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Scaling in Deep and Shallow Learning Architectures

New scaling law demonstrates how AI copes with changing categories

How can AI cope with changing categories?

Advanced confidence methods in deep learning

Bar-Ilan physicists engineer AI to increase confidence in autonomous vehicles

Bar-Ilan physicists engineer AI to increase confidence in autonomous vehicles

Towards a universal mechanism for successful deep learning

How can the brain compete with AI?

How can the brain compete with AI?
 

Efficient shallow learning mechanism as an alternative to deep learning

Scientists show how shallow learning mechanism used by the brain can compete with deep learning

Outsmarting AI: Brain’s Wide, Shallow Learning Redefines Efficiency

How Can the Human Brain Compete With Artificial Intelligence?

Wissenschaftler zeigen, wie der vom Gehirn genutzte flache Lernmechanismus mit tiefem Lernen konkurrieren kann
 

Enhancing the accuracies by performing pooling decisions adjacent to the output layer

Better paths yield better AI

Better paths yield better AI

Efficient shallow learning as an alternative to deep learning

Is Deep Learning a necessary ingredient for Artificial Intelligence?

Learning on tree architectures outperforms a convolutional feedforward network

Is brain learning weaker than artificial Intelligence?

Building a New Type of Efficient Artificial Intelligence Inspired
 

Brain inspired neuronal silencing mechanism to enable reliable sequence identification

Unreliable neurons improve brain functionalities

Unreliable neurons improve brain functionalities

Unreliable neurons improve brain functionalities

Unreliable neurons improve brain functionalities

Unreliable neurons improve brain functionalities

Unreliable neurons improve brain functionalities
 

Long anisotropic absolute refractory periods with rapid rise times to reliable responsiveness

Century-old assumption regarding neurons and brain activity disproved

Power-law scaling to assist with key challenges in artificial intelligence

Physics can assist with key challenges in artificial intelligence

A minority of self-organizing autonomous vehicles significantly increase freeway traffic flow

https://www.youtube.com/watch?v=fkju7WfX8sQ&feature=youtu.be

Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms

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Embedding information in physically generated random bit sequences while maintaining certified randomness

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Biological learning curves outperform existing ones in artificial intelligence algorithms

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Less Might Be More: Conduction Failure as a Factor Possibly Limiting the Efficacy of Higher Frequencies in rTMS Protocols

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Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links

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New Types of Experiments Reveal that a Neuron Functions as Multiple Independent Threshold Units

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Press articles on previous work

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