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#deeplearning

2 posts2 participants0 posts today
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Miguel Afonso Caetano

Gary Marcus is onto something in here. Maybe true AGI is not so impossible to reach after all. Just probably not in the near future but likely within 20 years.

"For all the efforts that OpenAI and other leaders of deep learning, such as Geoffrey Hinton and Yann LeCun, have put into running neurosymbolic AI, and me personally, down over the last decade, the cutting edge is finally, if quietly and without public acknowledgement, tilting towards neurosymbolic AI.

This essay explains what neurosymbolic AI is, why you should believe it, how deep learning advocates long fought against it, and how in 2025, OpenAI and xAI have accidentally vindicated it.

And it is about why, in 2025, neurosymbolic AI has emerged as the team to beat.

It is also an essay about sociology.

The essential premise of neurosymbolic AI is this: the two most common approaches to AI, neural networks and classical symbolic AI, have complementary strengths and weaknesses. Neural networks are good at learning but weak at generalization; symbolic systems are good at generalization, but not at learning."

garymarcus.substack.com/p/how-

Marcus on AI · How o3 and Grok 4 Accidentally Vindicated Neurosymbolic AIBy Gary Marcus
#AI#NeuralNetworks#DeepLearning
🧿🪬🍄🌈🎮💻🚲🥓🎃💀🏴🛻🇺🇸

#AI Identifies #FattyLiverDisease Using Chest #XRays

> Researchers trained the #deepLearning algorithm on 6,599 chest X-ray images from 4,414 patients, using controlled attenuation parameter scores as ground truth measures of #liver fat.

> This groundbreaking research opens up new avenues for early detection of this increasingly prevalent condition, which statistics suggest affects nearly one in four people globally.

bioengineer.org/ai-identifies-

bioengineer.org · AI Identifies Fatty Liver Disease Using Chest X-RaysBy Bioengineer
#artificialIntelligence#health#medicine
Continued thread
Petr Nuska

However, neither this method had a satisfactory data output that would suit the #RomChords project’s purposes. Even the state-of-the-art polyphonic detection algorithms (such as the one developed by Celemony Melodyne) produce a lot of noise, and cleaning the data to a standard usable for the project would be enormously laborious.

(Although it is possible that in a few years, progress in #DeepLearning will make this method much more effective and efficient).

#RomaniChords

🧵7/20

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