Archive for Alan Turing

and the travelling salesman is…

Posted in Books, pictures, Statistics, University life with tags , , , on July 21, 2017 by xi'an

Here is another attempt at using StippleGen on… Alan Turing‘s picture. My reason for attempting a travelling salesman rendering of this well-known picture towards creating a logo for PCI Comput Stats, the peer community project I am working on this summer. With the help of the originators of PCI Evol Biol.

fellowship openings at the Alan Turing Institute

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , , on November 17, 2015 by xi'an

[Verbatim from the  Alan Turing Institute webpage]Alan Turing Fellowships

This is a unique opportunity for early career researchers to join The Alan Turing Institute. The Alan Turing Institute is the UK’s new national data science institute, established to bring together world-leading expertise to provide leadership in the emerging field of data science. The Institute has been founded by the universities of Cambridge, Edinburgh, Oxford, UCL and Warwick and EPSRC.

Fellowships are available for 3 years with the potential for an additional 2 years of support following interim review. Fellows will pursue research based at the Institute hub in the British Library, London. Fellowships will be awarded to individual candidates and fellows will be employed by a joint venture partner university (Cambridge, Edinburgh, Oxford, UCL or Warwick).

Key requirements: Successful candidates are expected to have i) a PhD in a data science (or adjacent) subject (or to have submitted their doctorate before taking up the post), ii) an excellent publication record and/or demonstrated excellent research potential such as via preprints, iii) a novel and challenging research agenda that will advance the strategic objectives of the Institute, and iv) leadership potential. Fellowships are open to all qualified applicants regardless of background.

Alan Turing Fellowship applications can be made in all data science research areas. The Institute’s research roadmap is available here. In addition to this open call, there are two specific fellowship programmes:

Fellowships addressing data-centric engineering

The Lloyd’s Register Foundation (LRF) / Alan Turing Institute programme to support data-centric engineering is a 5-year, £10M global programme, delivered through a partnership between LRF and the Alan Turing Institute. This programme will secure high technical standards (for example the next-generation algorithms and analytics) to enhance the safety of life and property around the major infrastructure upon which modern society relies. For further information on data-centric engineering, see LRF’s Foresight Review of Big Data. Applications for Fellowships under this call, which address the aims of the LRF/Turing programme, may also be considered for funding under the data-centric engineering programme. Fellowships awarded under this programme may vary from the conditions given above; for more details contact

Fellowships addressing data analytics and high-performance computing

Intel and the Alan Turing Institute will be supporting additional Fellowships in data analytics and high-performance computing. Applications for Fellowships under this call may also be considered for funding under the joint Intel-Alan Turing Institute programme. Fellowships awarded under this joint programme may vary from the conditions given above; for more details contact

Download full information on the Turing fellowships here

Diversity and equality are promoted in all aspects of the recruitment and career management of our researchers. In keeping with the principles of the Institute, we especially encourage applications from female researchers

Turing’s Bayesian contributions

Posted in Books, Kids, pictures, Running, Statistics, University life with tags , , , , , , , , , , , , on March 17, 2015 by xi'an

Following The Imitation Game, this recent movie about Alan Turing played by Benedict “Sherlock” Cumberbatch, been aired in French theatres, one of my colleagues in Dauphine asked me about the Bayesian contributions of Turing. I first tried to check in Sharon McGrayne‘s book, but realised it had vanished from my bookshelves, presumably lent to someone a while ago. (Please return it at your earliest convenience!) So I told him about the Bayesian principle of updating priors with data and prior probabilities with likelihood evidence in code detecting algorithms and ultimately machines at Bletchley Park… I could not got much farther than that and hence went checking on Internet for more fodder.

“Turing was one of the independent inventors of sequential analysis for which he naturally made use of the logarithm of the Bayes factor.” (p.393)

I came upon a few interesting entries but the most amazìng one was a 1979 note by I.J. Good (assistant of Turing during the War) published in Biometrika retracing the contributions of Alan Mathison Turing during the War. From those few pages, it emerges that Turing’s statistical ideas revolved around the Bayes factor that Turing used “without the qualification `Bayes’.” (p.393) He also introduced the notion of ban as a unit for the weight of evidence, in connection with the town of Banbury (UK) where specially formatted sheets of papers were printed “for carrying out an important classified process called Banburismus” (p.394). Which shows that even in 1979, Good did not dare to get into the details of Turing’s work during the War… And explains why he was testing simple statistical hypothesis against simple statistical hypothesis. Good also credits Turing for the expected weight of evidence, which is another name for the Kullback-Leibler divergence and for Shannon’s information, whom Turing would visit in the U.S. after the War. In the final sections of the note, Turing is also associated with Gini’s index, the estimation of the number of species (processed by Good from Turing’s suggestion in a 1953 Biometrika paper, that is, prior to Turing’s suicide. In fact, Good states in this paper that “a very large part of the credit for the present paper should be given to [Turing]”, p.237), and empirical Bayes.

