Interpersonal synch is stronger face-to-face than in video chat

Face-to-face interaction elicits more inter-brain synchronization than video-chat interaction. Moreover, associations with behavior emerge only during f2f interaction — with right temporal-temporal synchrony linked to shared gaze and right frontal-frontal to engagement & empathy. pic.twitter.com/oIQkgOJkVX

— Guillaume Dumas (@introspection) October 16, 2022

Thanks and kudos to my amazing collaborators @Lin_Scw @yoniphd @AmirDJV @YaaraShapira and of course @ruthfeldmanlab 🙌 Also grateful for the continuous support of @IVADO_Qc @FRQS1 @Med_UMontreal @CR_CHUSJ 🙏

— Guillaume Dumas (@introspection) October 16, 2022

4 steaks on a colorful plate

5 propositions about art from a 7-year old

I’m writing my book ‘Children are artists’ based on my PhD. This is one of my favourite quotes from a child, 7 years and full of wisdom. pic.twitter.com/uB8lNzWyy9

— Dr Penny Hay 💙💚🌳🌹❤️🌈 (@PenAHay) January 5, 2022

Friendship between a [toy] gorilla and a frog

Chimps and gorillas can be friends

Interspecific interactions between sympatric apes https://t.co/B69OqvG4U6

— Bill Benzon, BAM! Bootstrapping Artificial Minds (@bbenzon) October 12, 2022

Abstract from article linked above:

Highlights

• First evidence of social relationships between chimpanzees and gorillas is reported
• Social ties between chimpanzees and gorillas persisted over years and across contexts
• Ape species engaged in a wide range of interactions, from play to aggression
• Coexisting great apes may inform us about interactions between some early hominins

Summary

Gorillas reside in sympatry with chimpanzees over the majority of their range. Compiling all known reports of overlap between apes and augmenting these with observations made over twenty years in the Ndoki Forest, we examine the potential predation-related, foraging, and social contexts of interspecific associations between gorillas and chimpanzees. We reveal a greater diversity of interactions than previously recognized, which range from play to lethal aggression. Furthermore, there are indications that interactions between ape species may serve multiple functions. Interactions between gorillas and chimpanzees were most common during foraging activities, but they also overlapped in several other contexts. From a social perspective, we provide evidence of consistent relationships between particular chimpanzee-gorilla dyads. In addition to providing new insights into extant primate community dynamics, the diversity of interactions between apes points to an entirely new field of study in early human origins as early hominins also likely had opportunities to associate. 

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Here's the story as reported in the NYTimes. From the article:

Social apes occasionally stepped on one another’s toes, and the team noted moments of friction. But aggressive interactions rarely got beyond yelled warnings, and never escalated to the kind of lethal interspecies attacks seen at sites in Gabon.

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“They’re not spending all of their time together, but they’re definitely coming together more consistently and regularly than we’d anticipated,” Dr. Sanz said. “These social ties are not what we’d have been expecting if these were just chance interactions in a foraging landscape.”

These sorts of groupings don’t seem to help ward off predation, Dr. Sanz and her colleagues found. Instead, maintaining friendly relationships seems to open up new feeding opportunities, with apes of different species sometimes alerting one another to fruits that are harder to spot. Co-feeding, in turn, gives apes a chance to make lasting relationships.

“Five or 10 years down the road, these individuals on the landscape know each other — they grew up together, they interacted every week or so at different types of food resources,” Dr. Sanz said.

It’s notable that these connections often start with play between two similar species, said Frans de Waal, a primatologist at Emory University who was not involved with the study. “It must be as much fun for them to play together as it is between us and, say, a dog or other companion animal. It expands how we look at primate social systems, which traditionally is entirely within species.”

