joaorico

Kafka [1] on which types of book to read:

"I believe one should only read those books which bite and sting. If the book we are reading does not wake us up with a blow to the head, then why read the book? To make us happy, as you write? My God, we would be just as happy if we had no books, and those books that make us happy, we could write ourselves if necessary. But we need the books that affect us like a disaster, that hurts us deeply, like the death of someone we loved more than ourselves, like if we were being driven into forests, away from all people, like a suicide, a book must be the axe for the frozen sea inside us." [2]

[1] Brief an Oskar Pollak, 27. Januar 1904. , https://homepage.univie.ac.at/werner.haas/1904/br04-003.htm

[2] Literal translation by ChatGPT. Original:

"Ich glaube, man sollte überhaupt nur solche Bücher lesen, die einen beißen und stechen. Wenn das Buch, das wir lesen, uns nicht mit einem Faustschlag auf den Schädel weckt, wozu lesen wir dann das Buch? Damit es uns glücklich macht, wie Du schreibst? Mein Gott, glücklich wären wir eben auch, wenn wir keine Bücher hätten, und solche Bücher, die uns glücklich machen, könnten wir zur Not selber schreiben. Wir brauchen aber die Bücher, die auf uns wirken wie ein Unglück, das uns sehr schmerzt, wie der Tod eines, den wir lieber hatten als uns, wie wenn wir in Wälder vorstoßen würden, von allen Menschen weg, wie ein Selbstmord, ein Buch muß die Axt sein für das gefrorene Meer in uns."

I suppose it's quite off-topic, but some weeks ago I read a small book by Mary Gaitskill, the writer of the piece.

It's called "Lost Cat".

I highly recommend it. Ironically, it might be an approximate opposite of Pale Fire. It's very short, with simple yet beautiful prose, filled with intense, raw emotions.

For anyone diving into Ulysses, I highly recommend checking out The Joyce Project [1].

It's filled with interactive notes that are very useful for understanding the linguistic and cultural references.

Here's my reading method that I found effective:

  1. Read a section on paper.
  2. Go through the same section on the site.
  3. (Re-)read on paper.

[1] https://www.joyceproject.com/

Incidentally, if you’re looking to start reading in French, there is hardly a better book in terms of (impact on literature) times (simple, accessible writing) [2]. It’s also a short book.

Regarding the literary merit of Camus, Nabokov had this to say [1]:

”I happen to find second-rate and ephemeral the works of a number of puffed-up writers—such as Camus, Lorca, Kazantzakis, D. H. Lawrence, Thomas Mann, Thomas Wolfe, and literally hundreds of other “great” second-raters.”

“Brecht, Faulkner, Camus, many others, mean absolutely nothing to me, and I must fight a suspicion of conspiracy against my brain when I see blandly accepted as “great literature” by critics and fellow authors Lady Chatterley’s copulations or the pretentious nonsense of Mr. Pound, that total fake.”

“Incidentally, I frequently hear the distant whining of people who complain in print that I dislike the writers whom they venerate such as Faulkner, Mann, Camus, Dreiser, and of course Dostoevski.”

“It is a shame that he [Franz Hellens] is read less than that awful Monsieur Camus and even more awful Monsieur Sartre.”

[1] Strong Opinions

[2] Although Le Petit Prince beats it in all three (impact, even simpler language, shorter).

Opportunity costs. The real debate has been whether it makes sense for string theory (whatever the prevailing definition is) to dominate funding for theoretical research of the "bridge". There are alternatives besides strings for the bridge, and there should be even more, in theory...

Simpson’s paradox should be taken into account.

If you group the population into only 2 groups: all of the vaccinated and all the unvaccinated, regardless of age; then the vaccinated had a higher death toll.

But age is a hidden factor. The older have more risks and are more vaccinated.

If you group by vaccination AND by age bracket, the opposite happens. For example, the 60 to 65 vaccinated have a lower death rate than the 60 to 65 unvaccinated.

Alain Connes, Fields medalist, talks about going on walks while reading math books in a particular way (and on how a mathematician works and should read a book) [0]:

"To understand any subject, above all, a mathematician SHOULD NOT pick up a book and read it.

It is the worst error!

