Deemah ๐บ๐ฆ ๐ณ๐ด ๐ธ๐ช retweeted
For my daughter, Lana:
supportnow.org/lana-rasmusseโฆ
I was today years old when I learned that metaphysical commitments are called hinges. #philosophy #wittgenstein
Deemah ๐บ๐ฆ ๐ณ๐ด ๐ธ๐ช retweeted
It is by logic that we prove, but by intuition that we discover.
To know how to criticize is good, to know how to create is better.
-- H. Poincarรฉ,
Science and Method (1908), Part II, Ch. 2.
Deemah ๐บ๐ฆ ๐ณ๐ด ๐ธ๐ช retweeted
Here it is - the official, revised, peer-reviewed version of my Platonic Space paper. mdpi.com/2409-9287/11/5/161 Of all the many unpopular positions Iโve taken over the decades - bitter controversies around the origin of left-right asymmetry in embryogenesis, bioelectricity and genetics, diverse intelligence, etc., this one has by far generated the most pushback: serious (grateful for those!) and energetic attempts to move me to other views, pleas to just drop it and not talk about it (for several different reasons), nasty emails and accusations, impacts on reviews of papers that have nothing to do with this, etc. Kind of amazing to me how incendiary this is. What can I say... Our job is to call it as we see it, and right now for me, this is it. Apologies to collaborators and colleagues for any shrapnel! Time will tell if this pans out or not; I've placed my bets. And btw, if you think this stuff is weird and uncomfortable, just waitโฆ Thereโs much more on the way. The knob turns slowly but as long as the data keep coming, I'm going to say what I think it all means and follow it to the next steps it enables. Buckle up!
You must imagine Sisyphus happy
์ธํฌ์์์ ๋ฌผ์ง์ ์ด๋ฐํ๋ ์ด๋ ๋จ๋ฐฑ์ง์ ๋ชจ์ต ํค๋ค์ ๊ท์ฝ๋ค
Readers added context they thought people might want to know
์ด ์์์ ํค๋ค์ ์ ๊ตฌ์กฐ ๊ธฐ๋ฐ ์ปดํจํฐ ์์ฑ ์ ๋๋ฉ์ด์
์ผ๋ก, ์ค์ ์ธํฌ ๋ด๋ถ ํ๋ฏธ๊ฒฝ ์์์ด ์๋๋๋ค. ๋จ๋ฐฑ์ง ํฌ๊ธฐ๊ฐ ๋๋ฌด ์์ ์ผ๋ฐ ํ๋ฏธ๊ฒฝ์ผ๋ก ์ด๋ฐ ๋ชจ์ต ๊ด์ฐฐ ๋ถ๊ฐํฉ๋๋ค.
en.wikipedia.org/wiki/Kinesin
easternblot.net/2016/02/12/imaโฆ
physicstoday.aip.org/news/superresoโฆ
Deemah ๐บ๐ฆ ๐ณ๐ด ๐ธ๐ช retweeted
Scientists and philosophers have fallen for a tempting buzzword: โemergence.โ
Itโs invoked to explain life, consciousness, and the flow of time: when simple parts combine, it is claimed, they sometimes produce new entities with powers their parts could never predict.
But philosopher John Heil calls this out as an intellectual sleight of hand. โEmergence,โ he argues, doesnโt reveal hidden truthsโit masks our ignorance, mistaking gaps in explanation for gaps in reality.
"Philosophers disagree on many thingsโon the nature of causation, for instanceโbut disagreements over emergence are more unsettling," argues Heil.
Itโs time to drop the magic word and face the real challenge: uncovering, in concrete detail, how simple parts can give rise to complex wholes.
Tap the link now to read more. iai.tv/articles/emergence-exโฆ
Deemah ๐บ๐ฆ ๐ณ๐ด ๐ธ๐ช retweeted
Pleased to announce that Tuesday my new book came out. But the three free version is here, and I thought Iโd share what i consider to be the strengths of it. 1/n
mixtape.scunning.com
Deemah ๐บ๐ฆ ๐ณ๐ด ๐ธ๐ช retweeted
Announcing: my final Cornell-era academic project - launching today!
It's called "Bayesian Decision-making Algorithms" - a book which tries to write down everything important I've learned in ten years of research.
Chapters 1 and 2 are now online!
๐ Link and more below! ๐
URL: bayesianalgorithms.com
Decision-making is ubiquitous in life - taking actions, seeing what happens, learning, doing a better job next time. In doing so, we plan using our mind's model of reality.
Bayesian decision-making casts this paradigm into math - by using a Bayesian model to quantify uncertainty about reality. The question becomes: how do we use this uncertainty to take better actions?
This is not as easy as it looks. Due to the presence of explore-exploit tradeoffs, maximizing expected value is often bad: strategies that do so can get stuck in bad actions because they don't try enough new things to correctly learn about the world.
The fix is to consider more sophisticated strategies for exploration. In this book, I'll show you the major known algorithm classes which balance tradeoffs like the above, and will teach you how they work.
This book represents the conclusion of an intellectual journey that began at the start of my twenties.
Its primary goal is to inspire the next generation to learn about Bayesian thinking, to enable others to do bigger and more creative things with it.
Especially in realistic situations where we do not assume the Bayesian model exactly matches reality.
In particular, I think there is a chance Bayesian decision-making is one of the "right abstractions" for understanding how large language models actually work, including how they handle exploration. You should consider working on this!
I'm writing the book build-in-public style - I'll be posting other chapters when they're done, which is hopefully soon.
I know exactly what I want to write, the remaining work is to do so. Stay tuned for that.
I don't know how long it will take me to finish. Among my ambitions, the book is number two on the priority list - nonetheless, writing it is possible, because priority one is bottlenecked by the number of days in a year, not the number of hours in a day. More on that another time.
In the meantime, I'd love your comments! Please have a look, and tell me what you think!
Deemah ๐บ๐ฆ ๐ณ๐ด ๐ธ๐ช retweeted
"Mathematical Methods in Data Science (with Python)" by Sebastien Roch
mmids-textbook.github.io
Deemah ๐บ๐ฆ ๐ณ๐ด ๐ธ๐ช retweeted
Statistical Rethinking 2026 is done: 20 new lectures emphasizing logical & critical statistical workflow, from basics of probability to causal inference to reliable computation to sensitivity. It's all free, made just for you. Lecture list & links: github.com/rmcelreath/stat_rโฆ
Deemah ๐บ๐ฆ ๐ณ๐ด ๐ธ๐ช retweeted
Why do two people watch the same video, read the same headline, or witness the same event and walk away with completely different conclusions?
The answer isnโt stubbornness or bad faith. Itโs the science of perception.
Learn more: at.apa.org/34c474
This insightful article breaks down the Jacobian, explaining how matrix determinants scale space, distort areas, and make complex coordinate transformations like polar and spherical coordinates actually make sense. Whether you're optimizing machine learning models or solving multidimensional integrals, grasping the Jacobian is essential. proswit.com/jacobian_explainโฆ
How Small Postgres Metadata Tables Quietly Throttle Your Largest Queries | HackerNoon hackernoon.com/how-small-posโฆ
Fascinating interview with excellent Howard Robinson youtube.com/watch?v=9T60xaPdโฆ
Michael Huemer ๐ช @Michael__Huemer
doi.org/10.53765/20512201.33โฆ


















