unclear: mixed, mostly math and instructions

What this neuron does

The strongest examples are weak and scattered, across geometry, folding paper, decision trees and graph theory, with no obvious shared thing. Negative examples are also weak. The pattern is not clear enough to name confidently.

Written by Claude from this neuron's activations and steering outputs. Confidence: low. Think it is wrong? Flag it (opens a prefilled issue on GitHub).

Fires most on

The text windows where this neuron's activation was highest. Amber marks positive activation; the outlined token is the peak. Hover a token for its strength.

1. #1 peak

   . Part A: Short Answer (1 problems, 12 points). 1. Let S and T be linear spaces and A : S T be a linear map. Say V and W are particular solutions of the equations A V = Y

2. #2 peak

   paper they like. Here’s the easiest way I know.⏎- Choose any piece of paper.⏎- Fold the paper in half.⏎- Starting and ending at the fold, make the first stroke of the number 2.⏎- Cut along the line

3. #3 peak

   circle and. . . .⏎X is a moveable point on the hypotenuse, and P and Q are the feet⏎of the perpendiculars from X to the sides of a right angled⏎triangle. What position of X makes the length

4. #4 peak

   an even 90 degree piece, this will be very easy. Draw 3 circles from the midpoint of the existing circles(From the left circle go up until it is parallel with the other circle, draw the new cirles from there)

5. #5 peak

   are the lines that never intersect and perpendicular lines are the lines that intersect at 90 degrees.⏎Answer: Lines with the same slope are parallel and if the slope of one line is the negative reciprocal of the second line, then they are perpendicular

6. #6 peak

   equal.⏎Label this plum tree graph to make it totally magic!⏎A graph is a set of vertices (or nodes), together with a set of edges (or arcs).⏎Look at

Steering

Push this neuron up or down while the model writes, and watch what changes. The outputs were generated ahead of time, so nothing runs in your browser.

Most negative on

Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.

1. #1 peak

   present in the data . There are many deep learning techniques currently used in various applications. These include auto-encoders, stacked auto-encoders, restricted Boltzmann machines (RBM), deep belief networks (DBN) and deep convolution

2. #2 peak

   we detect circadian cellular change that is indicative of cancer, and do not rely on specular reflective capabilities of varying tissue densities to make our diagnosis. Our historic false negative result of 17.<|endoftext|>Native Animals of the Thar Desert⏎The

3. #3 peak

   1, 86. Our estimates predicts 32 members of this sequence starting from 6. In fact, the sequence has 30 conjectured members. Similarly, our estimate predicts 2.5 members starting from 86

4. #4 peak

   her hypothesis will fail.⏎If the hypothesis is able to withstand 100 or 1000 experiments, then the hypothesis has high probability of being true. But if hypothesis failed in most or all of the 100 or

Which neurons in earlier layers feed this one, and which later ones it feeds. Amber links excite, blue links suppress; longer bars are stronger.

Activation histogram

How often this neuron fires at each strength. Almost always it does close to nothing (the tall bars in the middle); the snippets above are the rare moments it fired hard, amber for positive and blue for negative.

Logit lens

The words this neuron nudges the model toward (amber) or away from (blue) when it fires. A hint, not a label: many neurons work through later layers instead.