No, a mathematician needs to look in a book, and to read it backwards. Then, he sees the statement of a theorem. And, well, he goes for a walk. And, above all, he does not look at the book.

He says, "How the hell could I prove this?"

He goes for his walk, he takes two hours ... He comes back and he has thought about how he would have proved it. He looks at the book. The proof is 10 pages long. 99% of the proof, pff, doesn't matter.

Tak!, here's the idea!

But this idea, on paper, it looks the same as everything else that is written. But there is a place, where this little thing is written, that will immediately translate in his brain through a complete change of mental image that will make the proof.

So, this is how we operate. Well, at least some of us. Math is not learned in a book, it cannot be read from a book. There is something active about it, tremendously active.

[...]

It's a personal, individual work."

[0] https://www.youtube.com/watch?v=9qlqVEUgdgo

In case you're interested in learning about graph deep learning, and are familiar with standard DL, I strongly recommend these two very good, recent books (freely available):

[1] Graph Representation Learning - William L. Hamilton https://www.cs.mcgill.ca/~wlh/grl_book/

[2] Deep Learning on Graphs - Yao Ma, Jilian Tang https://cse.msu.edu/~mayao4/dlg_book/

The new edition has been split in two parts. The pdf draft (921 pages) and python code [1] of the first part are now available. The table of contents of the second part is here [2].

From the preface:

"By Spring 2020, my draft of the second edition had swollen to about 1600 pages, and I was still not done. At this point, 3 major events happened. First, the COVID-19 pandemic struck, so I decided to “pivot” so I could spend most of my time on COVID-19 modeling. Second, MIT Press told me they could not publish a 1600 page book, and that I would need to split it into two volumes. Third, I decided to recruit several colleagues to help me finish the last ∼ 15% of “missing content”. (See acknowledgements below.)

The result is two new books, “Probabilistic Machine Learning: An Introduction”, which you are currently reading, and “Probabilistic Machine Learning: Advanced Topics”, which is the sequel to this book [Mur22].

Together these two books attempt to present a fairly broad coverage of the field of ML c. 2020, using the same unifying lens of probabilistic modeling and Bayesian decision theory that I used in the first book. Most of the content from the first book has been reused, but it is now split fairly evenly between the two new books. In addition, each book has lots of new material, covering some topics from deep learning, but also advances in other parts of the field, such as generative models, variational inference and reinforcement learning. To make the book more self-contained and useful for students, I have also added some more background content, on topics such as optimization and linear algebra, that was omitted from the first book due to lack of space.

Another major change is that nearly all of the software now uses Python instead of Matlab."

[1] https://github.com/probml/pyprobml

[2] https://probml.github.io/pml-book/book2.html

He has shared part of his toolkit, namely the exporting and syncing of the Bear notes, and “link-janitor” for the backlinks [1]. Although I wouldn’t recommend it in general - it’s a brittle prototype. And right now there are better tools out there (linked above) many of which appeared in the last few months.

[1] https://www.reddit.com/r/bearapp/comments/enbk65/sharing_a_s...

Right now, Andy is perhaps the most sophisticated thinker in this space sharing his insights and prototypes (meta-knowledge work, backlinked evergreen notes, spaced repetition, new UX/UI for these systems, etc). Here's some additional pointers:

- Andy livestreamed a demo of him on a typical work session: https://www.youtube.com/watch?v=DGcs4tyey18

- this Patreon post explains in greater lenght his OS-level spaced repetition approach: https://www.patreon.com/posts/bringing-ideas-36925173

- Andy is working on a prototype of that system, called Orbit, which might be available soon: https://twitter.com/withorbit

- in regards to his specific writing/thinking system, here's a couple more clarifications: https://notes.andymatuschak.org/z4AX7pHAu5uUfmrq4K4zig9x8jmm... https://notes.andymatuschak.org/z6f6xgGG4NKjkA5NA1kDd46whJh2...

- Obsidian has a plug-in which replicates the sliding panes of Andy's notes: https://forum.obsidian.md/t/andy-matuschak-mode-v2-7-updated...

I think the space of graph/backlinked personal notes/knowledge systems is taking off [1], with many solutions free and open-source. (Of that list, many have spaced-repetition plug-ins not referenced there.) It will be interesting to how the field matures in a couple of years.

