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Nature tidbits [23 October 2025]

Posted in Books, Kids, pictures, Running, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on November 27, 2025 by xi'an

In this October issue of nature, plenty of the “usual” topics, namely AI and Trump.2.0 wrecking balls, along with two cosmology entries that related to my trip to the early universe last week, and a pros-and-cons opposition about animal testing,

a discussion on the nature of the “little red dots” that have been recently observed and whose nature remains open, the most popular explanation (I was given during lunch) being black holes surrounded by gas (even though I cannot understand why the gas is not attracted by the black hole!) [and would have produced a more exciting cover!]

a review of the recent book Discordance: The Troubled History of the Hubble Constant by Jim Baggott, entitled Why we still don’t understand the Universe — even after a century of dispute! A review that regrets that more time is spent on the Hubble “constant” (which varies with time!) rather than more controversial issues like dark matter and dark energy (And strangely bemoans that the book is focussed on scientific developments, missing sociological ones. Duh?! (Bonus for a picture of suit-and-tie Edwin Hubble sitting at the centre of a telescope),

two entries on the well-being [or lack thereof] of PhD students, with nothing particularly surprising (eg, inclusivity and respect help!), and Brazil, Australia and Italy ranking top locations but in a comparative study that does not mention France (as often in international comparisons found in Nature) despite the place being in the top 10 countries delivering PhD degrees, not that I believe PhD students are particularly well-treated in French academia!, the (unexplained) surprise being Italy ranking so high given the close resemblance between the two countries (low stipends, shortage of postdoc and permanent positions, high teaching loads for the advisor, limited travel budgets),

a conference (purposedly) made of AI-written papers reviewed by AI referees, Agents4Science 2025, how universities are rushed into adapting to AI-fluent students, whose skills are changing, and the rise in fake authors produced by paper mills, with a limited range of acceptable solutions,

why Trump 2.0‘s blackmail on pharmaceutical companies is counter-productive and likely to slow down progress, and why his massive increase of highly qualified scientists is shooting (or nuking) USelf in the foot, given the huge proportion) of im/emigrated Nobel prize winners (for physics, chemistry, and medicine), along the (post-) Nobel prize in economics is a direct or indirect reply to this regression by awarding the Prize to economists who worked on the importance of creativity and science on growth (not very surprising at first look!)

Nature tidbits [30th October 2025]

Posted in Books, Kids, pictures, Running, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on November 22, 2025 by xi'an

In this October issue of nature, items of interest (to me) [already highlighted in an emailed News Highlight]:

Google claim a significant ‘quantum advantage’ by quantum-echoes algorithms that they say is 13,000 faster than classical algorithms, but it is unclear from the nature article where and when their algorithms can be used, apparently missing to apply to realistic scientific applications… (Along with a paper on an “atom-array architecture that enables continuous operation with reloading rates of up to 30,000 initialized qubits per second while preserving coherence across a rearranged large-scale qubit array”.)

On the Trump vs. Science scene, a predicted drop in PhD admissions as an adaptation to (uncertain) Trump’s cuts, bans, visa restrictions, and other attempts at arm-bending and blackmailing  (And the novelty of me receiving applications from the US, a first!) Some U.S. departments have simply cancelled PhD admissions… But some universities have so far resisted the Orange pressure. Namely, the MIT, Brown University, (good old) Penn, UCLA, the University of Virginia (albeit agreeing to a deal), Dartmouth College, and the University of Arizona in Tucson. Vanderbilt has given in. (UT Austin and Harvard seem to be continuing the discussion with the Trump administration.) Meanwhile, China is zooming past! The issue also contains articles on how fundamental science discoveries have had hugely practical consequences, in case one need argue with a sceptic.

As a less urgent issue, some researchers at Institut Pasteur in Paris identified new diseases that did not help Napoléon’s Grande Armée as it retreated from Moscow, from the DNA of 13 soldiers buried in Lithuania. (With nature failing to give credit to the painter Adolph Northen for his famous “Napoleon’s retreat from Moscow” illustrating the story, attributed to the researchers in the paper!)

In this period of ERC announcements of their grantees, an analysis of the two-digit rise in applications. Unsurprising, given the international context. Along with a decrease in funding due to a lack of adjustment against inflation since 2007. (Incidentally, I found out this week that Torsten Elßin—at the Max Planck Institute for Astrophysics—I visited once had been selected for a Synergy grant on a 3D Milky Way Atlas. Along another cosmology Synergy grant at MPA on the Epoch of Reionization.)

A runner’s must-read that starts with the statement “the human body has a ‘metabolic ceiling’ that even the most extreme athletes cannot surpass”. Which would be 2.4 times the basal metabolic rate (BMR) for extended periods—by which the authors mean 30 weeks and over!, not a half-marathon. Not so exciting a paper in the end.

