ED Surge
NOVEMBER 5, 2024
Wuori’s arguments throughout the concise, 101-page book are premised on what he calls “The Three Simple Truths of Early Development”: Learning begins in utero and never stops. The period from prenatal to age 3 is a uniquely consequential window of human development during which the fundamental architecture of the brain is “wired.”
ED Surge
MAY 17, 2022
Medicine and nursing programs have been natural fits, but some in the humanities are experimenting too, such as in architecture and film. Researchers at the University of Michigan have developed a VR experience that lets students step into the virtual set of a final scene in the classic Orson Welles film “Citizen Kane.” “You
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STEM Education Guide
MARCH 4, 2023
In this article, we’re looking at both sides of the argument. After all, you can’t make an eye popping sculpture or build jaw dropping architecture without engineering and mathematics. Should the arts be included in STEM education? First, a bit of background on STEM education.
STEMe
MARCH 31, 2024
A neural network is a computer model based on the brain and nervous system, while a transformer model is a data architecture used to learn about language and other tasks. Because of the general skepticism, the argument about whether AI could have a negative or positive impact continues.
Stephen Wolfram
MARCH 5, 2024
of what’s now Wolfram Language —we were trying to develop algorithms to compute hundreds of mathematical special functions over very broad ranges of arguments. Perhaps even the architecture of the network can change. Probably it’s because neural nets capture the architectural essence of actual brains.
Stephen Wolfram
FEBRUARY 2, 2023
I think Yves Pomeau already had a theoretical argument for this, but as far as I was concerned, it was (at least at first) just a “next thing to try”.
Stephen Wolfram
NOVEMBER 10, 2021
And we can trace the argument for this to the Principle of Computational Equivalence. In the analogy of artificial neural networks, different networks will tend to have different “internal representations” because this depends not only on the network architecture, but also on the particular training data that the network has “experienced”.
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