The cultural evolution of emotion

Nature reviews psychology

The cultural evolution of emotionhttps://t.co/kiGsu673UV

— CLaE (@leafs_s) October 8, 2022

The abstract of the linked article:

Scholarly debates about the nature of human emotion traditionally pit biological and cultural influences against one another. Although many existing theories acknowledge the role of culture, they mostly treat emotion categories such as ‘anger’ as biological products. In this Perspective, we summarize traditional assumptions about the roles of biology and culture in emotion alongside supporting and conflicting empirical evidence. Building on constructionist models of emotion, we introduce a cultural evolutionary perspective that moves beyond a strict biology-versus-culture dichotomy. This cultural evolutionary perspective uses dual inheritance models of cultural transmission to explain how variation in emotion can arise across groups, how affect-laden information can travel throughout populations, and why people in different cultures use both similar and different emotion concepts and non-verbal expressions. This cultural evolution framework allows for new hypotheses about the development of emotion categories and challenges longstanding claims about the universality of emotion.

Tree trunks and greens

Hedging THE EVENT: Yes, the super-rich are different from you and me, they think they can buy their way out of mortality

I'm bouncing this to the top because it links up with my current interest in AI Doom. Rushkoff has turned this article into a book, reviewed by Cory Doctorow, Survival of the Richest.

 

* * * * *

 

Douglas Rushkoff was recently paid "about half my annual professor’s salary " to talk (republished at at The Guardian) with "five super-wealthy guys — yes, all men — from the upper echelon of the hedge fund world."

Which region will be less impacted by the coming climate crisis: New Zealand or Alaska? Is Google really building Ray Kurzweil a home for his brain, and will his consciousness live through the transition, or will it die and be reborn as a whole new one? Finally, the CEO of a brokerage house explained that he had nearly completed building his own underground bunker system and asked, “How do I maintain authority over my security force after the event?”

The Event. That was their euphemism for the environmental collapse, social unrest, nuclear explosion, unstoppable virus, or Mr. Robot hack that takes everything down.

This single question occupied us for the rest of the hour. They knew armed guards would be required to protect their compounds from the angry mobs. But how would they pay the guards once money was worthless? What would stop the guards from choosing their own leader? The billionaires considered using special combination locks on the food supply that only they knew. Or making guards wear disciplinary collars of some kind in return for their survival. Or maybe building robots to serve as guards and workers — if that technology could be developed in time.

That’s when it hit me: At least as far as these gentlemen were concerned, this was a talk about the future of technology.

This, of course, is nuts.

There’s nothing wrong with madly optimistic appraisals of how technology might benefit human society. But the current drive for a post-human utopia is something else. It’s less a vision for the wholesale migration of humanity to a new a state of being than a quest to transcend all that is human: the body, interdependence, compassion, vulnerability, and complexity. As technology philosophers have been pointing out for years, now, the transhumanist vision too easily reduces all of reality to data, concluding that “humans are nothing but information-processing objects.”

How'd we come to this?

Of course, it wasn’t always this way. There was a brief moment, in the early 1990s, when the digital future felt open-ended and up for our invention. Technology was becoming a playground for the counterculture, who saw in it the opportunity to create a more inclusive, distributed, and pro-human future. But established business interests only saw new potentials for the same old extraction, and too many technologists were seduced by unicorn IPOs. Digital futures became understood more like stock futures or cotton futures — something to predict and make bets on. So nearly every speech, article, study, documentary, or white paper was seen as relevant only insofar as it pointed to a ticker symbol. The future became less a thing we create through our present-day choices or hopes for humankind than a predestined scenario we bet on with our venture capital but arrive at passively.

This freed everyone from the moral implications of their activities. Technology development became less a story of collective flourishing than personal survival. [...] So instead of considering the practical ethics of impoverishing and exploiting the many in the name of the few, most academics, journalists, and science-fiction writers instead considered much more abstract and fanciful conundrums: Is it fair for a stock trader to use smart drugs? Should children get implants for foreign languages? Do we want autonomous vehicles to prioritize the lives of pedestrians over those of its passengers? Should the first Mars colonies be run as democracies? Does changing my DNA undermine my identity? Should robots have rights?

