**Y**et another indulgence during the coronavirus quarantine was watching the series Tales from the Loop (on Amazon Prime), a science-fiction show mixing the mundane with the supernatural, as far as space opera as one can imagine. No superheroes or super-villains, but simple glitches in an otherwise sleepy Midwest small town, operating a synchrotron that opens possibilities beyond the rules of physics, especially about time. A sort of minimalist dystopia. Some critics complained at the pace or the lack of plot, which is completely beyond the point imho, as the inner life of the characters overwhelms the need for action, if any, and leaves one with bittersweet regrets in the same way closing a Maupassant or a Brontë novel makes one feel sorry for the characters and their lost opportunities. Amazingly, the idea for the show started from the eerily beautiful digital paintings of Simon Stålenhag, where he inserted rusting robots and other futuristic but decaying elements in otherwise old-fashioned (I mean from the 1980’s!, with floppy disk computers!) semi-urban landscapes. The main characters are often children and teenagers, who either perceive better than their elders the surreal capacities of their environment or are yet able to question reality into a learning experience. Rarely a happy one, although the episode corresponding to the above painting is a moving exception. Each episode is directed by a different person, including Mark Romanek (who filmed the dystopian Never let me go) and Jodie Foster for the last one. Which explains for different moods from one to the next although there is never a discontinuity in the narrative. And the hauntingly beautiful music is from Philip Glass. Highly recommended!

## Archive for robots

## Tales from the Loop

Posted in Books, Kids, pictures with tags digital painting, dystopia, Emily Brontë, Färingsö, floppy disk, Maupassant, Midwest, particle accelerator, robots, Simon Stålenhag, Slingan, Sweden, synchrotron, Tales from the Loop on May 10, 2020 by xi'an## Statistical rethinking [book review]

Posted in Books, Kids, R, Statistics, University life with tags Amazon, Bayes theorem, Bayesian data analysis, Bayesian Essentials with R, book review, CHANCE, code, convergence diagnostics, E.T. Jaynes, generalised linear models, golem, maths, matrix algebra, MCMC algorithms, mixtures of distributions, Monte Carlo Statistical Methods, Prague, R, robots, STAN, statistical modelling, Statistical rethinking on April 6, 2016 by xi'anStatistical Rethinking: A Bayesian Course with Examples in R and Stan is a new book by Richard McElreath that CRC Press sent me for review in CHANCE. While the book was already discussed on Andrew’s blog three months ago, and [rightly so!] enthusiastically recommended by Rasmus Bååth on Amazon, here are the reasons why I am quite impressed by Statistical Rethinking!

“Make no mistake: you will wreck Prague eventually.” (p.10)

While the book has a lot in common with Bayesian Data Analysis, from being in the same CRC series to adopting a pragmatic and weakly informative approach to Bayesian analysis, to supporting the use of STAN, it also nicely develops its own ecosystem and idiosyncrasies, with a noticeable Jaynesian bent. To start with, I like the highly personal style with clear attempts to make the concepts memorable for students by resorting to external concepts. The best example is the call to the myth of the golem in the first chapter, which McElreath uses as an warning for the use of statistical models (which almost are anagrams to golems!). Golems and models [and robots, another concept invented in Prague!] are man-made devices that strive to accomplish the goal set to them without heeding the consequences of their actions. This first chapter of Statistical Rethinking is setting the ground for the rest of the book and gets quite philosophical (albeit in a readable way!) as a result. In particular, there is a most coherent call against hypothesis testing, which by itself justifies the title of the book. Continue reading

## Paris Machine Learning Meeting #10 Season 2

Posted in Books, Kids, pictures, Statistics, University life with tags Berlin, data, Jussieu, machine learning, Matlab, Paris Machine Learning Applications group, RER B, robots, Toronto, Université Pierre et Marie Curie, Vowpal on June 17, 2015 by xi'an**T**onight, I am invited to give a speed-presenting talk at the Paris Machine Learning last meeting of Season 2, with the themes of DL, Recovering Robots, Vowpal Wabbit, Predcsis, Matlab, and Bayesian test [by yours truly!] The meeting will take place in Jussieu, Amphi 25, Here are my slides for the meeting:

As it happened, the meeting was quite crowded with talks and plagued with technical difficulties in transmitting talks from Berlin and Toronto, so I came to talk about three hours after the beginning, which was less than optimal for the most technical presentation of the evening. I actually wonder if I even managed to carry the main idea of replacing Bayes factors with posteriors of the mixture weight! *[I had plenty of time to reflect upon this on my way back home as I had to wait for several and rare and crowded RER trains until one had enough room for me and my bike!]*

## lock in [book review]

Posted in Books, Kids, Travel with tags Cory Doctorow, flu, John Scalzi, Lock In, redshirts, robots, science fiction on January 17, 2015 by xi'an **A**s mentioned in my recent review of Redshirts, I was planning to read John Scalzi’s most recent novel, Lock In, if only to check whether or not Redshirts was an isolated accident! This was the third book from “the pile” that I read through the Yule break and, indeed, it was a worthwhile attempt as the book stands miles above Redshirts…

The story is set in a very convincing near-future America where a significant part of the population is locked by a super-flu into a full paralysis that forces them to rely on robot-like interfaces to interact with unlocked humans. While the book is not all that specific on how the robotic control operates, except from using an inserted “artificial neural network” inside the “locked-in” brains, Scalzi manages to make it sound quite realistic, with societal and corporation issues at the forefront. To the point of selling really well the (usually lame) notion of instantaneous relocation at the other end of the US. And with the bare minimum of changes to the current society, which makes it easier to buy. I have not been that enthralled by a science-fiction universe for quite a while. I also enjoyed how the economics of this development of a new class of citizens was rendered, the book rotating around the consequences of the ending of heavy governmental intervention in lock in research.

