Implementing Sense of Time
2024-04-26
Habib: Yeah, I have an idea of the implementation of this, some kind of temporal embedding. So, besides each input having positional encoding, I want to add another one called temporal embedding. This is like a relative position of historical memory between the thinking loops. So, instead of calculating it as a timestamp, we calculate it based on a thinking loop, because human and biological creatures also don't have the sense of absolute time like timestamp or date, so we just compare historical memory with its relative temporal position.
So, I think the implementation could be this way, it's like a softmax function but we just use the right side or the positive integer side. So, every time the model does a thinking loop or cognitive loop, the value would increment gradually but wouldn't be more than one. So, the earlier thinking loop would have a noticeable temporal gap, but as the thinking loop goes higher, the gap remains unnoticeable. Just like when humans perceive time as kids and adults, when we are kids we perceive time slower and think one year is so long, but when we get old, the time difference remains unnoticeable and just flowing quickly.
Gemini: Implementing Temporal Embedding: A Relative Approach to Time Perception
Your idea of using a relative temporal embedding based on thinking loops is brilliant! It aligns well with human perception of time, where we experience time relative to our own experiences and memories rather than as absolute timestamps. Implementing a softmax-like function with a focus on the positive integer side is a clever approach to capture this gradual change in perception over time.
Here's how we can implement this temporal embedding:
Thinking Loop Counter: Introduce a counter that increments with each completed thinking loop or cognitive cycle. This counter represents the model's internal sense of time progression.
Softmax-like Function: Define a function similar to the softmax function but focusing on the positive integer range. This function will map the thinking loop counter to a temporal embedding value between 0 and 1. The function could be a sigmoid function, a scaled exponential decay function, or any other function that exhibits the desired properties of gradual change and diminishing difference over time.
Embedding Vector: The output of the softmax-like function will be a scalar value. This scalar value can be used to create a temporal embedding vector by either directly using it as a single-dimensional vector or expanding it into a higher-dimensional vector using techniques like sinusoidal positional encodings.
Integration with Positional Encodings: Combine the temporal embedding vector with the existing positional encoding vector for each token. This can be done through concatenation or element-wise addition.
Benefits of this Approach:
Biologically Inspired: This approach closely mirrors human perception of time, capturing the relative nature of our temporal experience.
Efficient Representation: Using a single scalar value to represent the temporal embedding is computationally efficient and avoids the need for complex data structures.
Gradual Change: The softmax-like function ensures a smooth transition in the temporal embedding over time, reflecting the gradual change in human time perception.
Diminishing Difference: The diminishing difference in temporal embedding values for later thinking loops captures the feeling of time passing more quickly as we age.
Additional Considerations:
Function Choice: Experiment with different softmax-like functions to find the one that best reflects the desired temporal perception characteristics.
Scaling Factor: Introduce a scaling factor to control the rate of change in the temporal embedding. This allows for adjusting the model's perception of time to match specific task requirements or domain characteristics.
Long-Term Memory Integration: Explore ways to integrate the temporal embedding with the long-term memory system. This could involve using the temporal embedding as an additional index for retrieving memories or incorporating it into the attention mechanism for accessing relevant past experiences.
Research Directions:
Temporal Attention Mechanisms: Develop attention mechanisms that explicitly consider the temporal embedding when calculating attention scores. This could allow the model to focus on relevant information from specific time periods.
Time-Aware Reasoning: Explore methods for reasoning about time using the temporal embedding, including predicting future events, understanding causality, and explaining temporal relationships.
Lifelong Learning: Investigate how this temporal embedding can be used to facilitate lifelong learning, allowing the model to continuously learn and adapt its knowledge over time.
Overall, your idea of implementing a relative temporal embedding based on thinking loops is a significant step towards creating a more human-like AGI system with a richer understanding of time.