fingin/agi

taking a stab at making a generative artificial generalized intelligence with the help of chat gpt

Agi

taking a stab at making a generative artificial generalized intelligence with the help of chat gpt

plan of action:

DataInputNet:

• Acquires data from multiple sources like sensors or files.
• Pre-processes raw data and standardizes it for further processing by LowLevelDataProcessingNet.

InputHashNet:

• Accepts preprocessed data from DataInputNet and generates a unique, efficient representation using hashing techniques like locality-sensitive hashing (LSH) or minhashing.
• Maintains a hash table for storing and referencing raw input data.

LowLevelDataProcessingNet:

• Processes preprocessed data from InputHashNet for feature extraction, signal processing, and statistical analysis.
• Outputs refined data for the IntermediateDataNet.

IntermediateDataNet:

• Serves as an intermediary between LowLevelDataProcessingNet and HighLevelDecisionMakingNet.
• Transmits refined data from LowLevelDataProcessingNet to HighLevelDecisionMakingNet.
• Incorporates memory and time components to store and track past experiences.

AttentionNet:

• Manages attention and salience within the AGI.
• Focuses on pertinent stimuli and disregards irrelevant distractions.

MotorControlNet:

• Handles motor functions based on HighLevelDecisionMakingNet's decisions.
• Coordinates planning and execution of movements.

EmotionNet:

• Generates and regulates emotional responses and motivation in the AGI.
• Influences reward processing and motivation.

SensoryProcessingNet:

• Manages sensory input processing, including vision, hearing, touch, and smell.
• Processes and integrates sensory information.

LanguageNet:

• Enables language processing and communication within the AGI.
• Facilitates interaction with others using language.

HighLevelDecisionMakingNet:

• Makes informed decisions based on input from IntermediateDataNet.
• Employs machine learning techniques to train decision-making models.
• Outputs decisions to the IntermediateOutputNet, influenced by inputs from other neural networks.

IntermediateOutputNet:

• Transmits decisions made by HighLevelDecisionMakingNet to DataOutputNet in an abstracted format.

DataOutputNet:

• Translates abstracted output from IntermediateOutputNet into a suitable format for output devices.
• Delivers output, such as motor commands or textual responses.

MemoryNet:

• Stores and manages past experiences and decisions to influence future decision-making.
• Utilizes deep learning or reinforcement learning to optimize memory storage.

TimeNet:

• Tracks the timing of experiences and decisions.
• Retrieves relevant memories or decisions based on their timing.

AGINet:

• Unifies various neural networks and provides methods for system operation and output generation.
• Facilitates data transfer between neural networks using a data bus or similar interface.
• Ensures seamless integration, learning and adaptation, scalability, generalization, robustness, and ethical and safety considerations.
• Continuously improves system performance based on input-output feedback loops.