So, if you've been reading my Less Wrong posts over the last twenty-one months, you'll notice that I've been doing a lot of philosophy-of-language blogging lately! Those posts are:

• "Where to Draw the Boundaries?"
• "Schelling Concepts, and Simple Membership Tests"
• "Maybe Lying Doesn't Exist"
• "Algorithms of Deception!"
• "Firming Up Not-Lying Around Its Edge-Cases Is Less Broadly Useful Than One Might Initially Think"
• "Philosophy in the Darkest Timeline: Basics of the Evolution of Meaning"
• "Maybe Lying Can't Exist?!"
• "Unnatural Categories Are Optimized for Deception" (forthcoming)

But, if you didn't know, the reason I've been doing so much philosophy-of-language blogging lately is because I was frustrated because I thought a lot of people were motivatedly getting the philosophy of language wrong for political convenience, specifically around transgender issues. (I think assertions like "trans women are women" and similar need to be argued for on the empirical merits; you can't just define them to be true.)

In particular, "Where to Draw the Boundaries?"...

Much has been written about the fundamental opposition between explore and exploit, chaos and order, yin and yang. In this post I make two observations about the psychology of this opposition.

In the first part, I challenge the metaphor of the comfort zone: a slowly-changing region in activity-space where everything inside is comfortable and everything outside induces anxiety. The point is that anxiety depends not only on the spookiness of the activity itself, but on one's proximity to safety. I am afraid because it is dark; I am terrified because the light switch is all the way down the hall. In particular, it is possible to reduce anxiety by bringing your comfort zone with you in the form of a safety behavior or a trusted companion. This serves as an alternative to Comfort Zone Expansion.

In the second part, I note that explore and exploit are often embodied in the human personality...

"Gnomish helms should not function.  Their very construction seems to defy the nature of thaumaturgical law.  In fact, they are impossible.  Like most products of gnomish minds, they include a large number of bells and whistles, and very little substance.  Those that work usually have a minor helm contained within, always hidden away, disguised to appear innocuous and inessential."
              -- Spelljammer campaign set

We have seen that knowledge implies mutual information between a mind and its environment, and we have seen that this mutual information is negentropy in a very physical sense:  If you know where molecules are and how fast they're moving, you can turn heat into work via a Maxwell's Demon / Szilard engine.

We have seen that forming true beliefs without evidence is the same sort of improbability as a hot glass of water spontaneously reorganizing into ice cubes and electricity.  Rationality takes "work" in a thermodynamic sense, not just the sense of mental effort;...

I was chatting with friends about the Covid vaccine and they were concerned about side effects. I don’t expect significant side effects but don’t really have a principled analysis as to why not.

What kind of probability should one assign to significant side effects from a vaccine, particularly long term side effects which are less likely to be picked up in shorter testing regimes?

In this essay, I will argue that the analysis of Sandberg et al. is flawed in a number of key respects, and as a result the Fermi Paradox remains an open question. Here I briefly list the key problems with the Sandberg et al. paper, before proceeding to discuss each in more detail.

1. The method used of multiplying uncertainties of many small numbers, most of which have an upper bound of one, is biased towards yielding a result of a high probability of Earth being unique, while also leading to various dubious results.
2. The key result of the paper is driven largely by uncertainty in the parameter fl, which is modeled in an unusual way without clear justification.
3. Adoption of slightly different (and I believe more plausible) modelling choices and parameter values yields totally different results, which do not result in the Fermi paradox being dissolved. I illustrate this by re-estimating the Sandberg et al. models using different parameters and modelling assumptions.

I didn’t learn about history very well prior to my thirties somehow, but lately I’ve been variously trying to rectify this. Lately I’ve been reading Howard Zinn’s People’s History of the United States, listening to Steven Pinker’s the Better Angels of Our Nature, watching Ken Burns and Lynn Novick’s documentary about the Vietnam War and watching Oversimplified history videos on YouTube (which I find too lighthearted for the subject matter, but if you want to squeeze extra history learning in your leisure and dessert time, compromises can be worth it.)

There is a basic feature of all this that I’m perpetually confused about: how has there been so much energy for going to war?

It’s hard to explain my confusion, because in each particular case, there might be plenty of plausible motives given–someone wants ‘power’, or to ‘reunite their country’, or there is some customary enemy, or that enemy might attack them...

I started this post thinking that I would write all the rules for evaluating Jacobians of neural network parameters in specific cases. But while this would certainly be useful for grokking deep learning papers, frankly it's difficult to write that in Latex and the people who have written The Matrix Calculus You Need For Deep Learning paper have already done it much better than I can do.

Rather, I consider my comparative advantage here to provide some expansion on why we should use Jacobians in the first place. If you were to just read the paper above, you might start to think that Jacobians are just notational perks. I hope to convince you that they are much more than that. In at least one setting, Jacobians provide a mathematical framework for analyzing the input-output behavior of deep neural networks, which can help us see things which we might have missed without... ,

Real world examples of the Parable of Predict-O-Matic show that trust in predictive accuracy has the power to shape world events. Accuracy brings trust, and trust brings power.

It's therefore a bit surprising that more people don't publish verifiable forecasts about major world events, along the lines of Philip Tetlock's Superforecasting. Even if they were guessing randomly, a lucky streak might bring fame and fortune. Publishing your forecasts would be like buying a lottery ticket.

The explanation is that society defends itself against this threat by demanding that forecasters earn the right to predict. Hence, society's obsession with credentials, the struggle over who gets to grant them, and constant attempts to grab the right to predict by unlawful or deceptive means. By requiring that wannabe prognosticators earn a graduate degree before they're taken seriously, and by limit their forecasting privileges to their field of expertise, we hope to cut down on the...