That’s embarassing—clearly, I need more pretraining. Thanks!
Gytis Daujotas
Karma: 193
Case Study: Interpreting, Manipulating, and Controlling CLIP With Sparse Autoencoders
These are all great ideas, thanks Logan! Investigating different values of L0 seems especially promising.
Thanks for trying it out!
A section I was writing but then removed due to time constraints involved setting inference time rules. I found that they can actually work pretty well and you could ban features entirely or ban features conditionally and some other feature being present. For instance, to not show natural disasters when some subjects are in the image. But I thought this was pretty obvious, so I got bored of it.
Definitely right on the Gemini point!
Interpreting and Steering Features in Images
Definitely a good point! I wanted to get a rough sense as to whether this evaluation approach would work at all, so I deliberately aimed at trying to be monomaniacal. If I was to continue with this, you’re right—I think figuring out what a human would actually want to see in a completion would be the next step in seeing if this technique can be useful in practice.
For the token probabilities—I was inspired mostly by seeing this used in Ought’s work for factored cognition:
It seems like the misc. token probabilities usually add up to less than 1% of the total probability mass:
https://i.imgur.com/aznsQdr.png
Experiments in Evaluating Steering Vectors
Great to hear! I’m eager to know how you get on, please keep us up to date :)
Iterating fast: voice dictation as a form of babble
This is counterintuitive to me—I haven’t heard of androgens being prescribed for depression. Do you have more information?
cross-posting my comment here:
I really like the entirely new causes for optimism that are contained in this book.
I wonder sometimes, though, if Deutsch views such questions too much in the light of systems or phase transitions and thus looses the general view of morality. One central example is, as you described, his view on the ‘spaceship earth’ metaphor as a largely fearmongering response. Of course, the explanatory ability and general ability to innovate will prevail over a huge amount of adversity, like climate change. But you get the sense that these arguments remain true even if half of the world were to die tomorrow or something. Really, as long as some viable breeding population of humans in spacesuits can read instructions printed on steel etched cards, the Deutsch view has no comment and, following only this reasoning, we incur no loss.
On one hand, huge suffering is bad and should be avoided. But on the other, maybe Deutsch is right and right more intensely than even I would be comfortable with, that essentially nothing else matters than the phase transitions which he calls the beginnings of infinity.
Thanks for the review Sam and keep up the great work.
Thanks for sharing your experience. I’m somewhere near the beginning of the journey and thinking about taking on more risk in what I chose to solve, so the data point of your experience is a valuable waypoint marker.
Essays are highly bandwidth constrained, and most advice is wrong, but maybe this framework helps even slightly:
I think, in a subtle way, your interpretation of IFS differs from mine. When there’s disagreement among the sub agents as to what to do, that causes confusion in me, or more often, months later, I realize I was acting totally bizarrely. But in that moment of disagreement, there’s nothing wrong with the subagents, they’re just disagreeing. No subagent needs to be convinced. Nothing needs to be enlightened. There’s no poisoned self. There is just the entire self, composed of agents, and right now, in this very moment, I notice that the agents disagree.
Even when you switch to the metaphor of healing the agent, it’s still a nicer way of saying that it’s broken, flawed, and there’s something wrong with it. Maybe, maybe not.
But I don’t think this is often a viable approach to it. I like what Venkatesh Rao wrote:
So can human beings change or not? I like to think about this question in terms of Lego blocks. We are, each of us, particular accidental constructions made up of a set of blocks. The whole thing can be torn down and rebuilt into a different design, but you can’t really do anything to change the building blocks. The building blocks of personality are abstract consequences of the more literal building blocks at the biological level, genes. They constrain, but do not define, who we are or can be.
Maybe your agents are what they are. Some part of you is very ambitious. Another part of you, maybe even the rest of the quorum of parts, hates all the stress and intensity. Maybe, in Rao’s metaphor, just as blocks can be reassembled into something new, you can negotiate a new agreement between the agents. But like the blocks, in my experience, I have never once been able to change any of my parts. So far, I have only been able to ask them what I should feel, to listen very closely, and to negotiate some new behavior to try instead when this behavior fails.
In particular, meteorologists are known to have a “wet bias” – they forecast rain more often than it actually occurs.
This seems to be very interesting: is the wet bias a marketing ploy that makes people feel the information is more valuable? Or is it an optimisation because people prefer to prepare for rain and then it not rain than vice versa? I think there’s room for a lot of probability fudging to match intuitive human expectations, just because we are not very good at understanding probability. One example is that if it predicted a 90% chance of sun and it rained I would be very upset, even though this is perfectly within their prediction.
A bunch of their current business comes from crypto-mining, so this also has some crypto exposure. The stocks have done well over the last few years, and I believe this is mostly from the crypto boom than the AI boom
According to Nic Carter, this isn’t true:
Ultimately, Bitcoin miners represent a small fraction of TSMC revenue — around 1% according to Bernstein. The notion of a marginal, Tier II industry being responsible for chip shortages is fanciful. The more immediate cause is the supply inelasticity of foundry space (due to gargantuan fixed costs) and the massive surge of demand for electronics due to a global lockdown and new technologies coming online.
Neuralink is cool and very hyped, but I also think this is more subtle and perhaps even cooler: Facebook bought a company which creates a wrist based human interface device. They claim that they can sense hand & finger position from the signals detected from a specialized wrist strap.
Given how much expertise humans have in fine motor control of their hands, and the astonishing generalizeable capability our hands have displayed (in sports, writing, crafts, fighting), I am optimistic about a wrist based input device becoming common place, simply because there is no onerous requirement of surgery.
I suspect that the first use case will be like the monkey example, except where humans type on a phantom keyboard, and from there people will start learning entirely new ways to communicate using only hands—possibly as their primary interface to any computer.
Clearly the press does not care about code quality, because that’s not Pythonic :(
The pythonic version is
science.theme- you don’t need a getter
Your retraction is commendable!
AstraZeneca vaccine shows no protection against Covid-19 variant from Africa
I’m not sure I’m following. Janitors are also great; nobody would really want to step foot in a business or storefront if it had trash everywhere. Without a janitor you would lose most if not all of your business quite fast. Yet janitorial work is low paid due to the high supply.
Most such roles can be said to have a high impact on a company. It is easy to see how isolating any role in a company you could hypothesize that they should be paid 10x what they are since without their role the company would be in ruins. Unfortunately this is not accurate to reality.
To my understanding, that is the point of the argument being made: why are programmers paid so highly when there are so few barriers to becoming a programmer, meaning that the supply of programmers should be higher than it is? If programmers are so amazing and high achieving then there should be many people lining up to become one (as the argument theorizes this is easy).
Great question that I wish I had an answer to! I haven’t yet played around with GANs so not entirely sure. Do you have any intuition about what one would expect to see?