Asking these sorts of questions, while philosophically entertaining, is a poor substitute for wrestling with the real moral quandaries associated with unbridled technological development in the name of corporate capitalism.

* * * * *

Here's a fictional addendum: It's from Kim Stanley Robinson's New York 2140. 

By that time the sea had risen 50 feet above where it had been in the 20th century, changing the city in substantial ways. Extreme income inequality still existed. And now a hurricane had hit the city with a 20 foot surge. Major disaster. An angry mob is headed toward some super-luxury residential towers north of Harlem. The private security forces starts firing over their heads. Inspector Gen is with a contingent of police between the mob and the towers. Gen gets the private security to go inside and starts talking with their boss, now under arrest, whom she’d met a week earlier on the water. He’s thinking (p. 506):

And it also looked like he was considering his options, not as this tower’s security head, but as an individual who could get sued or go to jail. Who had perhaps made mistakes, after being ordered to do an illegal and impossible thing, by bosses who did not care about him. Best options for himself, he was now considering.

Hmmm.

Inside looking out [the Malibu]

AI-Doom – Just who is a cargo cult?

I spotted this post on LessWrong two months ago, jam-mosig, Paper reading as a Cargo Cult. It was the presence of the word “cult” in the title that caught my attention. Why? Because I think that belief in AI-Doom is ‘cult’ behavior – see my post in 3 Quarks Daily, On the Cult of AI Doom – but I’ve got reservations about the word ‘cult.’? Why? Well, I’m not clear on just what the word means, how to apply it to the world. Here’s the problem, sorta’. Every now and again I’ve seen a discussion of the difference between cult and religion in which it is observed that a religion is just a cult that has managed to achieve legitimacy. That is to say, the difference is not a matter of the beliefs, but of their social standing. This post is not a place to attempt to hash that out, but I think it’s a legitimate issue.

However, LessWrong is a place where AI Doom (that is, AI as existential risk) is a legitimate complex of beliefs. It is one of the central sites on the web for discussions of those ideas. Thus it is interesting and significant to see the word used on that site. It’s a sure indication that the issue of broader legitimacy is explicitly recognized in that world.

Note, however, that the post isn’t specifically about belief in AI Doom, but rather about the broader issue of AI alignment, where the possibility that AI presents an existential risk is only one issue among many. But it is the most extreme and distressing one.

* * * * *

Having said that, let’s take a look at the post. Here’s how the post opens:

I have come across various people (including my past self) who meet up regularly to study, e.g., alignment forum posts and discuss them. This helps people bond over their common believes, fears, and interests, which I think is good, but in no way is this ever going to lead anyone to find a solution to the alignment problem. In this post I'll reason why this doesn't help, and what I think we should do instead.

The cult

Reading good papers can be fun. You learn something interesting and, if the topic is hard but well presented by the authors, you get a kick from finally understanding something complicated. But is what you learned actually useful for the problem at hand? What is the question that drove you to read this paper in such detail?

Yes, you need to regularly skim papers for fun, so you get an idea of what's out there and where to look when you need something. You also need to absorb terminology and good writing practice, so you can communicate your own research. Yet, I believe that fun-reading should only occupy a tiny fraction of your time, as you have more important things to do (see next section).

Despite its relative unimportance, paper reading groups tend to focus a lot on this fun-reading aspect. They are more of a social gathering than a mechanism to boost progress.

The post is, in effect, distinguishing between a serious concern about AI alignment (which includes AI Doom) and a more superficial commitment. This more superficial commitment is (thus) cultlike and centers on social activity, not intellectual investigation.

After some more remarks about ‘the cult’ jam-mosig takes up the issue of “actual science”:

To drive scientific progress means to do something that nobody else has ever done before. This means that your idea or line of research tends to seem strange to others (at first sight). At the same time, it also tends to seem obvious to you - it's just the natural next step when you take seriously what you've learnt so far.

