Sounds and chimpanzees

Surfing the internet, I noted two pieces of news related to Chimpanzees and sounds.

The first article is about the rhythms that different groups of chimpanzees drum with, indicating that chimpanzees share musicality with humans.

Chimpanzee groups drum with distinct rhythms

New research from a team of cognitive scientists and evolutionary biologists finds that chimpanzees drum rhythmically, using regular spacing between drum hits. Their results show that eastern and western chimpanzees — two distinct subspecies — drum with distinguishable rhythms. The researchers say these findings suggest that the building blocks of human musicality arose in a common ancestor of chimpanzees and humans.

The actual science article was printed in Current Biology: Chimpanzee drumming shows rhythmicity and subspecies variation

The second article is about how wild chimpanzees alter the meaning of singles calls when embedding them in longer calls, indicating a linguistic complexity that hasn’t been documented before outside the human species.

The origins of language (horrible headline given by ScienceDaily)

Wild chimpanzees alter the meaning of single calls when embedding them into diverse call combinations, mirroring linguistic operations in human language. Human language, however, allows an infinite generation of meaning by combining phonemes into words and words into sentences. This contrasts with the very few meaningful combinations reported in animals, leaving the mystery of human language evolution unresolved.

Chimpanzees are capable of complex communication: The human capacity for language may not be as unique as previously thought. Chimpanzees have a complex communication system that allows them to combine calls to create new meanings, similar to human language. Combining calls creatively: Chimpanzees use four ways to change meaning when combining single calls into two-call combinations, including compositional and non-compositional combinations, and they use a large variety of call combinations in a wide range of contexts.

The science article is in ScienceAdvances: Versatile use of chimpanzee call combinations promotes meaning expansion

Abstract

Language is a combinatorial communication system able to generate an infinite number of meanings. Nonhuman animals use several combinatorial mechanisms to expand meanings, but maximum one mechanism is reported per species, suggesting an evolutionary leap to human language. We tested whether chimpanzees use several meaning-expanding mechanisms. We recorded 4323 utterances in 53 wild chimpanzees and compared the events in which chimpanzees emitted two-call vocal combinations (bigrams) with those eliciting the component calls. Examining 16 bigrams, we found four combinatorial mechanisms whereby bigram meanings were or were not derived from the meaning of their parts—compositional or noncompositional combinations, respectively. Chimpanzees used each mechanism in several bigrams across a wide range of daily events. This combinatorial system allows encoding many more meanings than there are call types. Such a system in nonhuman animals has never been documented and may be transitional between rudimentary systems and open-ended systems like human language.

I found it interesting that two articles came out at the same time, seemingly unrelated, that both indicates that we share more traits with our Chimpanzee relatives than previously thought

 

Time for a new pope

Pope Francis has died, Vatican says

Pope Francis, the first Latin American leader of the Roman Catholic Church, has died, the Vatican said on Monday, ending an often turbulent reign marked by division and tension as he sought to overhaul the hidebound institution.

Pope Francis is probably the best Catholic pope in my lifetime, but he still represented a bigoted organization, covering up for child molesters. The same will be true of the next pope.

Disable AI training on your YouTube videos

Recently I have come across a bunch of videos on YouTube with AI generated descriptions under the videos. I presume most people who have these don’t know how to switch this feature off.

Here is an example of what I am talking about – an added description field under the video.

If you want to switch it off for you account, you have to go through YouTube studio – this is done through the menu you find if you click on your head in the top right corner

After navigating to YouTube Studio, you have to click on “settings”

In settings, you have to choose “Channel” and the tab “Advanced settings”. After doing that, you can scroll down to “Third-party training”, which you should switch off.

Two deaths of note

Every week there are noteworthy people who pass away, but sometimes their deaths hit a little harder.

This week we lost Joe Nickell and Kevin Drum.

