Mastodawn

The world’s first “biological computer” that fuses human brain cells with silicon hardware to form fluid neural networks has been commercially launched. #frightening

#biologicalcomputer #Neuralink #humancomputers #ai #australia #CorticalLabs #cl1 #sbi #synthetic_biological_intelligence #llm #neuron #neuronalnetworks

https://newatlas.com/brain/cortical-bioengineered-intelligence/

World's first "Synthetic Biological Intelligence" runs on living human cells

The world's first "biological computer" that fuses human brain cells with silicon hardware to form fluid neural networks has been commercially launched, ushering in a new age of AI technology. The CL1, from Australian company Cortical Labs, offers a whole new kind of computing intelligence – one…

New Atlas

fatuus Mar 2

#Meta announcing being able to 'read thoughts'

https://youtu.be/ko9IOF3o-qk?si=OBnOK9YK7h-j5DbD

What could go wrong ? 😁

#facebook #ai #NeuronalNetworks #whatcouldgowrong #whatcouldpossiblygowrong

They Did It: Meta Just Announced an AI That Reads Thoughts—The World Will Never Be the Same!

YouTube

cr0n0s:~🐧📡⌨️ 🛠️ #Nov 27, 2024

[2024] Cibercriminales con IA

- Deepfakes: LipSync & FaceSwapping
- DeepNudes & DeepFakePorn
- Clonned Voices : Biometry & Scams
- Meta Human

https://www.youtube.com/watch?v=bBAAjNhUGIk

#machinelearning #bigdata #ia #amazon #google #NeuronalNetworks #tiktok #tesla #Microsoft

[2024] Cibercriminales con IA

YouTube

Fabrizio Musacchio Dec 8, 2023

With the success of #DeepNeuralNetworks in building #AI systems, one might wonder if #Bayesian models are no longer significant. New paper by Thomas Griffiths and colleagues argues the opposite: these approaches complement each other, creating new opportunities to use #Bayes to understand intelligent machines 🤖

📔 "Bayes in the age of intelligent machines", Griffiths et al. (2023)
🌍 https://arxiv.org/abs/2311.10206

#DNN #NeuronalNetworks

Bayes in the age of intelligent machines

The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case, and that in fact these systems offer new opportunities for Bayesian modeling. Specifically, we argue that Bayesian models of cognition and artificial neural networks lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, where a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.

arXiv.org