This document proposes a Hybrid Intelligent Interface (HII) that can: 1. Explain complex systems of human thought as executable scripts without coding. 2. Allow users to program and create concepts without extensive details. 3. Conceptualize values and machine learning through sample training to select signals, borders, and spots in images. The HII is demonstrated through a sample script that loads an image, trains classifiers on samples to select areas, and calculates parameters without coding. While not a fully realized technology, it shows the potential for a quasi-natural language interface to AI systems.

1. Hybrid Intelligent Interface
Alexey Egorov
Senior AI Developer
Cognifield, LLC
alex.L.egorov@gmail.com
Denver
2019
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2. Part I. Common
About Machine Learning as Software 2.0
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3. Difference between usual Software and ML (Software 2.0)
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4. Machine Learning: Myths & Biases
4

5. Machine Learning: Advantages
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6. Machine Learning: Problems
6

7. 7
Adversarial examples

8. Machine Learning: Trends
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9. Part II
Hybrid Intelligent Interface
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10. Hybrid Intelligent Interface
1. A technology to explain to
complex system of human
thoughts as executable script.
2. An ability to program without
coding.
3. An ability to create a concept
without describing much
detail.
Notice: now, Hybrid Interface is an hypothetical technology, it does not exist as whole robust variant, just in separate dummies and
prototypes
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11. Ontology
* Element as an unit
* Group as an access
* Descriptor as a storage
* Interface as a role
* Function as an element
* Container as an instance
* Relation as a structure
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12. Ontology. Elements
● Any instance is an instance of
interface (object oriented class
analog)
● Interface has descriptors, witch
defines properties (attributes), and
relations
● Instance exists as a part of integrity
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13. Ontology. Events
● Any sentence in the system is an
event
● Events have two spaces: localization
(spatial, temporal, causal, objective,
reliability, modality, perceptibility)
and lapse
● Lapse is a duration: local, stage and
permanent: event, process, and
status
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14. Controlled
Language
* Easy to use class or instance of class
without hard design experience
* Easy to describe as a new object
and to define its structure
* Easy to set up logical structure and
conditions
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15. ● Any proposition is a text about
scene
● Any scene is a model
● Any scene is a complement of
sentences and markups
● Markup is defined by rules and
predicates
● Sentence is defined by name (of
domain specific) and language
Controlled Language
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16. Machine
Conceptualization
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17. Value
Value as a quantity
Fuzzy ‘Linguistic’ Value
Value as a distribution of parameter
30 mins‘Conceptualized’ Value
Value as a data approximation
The Value Evolution
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18. //-------------------------------------------
// 1. load the scene image
//-------------------------------------------
define element
element has get, set, print, max, min, len, sum,
count
define point as element
point has x, y, color
define file as element
file has path
define image as element, file
image has {point}, width, height
declare scene as image
get scene from file where path is 'scene.xml'
print scene as image
//-------------------------------------------
Test sample of HII implementing. 1
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19. Test sample of HII implementing. 2
//-------------------------------------------
// 2. train the signal by samples
//-------------------------------------------
define ml_function as element
ml_function has method, path, train
declare signal as {image}, ml_function
set method of signal to 'M_012'
train signal where path is 'Signal Samples'
//-------------------------------------------
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20. Test sample of HII implementing. 3
//-------------------------------------------
// 3. select the signal area
//-------------------------------------------
declare selected_area as image
for each point in scene
add point to selected_area
where point is signal
set color of selected_area to white
print selected_area as image
//-------------------------------------------
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21. Test sample of HII implementing. 4
//-------------------------------------------
// 4. train and select the border by samples
//-------------------------------------------
declare border_signal as {image}, ml_function
set method of border_signal to 'M_045'
train border_signal where path is 'Border Samples'
print border_signal as {image}
declare borders as image
for each point in selected_area
add point to borders
where point is border_signal
set color of borders to red
print borders as image
//-------------------------------------------
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22. Test sample of HII implementing. 5
//-------------------------------------------
// 5. train and select the spots by samples
//-------------------------------------------
declare neig_signal as {image}, ml_function
set method of neig_signal to 'M_011'
train neig_signal where path is 'Neighbor Samples'
print neig_signal as {image}
declare neighborhood as image
for each point in selected_area
add point to {neighborhood} by index
where point is neig_signal
set color of {neighborhood} to random by index
print {neighborhood} as image
//-------------------------------------------
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23. Test sample of HII implementing. Final
//-------------------------------------------
// 6. calculation of parameters
//-------------------------------------------
neighborhood has index, x, y, width, height, square
where x is min of x
where y is min of y
where width is len of max of x and min of x
where height is len of max of y and min of y
where square is count of {color}
print {neighborhood} as data
//-------------------------------------------
---------------------------------------
ind x y w h sq
---------------------------------------
1 595 477 114 33 2854
2 526 388 125 94 8680
3 442 227 128 143 10980
4 369 327 167 167 15377
5 323 217 99 100 7911
6 284 142 78 74 4262
7 262 3 120 84 7771
8 191 477 92 33 2065
9 118 59 276 433 42135
10 14 176 168 231 25267
11 1 393 133 117 9693
12 1 1 69 140 8195
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24. HII implementing, conclusion
* We demonstrated a possibility to use quasi natural language as a script without
coding
* We defined all objects through ontology module without logical definition
* We defined some concepts through collection of samples by machine learning
conceptualization
* We solved our task by using only declared tools
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25. AGI perspective, architecture
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26. AGI perspective, inner organisation
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27. Alexey Egorov contacts:
alex.L.egorov@gmail.com
http://egorov.cognifield.com
https://www.linkedin.com/in/alexey-egorov-3ba92617/
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