Joe Nickell was a skeptic of the old school, investigating paranormal mysteries and hoaxes. I never had anything directly to do with him, but I admired his work, and have heard a lot of good stuff about him from people I respect. Here is Skeptical Inquirer’s obituary: Remembering Joe Nickell, Iconic Skeptic and Investigator

Kevin Drum was an influential progressive blogger back when blogging was a huge thing. He blogged under the name Calpundit, until he moved his blog, under the name Political Animal, to Washington Monthly. He later moved to Mother Jones, and more or less took over their website. Kevin was not without some serious flaws, but he was also willing to change his mind – for example, he supported the Iraq War before it actually started, but changed his mind, and was against it by the time the war actually started. I haven’t read Kevin Drum for years, but in his heyday, he was one of my must-read blogs. His family’s notice of his death can be read here.

 

The UK has a misinformation problem

As many of you probably know, there is a currently an anti-immigration movement in the UK, which unsurprisingly is supported by Musk who uses his platform to amplify their messages – messages that are mostly build upon lies and misinformation.

Here is a great article in Prospect Magazine addressing these lies and misinformation.

Immigration myths are everywhere

The media is flooded with outright lies and misleading statistics. Countering the falsehoods is arduous work

The article starts out with a great example

“One in 12 in Londoners is illegal migrant”; this was a front-page splash in the Telegraph, picked up and repeated across not just the right-wing press but in “mainstream” publications and by supposedly respectable but gullible or lazy commentators, not to mention Nigel Farage and Lord Frost, and no doubt other eminent politicians.

In fact, this claim contained not just one mistake but several. It was based not on new research but on a rehash of existing and now outdated estimates for the UK’s undocumented population. It took the upper limit of a wide estimate as fact—a more accurate description of this estimate would have been “between 1 in 13 and 1 in 20”.

Worse still, it omitted to note that the higher estimates include a large number of people who have indefinite leave to remain, and so are not, and in most cases never have been, irregular migrants, as well as children born in the UK, who may indeed be irregular but are most certainly not migrants.

Following my complaints to Ipso, the press regulator, the Telegraph and others corrected the story, albeit inadequately, and in small print on the inside pages. Ipso has the power to require them to publish a front-page correction, and have done so in the past; but their ruling will not come for some months.

There is no doubt that this is part of a broader strategy; the author of the Telegraph story, Sam Ashworth-Hayes, is not a “journalist” in the old-fashioned sense of the word, but an anti-immigration zealot, whose screeds usually appear on the Opinion page and who is part of a broader network of young right-wing activists.

The playbook is simple, drawn partly from the US, but adapted to the more centralised UK media landscape, where there is less of a clear firewall between “old” media and more overtly propagandistic outlets such as GB News, with many commentators featuring in both. Flood the zone with a mixture of lies, half-truths, misleading claims and statistics taken out of context, often sourced from “thinktanks” with little or no actual expertise. By the time these are belatedly corrected, or put in context, move on.

People like Jonathan Portes, who wrote this article, is fighting the good fight, but it is hard to counter lies and especially misinformation. This doesn’t mean we shouldn’t do it – it just means that we need to be aware of the limits, and make sure to take a multi-pronged approach while fighting this.

A new podcast – the Know Rogan Experience

A great new podcast has come into existence: The Know Rogan Experience,

The podcast is hosted brilliantly by Michael “Marsh” Marshall and Cecil Cicirello. Marsh is the editor of the UK Skeptic Magazine, one of the hosts of Skeptics with a K, and one of the organizers behind the QED conference. Cecil is one of the hosts of Cognitive Dissonance. So the hosts certainly have their skeptical credential in order.

