1.01STIMULI MYTH
To see you have to look
Entities are not to be multiplied beyond necessity.
If I can't build it, I don't understand it.
1.01STIMULI MYTH
To see you have to look
1.02HOW TO LEARN PERMANENTLY
Achilles heel of artificial neural network
1.03ARCHITECTURE
Building blocks for AGI
1.04WHERE LEARNING AND REASONING ARE HIDDEN
Pattern mining
1.05DIY PATTERN MINING
What's under the hood
1.06SEMANTIC STORAGE
The simpler, the more versatile
1.07SEMANTIC STORAGE II
How to find analogues
1.08NUTS AND BOLTS OF THE DECISION MAKING
Discrete events in the continual world
1.09PROBABILITY IN DECISION MAKING
To have and have not
1.10DECISION-MAKING FUNDAMENTALS
Science and art of compromises
1.11ARTIFICIAL GENERAL VISION
Humal-like active vision
1.12AGI INFRASTRUCTURE
Technological aspects
1.13COMPUTATIONAL PROBLEM vs COMPUTATIONAL SERVICE
Why Turing Machine inapplicable for AGI analysis
1.14FUTURE OF THE AGI
Possible breakthrough points
1.15HUMAN-MACHINE INTERFACE
AGI introspection implementation
1.16HUMAN-MACHINE INTERFACE II
Way to specify an entity
1.17AGI: VISUAL ATTENTION
An engineering approach to building a scene description
1.18AGI: TEXTS VS KNOWLEDGE
Is it possible to kearn about the real world by reading many texts?
1.19YIN AND YANG OF DECISION MAKING
Two-tier hybrid approach
1.20SEMANTIC STORAGE III
Integrity and consistency
1.21AGI: PLANNING
Optimization as decision-making basis
1.22AGI: EFFECTORS
Sensors and actuators
1.23AGI: TESTING
Classification by environment
1.24AGI: GENERALITY
How general can be modules of the AGI system
1.25AGI IN A WORLD OF TRAJECTORIES
How continuity affects AGI design
1.26AGI: INTELLIGENCE LEVELS
Classification by capability
1.27AGI: CAUSALITY
How to investigate the causes of events
1.28AGI: STRUCTURES DISCOVERING
Finding constancy in a volatile World
1.29AGI: PROTOTYPE
Compromise between simplicity and cogency
1.30NEXT VOLUME OF THE AGI SAGA
Transition to details
2.01AGI: SITUATION ROOM
The main cognitive unit
2.02TIME, SPACE, CAUSALITY, NEURAL NETWORKS, AND TEXT
Fundamental specificity of the natural environment
2.03AGI: ACTIONS
What the AGI system should know about itself
2.04AGI: STRUCTURING THE OBSERVABLE
How to detect unknown things and explain it
2.05AGI: KNOWLEDGE REPRESENTATION
How data representation affects the capabilities of an intelligent system
2.06AGI: CONTINUITY IMPLEMENTATION
Representation of quantities as functions of time
2.07SEMANTIC DATA TYPES
How to construct invariants
2.08TO PLAN OR NOT TO PLAN
False alternativeness
2.09THREE-VALUES LOGIC
Utility of having the unknown
2.10CAUSE AND EFFECT RELATIONSHIPS II
More details
2.11TO PLAN OR NOT TO PLAN II
The most robust plan is do not have a plan
2.12AGI: FROM THE DEFINITION TO THE IMPLEMENTATION
Crossroad of philosophy and technology
2.13AGI: KNOWLEDGE MINING vs. KNOWLEDGE TRANSFER
Society role and reinforcement learning weakness
2.14LANGUAGE AND "Language Models"
2.15THE BRIGHT FUTURE OF ARTIFICIAL INTELLIGENCE IS NOT WHERE EXPECTED
To build the best, you need to abandon the old
2.16AGI: WHERE EVENTS COME FROM
The internal representation of the event detector
2.17CONVERSATIONAL ARTIFICIAL INTELLIGENCE
Ease of imitation and the illusion of ease of implementation
2.18AGI: INFORMATIVENESS OF THE DISCONTINUITY POINTS
Ease of imitation and the illusion of ease of implementation
2.19AGI: REPRESENTATION OF THE CONTINUAL OBJECTS
Descriptive functions
2.20ARTIFICIAL INTELLIGENCE vs ARTIFICIAL HUMAN
Hidden belief in anthropomorphism
2.21AUTONOMOUS LEARNING: WHAT IT MEANS
Ad hoc learning can't be reduced to statistics
2.22WHY IS AGI NOT DEVELOPED YET
AGI-22
2.23AI: SLY TERMINOLOGY
2.24WHAT IS INTELLIGENCE?
How to distinguish artificial intelligence system from others
2.25CAUSALITY DISCOVERING
How statistics can help
2.26ROLES IN THE "AGI" THEATER
Inventor, Scientist, Guru
2.27CAUSALITY DISCOVERING
Superiority of non-statistical methods
2.28AGI: CONCEPT GROUNDING
Object - concept - symbol
2.29AGI: OBJECT DETECTION+IDENTIFICATION VS RECOGNITION
2.30ChatGPT
2.31ChatGPT IN PROGRAMMING
Limits of capabilities
2.32HIDDEN PART OF THE INTELLIGENCE "ICEBERG"
Two phases of information processing
2.33OBSERVABILITY OF THE INTELLECT
2.34AGI: OBJECT` TRACKING
One term, many meanings
2.35AGI: ARTIFICIAL NEURAL NETS
Engineering point of view
2.37AGI: IMPLICIT ASSUMPTIONS
And how they affected Large Language Models
2.38GPT: TO BE OR NOT TO BE
Luddite scientists as a test for humanity