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An AI that spots Parkinson’s earlier than doctors? Then helps slow it down by guiding light therapy to the brain? Also, why image models feel ‘creative’ with just trained data?
What are on FIRE 🔥
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AI INSIGHTS
What if AI could not only spot Parkinson’s earlier than doctors, but also guide a treatment that slows it down? That’s exactly what Rresearchers just pulled off, in mice.
How the AI Works
They built a 3D pose-estimation system that tracked 340+ movement features in mice.
It outputs a score that’s 90% accurate, even picking up early-stage PD weeks before normal tests.
How the Therapy Works
With wireless LED cages, researchers pulsed brain regions with blue light on different schedules.
In mild PD (Parkinson’s disease) mice, alternate-day light:
Prevented severe PD progression
Preserved ~90% of dopamine neurons (vs. ~38% untreated)
Restored gait, coordination, and reduced tremors
In severe PD mice, the effect was limited but still showed promise.
This feels like a blueprint for closed-loop neuro-medicine: AI diagnoses → Optogenetics delivers a targeted therapy → Both adapt over time, making treatment smarter.
But it’s still early, mouse models ≠ humans, but it’s a rare case we could see how AI + bioengineering could handle diseases once thought untouchable.
Why It Matters: AI clearly outperformed traditional tests in sensitivity and early detection. Perhaps, it could guide precision treatments for PD and other neurodegenerative disorders. Soon, hopefully, we’ll see it in real life.
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TODAY IN AI
AI HIGHLIGHTS
👔 LinkedIn now is broken with ghost jobs & 3rd party offshore agencies. A Redditor used AI to build advanced filters & scrape over 4.1M jobs directly. It's free to use here.
🛠 Andrew Ng, who built Google Brain, co-founded Coursera, showed 5 opportunities creating more millionaires than anything before. Here's what you should know.
🍽️ AI ASMR food challenges went viral again, creators pulled millions of views. All of it possible with BasedLabs AI (or maybe Higgsfield AI, we think). Here’re 7 examples.
📃 Anthropic released one of the best series on prompt engineering. It's literally everything you need to know with a comprehensive step-by-step course. Save it here.
🕸️ AI crawlers and fetchers from Meta and OpenAI are overwhelming websites with excessive traffic, with one bot hitting 39K requests/minute. Small sites are struggling.
🌐 YouTube secretly used AI to edit 20+ billion Shorts without telling creators. Only visible if you really look. But once noticed, it’s disturbing. Did yours change already?
💰 AI Daily Fundraising: FieldAI raised $405M to build a universal AI "robot brain" that works across all robot types. Backed by Bezos, Temasek, Intel, it adds physics for safer, smarter decisions.
AI SOURCES FROM AI FIRE
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AI QUICK HITS
📖 Grok 2.5 model is now open source. Grok 3 will be next in 6 months
🤝 Meta partnered with Midjourney on AI image and video models
🛑 OpenAI warned against SPVs & other ‘unauthorized’ investments
🎭 Aesthetic AI art became indistinguishable from real work on test
🐝 Why tiny bee brain movements could hold the key to smarter AI?
AI CHART
Why image models like DALL·E, Imagen, or Stable Diffusion feel creative? They were designed to mimic their training data, yet they improvise, sometimes eerily well.
→ It’s an inevitable by-product of their architecture.
In theory, diffusion models should just memorize images. In practice, they improvise, like blending patterns into novel, coherent outputs. The science behind it:
Diffusion models work by denoising → turning static back into an image.
Two quirks drive their creativity:
Locality → they focus on one patch of pixels at a time.
Equivariance → if you shift an input, they shift the output the same way.
These constraints mean models don’t recreate exact replicas, they assemble patches in unexpected ways.
Researchers built a math-driven “ELS machine” & tested it against trained diffusion models → 90% identical outputs.
AI’s apparent creativity is a deterministic outcome of its design. Human + AI creativity may share roots: both recombine what they’ve seen into something “new.” That might not be so different from how our brains work.
By forcing models to build the world piece by piece, we get originality “for free.”
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The AI Fire Team
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