The podcast focuses on Joe Rogan, and each episode covers an episode of The Joe Rogan Experience, going through the bullshit, falsehoods, and the very few factually correct statements

Machine learning has a pseudoscience problem

I saw this interesting paper linked on Bluesky

The reanimation of pseudoscience in machine learning and its ethical repercussions

It is from Patterns Volume 5Issue 9, September 13 2024, and talks about the harms of ML throughs its promotion of pseudo-science, or as the paper states:

The bigger picture

Machine learning has a pseudoscience problem. An abundance of ethical issues arising from the use of machine learning (ML)-based technologies—by now, well documented—is inextricably entwined with the systematic epistemic misuse of these tools. We take a recent resurgence of deep learning-assisted physiognomic research as a case study in the relationship between ML-based pseudoscience and attendant social harms—the standard purview of “AI ethics.” In practice, the epistemic and ethical dimensions of ML misuse often arise from shared underlying reasons and are resolvable by the same pathways. Recent use of ML toward the ends of predicting protected attributes from photographs highlights the need for philosophical, historical, and domain-specific perspectives of particular sciences in the prevention and remediation of misused ML.

Summary

The present perspective outlines how epistemically baseless and ethically pernicious paradigms are recycled back into the scientific literature via machine learning (ML) and explores connections between these two dimensions of failure. We hold up the renewed emergence of physiognomic methods, facilitated by ML, as a case study in the harmful repercussions of ML-laundered junk science. A summary and analysis of several such studies is delivered, with attention to the means by which unsound research lends itself to social harms. We explore some of the many factors contributing to poor practice in applied ML. In conclusion, we offer resources for research best practices to developers and practitioners.
The problem is simply put that the people responsible for the ML, cannot evaluate the data they feed into the ML. Or as the paper explains:
When embarking on a project in applied ML, it is not standard practice to read the historical legacy of domain-specific research. For any applied ML project, there exists a field or fields of research devoted to the study of that subject matter, be it on housing markets or human emotions. This ahistoricity contributes to a lack of understanding of the subject matter and of the evolution of methods with which it has been studied. The wealth of both subject-matter expertise and methodological training possessed by trained scientists is typically not known to ML developers and practitioners.
The gatekeeping methods present in scientific disciplines that typically prevent pseudoscientific research practices from getting through are not present for applied ML in either industry or academic research settings. The same lack of domain expertise and subject-matter-specific methodological training characteristic of those undertaking applied ML projects is typically also lacking in corporate oversight mechanisms as well as among reviewers at generalist ML conferences. ML has largely shrugged off the yoke of traditional peer-review mechanisms, opting instead to disseminate research via online archive platforms. ML scholars do not submit their work to refereed academic journals. Research in ML receives visibility and acclaim when it is accepted for presentation at a prestigious conference. However, it is typically shared and cited, and its methods built upon and extended, without first having gone through a peer-review process. This changes the function of refereeing scholarship. The peer-review process that does exist for ML conferences does not exist for the purpose of selecting which work is suitable for public consumption but, rather, as a kind of merit-awarding mechanism. The process awards (the appearance of) novelty and clear quantitative results. Even relative to the modified functional role of refereeing in ML, however, peer-reviewing procedures in the field are widely acknowledged to be ineffective and unprincipled. Reviewers are often overburdened and ill-equipped to the task. What is more, they are neither trained nor incentivized to review fairly or to prioritize meaningful measures of success and adequacy in the work they are reviewing.
This brings us to the matter of perverse incentives in ML engineering and scholarship. Both ML qua academic field and ML qua software engineering profession possess a culture that pushes to maximize output and quantitative gains at the cost of appropriate training and quality control. In most scientific domains, a student is not standardly expected to publish until the PhD, at which point they have typically had at least half a decade of training in the field. Within ML, it is now typical for students to have their names on several papers upon exiting their undergraduate. The incentives force scholars and scholars in training to churn out ever higher quantities of research. As limited biological agents, however, there is a bottleneck on time and critical thought that can be devoted to research. As quantity of output is pushed ever higher, the quality of scholarship necessarily degrades.
The field of ML has a culture of obsession with quantification—a kind of “measurement mania.” Determinations of success or failure at every stage and level are made quantitatively. Quantitative measures are intrinsically limited in how informative they can be—they are, as we have said, only informative to the extent that they are lent content by a theory or narrative. Quantitative measure cannot, for instance, capture the relative soundness of problem formulation. It has been widely acknowledged that benchmarking is given undue import in the field of ML and, in many cases, is actively harmful in that it penalizes careful theorizing while rewarding kludgy or hardware-based solutions.
A further contributing factor is the increased distribution of labor within scientific and science-adjacent activities. The Taylorization or industrialization of science and engineering pushes its practitioners into increasingly specialized roles whose operations are increasingly opaque to one another. This fact is not intrinsically negative—its repercussions for the legitimacy of science can be, when care is taken, a net positive. In combination with the other facets already mentioned, however, it can cause a host of problems. Increasingly, scholars and industry actors outsource the collection and labeling of their data to third parties. When—as we have argued—much of the theoretical commitments of a modeling exercise come in at the level of data collection and labeling, offloading these tasks can have damaging repercussions for the epistemic integrity of research.
All of the above realities work alongside a basic fact of modern ML: its ease of use. With data in hand and the computing power necessary to train a model, it is possible to achieve publishable or actionable results with a few hours of scripting and write-up. The rapidity with which such models are able to be trained and deployed works alongside a lack of gatekeeping and critical oversight to ill effect.
In my opinion, the paper makes the case for a new process, where people who actually knows the field are part of vetting the data given to the ML model.

