Archive for Facebook

Prairie chair

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , on October 2, 2019 by xi'an

Today is the launching day of PRAIRIE, one of the four Instituts Interdisciplinaires d’Intelligence Artificielle (3IA) supported by the French government. Taking place in Paris Dauphine, with Yann Le Cun as guest speaker. I have been fortunate to be endowed with one of these chairs for the coming years, along with my CEREMADE colleagues Laurent Cohen and Irène Waldspurger.

AIQ [book review]

Posted in Books, Statistics with tags , , , , , , , , , , , , , , , , , , on January 11, 2019 by xi'an

AIQ was my Christmas day read, which I mostly read while the rest of the household was still sleeping. The book, written by two Bayesians, Nick Polson and James Scott, was published before the ISBA meeting last year, but I only bought it on my last trip to Warwick [as a Xmas present]. This is a pleasant book to read, especially while drinking tea by the fire!, well-written and full of facts and anecdotes I did not know or had forgotten (more below). Intended for a general audience, it is also quite light, from a technical side, rather obviously, but also from a philosophical side. While strongly positivist about the potential of AIs for the general good, it cannot be seen as an antidote to the doomlike Superintelligence by Nick Bostrom or the more factual Weapons of Maths Destruction by Cathy O’Neal. (Both commented on the ‘Og.)

Indeed, I find the book quite benevolent and maybe a wee bit too rosy in its assessment of AIs and the discussion on how Facebook and Russian intervention may have significantly to turn the White House Orange is missing [imho] the viral nature of the game, when endless loops of highly targeted posts can cut people from the most basic common sense. While the authors are “optimistic that, given the chance, people can be smart enough”, I do reflect on the sheer fact that the hoax that Hillary Clinton was involved in a child sex ring was ever considered seriously by people. To the point of someone shooting at the pizza restaurant. And I hence am much less optimistic at the ability for a large enough portion of the population, not even the majority, to keep a critical distance from the message carried by AI driven media. Similarly, while Nick and James point out (rather late in the book) that big data (meaning large data) is not necessarily good data for being unrepresentative at the population at large, they do not propose (in the book) highly convincing solutions to battle bias in existing and incoming AIs. Leading to a global worry that AIs may do well for a majority of the population and discriminate against a minority by the same reasoning. As described in Cathy O’Neal‘s book, and elsewhere, proprietary software does not even have to explain why it discriminates. More globally, the business school environment of the authors may have prevented them from stating a worry on the massive power grab by the AI-based companies, which genetically grow with little interest in democracy and states, as shown (again) by the recent election or their systematic fiscal optimisation. Or by the massive recourse to machine learning by Chinese authorities towards a social credit system grade for all citizens.

“La rage de vouloir conclure est une des manies les plus funestes et les plus stériles qui appartiennent à l’humanité. Chaque religion et chaque philosophie a prétendu avoir Dieu à elle, toiser l’infini et connaître la recette du bonheur.” Gustave Flaubert

I did not know about Henrietta Leavitt’s prediction rule for pulsating stars, behind Hubble’s discovery, which sounds like an astronomy dual to Rosalind Franklin’s DNA contribution. The use of Bayes’ rule for locating lost vessels is also found in The Theorem that would not die. Although I would have also mentioned its failure in locating Malaysia Airlines Flight 370. I had also never heard the great expression of “model rust. Nor the above quote from Flaubert. It seems I have recently spotted the story on how a 180⁰ switch in perspective on language understanding by machines brought the massive improvement that we witness today. But I cannot remember where. And I have also read about Newton missing the boat on the precision of the coinage accuracy (was it in Bryson’s book on the Royal Society?!), but with less neutral views on the role of Newton in the matter, as the Laplace of England would have benefited from keeping the lax measures of assessment.

Great to see friendly figures like Luke Bornn and Katherine Heller appearing in the pages. Luke for his work on the statistical analysis of basketball games, Katherine  for her work on predictive analytics in medicine. Reflecting on the missed opportunities represented by the accumulation of data on any patient throughout their life that is as grossly ignored nowadays as it was at Nightingale‘s time. The message of the chapter [on “The Lady with the Lamp”] may again be somewhat over-optimistic: while AI and health companies see clear incentives in developing more encompassing prediction and diagnostic techniques, this will only benefit patients who can afford the ensuing care. Which, given the state of health care systems in the most developed countries, is an decreasing proportion. Not to mention the less developed countries.

Overall, a nice read for the general public, de-dramatising the rise of the machines!, and mixing statistics and machine learning to explain the (human) intelligence behind the AIs. Nothing on the technical side, to be sure, but this was not the intention of the authors.

