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While AI chatbots get louder, Google’s Perch model is learning to listen. It’s decoding ecosystems from the canopy to the seafloor, helping scientists find endangered animals before it’s too late. Meanwhile, Duolingo’s AI pivot is making the app smarter but less lovable, and the Alan Turing Institute is raising a harder question: what kind of intelligence are we scaling, and who gets to define it?
📌 In today’s Generative AI Newsletter:
Perch helps scientists detect rare species through sound
Duolingo faces backlash for replacing humans with AI
Turing Institute calls for interpretive, human-centered AI
🌿Google's AI Decodes Nature's Sounds for a Louder Planet
Image source: Google DeepMind
Google DeepMind has open-sourced a powerful new version of Perch, its AI model for decoding the sounds of the natural world. With this update, Perch can help scientists sift through millions of hours of wildlife audio to track endangered species across forests, oceans, and everything in between.
Here’s what’s echoing through the ecosystem:
More data, more depth: The new Perch is trained on twice as much data as before, spanning birds, mammals, amphibians, and even human-made noise.
One model, many missions: From counting newborn animals to identifying elusive species in remote soundscapes, Perch supports a wide range of conservation tasks.
Smarter with less: Open-source tools include vector search and active learning, letting researchers build species detectors from just a single sound.
Real-world wins: Perch helped find a new population of the rare Plains Wanderer in Australia and sped up honey creeper tracking in Hawaiʻi by 50x.
Every forest has a voice, and now, AI can finally hear it. Perch is becoming a tool for empathy at scale, helping conservationists recognize the sounds of life before they fall silent. As microphones keep listening from the canopy to the seafloor, this model gives scientists the means to act faster, with sharper clarity. In a world that often forgets what it’s losing, Perch helps us listen closely, and maybe hold on a little longer.
🦉Duolingo Dilemma: When Human-Built Charm Gives Way to AI
Image from the Duolingo widget when your streak (represented by flames) is in danger of being broken. Image from Psychologs Magazine
Duolingo’s shift to an AI-first model sparked loud backlash online, but the company seems unfazed. Despite cutting contract staff and leaning into generative tools, it’s doubling down on automation while watching public opinion split in real time.
Here’s how the sentiment battle is playing out:
Critics say the app feels colder: Long-time users argue AI-made lessons lack the charm and nuance of human-crafted ones.
Social media sentiment turned sour: CEO Luis von Ahn admitted the backlash pushed Duolingo to tone down its edgy posts and “get sentiment more positive.”
TikTok comments remain skeptical: Fans jokingly accuse even real employees of being AI, to which Duolingo now replies with carefully crafted PR reassurances.
Perception split from performance: The app is growing, but users are debating whether scale came at the cost of soul..
“People loved the weirdness,” one top commenter wrote on social media. “Now it just feels like every other app.” Another added, “I’m learning Spanish, not machine learning.” Duolingo says it's “putting learners first,” but the real lesson may be that scaling fast with AI means losing a little of the human voice and users are fluent enough to notice.
👩🏫The Alan Turing Institute Argues for Humanities in AI
A new UK-led initiative called Doing AI Differently is challenging the math-heavy mindset of AI development and calling for a shift toward human-centered, interpretive systems. Spearheaded by the Alan Turing Institute and partners across academia and industry, the project urges a cultural reckoning with how AI is built and deployed before it's too late.
Here’s what the project is proposing:
Interpretive AI: Instead of treating AI like a calculator, the initiative wants systems that understand ambiguity, culture, and context — like humans do.
Breaking the recipe loop: Today’s models are too uniform. The report compares it to every baker using the same cake recipe. The result? Bias baked in at scale.
Learning from past tech failures: Researchers draw a clear line from social media’s societal fallout to the risks of rushing AI without interpretive depth.
Funding on both sides of the Atlantic: The UK and Canada are launching a joint call to fund cross-border research that can build safer, more human-aligned AI.
“We have a narrowing window to build in interpretive capabilities from the ground up,” warns Professor Drew Hemment. “What AI creates are cultural artifacts — not just outputs.” In a world racing to scale intelligence, this project is asking a different question: what kind of intelligence are we scaling?
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¹ MarketsandMarkets, “AI agents market worth $52.62 billion by 2030,” PR Newswire, July 10, 2024.
² McKinsey & Company, “The economic potential of generative AI,” June 2023.
³ Pew Research Center, “AI in daily life,” March 2023.
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