Looking for news sources

It has long been know that a number of news sources in the US is completely unreliable, and work to present a far-right viewpoint as standard, and recirculates right-winged talking points disguised as news. During the last election, we also observed that old established news sources, such as New York Times and Washington Post where completely craven, and could not be trusted to accurately report the news.

So, where should one look for reliable news sources? Well, it is hard to fine, but it turns out that you have to look outside traditional news sources, to magazines that still maintain some standards – such unlikely magazines as Teen Vogue and Wired. And now Eater.

What Should You Do if ICE Comes to Your Restaurant?

In an interview on CNN, border czar Tom Homan bemoaned that immigrants in the U.S. were too informed. “Sanctuary citizens are making it very difficult to arrest the criminals,” he said, apparently annoyed that immigrants would be aware of basic rights they are owed by the government he works for. “For instance, Chicago, very well-educated. They’ve been educated how to defy ICE, how to hide from ICE.”

The ICE raids that the Trump administration has ordered across the country rely on fear, and there’s no shortage of that. Farm workers aren’t showing up to pick fruit, and street vendors aren’t showing up to run their businesses. They’re keeping their children home from school. “People in the neighborhood are talking about it. Agents are coming around, and people are scared to go to work,” a restaurateur in Queens told GrubStreet.

According to the American Immigration Council, immigrants of all statuses make up 22 percent of the food service industry, and ICE agents are sweeping up everyone in their raids on businesses, even U.S. citizens. “ICE is quite emboldened with the new administration, and they are not respecting people’s rights,” says Jessie Hahn, senior counsel for labor and employment policy at the National Immigration Law Center. “It just creates a lot of chaos, and they need to be held accountable.” And how an ICE raid may play out in a business is different from what it could look like on the street or in an individual home, which is why it’s important for all restaurant and food industry workers to know what to do if ICE shows up at work. We spoke to Hahn, and consulted other experts, about how restaurant workers and owners can work together to keep everyone safe.

Bloodywood

Yesterday evening I was at a concert with the Indian heavy band Bloodywood. If you get a chance to see them, I highly recommend it – they are full of energy. Of course, it didn’t harm that their first song on their playlist was Dana Dan, an aggressive anti-sexual assault song.

Lazy linking

First of all, I have created a bluesky account – feel free to follow me. I don’t know how active it will be, but I will try to give it a chance.

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In These Times has reprinted two works by Toni Morrison

Toni Morrison on Fascism and Censorship

In this reprint of “Peril” and “Racism and Fascism,” Toni Morrison warns of the creative depths of fascism’s reach.

