Archive for Google

travelling from pub to pub [in a straight line]

Posted in pictures, Wines with tags , , , , , , , , , , , on October 30, 2016 by xi'an

Above is the solution produced by a team at the University of Waterloo to the travelling salesman problem of linking all pubs in the UK (which includes pubs in Northern Ireland as well as some Scottish islands—even though I doubt there is no pub at all on the Island of Skye! They also missed a lot of pubs in Glasgow! And worst gaffe of all, they did not include the Clachaigh Inn, probably the best pub on Earth…). This path links over 24 thousand pubs, which is less than the largest travelling salesman problem solved at the current time, except that this case used the exact distances provided by Google maps. Of course, it would somehow make more sense to increase the distances by random amounts as the pub visits increase, unless the visitor sticks to tonic. Or tea.

eagle and child

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!).

Le Monde on the “dangers” of mathematics

Posted in Books, Statistics, Travel, University life with tags , , , , on October 12, 2015 by xi'an

“La responsabilité des mathématiciens semble engagée.”

This post is presumably aiming at a very small (French speaking) audience, but Le Monde published a central Science leaflet this week on the dangers of using uncontrolled mathematical modelling. Resulting in a mismatch of platitudes and absurdities. Blaming mathematicians for about every misappropriate use of mathematics and even more statistics, from the lack of reproducibility in published psychology studies and the poor predictions of flu epidemics by Google to the sub-prime crisis and the prosecutor fallacy. Quoting judicial miscarriages like the case of Lucy de Berk when the statistical arguments were administrated by a psychologist, while a statistician, Richard Gill, was instrumental in reopening the case by demonstrating those arguments were wrong. Objecting to the use of logistic regression for profiling inmates on the probability of recidivism. &tc., &tc… The only item of interest in this really poor article is the announcement of a semester workshop at the Isaac Newton Institute on the use of mathematics in criminal sciences. Which after verification is a workshop on probability and statistics in forensic sciences. With Richard Gill as one of the organisers.

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!

rise of the B word

Posted in Statistics with tags , , , on February 26, 2013 by xi'an

comparison of the uses of the words Bayesian, maximum likelihood, and frequentist, using Google NgramWhile preparing a book chapter, I checked on Google Ngram viewer the comparative uses of the words Bayesian (blue), maximum likelihood (red) and frequentist (yellow), producing the above (screen-copy quality, I am afraid!). It shows an increase of the use of the B word from the early 80’s and not the sudden rise in the 90’s I was expecting. The inclusion of “frequentist” is definitely in the joking mode, as this is not a qualification used by frequentists to describe their methods. In other words (!), “frequentist” does not occur very often in frequentist papers (and not as often as in Bayesian papers!)…