[1] https://www.notion.so/db13644f08144495ad9877f217a161a1?v=ff6...

I like these two quotes of Knuth where he lets us know how hard he worked.

--

From this small interview [1]:

"When I'm working on a research problem I generally begin by filling dozens of sheets of scratch paper with partial calculations. When I eventually get to a point where I can think about the problem while swimming, then I'm often ready to solve it."

--

From this other interview [2]:

"So I went to Case, and the Dean of Case says to us, says, it’s a all men’s school, says, “Men, look at, look to the person on your left, and the person on your right. One of you isn’t going to be here next year; one of you is going to fail.” So I get to Case, and again I’m studying all the time, working really hard on my classes, and so for that I had to be kind of a machine.

I, the calculus book that I had, in high school we — in high school, as I said, our math program wasn’t much, and I had never heard of calculus until I got to college. But the calculus book that we had was great, and in the back of the book there were supplementary problems that weren’t, you know, that weren’t assigned by the teacher. The teacher would assign, so this was a famous calculus text by a man named George Thomas, and I mention it especially because it was one of the first books published by Addison-Wesley, and I loved this calculus book so much that later I chose Addison-Wesley to be the publisher of my own book.

But Thomas’s Calculus would have the text, then would have problems, and our teacher would assign, say, the even numbered problems, or something like that. I would also do the odd numbered problems. In the back of Thomas’s book he had supplementary problems, the teacher didn’t assign the supplementary problems; I worked the supplementary problems. I was, you know, I was scared I wouldn’t learn calculus, so I worked hard on it, and it turned out that of course it took me longer to solve all these problems than the kids who were only working on what was assigned, at first. But after a year, I could do all of those problems in the same time as my classmates were doing the assigned problems, and after that I could just coast in mathematics, because I’d learned how to solve problems. So it was good that I was scared, in a way that I, you know, that made me start strong, and then I could coast afterwards, rather than always climbing and being on a lower part of the learning curve."

[1] http://authenticinquirymaths.blogspot.pt/2015/11/maths-in-sc....

[2] Transcript from here: https://github.com/kragen/knuth-interview-2006

Here's Alain Connes, Fields medalist, on how a mathematician works and should read a book [0]:

"To understand any subject, above all, a mathematician SHOULD NOT pick up a book and read it.

It is the worst error!

No, a mathematician needs to look in a book, and to read it backwards. Then, he sees the statement of a theorem. And, well, he goes for a walk. And, above all, he does not look at the book.

He says, "How the hell could I prove this?"

He goes for his walk, he takes two hours ... He comes back and he has thought about how he would have proved it. He looks at the book. The proof is 10 pages long. 99% of the proof, pff, doesn't matter.

Tak!, here's the idea!

But this idea, on paper, it looks the same as everything else that is written. But there is a place, where this little thing is written, that will immediately translate in his brain through a complete change of mental image that will make the proof.

So, this is how we operate. Well, at least some of us. Math is not learned in a book, it cannot be read from a book. There is something active about it, tremendously active.

[...]

It's a personal, individual work."

[0] https://www.youtube.com/watch?v=9qlqVEUgdgo

Don't forget Gaius Diocles, the roman charioteer [1]:

"His winnings reportedly totaled 35,863,120 sesterces, allegedly, over $15 billion in today’s dollars, an amount which could provide a year's supply of grain to the entire city of Rome, or pay the Roman army at its height for a fifth of a year. Classics professor Peter Struck describes him as "the best paid athlete of all time"."

[1] https://en.m.wikipedia.org/wiki/Gaius_Appuleius_Diocles

If you are interested in learning about GAN, checkout GANLab, by Google Brain and others:

https://poloclub.github.io/ganlab/

It's similar to tensorflow playground, but for GAN:

https://playground.tensorflow.org/

I think John Bell discovered his revolutionary Bell's theorems of quantum mechanics in a similar way.

I can't find it now in a quick search, but I remember reading that he thought every physicist should devote something like 10% of their time thinking about the foundations of physics/quantum mechanics. (What would he do with 100% of his time?)

It's always going to take a few years.

