Nature snapshots [and snide shots]

A very rich issue of Nature I received [late] just before leaving for Warwick with a series of reviews on quantum computing, presenting machine learning as the most like immediate application of this new type of computing. Also including irate letters and an embarassed correction of an editorial published the week before reflecting on the need (or lack thereof) to remove or augment statues of scientists whose methods were unethical, even when eventually producing long lasting advances. (Like the 19th Century gynecologist J. Marion Sims experimenting on female slaves.) And a review of a book on the fascinating topic of Chinese typewriters. And this picture above of a flooded playground that looks like a piece of abstract art thanks to the muddy background.

“Quantum mechanics is well known to produce atypical patterns in data. Classical machine learning methods such as deep neural networks frequently have the feature that they can both recognize statistical patterns in data and produce data that possess the same statistical patterns: they recognize the patterns that they produce. This observation suggests the following hope. If small quantum information processors can produce statistical patterns that are computationally difficult for a classical computer to produce, then perhaps they can also recognize patterns that are equally difficult to recognize classically.” Jacob Biamonte et al., Nature, 14 Sept 2017

One of the review papers on quantum computing is about quantum machine learning. Although like Jon Snow I know nothing about this, I find it rather dull as it spends most of its space on explaining existing methods like PCA and support vector machines. Rather than exploring potential paradigm shifts offered by the exotic nature of quantum computing. Like moving to Bayesian logic that mimics a whole posterior rather than produces estimates or model probabilities. And away from linear representations. (The paper mentions a O(√N) speedup for Bayesian inference in a table, but does not tell more, which may thus be only about MAP estimators for all I know.) I also disagree with the brave new World tone of the above quote or misunderstand its meaning. Since atypical and statistical cannot but clash, “universal deep quantum learners may recognize and classify patterns that classical computers cannot” does not have a proper meaning. The paper contains a vignette about quantum Boltzman machines that finds a minimum entropy approximation to a four state distribution, with comments that seem to indicate an ability to simulate from this system.

stop the rot!

Several entries in Nature this week about predatory journals. Both from Ottawa Hospital Research Institute. One emanates from the publication officer at the Institute, whose role is “dedicated to educating researchers and guiding them in their journal submission”. And telling the tale of a senior scientist finding out a paper submitted to a predatory journal and later rescinded was nonetheless published by the said journal. Which reminded me of a similar misadventure that occurred to me a few years ago. After having a discussion of an earlier paper therein rejected from The American Statistician, my PhD student Kaniav Kamary and I resubmitted it to the Journal of Applied & Computational Mathematics, from which I had received an email a few weeks earlier asking me in flowery terms for a paper. When the paper got accepted as such two days after submission, I got alarmed and realised this was a predatory journal, which title played with the quasi homonymous Journal of Computational and Applied Mathematics (Elsevier) and International Journal of Applied and Computational Mathematics (Springer). Just like the authors in the above story, we wrote back to the editors, telling them we were rescinding our submission, but never got back any reply or request of copyright transfer. Instead, requests for (diminishing) payments were regularly sent to us, for almost a year, until they ceased. In the meanwhile, the paper had been posted on the “journal” website and no further email of ours, including some from our University legal officer, induced a reply or action from the journal…

The second article in Nature is from a group of epidemiologists at the same institute, producing statistics about biomedical publications in predatory journals (characterised as such by the defunct Beall blacklist). And being much more vehement about the danger represented by these journals, which “articles we examined were atrocious in terms of reporting”, and authors submitting to them, as unethical for wasting human and animal observations. The authors of this article identify thirteen characteristics for spotting predatory journals, the first one being “low article-processing fees”, our own misadventure being the opposite. And they ask for higher control and auditing from the funding institutions over their researchers… Besides adding an extra-layer to the bureaucracy, I fear this is rather naïve, as if the boundary between predatory and non-predatory journals was crystal clear, rather than a murky continuum. And putting the blame solely on the researchers rather than sharing it with institutions always eager to push their bibliometrics towards more automation of the assessment of their researchers.

the “myth of the miracle machine”

In what appears to be a regular contribution of his to Nature, Daniel Sarewitz recently wrote a “personal take on events” that I find quite reactionary, the more because it comes from an academic. And I wonder why Nature chose to publish his opinion piece. Every other month! The arguments of the author is that basic science should be defunded in favour of “use-inspired” research, “mission oriented” programmes, “societal needs and socially valuable knowledge”… The reason being that it is a better use of public money and that scientists are just another interest group that should not be left to its own device. This is not a new tune, calls to cut down funding fundamental research emerge regularly as an easily found culprit for saving “taxpayer money”, and it is the simplest mean of rejecting a research proposal by blaming its lack of clear applicability. Of course, when looking a bit wider, one can check this piece bemoaning the Democrat inclinations of most scientists. Or that one that science should sometimes give way to religion. With the definitive argument that, for most people, the maths behind scientific models are so complex that they must turn to an act of faith… Yes, I do wonder at Nature providing Sarewitz with such a wide-ranging tribune.

how many academics does it take to change… a p-value threshold?

“…a critical mass of researchers now endorse this change.”

