Mark McDonough

• We conclude with a queer bestial ethics of avowal, one that dispenses with anthopatriarchal innocence towards a more capacious embrace of the panspecies desire for touch and thriving

• The judge also found that it violated free speech rights by requiring teachers to use pronouns aligning with a student’s gender identity.

   

  “The First Amendment does not permit the government to chill speech or compel affirmance of a belief with which the speaker disagrees in this manner,” Reeves wrote.

• Decoding whalespeak and other animal languages 

    

    Researchers know the enigmatic clicks made by sperm whales vary in tempo, rhythm and length, but what the animals are saying with these sounds — produced through spermaceti organs in their bulbous heads — remains a mystery to human ears.

• Machine learning, however, has helped scientists analyze nearly 9,000 recorded click sequences, called codas, that represent the voices of approximately 60 sperm whales in the Caribbean Sea. The work may one day make it possible for humans to communicate with the marine animals. 

    

    The scientists examined the timing and frequency of codas in solitary whale utterances, in choruses, and in call-and-response exchanges between the marine giants. When visualized with artificial intelligence, previously unseen coda patterns emerged in what the researchers described as akin to phonetics in human communication.

• In all, the program detected 18 types of rhythm (the sequence of intervals between clicks), five types of tempo (the duration of the entire coda), three types of rubato (variations in duration), and two types of ornamentation — an “extra click” added at the end of a coda in a group of shorter codas. 

    

    These features could all be mixed and matched to form an “enormous repertoire” of phrases, the scientists reported in May. However, the approach has its limitations. While machine learning is adept at identifying patterns, it doesn’t shed light on the meaning. 

    

    A next step, according to the study, is interactive experimentation with whales, along with observations of whale behavior, which could be an important part of unraveling the syntax of sperm whale click sequences.

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• We cannot do anything with reasoning that works like this. It begins with its conclusion that men and male sexuality are defined by something dark, dangerous and ugly and that gay men’s sexuality, unconstrained by the influence of an imagined pure and virtuous female sexuality, represents a particularly concentrated variation of this vile and contaminating depravity. It then works backwards from there. No amount of reasoned argument or evidence to the contrary will reach true believers in this narrative. They are prisoners of their own minds and the rest of us can only be thankful we do not have to live in such minds.
• However, I would suggest that there is genuine cause to be concerned for young gay men and especially for boys who are or will soon be recognising themselves to be gay and who are particularly likely to operate more in the social media world of narratives than in the real one. For them, we must fear the homophobic narratives of the ‘queer theory’ brand of Critical Social Justice activism which will try to convince some of them struggling with their sexuality that they are actually girls and the rest that it is transphobic to be solely same-sex attracted.

• Further, vegans are perceived as less masculine than omnivores
  (Thomas, 2016), and omnivorous men exhibit more negative attitudes toward vegans than omniv-
  orous women do (Judge & Wilson, 2019). In addition, vegan men face more negative prejudice
  than vegan women do. In a simulated job application context, for example, vegan men were con-
  sidered less competent than their omnivore counterparts, while vegan and omnivore women were
  assessed as equally competent (Adamczyk & Maison, 2023). Men therefore have fewer personal
  incentives to become vegan, and face more obstacles if they choose this path

• “In the back end, these algorithms that need to be running for any generative AI model are fundamentally very, very different from the traditional kind of Google Search or email,” says Sajjad Moazeni, a computer engineering researcher at the University of Washington. “For basic services, those were very light in terms of the amount of data that needed to go back and forth between the processors.” In comparison, Moazeni estimates generative AI applications are around 100 to 1,000 times more computationally intensive.
• Despite an upwards trend of energy needs at data centers, it’s still a small percentage of the amount of energy humans use overall. Fengqi You, an energy systems engineering researcher at Cornell, mentions oil refineries, buildings, and transportation as more impactful at the present moment. “Those sectors use much more energy compared to AI data centers right now,” he says. Keeping that in mind, AI’s energy consumption footprint could continue to grow in the near future, as generative AI tools are integrated into more corners of the internet and adopted by more users online.
• While it may appear similar at first, the impact on the local environment from companies operating giant data centers is not comparable to the impact from residents who may take multiple bubble baths a week or leave the faucet running while they brush their teeth. “They're different from normal, residential users. When we get the water from the utility, and then we discharge the water back to the sewage immediately, we are just withdrawing water—we're not consuming water,” Ren says. “A data center takes the water from this utility, and they evaporate the water into the sky, into the atmosphere.” He says the water consumed by data centers may not return to the earth’s surface until a year later.

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