🚨 Today (Fri 23rd) is the last chance to download the full 109 page BBS collection on 'conscious AI and biological naturalism', including target article, 50 commentaries, and my author response. Don't miss out (& do click 'save pdf')! doi.org/10.1017/S014...
Nice! My first article is grad school was a response to people who were trying to bring the levels of selection debate into the dynamical context and doing it wrong. This approach looks much better.
philpapers.org/rec/WEIITA-4
If you are a philosopher of biology, you’ve probably heard that group selection and kin selection are 'formally equivalent', just two different ways of talking about the same thing.
link.springer.com/article/10.1...
⚡ Out now:
Psychological and health sciences increasingly study how phenomena dynamically influence and reinforce one another over time: for example, to understand the mechanisms maintaining mental disorders.
Can we identify reliable clusters of these dynamics?
A thread 🧵
osf.io/preprints/ps...
The complete version of @phaueis.eurosky.social and my BBS target article, "Metabolic considerations for cognitive modeling," is now available. It includes 32 commentaries and our response.
Please share
#philsci#philsky#cogsci
Check out my new paper on using causal models and thermodynamics to figure out why causes precede their effects. It's got everything!: Dynamic Causal Models, David Lewis, Maxwell's Demons, and Causal Abstraction.
philsci-archive.pitt.edu/id/eprint/31...
I kinda get why people hate on fussy philosophical stuff, but on the other hand don't you think we live in a time where it'd be especially helpful to have rigorous conversations about things like personhood, meaning in life, the point of art, the point of pure mathematics, & operationalizing ethics?
I find it frustrating how philosophy journals include references in the word count. It encourages omitting all but essential citations, and reduces the citation rate for everyone in the field.
Also, to everyone I didn't cite -- you were probably in the earlier draft!
Briefly popping my head back in to unveil the cover of my upcoming book, LIFE BEYOND MEASURE: Nature's Hidden Wonders at Every Scale. It’ll be out on April 6 in the U.S. and April 29 in the U.K. and you can pre-order it right now! www.penguinrandomhouse.com/books/757705... 1/
Last night I saw Leslie Liao at The Port, in Baltimore, and she's quickly becoming one of my favorite up-and-coming comedians. Underlying her set was a tension between a desire for intimacy and the physical joy of being unobserved that seems to be especiallly pressing for millenials.
portcomedy.com
Astrophotographer Adam (AJ) Smadi caught this image a few hours ago from Washington state.
A thin crescent Moon, sunlight illuminating the rugged lunar surface.
And 2,400 times farther away—and on the other side of the Sun—mighty Jupiter, and its moons Io (left) and Ganymede (right).
My book diet has come to include memoirs that are not quite self-help books, but that tend a bit in that direction. About 100% of them at some point discuss Kafka in detail. I wonder if his centrality has risen recently, as he’s been glommed onto by anyone thinking about shame, writer’s block, etc.
My invited ICML talk "Evaluating Surrogate Metrics Under Optimization Pressure: A Causal Perspective" is online. I use dynamic causal models and control feedback theory to disentangle types of AI trustworthiness, and illuminate policy failures involving Goodhart's Law.
icml.cc/virtual/2026...
Check out my newest attempt to help people understand causal mediation. This one covers just the direct effect (indirect effects are next episode) since if people truly understood the difference between CDEs and NDEs that would already clear up considerable confusion.
youtu.be/gl5-HRCUfw0?...
Guy just now: what day is today? Wednesday, Thursday?
Me: it’s Friday
Guy: what time you got?
Me: it’s a few minutes after 9
Guy: so, like 9:10?
Me: 9:04
I feel like the level of desired precision really escalated quickly. I hope he’s not late for something!
Check out my newest attempt to help people understand causal mediation. This one covers just the direct effect (indirect effects are next episode) since if people truly understood the difference between CDEs and NDEs that would already clear up considerable confusion.
youtu.be/gl5-HRCUfw0?...
Five years from now, when more people have internalized that the limits of language are the limits of LLMs, will they turn to analytic or continental philosophy? And who can better help?
New paper out now 🥳 When psychologists discuss generalisability, they often refer to vague notions of representativeness. We provide an accessible intro to the total survey error framework as a tool to reason about this more rigorously.
w @taymalsalti.bsky.social@ruben.the100.ci >
Who else here is going to be at ICML in Seoul and might be up for talking causal? (Also feel free to plug your talk in the comments!)
Info on my talk/workshop is here: sites.google.com/view/philmli...
What are the implications of the fact that social media hooked us by making us anxious and depressed, while LLMs seem optimized to make us over-confident and manic? Any good discussions of this?
If you've been thinking of reading @add-hawk.bsky.social's the score, go ahead and do so. It's an extremely pleasurable read, without a wasted page.
I'd like to consider one (AI-related) nuance that perhaps gets lost in his discussion of objectivity...
www.penguinrandomhouse.com/books/735252...
Nguyen does value independence, in that what makes games non-soul-deadening is the plurality of values across games, and the book is filled with cases of independence-destroying homgenizations. Only he relates it to the incentive-stucturing implications of metrics, rather than their epistemology...
Here I'm suggesting that these insights might also have relevance to those of use who are thinking of how to design better metrics (which is not his project).
Hopefully it's clear than none of this is an attack on Nguyen, but an attempt to further savor the feast his book provides.
If you've been thinking of reading @add-hawk.bsky.social's the score, go ahead and do so. It's an extremely pleasurable read, without a wasted page.