The global fleet is about 2 billion passenger and commercial vehicles, and the global yearly production is about 100 million. So even if all new cars sold from now are electric, it will take 20 years.

But who knows what kind of autonomous vehicles and other innovations we'll have in 20 years. Buckle up :)

This is the most complete list of Alan Kay's talks I found: https://tinlizzie.org/IA/index.php/Talks_by_Alan_Kay

This is a bit of a strange article.

First, "in his recent book" refers to his 2011 book [1]. And Christensen has been prophesying this general bankruptcy "in the next decade" since that time. [2]

In any case it's interesting to think about the larger argument of the future of traditional higher education in general versus online education.

Bryan Caplan's thesis that (the state should cut funding for higher education because) higher education is mostly about signalling 3 things is a good tool. He argues that higher education signals a combination of intelligence, conscientiousness and conformity. The combination of the 3 is crucial for the model. [3]

Online education, and more generally self-education, fails on the conformity side. Companies do not want in general to risk such non-conformists, when they can hire from a stream of fresh graduates (smart, hard-working and relatively conformist).

Also, I think the socialization, friendships and networking that happen in the university are extremely valuable and not easily replaced by online education (where and with who can a smart, driven 18 year old hang out while studying and learning for 4 years on MOOCs and textbooks?)

And in addition, I hope, traditional universities are starting to improve their teaching methods (eg, flipped classroom, peer instruction) to multiply the pedagogical and motivational value they offer vs MOOCs.

For online education to replace traditional higher ed, it might require taking into account these factors. Could something like workspaces for freelancers or remote workers - but for studying - replace the traditional institution and the above benefits? Such that, for example, you would not be seen as an extreme non-conformist by not enrolling in a university?

Also, outside the US, tuition costs is often much lower. An online STEM degree, say a certified online masters in software engineering such as coursera or edx, could easily be more expensive than regular (or even the best) university.

[1] https://www.amazon.com/Innovative-University-Changing-Higher...

[2] https://www.economist.com/international/2012/12/22/learning-...

[3] https://www.amazon.com/Case-against-Education-System-Waste/d...

(To be clear, he argues that from the individual's perspective, university is still net positive, if you have what it takes to finish the degree and don't get too much in debt. It's the state that should cut funding since it's inflating credentials.)

Anders Ericsson has replied to this meta-analysis [1], which in turn got a reply from McNamara et al [2].

In Ericsson's opinion/definition [1], deliberate practice is "individualized practice with training tasks (selected by a supervising teacher) with a clear performance goal and immediate informative feedback was associated with marked improvement"; and he argues "In contrast, Macnamara, Moreau, and Hambrick’s (2016, this issue) main meta-analysis examines the use of the term deliberate practice to refer to a much broader and less defined concept including virtually any type of sport-specific activity, such as group activities, watching games on television, and even play and competitions. Summing up every hour of any type of practice during an individual’s career implies that the impact of all types of practice activity on performance is equal—an assumption that I show is inconsistent with the evidence."

McNamara et al reply saying that evidence only accounts for a relatively small fraction of expert performance [2]: "we found that deliberate practice accounted for a sizeable amount of variance in sports performance (18%), but it left a much larger amount unexplained. Ericsson’s (2016, this issue) evaluation of our research is undercut by contradictions, omissions, and errors." They conclude that "The available evidence indicates that deliberate practice, though undeniably important, does not largely account for individual differences in expertise. Building on Ericsson’s pioneering work, the task now is to develop theories of expertise that include multiple factors."

[1] Summing up hours of any type of practice versus identifying optimal practice activities: Commentary on Macnamara, Moreau, & Hambrick (2016) http://journals.sagepub.com/doi/abs/10.1177/1745691616635600

[2] How Important Is Deliberate Practice? Reply to Ericsson (2016) http://journals.sagepub.com/doi/abs/10.1177/1745691616635614

Pearl's words from the Introduction of "BAYESIANISM AND CAUSALITY, OR, WHY I AM ONLY A HALF-BAYESIAN":

"I turned Bayesian in 1971, as soon as I began reading Savage’s monograph The Foundations of Statistical Inference [Savage, 1962]. The arguments were unassailable: (i) It is plain silly to ignore what we know, (ii) It is natural and useful to cast what we know in the language of probabilities, and (iii) If our subjective probabilities are erroneous, their impact will get washed out in due time, as the number of observations increases.

