NRN está construindo um ecossistema para acelerar o progresso em direção à Inteligência Geral Artificial (AGI) usando jogos e robótica como bancos de teste. A plataforma integra agentes de IA em experiências de jogo inovadoras e treina inteligência incorporada em simulação antes de implantar em robôs físicos.
Notícias
“We found that a single neuron can solve MNIST.” Konrad Kording's lab built a computational model of a single neuron that can recognize handwritten numbers — a hint that neurons can do more than we assumed. Full conversation on the @JuanBenet Podcast >>> https://t.co/uLosnelLsr https://t.
You're asking the right question of the wrong science. It really did help me to study Theory of Mind and Epigentics more than Neuroscience. Computational models are super helpful too. But yeah there are a lot of gaps/missing-layers. Now we do have full neuron maps of insects though.
I animated how a neural network learns, from one neuron to backpropagation, simple math, no calculus required. Best 8 minutes you can spend if it has only ever been a black box to you.
The entire core of a neural network on four cards. Neuron, forward pass, activations, backprop. Learn these four and you understand how every model from a perceptron to a transformer predicts and learns.
What can a neuron compute? Real biological neurons are complex, but how capable are they? Using a new method, we found that a single cortical neuron can classify cats vs dogs, recognize spoken words, and solve 10-bit parity, all tasks thought to require entire networks. (1/15)
RT @IdoAizenbud: What can a neuron compute? Real biological neurons are complex, but how capable are they? Using a new method, we found t…
The neuron. weighted sums, a bias, a nonlinear bend.
I would dispute that. If you simulated every single neuron of Jules Caesar precisely, Jules Caesar to the best of our understanding would be conscious. There's no law of physics that explain why the substrate would matter, and if consciousness goes beyond physics then the physical substrate definit
The neuron. weighted sums, a bias, a nonlinear bend.
A single neuron is not intelligent. A single transistor is not intelligent. Yet when billions of them interact and exchange signals according to simple rules, something quite remarkable emerges. We call that phenomenon intelligence.