High Signal Podcasts Evidence ledger
Method
Browse
← Back to evidence

Evidence receipt / prediction

Published · transcript-backed

Kenneth Stanley: prediction

5 Jul 2025 Machine Learning Street Talk The Fractured Entangled Representation Hypothesis (Intro)

“If you have an algorithm that's trying to follow a gradient by matching closer and closer and closer to the objective, getting a higher and higher score, you're going to get stuck in a dead end because of deception, because the things that lead to the thing you want actually don't look like the thing you want.”

— Kenneth Stanley

Source trail

Everything needed to verify it.

Speaker
Kenneth Stanley
Attribution
Verified speaker
Claim type
prediction
Recorded
5 Jul 2025
Publisher
Machine Learning Street Talk

Transcript context

…The most intuitive evidence comes from sweeping the parameters or the factored representations as Kenneth would call them. By changing a single connection in the network, you can actually see which factor of variation it represents. In this new type of network, sweeping these values results in a commensurate semantic change. It might be opening the mouth on a skull or winking the eye on a face or swinging the stem of an apple. It's like the network understands what these objects are at a deep level. In conventional networks, the same action just produces meaningless chaotic distortions. This is what we mean by the impostor. And in case you didn't get the memo, this is basically how chat g b t works now. We needed to have a huge number of free parameters in the network to make it trainable, right, to make it statistically tractable. But it's precisely that reason that we end up with a sand castle. It looks like a castle, but it doesn't have any structural joints. It doesn't look anything like we know a castle to be. Why is 1 network a sand castle and the other 1 the real deal? Well, the secret lies in abandoning the fixed objective in training and building bottom up, not chipping away top down like SGD does. We also need to embrace a counterintuitive notion called deception. Deception means the stepping stones that lead to these interesting artifacts that you might want to find don't resemble them. If you have an algorithm that's trying to follow a gradient by matching closer and closer and closer to the objective, getting a higher and higher score, you're going to get stuck in a dead end because of deception, because the things that lead to the thing you want actually don't look like the thing you want. And this is true in the lineage of many of these images in Pick Breeder. The paper showed the path to the skull in Pick Breeder. The key idea is that sometimes the stepping stones which lead to something important don't even resemble the thing you end up discovering. It might seem like total serendipity, total randomness, but humans have a nose for what is interesting, which has a lot to do with the foundational cognitive priors which nature has bestowed to us through constraints in our evolution and physical environment, and of course, our life experiences on top of that.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.

Search evidence