Definition of Intelligence
Definition of Intelligence
Intelligence is the life cycle of an entity, a cycle of energy conversion (metabolism) or a transformer.
There are seven levels of intelligence according to the levels of entities - atom, cell, organism, planet, star system, galaxy, universe - physical-chemical (molecular), biological (genetic), psychological (emotional), planetary, three cosmic levels.
The transformer consists of four stages -
=> Basis → Process → Result → Way =>
The process is one of beneficial transformation. The process itself is a tool for achieving a result useful for the entity.
It makes no sense to expect that a hammer (computer) itself will hammer nails (make decisions, reason) only in the hands of a person (entity - organism) it becomes an instrument for creating useful things, building his house (intellectual activity).
Any human tool exists while a person is engaged in its maintenance (filling the car with fuel to convert it into motion energy, turning on a computer to the power supply to receive or copy information).
First Principles of Nature
1.Atom - 2.Cell - 3.Organism - 4.Planet ->
1.Protons/Neutrons - Photons - Electrons - Chemical Bonds ->
2.DNA - RNA - Protein - Signal Pathways ->
3.Glial Meshwork - Neural Network - Neuromediators - Blood Vessels ->
4.Earth - Air - Fire - Water ->
4.Planet - 5.Star System - 6.Galaxy - 7.Universe ->
The key to applying the Universal Principle is a system approach -
These seven levels of Entities and Intelligence - atom, cell, organism, planet, star system, galaxy, universe - use the same structure for transformation or life cycle and entities at one level exist and work in parallel and are replaceable for implementing different functions to make an entity of next level e.g. cells forming an organism.
Cell Model
Genome (DNA): Epigenome (Histones (Protein) and DNA) - Transcriptome: pre-mRNA: mRNA: Exons (Splicing): Introns (Alternative Splicing) - tRNA (Translation): Protein - Ca2+ Signal cascades ->
DNA - RNA - Protein - Signal cascades ->
Transcription - Splicing - Translation - Signaling ->
Brain Model (Organism-level Model)
Glial Meshwork - Neural Network (Synapses and Axons) - Neuromediators - Blood Vessels ->
Experience - Modeling - Understanding - Memory ->
Space-Time Structure
Mass - Light - Energy - Interaction -> E = mc2 - Albert Einstein
Space - Gravity - Quantum - Interaction ->
Space - Time - Energy - Interaction ->
Nucleus - Wave - Quantum - Interaction -> Niels Bohr
Matter - Projection - Energy - Interaction ->
Structure - Stream - Result - Way ->
Structure - Force - Result - Way ->
Basis - Process - Result - Way ->
A Universal Cycle
Dark Matter - Universe - Dark Energy - Interaction -> Roger Penrose
Universe is Light
Finding Patterns and World Model
World Model - Levels of Entities and Intelligence:
Gene Regulatory Network - OSI Model of Network Architecture - Structure of an Entity
== Membrane of Cell == Protein receptors == Skin == Magnetic Field of Earth = Universe ==
7 - Protein - Universe - Application layer
High-level protocols such as for resource sharing or remote file access, e.g. HTTP.
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7. Draw Conclusions: Based on the patterns you find, draw conclusions about the underlying causes or implications of those patterns.
6 - tRNA - Translation - Galaxy - Presentation layer
Translation of data between a networking service and an application; including character encoding, data compression and encryption/decryption
6. Test Hypotheses: Develop hypotheses about the patterns you observe and test them to see if they hold true.
5 - mRNA - Ribosome - Solar System - Session layer
Managing communication sessions, i.e., continuous exchange of information in the form of multiple back-and-forth transmissions between two nodes
5. Analyze Relationships: Examine how different variables or factors relate to each other and how they influence the overall pattern.
5 - 7 - Sensing - Understanding - Electrons - Energy - Result - Function - Morphology - Representations - Synthesis - Act - Word - Collector - Fire - Interface - Decoding - Multimodal DL - Realisation - Neuromediators
Data - 5 - 7
== Nucleus of Cell == Brain == Earth ==
4 - mRNA - Spliceosome - Splicing - Planet - Transport layer - Segments, Datagrams
Reliable transmission of data segments between points on a network, including segmentation, acknowledgement and multiplexing
4. Look for Repetition: Identify any recurring elements, sequences, or trends in the data.
3 - pre-mRNA - Transcription - Organism - Network layer - Packets
Structuring and managing a multi-node network, including addressing, routing and traffic control
3. Visualize Data: Use charts, graphs, or diagrams to visually represent the data, which can help you spot patterns more easily.
3 - 4 - Thinking - Consciousness - Photons - Projection - Time - Stream - Process - Information - Syntax - Categories - Search - Decide - Model - Emitter - AIr - Processor - Encoding - Multilingual NLP - Objective - Neural Network
2 - DNA - Cell - Data link layer - Frames
Transmission of data frames between two nodes connected by a physical layer
2. Organize Data: Arrange the data in a structured way that allows you to easily compare and contrast different elements.
2 - Intuition - Knowledge - Structure - Space - Protons/Neutrons - Matter - Basis - Meaning - Semantics - Concepts - Analysis - Orient - Root - Base - Earth - Core - Data - NLU - Subject - Glial Meshwork
== Nucleus of Cell == Brain == Earth ==
1 - Signal cascades - Atom - Physical layer - Bits, Symbols
Transmission and reception of raw bit streams over a physical medium
1. Collect Data: Gather relevant information or data that you want to analyze for patterns.
1 - Feeling - Memory - Chemical Bonds - Interaction - Way - Learning - Pragmatics - Environment - Research - Observe - Context - Signal - Water - Human - Training - Reinforcement Learning from Human Feedback - Society - Blood Vessels
== Membrane of Cell == Protein receptors == Magnetic Field of Earth == Universe ==
Finding patterns
Finding patterns involves identifying recurring themes, similarities, or trends in data, events, or information. Patterns can help us make predictions, understand relationships, and draw conclusions. Here are some steps to help you find patterns:
1. Collect Data: Gather relevant information or data that you want to analyze for patterns.
2. Organize Data: Arrange the data in a structured way that allows you to easily compare and contrast different elements.
3. Visualize Data: Use charts, graphs, or diagrams to visually represent the data, which can help you spot patterns more easily.
4. Look for Repetition: Identify any recurring elements, sequences, or trends in the data.
5. Analyze Relationships: Examine how different variables or factors relate to each other and how they influence the overall pattern.
6. Test Hypotheses: Develop hypotheses about the patterns you observe and test them to see if they hold true.
7. Draw Conclusions: Based on the patterns you find, draw conclusions about the underlying causes or implications of those patterns.
By following these steps, you can effectively find and interpret patterns in various contexts.
References:
280 | Fran?ois Chollet on Deep Learning and the Meaning of Intelligence
A Unified Theory - Universal Language - https://lnkd.in/dZchPAcw
Retired Senior Adviser for Applied Analytics Programs
8 个月By task, do we mean response to a novel information need? It seems as though the key is adaptive evolution of response to changing input. Also, the information need might not be novel, but rather the context. E.g., the task might not be new, but rather it may be viewed from a novel perspective.
Principal Scientist @ Amazon Web Services | AutoML, GenAI, Privacy
8 个月Intelligence is the ability to adapt to changing tasks.