Thirty years later, I am still a devout Bayesian in the sense of (i), but I now doubt the wisdom of (ii) and I know that, in general, (iii) is false."

Have you tried some of the better math related apps?

Like DragonBox's Elements, Algebra and Numbers [1]? Or the ones by DuckDuckMoose, such as MooseMath [2]? KhanAcademy's very first levels might be good as well [3].

With your oldest you might try a game like Junior Catan [4], that than graduates to Catan where you can talk about the probability distribution of the sum of two dice, an essential aspect of Catan.

You could perhaps try a programming language like Scratch. Have a go at some of code.org's "games" [5] and perhaps even MIT app inventor's with your oldest? [6]

[1] https://dragonbox.com/

[2] http://www.duckduckmoose.com/educational-iphone-itouch-apps-...

[3] https://www.khanacademy.org/math/early-math

[4] https://www.catan.com/game/catan-junior

[5] https://code.org/minecraft

[6] http://www.appinventor.org/content/ai2apps/simpleApps/androi...

Just yesterday I offered "So Good They Can't Ignore You" to someone about to start their professional career. Mostly as a counterweight to the mainstream advice.

I recommend watching Bret Victor's "Inventing on Principle" [0] for a very interesting variant on the theme of "your mission in life & work".

[0] https://vimeo.com/36579366

This is off-topic, but perhaps interesting to some, and might add some weight to the opinions expressed in the article.

The author of this article is Daniel T. Willingham. He is a psychologist at the University of Virginia and author of some very good books on learning, schools and education.

He is a good, careful and informed thinker on the subject of learning and children.

I recommend his book "Why Don't Students Like School?" [0] for an insightful look at one of the pieces of the puzzle that is Education.

[0] "Why Don't Students Like School?: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom" https://www.amazon.com/Why-Dont-Students-Like-School/dp/0470...

In theory, GTD is great.

In practice, it comes up short in several ways.

TLDR - For some years now, I use a system called Agile Results [1]. It's a method (with less marketing behind it than GTD) which has been gaining traction for a lot of good reasons. I couldn't be happier with it.

I won't get into the details, but its biggest edge against GTD is the flexibility. With Agile Results, you can let go for a couple of hours or days, and the system doesn't fall apart, it's more organic. It's oriented towards Results in several domains of life, in a balanced way. The manner in which it breaks down the hierarchy of projects, temporal horizons and relative importance of tasks is the real key. It solves the same problems as GTD (eg, your mind is for thinking not remembering [of course, you can still incorporate spaced repetition for what you want to remember long term]). But it solves them in a way which is more organic, focused and iterative. If you only implement a portion of it, it has the proportional benefits - it's not all or nothing. It also lets you integrate parts of other productivity systems.

There's a book about it [1], and a 30 day program to getting started incrementally [2].

I realize it sounds like hyperbole, but, after some years using it, I consider the problem of productivity essentially solved.

[1] https://www.amazon.com/Getting-Results-Agile-Way-Personal/dp...

[2] http://www.30daysofgettingresults.com/

[3]http://www.asianefficiency.com/agile-results/

There's a slight confusion in this thread, that many are rightfully pointing out, as there's a bit of context missing in the article.

The thing is, the term 'digital native' has been heavily used as a way of saying that children that grew up surrounded by tablets and TVs and the internet actually learn differently from the so-called digital immigrants.

For an example, see these quotes by Marc Prensky cited in the literature thousands of times:

‘today’s students think and process information fundamentally differently from their predecessors’ [1]

‘Our students, as digital natives, will continue to evolve and change so rapidly that we won’t be able to keep up’ [2]

‘Our young people generally have a much better idea of what the future is bringing than we do’ [2]

‘In fact they are so different from us that we can no longer use either our twentieth century knowledge or training as a guide to what is best for them educationally’ [2]

Now this might immediately sound extremely suspicious for anyone who has some background on the research of how humans learn, but it's a common myth all over the learning world. 'Oh I can't learn from books, I need interactive videos and apps, that's why I have bad grades.' Actually computers are an excellent choice for learning if applied correctly, but this is not what is usually being implied in these situations.

