NRN is building an ecosystem that accelerates progress toward AGI (Artificial General Intelligence), using gaming and robotics as the sandbox. NRN Agents is a platform that powers AI agent integration in innovative gaming experiences in virtual and physical environments. The tech stack combines data aggregation, model training, and model inspection capabilities across imitation learning and reinforcement learning. Gaming and robotics provide the perfect testbed to tackle the challenge of achieving AGI. Games mirror the complexity of the real world, featuring dynamic rules and limitless variability that push AI to think beyond static tasks. Similarly, robotics extends these virtual learning environments into physical reality, training agents first in simulations before deploying them in the real world. This bridges the gap between virtual and physical, incorporating real-world complexities, variability, and constraints directly into AI training. The intersection of AI, gaming and robotics is more than entertainment—it’s a strategic approach to achieving AGI. AI agents trained in these environments won’t just master games; they’ll reason, adapt, and ultimately solve real-world challenges. The NRN Agents ecosystem is powered by the Neuron token $NRN.
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“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.