I'm happy to see The Variational Principles of Mechanics (by the great Lanczos) on that list. It really is deep, and I think it does a fantastic job of explaining classical mechanics. I've read the vast majority of the book multiple times.
Since we're on the topic of Sussman, has anyone here read through SICM? I've heard that the code is difficult to get to work, but does anyone have an opinion on the rest? I haven't had a chance to read it yet.
I referred to the Lanczos book quite often when I was working on my PhD. I've read the first part of SICM and skimmed the rest. Based on that, it looks like it does an excellent job in building up an understanding of how the math works. By the time I looked at SICM, I had implemented my own code to do something similar (coded in Maple). It looked like the progression of the code was well handled. On the whole, I think Lanczos is a better book, but SICM is fairly decent. I'm also partial to Meirovitch's Methods of Analytical Dynamics which I think does the best job of explaining the inclusion of non-conservative forces in the Principle of Least Action. This is needed for applied forces and torques like a motor. It also covers the inclusion of damping forces well. Like Lanczos, the Meirovitch book is available from Dover, so it's pretty inexpensive.
The SICM code really only works for a specific scheme interpreter, so if you have that it should be fine.
I bought Lanczos' book a month ago, I'm ~75 pages in (got other stuff to do). I will give SICM a try. I've just installed the 'ScmUtils Mechanics' package to accompany the book.
How can the code be difficult to get to work if he ships the required package with the book?
I cannot recommend 'The Society of Mind' enough. This was my first book before diving into Cognitive Science. Although it is mostly psychology(i.e.speculation), it is a brilliant book with insights and ideas.
Until very recently (mid 90's) many psychological phenomena were explained by childhood experiences, in line with Freud's psychoanalytical theory. Many of these have since been disproved. One criteria I have for trustworthiness of a scientific discipline is that of stability. If the previous theory has been found to be very wrong, very recently, then I am cautious to trust again.
Specifically note that his theory, although debunked, still lives on in philosophy and literature analysis. This "stickiness", that people refuse to give up the theory when proven wrong is additionally a bad sign.
People really like to trash Freud, but you have to put him into context. Before Freud, we had a few branches of psychology: philosophical psychology in England arguing about empiricism vs. nativism, Wundtian psychology in Germany sitting around asking very specific questions that they answered using introspection, and in the States we had the very first blossoming of the behaviorism that would dominate psychology in the States until the '60s[0]. Some of these approaches had a concept of the subconscious, but they all viewed it as a static warehouse for previous experience, and very few people thought about it in a serious way.
Freud's major contribution to psychology was that we actually have a dynamic subconscious that profoundly affects how we live our lives. This aspect of his theory has become so ingrained in our culture that it's hard to imagine the world before Freud. Also, that aspect of his theory has held up over the years.
Also, he got a number of things correct: many of his coping mechanisms have strong empirical support, for instance.
Freud was wrong in detail, but his overarching approach changed psychology for the better.
[0] Yes, I know this "history" is a vast oversimplification.
The concept of subconscious is much older than Freud, it was a staple of the romantics. Freud proposed a specific structure theory of the subconscious centred around the Oedipus complex. That specific theory is indefensible.
Psychology is a very broad area and it's not fair to judge it based upon Dr. Phil style counselling or literary criticism.
The psychologists I've worked with design experiments to attempt to determine fundamental mental capabilities in terms of perception, memory, spatial reasoning, etc. and how this can be applied to design safe and effective user interfaces, in particular for safety critical systems like aircraft.
Sure, the models they develop are likely just useful approximations, but it seems that models at the neuron level would be too unwieldy for these sorts of questions anyway.
At least in the US, up until the 50's/60's behaviorism (which at least claims to be based in observation) was a dominant school of thought in psychology.
"Empiricism" does not usually refer to taking measurements (which seems to be what you have in mind); it is a theory of epistemology which holds that knowledge is primarily experiential. (It just happens that many psychologists are causal empiricists, but it has nothing per se to do with their love of measurement.)
I bought the book some 15 years ago when my interest in AI was on its peak. I remember not being too impressed by it, but I always wanted to go back and give it a second try, especially since I cannot really remember the reason I didn't like it too much. I believe it was that I found some of the earlier ideas (every page basically describes one idea) not totally convincing, or maybe I just wasn't able to follow the later ones?
Quantum Computing since Democritus in high school reading list is very heavy. I have had Theory of Computation class in college and I can't read 20 pages of that book.
On the other hand, if you treat each page, each statement as something you have to completely internalize before moving onto the next page—including looking up all the prerequisite topics recursively on Wikipedia or in other texts—you might just end up teaching yourself up to college-level math while still in high school.
(I didn't do this myself with QCD, but I very nearly did it with SICP in middle school.)
To provide a second opinion: for me it was probably one of the best books I read last year -- extremely lucid writing and very lively. I think I would have loved it in high school.
QC since Democritus is a weird mix of textbook and popularization; I think you could absolutely read it without understanding the details and still enjoy it. You won't get as much, but you'll still get something.
I didn't understand much of algorithms or quantum computing when I first read the lecture notes the book is sourced from [1]. Was still worth it.
I'm reading QCsD right now and loving it. But in high school? That sounds crazy. It'd help a lot to already understand Godel's proof, big-O notation, and P-vs-NP. Also relativity and quantum mechanics. And maybe some group theory.
I'm a current HS senior who took a Theory of Computation class the year prior, and I got a few chapters into QCSD before realizing that I needed to learn some more about complexity theory before I read that book. I'm planning on trying again once the summer begins.
Now I'm about half-way through Godel, Esher, Bach, and I have to say that GEB and QCSD feel similar, with an overlap not only in theme but also in genre and style.