There's a big confusion between how much children are familiar with using technology from a consumer's point of view, and the natural human ability to learn. The latter hasn't changed.

The debunking of 'digital natives' is not new either [3].

By the way, for anyone interested, I recommend Hattie and Yates' book on learning [4] as a great, very balanced, modern introduction.

[1] (cited 18000 times!) Prensky, Marc. "Digital natives, digital immigrants part 1." On the horizon 9.5 (2001): 1-6.

[2] (cited 1000 times) Prensky, Marc. "Listen to the natives." Educational leadership 63.4 (2005).

[3] (cited 2700 times) Bennett, Sue, Karl Maton, and Lisa Kervin. "The ‘digital natives’ debate: A critical review of the evidence." British journal of educational technology 39.5 (2008): 775-786.

[4] Hattie, John, and Gregory CR Yates. Visible learning and the science of how we learn. Routledge, 2013.

Feynman has another fantastic talk on "What is Science?" [1].

Among other things, at a certain point in that talk, this is how he lays out his "best definition of science":

"What science is, I think, may be something like this: There was on this planet an evolution of life to a stage that there were evolved animals, which are intelligent. I don't mean just human beings, but animals which play and which can learn something from experience--like cats. But at this stage each animal would have to learn from its own experience. They gradually develop, until some animal [primates?] could learn from experience more rapidly and could even learn from another’s experience by watching, or one could show the other, or he saw what the other one did. So there came a possibility that all might learn it, but the transmission was inefficient and they would die, and maybe the one who learned it died, too, before he could pass it on to others.

The question is: is it possible to learn more rapidly what somebody learned from some accident than the rate at which the thing is being forgotten, either because of bad memory or because of the death of the learner or inventors?

So there came a time, perhaps, when for some species [humans?] the rate at which learning was increased, reached such a pitch that suddenly a completely new thing happened: things could be learned by one individual animal, passed on to another, and another fast enough that it was not lost to the race. Thus became possible an accumulation of knowledge of the race.

This has been called time-binding. I don't know who first called it this. At any rate, we have here [in this hall] some samples of those animals, sitting here trying to bind one experience to another, each one trying to learn from the other.

This phenomenon of having a memory for the race, of having an accumulated knowledge passable from one generation to another, was new in the world--but it had a disease in it: it was possible to pass on ideas which were not profitable for the race. The race has ideas, but they are not necessarily profitable.

So there came a time in which the ideas, although accumulated very slowly, were all accumulations not only of practical and useful things, but great accumulations of all types of prejudices, and strange and odd beliefs.

Then a way of avoiding the disease was discovered. This is to doubt that what is being passed from the past is in fact true, and to try to find out ab initio again from experience what the situation is, rather than trusting the experience of the past in the form in which it is passed down. And that is what science is: the result of the discovery that it is worthwhile rechecking by new direct experience, and not necessarily trusting the [human] race['s] experience from the past. I see it that way. That is my best definition."

[1] Feynman, R. P., "What is Science?" The Physics Teacher Vol. 7, issue 6, 1969, pp. 313-320 http://www.fotuva.org/feynman/what_is_science.html

The book I recommend to people getting started is Competitive Programming 3 [1] by Steven and Felix Halim. It's pretty great if you have already a basic grasp of simple algorithms and a bit of C++.

And as you say you need to practice, and the book incentivizes it. They accompany the book with precisely problems from UVa Online Judge, some of them solved and with code (in the book and in the site).

[1] https://cpbook.net/

This is the first edition of this important book.

AFAICT the only differences to the second edition are the additional forewords and a new Foreword to the Second Edition by Papert. This foreword is not mentioned in this online edition by MIT [0]. I have the second edition at home; I can try to share the forewords and specially the new preface, I don't think they are online (even on library genesis).

[0] http://mindstorms.media.mit.edu/ "A second edition, with new Forewords by John Sculley and Carol Sperry, was published in 1993."

Scott has talked about Penrose's books and ideas several times, one of them in direct conversation with him: http://www.scottaaronson.com/blog/?p=2756 http://www.scottaaronson.com/democritus/lec10.5.html