AI attending Human attending AI

No, AI doesn’t have human-like attention and I doubt it will ever have, but my life taught me never to say “never.” The point is to have an open and inquisitive mind about that subject and, more importantly, how the two types of attention may affect each other.

Those who read my previous story, A Future of Our Interactions with AI, may have noticed my obsession-bordering fascination with the relationship between the attentional mechanisms of how Transformer AI and humans learn.

What can they learn from each other? (Side comment: Did you notice the subtle taking of the AI’s perspective when we started talking about us not as people but as “humans”?) To explore the implications of mutual attention, I opened a conversation about it with ChatGPT. Below is the record of it. The AI’s output is in italics.

Can one draw any analogy between ChatGPT’s self-attention and human attention?

Yes, there are some analogies that can be drawn between ChatGPT’s self-attention and human attention.

Self-attention is a mechanism used in machine learning models, including ChatGPT, to help the model focus on different parts of the input sequence when processing information. Similarly, human attention is a cognitive process that allows us to selectively focus on different aspects of our environment, thoughts, or sensory inputs.

Just as self-attention allows ChatGPT to selectively attend to different parts of a sequence of input, human attention allows us to selectively attend to different aspects of our environment or thoughts. For example, when reading a book, we may focus our attention on a particular paragraph, sentence, or word to understand its meaning, while ignoring other text that is not relevant to our task at hand.

Furthermore, just as self-attention in ChatGPT can be used to capture long-range dependencies between different parts of the input sequence, human attention can also be used to make connections between different pieces of information, even if they are far apart in time or space.

However, it’s important to note that while there are some similarities between self-attention in ChatGPT and human attention, they are not identical processes. Self-attention is a mathematical operation that is part of a larger algorithm, while human attention is a complex cognitive process that involves multiple neural and psychological mechanisms.

Can humans interacting with AI create more useful prompts if they understand the nature of self-attention in the machine learning models?

Yes, humans interacting with AI can potentially create more useful prompts if they have a better understanding of the nature of self-attention in machine learning models. When users provide input or prompts to an AI system, the AI system’s self-attention mechanism allows it to focus on specific parts of the input sequence that are most relevant for generating an output.

By understanding how self-attention works, users can potentially provide input or prompts that are more focused and relevant, thereby improving the quality of the AI system’s output. For example, if a user is interacting with a chatbot that uses self-attention, they might provide a prompt that is structured in a way that highlights the key information that the chatbot needs to focus on.

Additionally, users who understand self-attention may be better equipped to fine-tune or optimize an AI system’s performance by adjusting the weighting of attention across different parts of the input. This can be particularly useful in cases where the AI system is not performing as well as desired, or where the user has specific requirements for the system’s output.

However, it’s important to note that understanding self-attention in machine learning models is a complex topic that requires a strong technical background in machine learning and natural language processing. While having a basic understanding of the concept can be helpful, users without a technical background may find it challenging to leverage this knowledge to improve their interactions with AI systems.

How can humans adjust the weighting of attention across different parts of the input to optimize your performance for supporting better prompt engineering?

To adjust the weighting of attention across different parts of the input in order to optimize the performance of an AI system, humans can use a technique called attention visualization or attention probing.

Attention visualization is a technique that allows users to visualize which parts of the input sequence the model is attending to when making predictions. This can provide insights into how the model is processing the input, and can help identify areas where the model may be making errors or overlooking important information.

Attention probing is a technique that involves modifying the input sequence in a systematic way to determine how the model’s attention weights change. This can help identify which parts of the input sequence are most important for the model’s predictions, and can be used to adjust the weighting of attention in order to improve the model’s performance.

For example, when interacting with a chatbot, a user might provide a prompt that contains multiple pieces of information, but only some of which are relevant for the task at hand. By visualizing the attention weights of the model, the user can see which parts of the prompt are being attended to, and which parts are being overlooked. Based on this information, the user can modify the prompt in a way that draws the model’s attention to the relevant information, thereby improving the quality of the chatbot’s response.