As predicted by the cover, a Royal Society meeting acknowledging the AI language models killed Turing’ test and questioning the next one. Since assessing the capacities and limitations of novel AIs and AGIs sounds more relevant and societally important. To wit, “;the Turing test of the future should question whether an AI is safe, reliable and provides meaningful benefit, he said, and should also ask who bears the cost of that benefit”. (As discussed in the book review of The Means of Prediction: How AI Really Works (and Who Benefits) by Maximilian Kasy, in the same volume. And yet another paper on AI biases.)

Nature snipets [17 April 2025]

Posted in Books, Kids, University life with tags , , , , , , , , , , , , on June 25, 2025 by xi'an

From the Nature 17 April Issue:

Data science ethics [book review]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on May 5, 2025 by xi'an

Data science ethics (concepts, techniques and cautionary tales), by David Martens, was published in 2022 by Oxford University Press. The book is inspired by the author’s  course on Data Science and ethics he has been teaching at the University of Antwerp. (With a link to his slides.) The 255p book proceeds by decomposing the ethics of data science into its different steps: data gathering (Chap. 2), data preprocessing (Chap. 3), modelling (Chap. 4), evaluation (Chap. 5), and deployment (Chap. 6). Following the `FAT Flow Framework´, where FAT stands for fairness, accountability, and transparency.

Do not expect much maths, stats, or anything quantitative: this book is mostly about concepts, even though some (mostly well-known) illustrations are provided. Chapter 2 includes a description of encryption (with homomorphic encryption treated in Chapter 4). And somewhat improbably quantum computing. Differential privacy gets a few pages with not a single formula (until Chapter 4, again).

Chapter 3 covers k-anonymity, record linkage, reidentification, (through cautionary tales) and discrimination through biases in the (learning) dataset.  Chapter 4 is defining ε differential privacy with the Laplace randomization as a possible implementation and with no critical stance on the limitations of the concept. The computation limitations of homomorphic encryption are more clearly pointed out. Federated learning is only quickly mentioned. The section about measuring fairness and reducing bias implies that some prior knowledge is available about whom is potentially discriminated and which covariates to add to the model. The last section on explicability of predictions is worthwhile in signalling the difficulty with most (black box) AI but the example opposing an SVM model to a logistic model is not tremendously convincing in that neither model is true.

Chapter 5 addresses the crucial challenge of ethical evaluation in a rather verbose and vague manner. For instance, with no instruction on how to resist adversarial attacks. Or criticising p-hacking and multiple testing while missing the elephant in the room (p-values!). Drifting from the topic when discussing the misdeeds of Diederik Stapel. Most of the same goes about Chapter 6 and its take on ethical deployment, when going through examples such as Google’s policies in China. Or general musing on the impact of AI on societal inequalities (with mentions of companies and CEOs who have since then back-pedalled on their ethical engagement). These chapters are lacking in tools and (more) practical recommendations.

One interesting aspect of the book is the attention paid to the EU(ropean) aspect of these concerns, through the GDPR (General DAta Protection Regulations) adopted by the European Parliament in 2016. (There is also a brief mention of China’s regulations, but no details beyond a reference. Maybe the Chinese edition differs.)

Nature tidbits

Posted in Books, pictures, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , on January 9, 2025 by xi'an

From the 12 December issue, lots of AI entries in Nature (soon moving to NAIture??), from the arrival of AGI, artificial general intelligence, and the usual barren call for companies to take responsibility (!) and equally repeated pious wishes for (better) controlling the incoming “human intelligent” AIs, to the two year anniversary of ChatGPT, which came as a significant support to non-native English speakers, if raising concern about the privacy losses in delivering unprotected data to the model,  to the incoming dearth of data to feed AIs (duh? why would new data be necessary for new AI systems?), with the danger that using AI generated data to train new AIs is not good being repeated anew, to the poor performances of LLMs on African languages, and the correlated dearth of funding in Africa, to DeepMind doing better than weather agency supercomputers to predict weather on a 15 day window, including extreme weather events (not much of a surprise, as climate change does not mean that history of past weather patterns cannot be exploited) albeit the probabilistic nature of the forecast seems to derive from the randomness of the starting conditions, hence depends on the choice of that distribution, to the (unsurprising) 50% productivity boost in design in a material science company afforded by seconding (or supplanting!) researchers with AI tools, to the poor design of bar plots (inc. Nature) that induce misunderstandings. A fair degree of double entries when considering the earlier 5 December issue, also read in the plane, with again fossilized poo, AI soon reaching human intelligence level (??),  and the AI computing gap between academy and industry. Beside this AI frenzy, a news article reporting on the EU trying to create an applied research council equivalent to what the ERC succeeded for academic research. (And I will not mention the digestive track article further than pointing out it added bromalite, cololite, and regurgitalite, to my digestive vocabulary!)