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photo 2018-01-16 13:20
Back cover of "Godel, Escher, Bach"

Finally finished 360 days later ... very tired but happy I got through it!

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review 2017-11-07 17:46
Incompleteness: “Build Deeper - Deep Learning Beginner's Guide by Thimira Amaratunga
Build Deeper: Deep Learning Beginners' Guide - Thimira Amaratunga


“epsilon”: 1e-07,

“float”: “float32”,


“backend”: “tensorflow”



In “Build Deeper - Deep Learning Beginner's Guide by Thimira Amaratunga


This book confirms other predictive system results that I have seen, where it has often been found that we human as a species who fancy ourselves as psychics or using other la-di-da methodologies can at best achieve around an 80% accuracy rate, even with good regular practice and tuning. The more accustomed you are toward reaching ever higher accuracy & precision percentile targets the more the distance to the next little increase in goal horizon. Still it does bring into question the abilities of Science and machine systems designing new machine systems, often through excluding what are regarded as unrepeatable subjective methods in favour of repeatable objectiveness. Outliers and other non-obvious patterns & so on are pushing back the boundaries at the edge of our cultural belief systems.


I don't think that any computer scientist would dispute the point that modern AI or machine learning is nowhere near the threshold of 'consciousness' or even 'general intelligence'. But it's not uncommon for words to have a different meaning within a technical field compared to how they are used in everyday communication. In regular English 'chaos' means unpredictable, whereas in mathematics it refers to the tendency of sensitive nonlinear systems to exhibit emergent attraction basins that can potentially be extremely predictable. Those are arguably even antonyms. Another example would be terms 'deterministic/nondeterministic' in Computer Science, which also differ strongly from their meanings in regular English. The point is that if you feel the need to grandstand on these trivialities, you clearly don't understand the fundamentals of the subject matter under discussion.



If you're into Computer Science and Machine Learning in particular, read on.

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review 2016-04-26 00:00
Gödel, Escher, Bach: An Eternal Golden Braid
Gödel, Escher, Bach: An Eternal Golden Braid - Douglas R. Hofstadter Preface to GEB's Twentieth-anniversary Edition
List of Illustrations
Words of Thanks

--Gödel, Escher, Bach: An Eternal Golden Braid

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review 2014-06-11 03:37
Gödel, Escher, Bach: An Eternal Golden Braid - Douglas R. Hofstadter

If I were clever enough, I would write this review as a fugue. This is the formal structure that Hofstadter uses throughoutGödel, Escher, Bach. Whether the whole book is a fugue, I'm not smart enough to tell. But the fugue is used as a metaphor for layers of brain activity, thoughts, superimposed over the “hardware” of the brain, the neurons.

In fact, though I would recommend starting at the beginning of the book, I suppose one might begin anywhere and read through and back again, a'la Finnegan's Wake. No, the book isn't designed this way, but considering that I couldn't discern a solid central idea until page 302 of the book, and that this was only one of several theses in the book, I wouldn't be surprised if it proved possible to begin anywhere.

The idea presented there is “To suggest ways of reconciling the software of mind with the hardware of brain is a main goal of this book.”

The question is, does it succeed? 

I would argue that it does not.

And it does not matter.

There are some works, such as Giorgio De Santilliana's Hamlet's Mill or Daniel Schacter's Searching for Memory that are so vast and all-encompassing that it is difficult to pin down one central thesis. These are the kind of works that you might not understand in your lifetime, the thoughts of a genius transposed directly to paper that, unless you are an equally-gifted person or a savant, you cannot hope to fully comprehend. Still, the threads and nuggets of gold that are spread throughout make it worth the time spent in the dark mines of incomprehension, if only to find that one fist-sized chunk of precious metal and appreciate its beauty set against the background of your own ignorance.

As far as I can tell, the book is really about intelligence, both human and artificial. Hofstadter does a lot of preliminary work priming the reader's brain with assumptions taken from theoretical mathematics and computer programming. But don't let that scare you off! I'm no math whiz, but I found most of the logical puzzles at least comprehensible after a few careful reads. Hofstadter also gives the occasional exercise, leaving the reader without an answer to his question. Like all good teachers, Hofstadter understands that the students who work things out on their own are the best prepared students. That doesn't meant that you won't understand many of the book's salient points if you can't successfully answer his questions. You can. But in order to understand the finer points, I suppose one would have to have a pretty good grasp on the answers to those questions.

I don't.

And it didn't matter.

What did matter, for me, was having a little bit of a background in the idea of nested hierarchies and a smidgen of knowledge in non-linear dynamics (aka “chaos theory”). For the former, I'd recommend Valerie Ahl's seminal Hierarchy Theory: A Vision, Vocabulary, and Epistemology . For the latter, just do what you were going to do anyway and look it up on Wikipedia. I won't tell anyone.

The idea of nested hierarchies is central to the understanding of what makes human intelligence different from machine intelligence. The short story is this: human thought is structured from the ground up according to the basic laws of physics, in particular, electricity, because it is through electricity that neural networks . . . well, network. The issue is that the layers interceding between neural electrical firings and human thought are tangled. They are explainable, or ought to be explainable, by a series of “tangled” layers that lead up to the higher functioning of thought. Again, this is one of the central points of the book.

And this is the point where Hofstadter utterly fails.

And it doesn't matter.

You see, Hofstadter never convincingly shows those transitional layers between neural activity and thought, though he claims they must be there. He claims that it should be possible to create an Artificial Intelligence (AI) that is every bit as human as human intelligence. The problem is, how do you define human intelligence?

