From Newsgroup: sci.physics

Hi,

Looks like the Causal AI viewpoint on neural
networks makes people comfortable. Some
work from Pittsburgh:

Causal Representation Learning and Generative AI
by Dr Kun Zhang #CausalNeSyAI
https://www.youtube.com/watch?v=UwcitNXphog

Kathleen M. Carley: Understanding Influence
A Network Science + AI Approach rCo IC2S2 2025 Keynote https://www.youtube.com/watch?v=iHjFXtIeXnw

So Gen Alpha will grow up with Causal AI,
while Gen Z just botched Scryer Prolog, its
definitively a dead horse now?

Bye

Mild Shock schrieb:

Hi,

Geoffrey E. Hinton, the Nobel Prize winner
for AI. He was already beating the drums
for ReLU in 2010:

HRectified Linear Units Improve Restricted Boltzmann Machines
Geoffrey E. Hinton & Vinod Nair - 2010 https://www.cs.toronto.edu/~fritz/absps/reluICML.pdf

Because ANNs (Artificial Neural Networks) were originally
designed with other functions, e.g. with Logistic function:

An artificial neuron is a mathematical function conceived
as a model of a biological neuron in a neural network. https://en.wikipedia.org/wiki/Artificial_neuron

If you populate additive factor graphs with log P,
you basically get multiplicative factor graphs.
So an ANN can express belief networks, right?

Bye

P.S.: What is all the hype about Causal AI, and
the Ladder of Causation |a la Judea Pearl?

Causal AI rCo the next gen AI
Prof. Sotirios A. Tsaftaris - 2025 https://www.youtube.com/watch?v=IelslFzdsYw

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