Neural Networks
![](/content/images/size/w1384/2023/09/kentlangley_neural_network_learning_like_a_child_e6ecf1f2-6671-4ff4-9a60-eafaa35f0f85.png)
Comparing Learning Mechanisms: Neural Networks vs. Human Children Paid Members Public
Can machines really learn as humans do? Large language models come close - soaking up data and recognizing patterns. But children have an edge - curiosity, emotion, social intelligence. As AI advances, understanding these cognitive parallels is key to the future.
![](/content/images/size/w1384/2022/03/female-programmer-scanning-her-face-with-biometric-security-technology-virtual-screen-digital-remix.jpg)
Future of Neural Network Technology Paid Members Public
Deep neural networks, or more broadly, learning models with deep embeddings, enable a wide range of applications on various levels: from biomedical data to language modeling.
![](/content/images/size/w1384/2022/03/BCI-Feb-22-Cover-Pic.png)
Accelerating The Journey Into The Brain Paid Members Public
They send and detect signals a million times smaller than those controlling your smartphone. AI software filters millions of seemingly random strains of neural chatter to discern chords of neural activity.
![](/content/images/size/w1384/2021/12/jj-ying-8bghKxNU1j0-unsplash.jpeg)
A Linear Brain in a Non-linear World: How to Reverse your Thinking with Neuroscience to Challenge, Experiment, and Explore Paid Members Public
I will try to answer this question by exploring major differences between linear and non-linear thinking and why humans are tuned to think linearly from a cognitive and probably neurophysiological perspective.