Alan Turing Institute

Posted in Books, pictures, Running, Statistics, University life with tags , , , , , , , , , on February 10, 2015 by xi'an


The University of Warwick is one of the five UK Universities (Cambridge, Edinburgh, Oxford, Warwick and UCL) to be part of the new Alan Turing Institute.To quote from the University press release,  “The Institute will build on the UK’s existing academic strengths and help position the country as a world leader in the analysis and application of big data and algorithm research. Its headquarters will be based at the British Library at the centre of London’s Knowledge Quarter.” The Institute will gather researchers from mathematics, statistics, computer sciences, and connected fields towards collegial and focussed research , which means in particular that it will hire a fairly large number of researchers in stats and machine-learning in the coming months. The Department of Statistics at Warwick was strongly involved in answering the call for the Institute and my friend and colleague Mark Girolami will the University leading figure at the Institute, alas meaning that we will meet even less frequently! Note that the call for the Chair of the Alan Turing Institute is now open, with deadline on March 15. [As a personal aside, I find the recognition that Alan Turing’s genius played a pivotal role in cracking the codes that helped us win the Second World War. It is therefore only right that our country’s top universities are chosen to lead this new institute named in his honour. by the Business Secretary does not absolve the legal system that drove Turing to suicide….]

interesting mis-quote

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , on September 25, 2014 by xi'an

At a recent conference on Big Data, one speaker mentioned this quote from Peter Norvig, the director of research at Google:

“All models are wrong, and increasingly you can succeed without them.”

quote that I found rather shocking, esp. when considering the amount of modelling behind Google tools. And coming from someone citing Kernel Methods for Pattern Analysis by Shawe-Taylor and Christianini as one of his favourite books and Bayesian Data Analysis as another one… Or displaying Bayes [or his alleged portrait] and Turing in his book cover. So I went searching on the Web for more information about this surprising quote. And found the explanation, as given by Peter Norvig himself:

“To set the record straight: That’s a silly statement, I didn’t say it, and I disagree with it.”

Which means that weird quotes have a high probability of being misquotes. And used by others to (obviously) support their own agenda. In the current case, Chris Anderson and his End of Theory paradigm. Briefly and mildly discussed by Andrew a few years ago.

Le Monde sans puzzle

Posted in Books, Kids, Statistics, University life with tags , , , , , , , on June 17, 2014 by xi'an

This week, Le Monde mathematical puzzle: is purely geometric, hence inappropriate for an R resolution. In the Science & Médecine leaflet, there is however an interesting central page about random generators, from the multiple usages of those in daily life to the consequences of poor generators on cryptography and data safety. The article is compiling an interview of Jean-Paul Delahaye on the topic with recent illustrations from cybersecurity. One final section gets rather incomprehensible: when discussing the dangers of seed generation, it states that “a poor management of the entropy means that an hacker can saturate the seed and take over the original randomness, weakening the whole system”. I am sure there is something real behind the imagery, but this does not make sense… Another insert mentions a possible random generator built out of the light detectors on a smartphone. And quantum physics. The society IDQ can indeed produce ultra-rapid random generators that way. And it also ran randomness tests summarised here. Using in particular George Marsaglia’s diehard battery.

Another column report that a robot passed the Turing test last week, on Turing‘s death anniversary. Meaning that 33% of the jury was convinced the robot’s answers were given by a human. This reminded me of the Most Human Human book sent to me by my friends from BYU. (A marginalia found in Le Monde is that the test was organised by Kevin Warwick…from the University of Coventry, a funny reversal of the University of Warwick sitting in Coventry! However, checking on his website showed that he has and had no affiliation with this university, being at the University of Reading instead.)


the most human human

Posted in Books, University life with tags , , , , , , , , , , , , , , on May 24, 2013 by xi'an

“…the story of Homo sapiens trying to stake a claim on shifting ground, flanked on both sides by beast and machine, pinned between meat and math.” (p.13)

No typo in the title, this is truly how this book by Brian Christian is called. It was kindly sent to me by my friends from BUY and I realised I could still write with my right hand when commenting on the margin. (I also found the most marvellous proof to a major theorem but the margin was just too small…)  “The most human human: What artificial intelligence teaches us about being alive” is about the Turing test, designed to test whether an unknown interlocutor is a human or a machine. And eventually doomed to fail.