...I got a few chapters into QCSD before realizing that I needed to learn some more about complexity theory before I read that book. I'm planning on trying again once the summer begins.
It might be a bit overkill, but if you go over the main chapters of Arora and Barak you should have more than enough background in complexity theory for your purposes.
Has anyone read the probability text mentioned in the list - Probability: The Logic of Science by Jaynes?
It looks like the text is freely available; I skimmed through the first chapter and it makes sense to me so far (I don't know how long that would hold true). I've been looking for a basic probability text for some time now, nothing too heavy but something to compensate for not having taken enough math in college.
I think Jaynes recommended Sivia & Skilling [1] as a companion book, but I cannot find the citation now. It might even make sense to read it beforehand.
Interesting to see Stranger in a Strange Land on his list. It shows up quite often on top lists of sci-fi books and Heinlein books. I'm halfway through it, after reading The Moon is a Harsh Mistress, and Stranger in a Strange Land is certainly the weaker book so far in my opinion.
As I struggle to wade through it, I wonder why it was ever as popular as it once was. What is it about this book that make so many people recommend it?
His writing is terrible on the level of style, characterisation, and even plot, but I think many of the ideas he expressed about human relations in his books are profound - that the rules which govern our societies are temporary, contingent and negotiable, that we don't live according to the principles we claim to, that free is often anything but, that colonies often become stronger than the parent society, that revolution comes when there is too big a gap between perception and reality, the tension between soldiers and citizens etc.
For me, I really like the first half of the book where he's viewing society from outside. It kind of falls apart for me when that is kind of set aside. Heinlein almost always has problems with the endings of his books. There's some that stay consistent throughout, like The Moon is a Harsh Mistress or Podkayne of Mars, but most kind of land with a thud. I love his books quite often for the ideas and settings, but yes, the writing isn't the best.
A funny comparison, since I could see The Moon is a Harsh Mistress getting compared to Ayn Rand due to the depictions of (and brief philosophical expositions on) stateless society.
Yea, definitely: I haven't read either of them in a while, but my memory is that The Moon is a Harsh Mistress felt like it had a plot with some philosophizing added on, but Stranger In A Strange Land was political exposition/description of wish-fulfillment orgies with an attempt at forming a plot around it, and that's why it is more like Ayn Rand novels.
I also read Time Enough for Love every few years. I've owned and read and loved much of Heinlein's work over my life, but it's the one that's stuck with me for the past decade.
My problem with Stranger in a Strange Land is it kind of falls apart around halfway to 2/3rds of the way through the book. The ending is pretty weak (as is most of Heinlein's endings). But, I found the first half of Stranger in a Strange Land to be excellent. In contrast, The Moon is a Harsh Mistress is a solid book all the way through. Also, a later book has a nice, little tie-in to it.
It is indeed one of the coolest papers and programs ever. KAM is a smart ODE solver, written in ZetaLisp on a Symbolics. It analyzes 2D pointsets created by any 2d equations, esp. non-linear ones. Typically a system of ordinary or partial differential equations, with a set of boundary and initial conditions. A typical non-linear physical system.
It creates MST's (Minimal spanning trees) of the calculated points to get the shape and number of curves, to see the number of clusters (checking the distance of the curves), and if the curves are linear or space filling.
Then the phase space is searched for initial states and end conditions, and to get useful summaries. It cannot do shape matching though, so repetitions and mirroring are not detected as such.
The goal is to get high-level descriptions of the model and the numerical dataset, and at which parameter ranges and conditions the system falls into chaos. Chaotic systems are bad for predictability but mostly good for engineering purposes.
Robert McIntyre is a wizard in his own right. He has taken the prowess of his mental faculties to the extreme, even mastering the art of bioluminescence.
Do people actually read all the books they recommend? I often tell people books are good, esp. books on some mathematical topic, without having read the entire thing myself, just selected parts as my inclination takes me. In fact, there are almost no textbooks I've read straight through.
I don't read whatever people recommend to me; I prioritize what's available based on what I think will have the greatest impact on me. But whatever I do read, I try to read it all the way through. Books have layers, like an onion. Each time you read a book, you peel back another layer of meaning. The first time I read a book, I don't let myself get hung up on what I don't understand; I just keep reading until I've reached the end. Then, if the book is really worth re-reading, I'll do it, and pay more attention to the details. You can get through a lot of tough material that way, and get 85-90% of the benefits as you would just sitting there, trying to grok every. Single. Word.
I do the same. I also have the philosophy that a book worth reading is worth re-reading. Sadly, you have to read it the first time without knowing if it will be worth reading at all.
On The Connection Machine: "Beautiful thesis, though it doesn't tell you anything you can really do today."
I don't understand: is data-parallel computing on a GPU much worse somehow? Or is it that there are better sources to read about data-parallel algorithms?
> is data-parallel computing on a GPU much worse somehow?
OpenCL is a very awkward way to do vector processing - everything is hard-coded to an abstract model of a typical consumer GPU memory hierarchy. CUDA is even worse with a ton of versions all having different limitations according to what the Nvidia chips can do.
It's awkward to do a lot of SIMD tasks on GPUs. The Connection Machine was a general-purpose SIMD originally designed for parallel graph algorithms.
Thanks. I've read Hillis's book but not studied the modern stuff; I'd gotten the impression the hardware was capable of about as much, and faster now, even if organized differently -- shared memory instead of a network -- and with less-pleasant languages.
That was pretty much where Thinking Machines was headed before bankruptcy - their third model (CM5) was a SPARC cluster primarily for running Connection Machine Fortran and C*.
Alan Kay's reading list: http://www.squeakland.org/resources/books/readingList.jsp
Bret Victor's reading list: http://worrydream.com/#!/Links