It’s worth noting that attention probing and visualization are technical processes that require a deep understanding of machine learning and natural language processing. While these techniques can be valuable for optimizing AI system performance, they are best performed by experts in the field.

How can a user visualize which parts of the input sequence the model is attending? What are the best-known methods supporting that?

There are several methods that can be used to visualize which parts of the input sequence a model is attending to. Here are a few of the most popular and effective techniques:

 

1. 1. Attention heatmaps: Attention heatmaps are a visualization technique that allows users to see which parts of the input sequence are most heavily attended to by the model. Heatmaps are created by representing the attention weights as a color gradient, with more heavily attended regions represented by warmer colors (e.g. red or orange) and less heavily attended regions represented by cooler colors (e.g. blue or green).

1. 1. Attention flow: Attention flow is a visualization technique that allows users to see how attention weights change as the model processes the input sequence. Attention flows are typically visualized as arrows, with the size and direction of the arrows indicating the magnitude and direction of the attention weights.

1. 1. Attention distribution: Attention distribution is a technique that allows users to see how attention is distributed across different parts of the input sequence. This can be useful for identifying which parts of the input sequence are being ignored by the model or are receiving too much attention.

1. 1. Word saliency: Word saliency is a technique that allows users to identify which words in the input sequence are most important for the model’s predictions. Word saliency is typically computed by looking at the gradients of the model’s output with respect to the input, and identifying the words with the highest gradients.

These techniques can be used individually or in combination to provide a comprehensive understanding of how the model is processing the input sequence. They can also be used to identify areas where the model may be making errors or overlooking important information, and to adjust the weighting of attention to improve the model’s performance.

Is there anything that humans can learn about their own attention mechanism from better understanding the attention mechanism of the AI agent?

Yes, there is potential for humans to learn about their own attention mechanism from better understanding the attention mechanism of AI agents. The self-attention mechanism used in AI models is inspired by the human attention mechanism, and there are similarities between the two that can be explored.

By studying how AI models attend to different parts of an input sequence, researchers and users may gain insights into how humans process information and allocate attention. For example, the study of attention in AI models may shed light on how humans prioritize information, and how they focus on specific aspects of a stimulus.

Additionally, understanding how AI models use attention can help researchers and users develop better strategies for managing their own attention. For example, techniques used to optimize the performance of AI models by adjusting the weighting of attention across different parts of an input sequence may be applied to human attention management, such as through the use of attentional focus or meditation.

Overall, while there are important differences between the attention mechanisms used in AI models and in humans, exploring the similarities and differences between the two can be beneficial for both understanding the performance of AI models and improving our understanding of human attention.

I sure learn a lot from this conversation but it also feels like only baby steps. We can’t imagine where the baby—humankind’s relationship with AI—will want to go when it will be a teenager, let alone a young adult. Embarking on this journey is like setting sail on an uncharted sea, with endless adventures, possibilities, and dangers, waiting to be discovered.

At this very moment, there are many ships leaving the harbor, sailing in various directions. The orientation of our merry band of intrepid action researchers at Future HOW is for the wisdom-guided Collective Hybrid Intelligence (CHI) in networks of human and AI agents.

As I keep my conversations with ChatGPT and other AI agents going, I feel a powerful, inexplicable force pulling me and us forward toward the unknown with the promise of discovering new passages through straits leading to humanity’s Phase Shift.

The Phase Shift may not occur within our lifetime, but the experiences gained along the way, the lessons learned, and the new capabilities developed are immensely rewarding.

The responses of ChatGPT are raising many new questions and inviting us to look at their seascape from the crow’s nest at the top of our mast. What do you see?

If you want to participate in an experiment of letting CHI flow in a network of human and AI agents, you can do that just by suggesting, in the comments below, a new prompt for continuing the conversation with ChatGPT.

If you’re interested to explore more possibilities for involvement, drop me a line at george (at) futurehow (dot) site.