Human Focus and Attention
When you're trying to solve a problem or understand a concept, the process
can be broken down into the following steps, which align closely with the query-key-value
attention mechanism:
1. Clarify
Your Mind on What You Seek (Query):
·
Query: This is your specific goal or the
question you want to answer. For instance, if you're looking for a way to solve
a specific type of math problem, your query might be "methods to solve
quadratic equations."
·
In human terms, this means you have a
clear idea of what you're searching for. You might even phrase it as a specific
question or a goal in your mind.
2. Limit
the Relevant Texts (Keys):
·
Keys: These are the potential sources of
information where you might find your answer. In a book, keys could be
different chapters, sections, or paragraphs.
·
As you read or search through the book,
your attention is drawn to parts of the text that are likely to contain
information relevant to your query. You mentally filter out sections that are
clearly not related to your query.
3. Clarify
What You Really Need to Find (Values):
·
Values: These are the pieces of information
within the keys that are relevant to your query. In our example, values could
be specific formulas, examples, or explanations of quadratic equations.
·
Your brain evaluates the keys and
extracts the most relevant values. For instance, it might highlight a specific
formula or a step-by-step solution that directly addresses your query.
Applying the Attention Mechanism
Here’s how this process looks in a neural network with an attention
mechanism:
1. Query: The neural network
receives a specific query vector representing the information it needs to find.
2. Keys: It then looks at all
the potential information (keys) in the input data. Each piece of data is
associated with a key.
3. Values: For each key, there
is a corresponding value which contains the actual information.
The attention
mechanism works by:
·
Calculating the relevance: The network computes
how relevant each key is to the query by calculating attention scores (often
using a dot product of the query and key vectors).
·
Weighting the values: The network uses
these scores to weigh the values. Higher scores mean more relevance and thus
more weight.
·
Aggregating the values: The weighted values
are then combined to form an output that is focused on the most relevant
information.
Human Attention and Learning
In human learning, this process is less mechanical but conceptually
similar:
·
You clarify your goal
(query), making sure you know what you’re looking for.
·
You scan through potential sources
(keys), identifying where relevant information might be located.
·
You focus on extracting relevant
information (values), filtering out what is not needed and
concentrating on what will help you solve your problem.
Example in Practice
Suppose you're reading a textbook to solve a physics problem about
projectile motion. Here’s how you might apply this:
1. Query: You decide you need
to find the formula for the range of a projectile.
2. Keys: You flip through the
chapters and sections that cover projectile motion.
3. Values: You find a section
with the heading "Range of a Projectile" and start reading. You focus
on the equations and examples that directly address your query.
In summary, the human process of focusing involves clarifying your
objective, filtering through relevant sources, and pinpointing the precise
information needed, closely mirroring the query-key-value attention mechanism
in neural networks
