poems that solve puzzles [book review]

Upon request, I received this book from Oxford University Press for review. Poems that Solve Puzzles is a nice title and its cover is quite to my linking (for once!). The author is Chris Bleakley, Head of the School of Computer Science at UCD.

“This book is for people that know algorithms are important, but have no idea what they are.”

These is the first sentence of the book and hence I am clearly falling outside the intended audience. When I asked OUP for a review copy, I was more thinking in terms of Robert Sedgewick’s Algorithms, whose first edition still sits on my shelves and which I read from first to last page when it appeared [and was part of my wife’s booklist]. This was (and is) indeed a fantastic book to learn how to build and optimise algorithms and I gain a lot from it (despite remaining a poor programmer!).

Back to poems, this one reads much more like an history of computer science for newbies than a deep entry into the “science of algorithms”, with imho too little on the algorithms themselves and their connections with computer languages and too much emphasis on the pomp and circumstances of computer science (like so-and-so got the ACM A.M. Turing Award in 19… and  retired in 19…). Beside the antique algorithms for finding primes, approximating π, and computing the (fast) Fourier transform (incl. John Tukey), the story moves quickly to the difference engine of Charles Babbage and Ada Lovelace, then to Turing’s machine, and artificial intelligence with the first checkers codes, which already included some learning aspects. Some sections on the ENIAC, John von Neumann and Stan Ulam, with the invention of Monte Carlo methods (but no word on MCMC). A bit of complexity theory (P versus NP) and then Internet, Amazon, Google, Facebook, Netflix… Finishing with neural networks (then and now), the unavoidable AlphaGo, and the incoming cryptocurrencies and quantum computers. All this makes for pleasant (if unsurprising) reading and could possibly captivate a young reader for whom computers are more than a gaming console or a more senior reader who so far stayed wary and away of computers. But I would have enjoyed much more a low-tech discussion on the construction, validation and optimisation of algorithms, namely a much soft(ware) version, as it would have made it much more distinct from the existing offer on the history of computer science.

[Disclaimer about potential self-plagiarism: this post or an edited version of it will eventually appear in my Books Review section in CHANCE.]

the beauty of maths in computer science [book review]

CRC Press sent me this book for review in CHANCE: Written by Jun Wu, “staff research scientist in Google who invented Google’s Chinese, Japanese, and Korean Web search algorithms”, and translated from the Chinese, 数学之美, originating from Google blog entries. (Meaning most references are pre-2010.) A large part of the book is about word processing and web navigation, which is the author’s research specialty. And not so much about mathematics. (When rereading the first chapters to start this review I then realised why the part about language processing in AIQ sounded familiar: I had read it in the Beauty of Mathematics in Computer Science.)

In the first chapter, about the history of languages, I found out, among other things, that ancient Jewish copists of the Bible had an error correcting algorithm consisting in giving each character a numerical equivalent, summing up each row, then all rows, and  checking the sum at the end of the page was the original one. The second chapter explains why the early attempts at language computer processing, based on grammar rules, were unsuccessful and how a statistical approach had broken the blockade. Explained via Markov chains in the following chapter. Along with the Good-Turing [Bayesian] estimate of the transition probabilities. Next comes a short and low-tech chapter on word segmentation. And then an introduction to hidden Markov models. Mentioning the Baum-Welch algorithm as a special case of EM, which makes a return by Chapter 26. Plus a chapter on entropies and Kullback-Leibler divergence.

A first intermede is provided by a chapter dedicated to the late Frederick Jelinek, the author’s mentor (including what I find a rather unfortunate equivalent drawn between the Nazi and Communist eras in Czechoslovakia, p.64). Chapter that sounds a wee bit too much like an extended obituary.

The next section of chapters is about search engines, with a few pages on Boolean logic, dynamic programming, graph theory, Google’s PageRank and TF-IDF (term frequency/inverse document frequency). Unsurprisingly, given that the entries were originally written for Google’s blog, Google’s tools and concepts keep popping throughout the entire book.

Another intermede about Amit Singhal, the designer of Google’s internal search ranking system, Ascorer. With another unfortunate equivalent with the AK-47 Kalashnikov rifle as “elegantly simple”, “effective, reliable, uncomplicated, and easy to implement or operate” (p.105). Even though I do get the (reason for the) analogy, using an equivalent tool which purpose is not to kill other people would have been just decent…

Then chapters on measuring proximity between news articles by (vectors in a 64,000 dimension vocabulary space and) their angle, and singular value decomposition, and turning URLs as long integers into 16 bytes random numbers by the Mersenne Twister (why random, except for encryption?), missing both the square in von Neumann’s first PRNG (p.124) and the opportunity to link the probability of overlap with the birthday problem (p.129). Followed by another chapter on cryptography, always a favourite in maths vulgarisation books (but with no mention made of the originators of public key cryptography, like James Hellis or the RSA trio, or of the impact of quantum computers on the reliability of these methods). And by an a-mathematic chapter on spam detection.

Another sequence of chapters cover maximum entropy models (in a rather incomprehensible way, I think, see p.159), continued with an interesting argument how Shannon’s first theorem predicts that it should be faster to type Chinese characters than Roman characters. Followed by the Bloom filter, which operates as an approximate Poisson variate. Then Bayesian networks where the “probability of any node is computed by Bayes’ formula” [not really]. With a slightly more advanced discussion on providing the highest posterior probability network. And conditional random fields, where the conditioning is not clearly discussed (p.192). Next are chapters about Viterbi’s algorithm (and successful career) and the EM algorithm, nicknamed “God’s algorithm” in the book (Chapter 26) although I never heard of this nickname previously.

The final two chapters are on neural networks and Big Data, clearly written later than the rest of the book, with the predictable illustration of AlphaGo (but without technical details). The twenty page chapter on Big Data does not contain a larger amount of mathematics, with no equation apart from Chebyshev’s inequality, and a frequency estimate for a conditional probability. But I learned about 23&me running genetic tests at a loss to build a huge (if biased) genetic database. (The bias in “Big Data” issues is actually not covered by this chapter.)

“One of my main objectives for writing the book is to introduce some mathematical knowledge related to the IT industry to people who do not work in the industry.”

To conclude, I found the book a fairly interesting insight on the vision of his field and job experience by a senior scientist at Google, with loads of anecdotes and some historical backgrounds, but very Google-centric and what I felt like an excessive amount of name dropping and of I did, I solved, I &tc. The title is rather misleading in my opinion as the amount of maths is very limited and rarely sufficient to connect with the subject at hand. Although this is quite a relative concept, I did not spot beauty therein but rather technical advances and trick, allowing the author and Google to beat the competition.