Der Kunst ihre Freiheit [and the scare of the nude]

Posted in Books, pictures, Travel with tags , , , , , , , , , , , , , on February 17, 2018 by xi'an

A poster campaign advertising for several exhibits of modernist painters in Vienna, including major paintings by Egon Schiele, has met with astonishing censoring from the transport companies posting these advertisements. (And by Facebook, which AIs are visibly too artificial and none too intelligent to [fail to] recognise well-known works of art.) Not very surprising, given the well-known conservatism of advertising units in transportation companies, but nonetheless appalling, especially when putting these posters against the truly indecent ones advertising for, e.g., gas guzzling machines and junk food.

major confUSion

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

crossing the Seine in RER C near Maison de la Radio, Nov. 09, 2012In a recent evening talk-show on France Inter, the French national public radio, the debate was about the [bad] surprise election of the donald and the fact that the media had missed the result, (self-)blaming a disconnection with the “real” country. One of the discussants, Julia Cagé, Professor of Economics at Science Po’, started the discussion with the amazing confusion [at 5’55”] between the probability that Hillary Clinton would win [evaluated at 84% on the last day] and the percentage of votes in her favour [which was around that figure in Manhattan]…

On a related if minor theme, my post on Flaxman et al.’s early [if preliminary] analysis of the said election got so many views that it became the most popular post for 2016! (If not competing with Ross Ihaka’s call to simply start over with R!)

And yet another related entry today in Libération, blaming the disastrous result partly on the social media and their algorithms (again!) that favour items of information (or dis-information) from the same perspective and do not rank those items by their reliability… The author of the tribune is an econometrician at Essec, but there is no methodological content in this ideological entry that seems to call for a super-monitor which would impose (how?) diversity and (which?) ranking on social media. A post-truth era, for sure! Shifting the blame from the deplorable voters themselves to anything else…

de.activated!

Posted in Kids, pictures, University life with tags , , on May 31, 2014 by xi'an

cutface

Advances in scalable Bayesian computation [day #3]

Posted in Books, Mountains, pictures, R, Statistics, University life with tags , , , , , , , , , , on March 6, 2014 by xi'an

polyptych painting within the TransCanada Pipeline Pavilion, Banff Centre, Banff, March 21, 2012We have now gone over the midpoint of our workshop Advances in Scalable Bayesian Computation with three talks in the morning and an open research or open air afternoon. (Maybe surprisingly I chose to stay indoors and work on a new research topic rather than trying cross-country skiing!) If I must give a theme for the day, it would be (jokingly) corporate Big data, as the three speakers spoke of problems and solutions connected with Google, Facebook and similar companies. First, Russ Salakhutdinov presented some  hierarchical structures on multimedia data, like connecting images and text, with obvious applications on Google. The first part described Boltzman machines with impressive posterior simulations of characters and images. (Check the video at 45:00.) Then Steve Scott gave us a Google motivated entry to embarrassingly parallel algorithms, along the lines of papers recently discussed on the ‘Og. (Too bad we forgot to start the video at the very beginning!) One of the novel things in the talk (for me) was the inclusion of BART in this framework, with the interesting feature that using the whole prior on each machine was way better than using a fraction of the prior, as predicted by the theory! And Joaquin Quinonero Candela provided examples of machine learning techniques used by Facebook to suggest friends and ads in a most efficient way (techniques remaining hidden!).

Even though the rest of the day was free, the two hours of exercising between the pool in the early morning and the climbing wall in the late afternoon left me with no energy to experiment curling with a large subsample of the conference attendees, much to my sorrow!

interacting particle systems as… facebook

Posted in Books, Statistics, University life with tags , , , , , on October 8, 2013 by xi'an

Among the many interesting arXived papers this Friday, I first read David Aldous’ “Interacting particle systems as stochastic social dynamics“. Being unfamiliar with those systems (despite having experts in offices down the hall in Paris-Dauphine!), I read this typology of potential models (published in Bernoulli) with a keen interest! The paper stemmed from a short course given in 2012 in Warwick and Cornell. I think the links exhibited there with (social) networks should be relevant for statisticians working on networks (!) and dynamic graphical models. Statistics is not mentioned in the paper, except for the (misleading) connection with statistical physics, but there is obviously a huge potential for statistical inference, from parameter estimation to model comparison. (As pointed out by David Aldous, there is usually “no data or evidence linking the model to the asserted real-world phenomena”.) The paper then introduces some basic models like the token, the pandemic and the averaging process, plus the voter model that relates to Kingman’s coalescent. A very nice read opening new vistas for sure (and a source of projects for graduate students most certainly!)