“The final test, for me, was to give the most uniquely human performance I could in Brighton, to attempt a successful defense against the machines.” (p.15)

What I had not realised earlier is that there is a competition every year running this test against a few AIs and a small group of humans, the judges (blindly) giving votes for each entity and selecting as a result the most human computer. And also the most human … human! This competition is called the Loebner Prize and it was taking place in Brighton, this most English of English seaside towns, in 2008 when Brian Christian took part in it (as a human, obviously!).

“Though both [sides] have made progress, the `algorithmic’ side of the field [of computer science] has, from Turing on, completely dominated the more `statistical’ side. That is, until recently.” (p.65)

I enjoyed the book, much more for the questions it brought out than for the answers it proposed, as the latter sounded unnecessarily conflictual to me, i.e. adopting a “us vs.’em” posture and whining about humanity not fighting hard enough to keep ahead of AIs… I dislike this idea of the AIs being the ennemy and of “humanity lost” the year AIs would fool the judges. While I enjoy the sci’ fi’ literature where this antagonism is exacerbated, from Blade Runner to Hyperion, to Neuromancer, I do not extrapolate those fantasised settings to the real world. For one thing, AIs are designed by humans, so having them winning this test (or winning against chess grand-masters) is a celebration of the human spirit, not a defeat! For another thing, we are talking about a fairly limited aspect of “humanity”, namely the ability to sustain a limited discussion with a set of judges on a restricted number of topics. I would be more worried if a humanoid robot managed to fool me by chatting with me for a whole transatlantic flight. For yet another thing, I do not see how this could reflect on the human race as a whole and indicate that it is regressing in any way. At most, it shows the judges were not trying hard enough (the questions reported in The most human human were not that exciting!) and maybe the human competitors had not intended to be perceived as humans.

“Does this suggest, I wonder, that entropy may be fractal?” (p.239)

Another issue that irked me in the author’s perspective is that he trained and elaborated a complex strategy to win the prize (sorry for the mini-spoiler: in case you did  not know, Brian did finish as the most human human). I do not know if this worry fear to appear less human than an AI was genuine or if it provided a convenient canvas for writing the book around the philosophical question of what makes us human(s). But it mostly highlights the artificial nature of the test, namely that one has to think in advance on the way conversations will be conducted, rather than engage into a genuine conversation with a stranger. This deserves the least human human label, in retrospect!

“So even if you’ve never heard of [Shanon entropy] beofre, something in your head intuits [it] every time you open your mouth.” (p.232)

The book spend a large amount of text/time on the victory of Deep Blue over Gary Kasparov (or, rather, on the defeat of Kasparov against Deep Blue), bemoaning the fact as the end of a golden age. I do not see the problem (and preferred the approach of Nate Silver‘s). The design of the Deep Blue software was a monument to the human mind, the victory did not diminish Kasparov who remains one of the greatest chess players ever, and I am not aware it changed chess playing (except when some players started cheating with the help of hidden computers!). The fact that players started learning more and more chess openings was a trend much before this competition. As noted in The most human human,  checkers had to change its rules once a complete analysis of the game had led to  a status-quo in the games. And this was before the computer era. In Glasgow, Scotland, in 1863. Just to draw another comparison: I like playing Sudoku and the fact that I designed a poor R code to solve Sudokus does not prevent me from playing, while my playing sometimes leads to improving the R code. The game of go could have been mentioned as well, since it proves harder to solve by AIs. But there is no reason this should not happen in a more or less near future…

“…we are ordering appetizers and saying something about Wikipedia, something about Thomas  Bayes, something about vegetarian dining…” (p.266)

While the author produces an interesting range of arguments about language, intelligence, humanity, he missed a part about the statistical modelling of languages, apart from a very brief mention of a Markov dependence. Which would have related to the AIs perspective. The overall flow is nice but somehow meandering and lacking in substance. Esp. in the last chapters. On a minor level, I also find that there are too many quotes from Hofstadter’ Gödel, Escher and Bach, as well as references to pop culture. I was surprised to find Thomas Bayes mentioned in the above quote, as it did not appear earlier, except in a back-note.

“A girl on the stairs listen to her father / Beat up her mother” C.D. Wright,  Tours

As a side note to Andrew, there was no mention made of Alan Turing’s chess rules in the book, even though both Turing and chess were central themes. I actually wondered if a Turing test could apply to AIs playing Turing’s chess: they would have to be carried by a small enough computer so that the robot could run around the house in a reasonable time. (I do not think chess-boxing should be considered in this case!)