Hofstadter presents the problem like this:

Historically, people have been naïve about what qualities, if mechanized, would undeniably constitute intelligence. Sometimes it seems as though each new step towards AI, rather than producing something which everyone agrees is real intelligence, merely reveals what real intelligence is not. If intelligence involves learning, creativity, emotional responses, a sense of beauty, a sense of self, then there is a long road ahead, and it may be that these will only be realized when we have totally duplicated a living brain.

One of the big issues in identifying whether an AI is actually intelligent is the notion of “slipperiness”. The concept here is that human thoughts can deal in a larger possibility space (my words) than machine “intelligence”. Hofstadter quotes from an article in The New Yorker, in which two statements are made that, while possible, would constitute lunacy on the part of anyone who actually believed them. They are:

If Leonardo da Vinci had been born a female the ceiling of the Sistine Chapel might never have been painted.

And if Michelangelo had been Siamese twins, the work would have been completed in half the time.

Then he points out another sentence that was “printed without blushing”:

I think he [Professor Philipp Frank] would have enjoyed both of these books enormously.

Hofstadter comments: “Now poor Professor Frank is dead; and clearly it is nonsense to suggest that someone could read books written after his death. So why wasn't this serious sentence scoffed at? Somehow, in some difficult-to-pin-down sense, the parameters slipped in this sentence do not violate our sense of 'possibility' as much as in the earlier examples.”

This allowable playfulness is something so complex and multi-layered, that an AI would be hard-pressed to correctly parse an “appropriate” reaction.

This is just one case portraying the difficulty inherent in trying to define and understand intelligence and the connection between brain hardware and mind-thought. The book is rife with them. I'm not convinced that Hofstadter was fully convinced that there will ever be a machine so “intelligent” as to completely mirror human thought.

And, one last time, it doesn't matter.

This book has set me to thinking, thinking hard, about what it means to be human. Not merely as an intellectual exercise, but deep in my emotional breadbasket, if you will, I feel human in a way that I can't explain when I think about the difficulty of trying to translate my hopes, fears, love, creativity, wordplay, happiness, sadness, and ambitions into machine language. There has been a lot of talk lately about “singularity,” that moment when machines become self-aware. I'm beginning to think that it will never happen. And I'm fine with that.

Besides, Hofstadter gives an implicit warning when quoting Marvin Minsky, who said:

When intelligent machines are constructed, we should not be surprised to find them as confused and as stubborn as men in their convictions about mind-matter, consciousness, free will, and the like.

In other words, if we do somehow construct true Artificial Intelligence, with the same capacity for thought and feeling as human beings, whose to say the “person” we create isn't going to turn out to be a real douchebag?

Terminator, anyone?

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review 2014-05-23 00:00
Gödel, Escher, Bach: An Eternal Golden Braid
Gödel, Escher, Bach: An Eternal Golden Braid - Douglas R. Hofstadter This is a book of brilliant insights separated by hundreds of pages of tangents.

It touches on a ridiculous number of topics: number theory, music theory, fugues, art, physics, linguistics, literature, cognition, calculus, logic, programming, recursion, molecular biology, Zen, and much more. Many of these are used as the basis for understanding cognition, knowledge and AI; some make for superb analogies to make it easier to understand these complicated topics; unfortunately, Hofstadter sometimes goes into way too much detail on these tangential topics, and occasionally, it just feels like he's showing off his (undeniably impressive) intellect.

It's a shame, because all of this extra material makes the book much harder to get through and actively distracts from some of the gems hidden within. If a good editor had chopped out ~300 pages, the book would've been perfect. As it is, it's only worth reading if you're willing to put in a ton of effort to get to some of the delightful parts.

My absolute favorite is the analogy that compares the human mind to a colony of ants; this is the absolute closest I've come to a vague understanding of how an intelligence could emerge from a bunch of simple, unintelligent parts. If you are skimming the book, make sure not to skip that chapter :)

Some great quotes:

“Hofstadter's Law: It always takes longer than you expect, even when you take into account Hofstadter's Law”

Tesler's Theorem: "AI is whatever hasn't been done yet".

“How gullible are you? Is your gullibility located in some "gullibility center" in your brain? Could a neurosurgeon reach in and perform some delicate operation to lower your gullibility, otherwise leaving you alone? If you believe this, you are pretty gullible, and should perhaps consider such an operation.”

“The paraphrase of Gödel's Theorem says that for any record player, there are records which it cannot play because they will cause its indirect self-destruction.”

"Relying on words to lead you to the truth is like relying on an incomplete formal system to lead you to the truth. A formal system will give you some truths, but as we shall soon see, a formal system, no matter how powerful—cannot lead to all truths."

"What is sacrificed is, of course, strict accuracy; what is gained is, I hope, a little insight."

"The naive assumption that all knowledge should be coded into passive pieces of data is actually contradicted by the most fundamental fact about computer design: that is, how to add, subtract, multiply, and so on is not coded into pieces of data stored in memory; it is, in fact, represented nowhere in memory, but rather in the wiring patterns of the hardware."

"When a human forgets, it most likely means that a high-level pointer has been lost - not that any information has been deleted or destroyed."

"It is amazing how deep this problem with the word 'the' is. It is probably safe to say that writing a program which can fully understand the top five words of English - 'the', 'of', 'and', 'a', and 'to' - would be equivalent to solving the entire problem of AI, and hence tantamount to knowing what intelligence and consciousness are."

"Perhaps the greatest contradiction in our lives, the hardest to handle, is the knowledge 'There was a time when I was not alive, and there will come a time when I am not alive.'"

"By the way, in passing, it is interesting to note that all results essentially dependent on the fusion of subject and object have been limitative results. In addition ot the limitative Theorems, there is Heisenberg's uncertainty principle, which says that measuring one quantity renders impossible the simultaneous measurement of a related quantity. I don't know why all those results are limitative."

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