrastructure improvements.

*** Setup of "intelligent", self-learning test stands, which collect their experiences gained so far by CAEBM technology in test stand-specific models (TSSMs), ready then to do additional test jobs in a fraction of time, because most often new test jobs can be mostly fullfiled by the TSSMs, and only a few additional test stand runs are needed, to "calibrate" the experience to the new test job.

*** Setup and maintenance of a know-how network of CAEBMs for the development of a family of products (eg a family of cars), to be used for super-fast development (and production) of customer-individual products.

*** and many many more...

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