The answer to the lightpulp question seems to be 72: Andrew sent me a short paper recently PsyarXived and to appear in Nature Human Behaviour following on the .005 not .05 tune we criticised in PNAS a while ago. (Actually a very short paper once the names and affiliations of all authors are taken away.) With indeed 72 authors, many of them my Bayesian friends! I figure the mass signature is aimed at convincing users of p-values of a consensus among statisticians. Or a “critical mass” as stated in the note. On the next week, Nature had an entry on this proposal. (With a survey on whether the p-value threshold should change!)

The argument therein [and hence my reservations] is about the same as in Val Johnson’s original PNAS paper, namely that .005 should become the reference cutoff when using p-values for discovering new effects. The tone of the note is mostly Bayesian in that it defends the Bayes factor as a better alternative I would call the b-value. And produces graphs that relate p-values to some minimax Bayes factors. In the simplest possible case of testing for the nullity of a normal mean. Which I do not think is particularly convincing when considering more realistic settings with (many) nuisance parameters and possible latent variables where numerical answers diverge between p-values and [an infinity of] b-values. And of course the unsolved issue of scaling the Bayes factor. (This without embarking anew upon a full-fledged criticism of the Bayes factor.) As usual, I am also skeptical of mentions of power, since I never truly understood the point of power, which depends on the alternative model, increasingly so with the complexity of this alternative. As argued in our letter to PNAS, the central issue that this proposal fails to address is the urgency in abandoning the notion [indoctrinated in generations of students that a single quantity and a single bound are the answers to testing issues. Changing the bound sounds like suggesting to paint afresh a building on the verge of collapsing.

the DeepMind debacle

“I hope for a world where data is at the heart of understanding and decision making. To achieve this we need better public dialogue.” Hetan Shah

As I was reading one of the Nature issues I brought on vacations, while the rain was falling on an aborted hiking day on the fringes of Monte Rosa, I came across a 20 July tribune by Hetan Shah, executive director of the RSS. A rare occurrence of a statistician’s perspective in Nature. The event prompting this column is the ruling against the Royal Free London hospital group providing patient data to DeepMind for predicting kidney. Without the patients’ agreement. And with enough information to identify the patients. The issues raised by Hetan Shah are that data transfers should become open, and that they should be commensurate in volume and details to the intended goals. And that public approval should be seeked. While I know nothing about this specific case, I find the article overly critical of DeepMind, which interest in health related problems is certainly not pure and disinterested but nonetheless can contribute advances in (personalised) care and prevention through its expertise in machine learning. (Disclaimer: I have neither connection nor conflict with the company!) And I do not see exactly how public approval or dialogue can help in making progress in handling data, unless I am mistaken in my understanding of “the public”. The article mentions the launch of a UK project on data ethics, involving several [public] institutions like the RSS: this is certainly commandable and may improve personal data is handled by companies, but I would not call this conglomerate representative of the public, which most likely does not really trust these institutions either…

Das Kapital [not a book review]

A rather bland article by Gareth Stedman Jones in Nature reminded me that the first volume of Karl Marx’ Das Kapital is 150 years old this year. Which makes it appear quite close in historical terms [just before the Franco-German war of 1870] and rather remote in scientific terms. I remember going painstakingly through the books in 1982 and 1983, mostly during weekly train trips between Paris and Caen, and not getting much out of it! Even with the help of a cartoon introduction I had received as a 1982 Xmas gift! I had no difficulty in reading the text per se, as opposed to my attempt of Kant’s Critique of Pure Reason the previous summer [along with the other attempt to windsurf!], as the discourse was definitely grounded in economics and not in philosophy. But the heavy prose did not deliver a convincing theory of the evolution of capitalism [and of its ineluctable demise]. While the fundamental argument of workers’ labour being an essential balance to investors’ capital for profitable production was clearly if extensively stated, the extrapolations on diminishing profits associated with decreasing labour input [and the resulting collapse] were murkier and sounded more ideological than scientific. Not that I claim any competence in the matter: my attempts at getting the concepts behind Marxist economics stopped at this point and I have not been seriously thinking about it since! But it still seems to me that the theory did age very well, missing the increasing power of financial agents in running companies. And of course [unsurprisingly] the numerical revolution and its impact on the (des)organisation of work and the disintegration of proletariat as Marx envisioned it. For instance turning former workers into forced and poor entrepreneurs (Uber, anyone?!). Not that the working conditions are particularly rosy for many, from a scarsity of low-skill jobs, to a nurtured competition between workers for existing jobs (leading to extremes like the scandalous zero hour contracts!), to minimum wages turned useless by the fragmentation of the working space and the explosion of housing costs in major cities, to the hopelessness of social democracies to get back some leverage on international companies…

crowd-based peer review

In clear connection with my earlier post on Peer Community In… and my visit this week to Montpellier towards starting a Peer Community In Computational Statistics, I read a tribune in Nature (1 June, p.9) by the editor of Synlett, Benjamin List, describing an experiment conducted by this journal in chemical synthesis. The approach was to post (volunteered) submitted papers on a platform accessible to a list of 100 reviewers, nominated by the editorial board, who could anonymously comment on the papers and read others’ equally anonymous comments. With a 72 hours deadline! According to Benjamin List (and based on  a large dataset of … 10 papers!), the outcome of the experiment is one of better quality return than with traditional reviewing policies. While Peer Community In… does not work exactly this way, and does not aim at operating as a journal, it is exciting and encouraging to see such experiments unfold!