Before I properly reconcile "strange" and "obvious" here, let me warn you of a trap: It is very easy to have an idea that seems obvious to you, but strange to others, when you are delusional. Especially when you are good at arguing, you can easily make yourself believe that you are right and everybody else is just not seeing it. Beware that trap.

I find that second paragraph interesting because it outlines the epistemological problem presented by cultishness. Cult beliefs are obvious to those in the cult, but strange to others.

While there’s more to the post, though not much more, that’s enough for my purpose, which is simply to show that the issue of cultishness is real within the AI alignment community. The author of the post seems intent on showing that, while they once engaged in this cultish behavior, they’ve since moved beyond it. But those other people, over there...they’ve got to change their ways.

Active Matter

"I consider the Organism, or natural Machine, a machine in which each part is a machine."
- #Leibniz

🦠 Energy → Work

"Active matter *employs* control, either internally [e.g., embryogenesis] or externally [e.g., with sheepdogs]."
- @SurajShankar92 pic.twitter.com/4l2NAG3F7C

— Santa Fe Institute (@sfiscience) October 5, 2022

Don't forget the irises

Linearly Mapping from Image to Text Space

Linearly Mapping from Image to Text Space
abs: https://t.co/ZyItiCfj0l pic.twitter.com/tWfKeovBVc

— AK (@_akhaliq) October 3, 2022

The paper's conclusion:

In this paper, we test the extent to which the representations of language models encode information about the non-linguistic world in terms of their ability to use image representations to perform vision-language tasks. We show through LiMBeR (Linearly Mapping Between Representation spaces) that training a linear (thus, distance-preserving) transformation to connect image features to an LM’s input space is competitive on image captioning and visual question answering benchmarks with similar models like MAGMA that tune both image and text networks. However, we also find that such transfer is highly dependant on the amount of linguistic supervision the image encoder backbone had during its pretraining phase. BEIT, which is a vision-only image encoder underperforms compared to CLIP, which was pretrained with natural language captions. We explore what conceptual information transfers successfully, and find through probing, clustering, and analysis of generated text that the representational similarity between LMs and vision-only image representations is mostly restricted to coarse-grained concepts of perceptual features. Our findings indicate that large LMs do appear to form models of the visual world along these perceptual concepts to some extent, but are biased to form categorical concepts of words that are not distinguished by vision-only models. We are excited by future work applying LiMBeR to other domains and modalities as a behavioral tool for understanding the representations of LMs and other deep neural networks.

Smoothies and Crabcakes

Semantic reconstruction of continuous language from non-invasive brain recordings

it is generally thought that fMRI responses are too slow for language decoding. to overcome this low temporal resolution, we developed a bayesian decoder that combines state of the art language models and encoding models to generate rapidly changing word sequences (2/7)

— Jerry Tang (@jerryptang) September 30, 2022

the same decoder also worked on brain responses while subjects imagined telling stories, even though the decoder was only trained on perceived speech data. we expect that training the decoder on some imagined speech data will further improve performance (4/7) pic.twitter.com/z63D7Xe3Sa

— Jerry Tang (@jerryptang) September 30, 2022

Abstract of linked article:

A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, decoders that reconstruct continuous language use invasive recordings from surgically implanted electrodes, while decoders that use non-invasive recordings can only identify stimuli from among a small set of letters, words, or phrases. Here we introduce a non-invasive decoder that reconstructs continuous natural language from cortical representations of semantic meaning recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech, and even silent videos, demonstrating that a single language decoder can be applied to a range of semantic tasks. To study how language is represented across the brain, we tested the decoder on different cortical networks, and found that natural language can be separately decoded from multiple cortical networks in each hemisphere. As brain-computer interfaces should respect mental privacy, we tested whether successful decoding requires subject cooperation, and found that subject cooperation is required both to train and to apply the decoder. Our study demonstrates that continuous language can be decoded from non-invasive brain recordings, enabling future multipurpose brain-computer interfaces.