I'd like to consider one (AI-related) nuance that perhaps gets lost in his discussion of objectivity...
www.penguinrandomhouse.com/books/735252...
For AI researchers reading Nguyen, the lesson they *should* take away is to become more critical about when to adopt metrics at all. But since I fear things will only become more metrified, I think the point I'm raising could potentially aid the discussion...
Namely, even from a purely knowledge-oriented perspective, one of the risks of AI is to render independent inputs dependent (e.g. linking your dating prospects to your credit score). So a fruitful question to ask is how we can try to preserve such independence...
If you've been thinking of reading @add-hawk.bsky.social's the score, go ahead and do so. It's an extremely pleasurable read, without a wasted page.
I'd like to consider one (AI-related) nuance that perhaps gets lost in his discussion of objectivity...
www.penguinrandomhouse.com/books/735252...
"Objective" is a slippery word. Just because aggregation removes independent sources of error, this doesn't entail that the aggregated concept (e.g. "health") picks out something real - only that it is invariant in a certain way. & which inputs to select and how to weigh them are value judgments...
But the phenomenon here does add some nuance to the discussion of local vs aggregated information. Epistemically, local (i.e. maximally contextualized) is not always most reliable. The right move isn't always to avoid aggregation, but to find the right level of aggregation...
If you've been thinking of reading @add-hawk.bsky.social's the score, go ahead and do so. It's an extremely pleasurable read, without a wasted page.
I'd like to consider one (AI-related) nuance that perhaps gets lost in his discussion of objectivity...
www.penguinrandomhouse.com/books/735252...
The arbitrariness claim is uncontestable -- we typically don't measure the complex quantity measure, but rather what is easiest to quantify and collect data on. More nefariously, our measurements can serve the interests or reflect the beliefs of those with more power...
On the other side of the scale, we have the phenomenon that aggregating a set of biased measurements of a complex quantity can provide a more reliable measure of that quantity, *provided that those measurements are independent*. (My favorite discussion of this is in Noise by Kahneman et al.'s)...
If you've been thinking of reading @add-hawk.bsky.social's the score, go ahead and do so. It's an extremely pleasurable read, without a wasted page.
I'd like to consider one (AI-related) nuance that perhaps gets lost in his discussion of objectivity...
www.penguinrandomhouse.com/books/735252...
Let me be clear that I do not dispute Nguyen's main claims about why game scores and metrics play different roles, and of the dangers of adopting metrics. Among these are examples of Goodhart's law, by which metrics become gamed and cease to serve their informational purpose...
Here I want to zoom in to the Q of how metrics should be epistemically evaluated. My claim: while he correctly emphasizes how metrics can lose objectivity due to arbitrariness, he neglects ways that metrics can gain objectivity through the aggregation of independent measurement...
"In the event of a nuclear holocaust, the only life remaining on Earth may be cockroaches, Keith Richards and physicists arguing about philosophical interpretations of quantum mechanics." physicsworld.com/a/experiment... 🧪⚛️
It's a relatively new course I give called Causal Thinking where the goal is to help them reason about whether or not the causal assumptions they rely on are satisfied and also how to reason when they are not satisfied. Basically I'm trying to address this gap:
bsky.app/profile/jere...
Causal inference courses: here's how you estimate causal effects and these are the assumptions you rely on and by the way the assumptions are always violated kthxbye
Thanks to Will for the shout out. I had a great time at the @applied-causal-graphs.de conference, which really made me optimistic about the quality of causal work being done. That I got to give a keynote there is something I still need to wrap my head around, but I'm glad it was so well received.
Should you be interested in a a less 'dude, where's my equilibrium' approach to dags, I can recommend @dagophile.bsky.social's 2020, 2021, and forthcoming on homeostasis and causal control. We just got a great keynote from him at @applied-causal-graphs.de on those topics.
Should you be interested in a a less 'dude, where's my equilibrium' approach to dags, I can recommend @dagophile.bsky.social's 2020, 2021, and forthcoming on homeostasis and causal control. We just got a great keynote from him at @applied-causal-graphs.de on those topics.
I’ve just finished this in the past week, but this is easily the best online course/series of lectures I’ve done. I’d tried it in a prior year and fallen on the wayside a bit, and I found the 2026 version with the split beginner/intermediate lectures much more approachable. Would recommend!!
Statistical Rethinking 2026 is done: 20 new lectures emphasizing logical and critical statistical workflow, from basics of probability theory to causal inference to reliable computation to sensitivity. It's all free, made just for you. Lecture list and links: github.com/rmcelreath/s...
This Thursday at noon I'll be presenting work on causation in physics at the Munich Center for Mathematical Philosophy. Feel free to join if you're around.
www.philosophie.lmu.de/mcmp/en/late...
This Thursday at noon I'll be presenting work on causation in physics at the Munich Center for Mathematical Philosophy. Feel free to join if you're around.
www.philosophie.lmu.de/mcmp/en/late...
Wimsatt: Perhaps, just perhaps, the best explanation of a phenomenon at a given level is to be found at the same level.
Kendler: Noone would seek to explain hypertension at the level of quarks.
A few of my favourite papers on this topic of absurd biological reductionism: 🧵
This...the fetishizing of unreplicable neuroscience in order to legitimize our work is self-defeating. We don't need to know that depression is in the brain (as opposed to what???) to know it is problematic. How much money have we wasted proving something like depression is "real"?