Now, the story itself is of a more classical nature in that the danger threatening the loked-in population is uncovered single-handedly by the rookie detective who conveniently happens to be the son of a very influential ex-basketball-player and hence to meet all the characters involved in the plot. This is pleasant but somewhat thin with a limited number of players considering the issues at stake and a rather artificial ending.

Look here for a more profound review by Cory Doctorow.

## Bayesian programming [book review]

Posted in Books, Kids, pictures, Statistics, University life with tags artificial intelligence, Bayesian inference, Bayesian programming, CHANCE, conjugate priors, E.T. Jaynes, graphical models, maximum entropy, Python, robots on March 3, 2014 by xi'an

“We now think the Bayesian Programming methodology and tools are reaching maturity. The goal of this book is to present them so that anyone is able to use them. We will, of course, continue to improve tools and develop new models. However, pursuing the idea that probability is an alternative to Boolean logic, we now have a new important research objective, which is to design specific hsrdware, inspired from biology, to build a Bayesian computer.”(p.xviii)

**O**n the plane to and from Montpellier, I took an extended look at Bayesian Programming a CRC Press book recently written by Pierre Bessière, Emmanuel Mazer, Juan-Manuel Ahuactzin, and Kamel Mekhnacha. *(Very nice picture of a fishing net on the cover, by the way!)* Despite the initial excitement at seeing a book which final goal was to achieve a Bayesian computer, as demonstrated by the above quote, I however soon found the book too arid to read due to its highly formalised presentation… The contents are clear indications that the approach is useful as they illustrate the use of Bayesian programming in different decision-making settings, including a collection of Python codes, so it brings an answer to the *what* but it somehow misses the *how* in that the construction of the priors and the derivation of the posteriors is not explained in a way one could replicate.

“A modeling methodology is not sufficient to run Bayesian programs. We also require an efficient Bayesian inference engine to automate the probabilistic calculus. This assumes we have a collection of inference algorithms adapted and tuned to more or less specific models and a software architecture to combine them in a coherent and unique tool.” (p.9)

**F**or instance, all models therein are described via the curly brace formalism summarised by

which quickly turns into an unpalatable object, as in this example taken from the online PhD thesis of Gabriel Synnaeve (where he applied Bayesian programming principles to a MMORPG called StarCraft and developed an AI (or bot) able to play BroodwarBotQ)

thesis that I found most interesting!

“Consequently, we have 21 × 16 = 336 bell-shaped distributions and we have 2 × 21 × 16 = 772 free parameters: 336 means and 336 standard deviations.¨(p.51)

**N**ow, getting back to the topic of the book, I can see connections with statistical problems and models, and not only via the application of Bayes’ theorem, when the purpose (or *Question*) is to take a decision, for instance in a robotic action. I still remain puzzled by the purpose of the book, since it starts with very low expectations on the reader, but hurries past notions like Kalman filters and Metropolis-Hastings algorithms in a few paragraphs. I do not get some of the details, like this notion of a discretised Gaussian distribution (I eventually found the place where the 772 prior parameters are “learned” in a phase called “identification”.)

“Thanks to conditional independence the curse of dimensionality has been broken! What has been shown to be true here for the required memory space is also true for the complexity of inferences. Conditional independence is the principal tool to keep the calculation tractable. Tractability of Bayesian inference computation is of course a major concern as it has been proved NP-hard (Cooper, 1990).”(p.74)

**T**he final chapters (Chap. 14 on “Bayesian inference algorithms revisited”, Chap. 15 on “Bayesian learning revisited” and Chap. 16 on “Frequently asked questions and frequently argued matters” [!]) are definitely those I found easiest to read and relate to. With mentions made of conjugate priors and of the EM algorithm as a (Bayes) classifier. The final chapter mentions BUGS, Hugin and… Stan! Plus a sequence of 23 PhD theses defended on Bayesian programming for robotics in the past 20 years. And explains the authors’ views on the difference between Bayesian programming and Bayesian networks (“any Bayesian network can be represented in the Bayesian programming formalism, but the opposite is not true”, p.316), between Bayesian programming and probabilistic programming (“we do not search to extend classical languages but rather to replace them by a new programming approach based on probability”, p.319), between Bayesian programming and Bayesian modelling (“Bayesian programming goes one step further”, p.317), with a further (self-)justification of why the book sticks to discrete variables, and further more philosophical sections referring to Jaynes and the principle of maximum entropy.

“The “objectivity” of the subjectivist approach then lies in the fact that two different subjects with same preliminary knowledge and same observations will inevitably reach the same conclusions.”(p.327)

Bayesian Programming thus provides a good snapshot of (or window on) what one can achieve in uncertain environment decision-making with Bayesian techniques. It shows a long-term reflection on those notions by Pierre Bessière, his colleagues and students. The topic is most likely too remote from my own interests for the above review to be complete. Therefore, if anyone is interested in reviewing any further this book for CHANCE, before I send the above to the journal, please contact me. (Usual provisions apply.)