From 1977 to 1979, June Jordan and Toni Morrison were both a part of The Sisterhood, a group of Black women writers who met in a New York City apartment to eat and drink together while discussing liberation. Whether addressing genocide, imperialism or the American literary establishment, the writers in the group, which included Alice Walker and Ntozake Shange, saw their work as a means to make interventions against dominant narratives of colonialism and oppression.

Their words ring prescient.

In ​“Peril” (2008) and ​“Racism and Fascism” (1995), reprinted below, Morrison recognizes what the creep of fascism looks like, particularly the censorship of dissent. Found here, in an excerpt of the essay ​“Life After Lebanon” (1984), Jordan reflects on the Israeli invasion of Lebanon in 1982 (funded by American taxes, of course), the backlash she faced after publicly condemning it and the fearless women who supported her in spite of it all. And in an excerpt of ​“Waking Up in the Middle of Some American Dreams” (1986), Jordan underscores the dire need for coalition-building across differences.

The article (and the excerpt I copied) also links to June Jordan’s works

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In the upcoming times, some of this might be useful for people

Anonymous — The Uber-Secret Handbook Version 3.0

This is not an endorsement of Anonymous, a group too undefined for me to have an overall opinion about, but rather a reference to a bunch of advice and practices that can help you stay safer on the internet in a changing future.

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Journalists flock to Bluesky as X becomes increasingly ‘toxic’

Journalists are finding more readers and less hate on Bluesky than on the platform they used to know as Twitter.

It is early days yet, but Bluesky seems like they take moderation seriously, and they allow you to filter messages on a much more fine-grained level than Twitter ever did.

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Concern Grows Around Billionaire Peter Thiel’s Period-Tracking App

Peter Thiel — the billionaire venture capitalist, Gawker destroyer and top dollar conservative campaign donor — is now backing a “femtech” app called 28, according to Vice News.

The app, created by a controversial women’s publication called Evie Magazine, claims to be a “cycle-based” nutrition and wellness program that helps women “reclaim control of [their] bodies, in the most natural way possible.”

This is important. Thiel is a big funder and backer of both Trump and the draconian policies to remove female autonomy

Neither the billionaire’s venture firm Thiel Capital nor Thiel himself are strangers to health and life science investments, but funding a fertility startup is a bit of a turn, especially at a moment during which a lot of Americans have just lost, rather than reclaimed, a significant degree of bodily autonomy.

And a closer look at the app, and those who made it, illuminates a powerful political intersection between tech, health, the wellness industry, and modern conservatism in which conspiracy theories and dubious pseudoscience are feeding a growing counter-counter-culture.

For starters, the science is sketchy. The core premise of the app appears to get women off modern birth control methods, like the hormonal pill or an intrauterine device. Its website is almost comically vague on what the alternative might be, but a focus on “cycles” suggests that it’s essentially the rhythm method, which basically entails attempting to avoid sex during ovulation. That’s fine in principle, but the rhythm method is statistically quite ineffective, with about a quarter of couples using it accidentally becoming pregnant over the average year.

The publication behind the app raises even more questions. Evie appears to be somewhat of a Cosmopolitan-meets-MindBodyGreen-meets-Tomi Lahren situation: makeup, workout, and holistic wellness tips and tricks are sandwiched between transphobic essays and anti-vaxx arguments. Its anti-birth control cycle-tracking app — which, yes, requires that women input potentially incriminating menstruation data — seems like a natural extension of that apparent mission: the rejection of a very specific version of “feminism” in order to embrace an old-meets-new version of traditional femininity.

The app seems like an attempt to gather data, while providing a pseudo-scientific method of birth control. It is easy to foresee a future where the app data will be used to force people to give birth in pregnancies which was caused by people trusting the method promoted by the app.

Thiel, and the companies he control, should never have access to any kind of health or personal data!