Archive for superintelligence

weapons of math destruction [fan]

Posted in Statistics with tags , , , , , , , , on September 20, 2017 by xi'an

As a [new] member of Parliement, Cédric Villani is now in charge of a committee on artificial intelligence, which goal is to assess the positive and negative sides of AI. And refers in Le Monde interview below to Weapons of Maths Destruction as impacting his views on the topic! Let us hope Superintelligence is no next on his reading list…

weapons of math destruction [book review]

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , on December 15, 2016 by xi'an

wmd As I had read many comments and reviews about this book, including one by Arthur Charpentier, on Freakonometrics, I eventually decided to buy it from my Amazon Associate savings (!). With a strong a priori bias, I am afraid, gathered from reading some excerpts, comments, and the overall advertising about it. And also because the book reminded me of another quantic swan. Not to mention the title. After reading it, I am afraid I cannot tell my ascertainment has changed much.

“Models are opinions embedded in mathematics.” (p.21)

The core message of this book is that the use of algorithms and AI methods to evaluate and rank people is unsatisfactory and unfair. From predicting recidivism to fire high school teachers, from rejecting loan applications to enticing the most challenged categories to enlist for for-profit colleges. Which is indeed unsatisfactory and unfair. Just like using the h index and citation ranking for promotion or hiring. (The book mentions the controversial hiring of many adjunct faculty by KAU to boost its ranking.) But this conclusion is not enough of an argument to write a whole book. Or even to blame mathematics for the unfairness: as far as I can tell, mathematics has nothing to do with unfairness. Some analysts crunch numbers, produce a score, and then managers make poor decisions. The use of mathematics throughout the book is thus completely inappropriate, when the author means statistics, machine learning, data mining, predictive algorithms, neural networks, &tc. (OK, there is a small section on Operations Research on p.127, but I figure deep learning can bypass the maths.) Continue reading

the girl with the spider’s nest [book review]

Posted in Books with tags , , , , , , , , , , , , on June 12, 2016 by xi'an

“..the Millennium Trilogy was messier and more eccentric than much popular fiction, a genre that can lean towards standardisation. Lagercrantz’s continuation, while never formulaic, is a cleaner and tighter read than the originals…” The Guardian

“…while Mr. Lagercrantz never makes the N.S.A.’s involvement in the case Salander and Blomkvist are investigating remotely convincing, he writes with such assurance and velocity in the later portions of the book that he powers through these more dubious passages.” The New York Times

Millennium is a bit like chocolate addiction, when I carefully pack away the remains of a Lindt tablet, only to get back to it for another row half-an-hour later… The style of the series is rudimentary, the story is just implausible, the message is shaky, as explained in my earlier reviews, and still, still, I just got back from binge reading a new volume, even though it was written by another author. David Lagercrantz. Who also managed to write a biography of both Alan Turing and a (former Paris) footballer competing with Chuck Norris…

I had misgivings, to start with, about another author taking over the commercial massive success of the previous author (towards a further commercial massive success, apparently, to judge from the 7,575 customer reviews there!). With much less legitimacy (if any) than, say, Brandon Sanderson taking over Robert Jordan to complete the Wheel of Time. (Although this sequel is completely legit, since Stieg Larsson’s family controls his literary estate and hired David Lagercrantz.)  On the other hand, I do not have the highest respect for the literary qualities of the series, beyond inducing a remarkable crave for the next page that kept me awake part of both nights when I read The Girl with the Spider’s web. A feature that, in my opinion, relates to the essentially commercial nature of the product (and that is compounded by the mere £3.00 it cost me in a Coventry supermarket!).

Without getting into spoilers, the current story revolves around the complicated family tree of Lisbeth Salander, the endless fight of the Millennium editors against market forces, the murky waters of hacking and of intelligence companies, plus some lines about NSA’s Egotistical Giraffe, quantum computing, public-key encryption, and resolution by elliptic curve factorization. Story that remains as enjoyable as the previous volumes, even though it may be lacking in the psychology of the characters.  Given the extreme implausibility of the intelligence central plot, I am rather surprised at the very positive reviews found in the press, as shown by both quotes reproduced above…

One of the threads exploited in the book is the threat represented by super-intelligence, that is when AIs become much more intelligent than humans. This should ring a bell as this is the theme of Super-Intelligence, the book by Nick Bostrom I reviewed a few months ago. Although this volume of Millenium only broaches upon the topic, and while there is no reason to imagine a direct connection between both books, even though Lagercrantz may have read the popular book of a fellow Swede, I find the setting both amazing and so representative of the way the book ingratiates itself into the main computer culture memes.

go, go, go…deeper!

Posted in pictures, Statistics with tags , , , , , , , , , , on February 19, 2016 by xi'an

While visiting Warwick, last week, I came across the very issue of Nature with the highly advertised paper of David Silver and co-authors from DeepMind detailing how they designed their Go player algorithm that bested a European Go master five games in a row last September. Which is a rather unexpected and definitely brilliant feat given the state of the art! And compares (in terms of importance, if not of approach) with the victory of IBM Deep Blue over Gary Kasparov 20 years ago… (Another deep algorithm, showing that the attraction of programmers for this label has not died off over the years!)This paper is not the easiest to read (especially over breakfast), with (obviously) missing details, but I gathered interesting titbits from this cursory read. One being the reinforced learning step where the predictor is improved by being applied against earlier versions. While this can lead to overfitting, the authors used randomisation to reduce this feature. This made me wonder if a similar step could be on predictors like random forests. E.g., by weighting the trees or the probability of including a predictor or another.Another feature of major interest is their parallel use of two neural networks in the decision-making, a first one estimating a probability distribution over moves learned from millions of human Go games and a second one returning a utility or value for each possible move. The first network is used for tree exploration with Monte Carlo steps, while the second leads to the final decision.

This is a fairly good commercial argument for machine learning techniques (and for DeepMind as well), but I do not agree with the doom-sayers predicting the rise of the machines and our soon to be annihilation! (Which is the major theme of Superintelligence.) This result shows that, with enough learning data and sufficiently high optimising power and skills, it is possible to produce an excellent predictor of the set of Go moves leading to a victory. Without the brute force strategy of Deep Blue that simply explored the tree of possible games to a much more remote future than a human player could do (along with the  perfect memory of a lot of games). I actually wonder if DeepMind has also designed a chess algorithm on the same principles: there is no reason why it should no work. However, this success does not predict the soon to come emergence of AI’s able to deal with vaguer and broader scopes: in that sense, automated drivers are much more of an advance (unless they start bumping into other cars and pedestrians on a regular basis!).