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The next interface isn’t a screen — it’s neural. PL network team Precision Neuroscience is working at the intersection of neuroscience and digital interaction, exploring how neural signals can help people with neurodegenerative diseases.

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Protocol Labs

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"An exciting opportunity to make fundamental discoveries." 🌟Apply by Dec. 16: to The Shanahan Foundation Fellowship at the Interface of Data and Neuroscience. Experience in #neuroscience is not needed.
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"An exciting opportunity to make fundamental discoveries." 🌟Apply by Dec. 16: to The Shanahan Foundation Fellowship at the Interface of Data and Neuroscience. Experience in #neuroscience is not needed.

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The universal approximation theorem states that a neural network with one hidden layer can approximate continuous functions on compact sets with any desired precision.
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Meta Develops Non-Invasive Neural Interface for Next-Generation Interaction Meta Reality Labs is advancing wrist-based neural interface technology designed to enable more natural and intuitive interaction with future computing platforms. The non-invasive device leverages finger-tracking to interpret user intent. Meta’s CEO has indicated that the technology could reach commercialization within the next few years. Video source: Meta #Meta #VR #AR #NeuralWristband #tech ----------------------- Wanna get your company on Wevolver too? Learn how:

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The age of Neural Engineering is here... and Max Hodak's Science Corp is on the frontier: ‣ Curing blindness ‣ Merging mind & machine with BCIs ‣ Today, revealing a new kind of neural probe ‣ And exploring consciousness itself on Episode 65 of S3.

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With OpenAI, Figure 01 can now have full conversations with people -OpenAI models provide high-level visual and language intelligence -Figure neural networks deliver fast, low-level, dexterous robot actions Everything in this video is a neural network:
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With OpenAI, Figure 01 can now have full conversations with people -OpenAI models provide high-level visual and language intelligence -Figure neural networks deliver fast, low-level, dexterous robot actions Everything in this video is a neural network:

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We're building a surgical robot capable of reaching any brain region. The goal: a generalized neural interface to help solve any condition that originates in the brain.

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"In the future, you won't need any software at all. The neural network will completely replace it." - stephen balaban, co-founder of Lambda "Here's the idea: instead of generating a program, just go to ChatGPT and say, 'Hi, please behave like a calculator or a spreadsheet.'" "'Generate an ASCII user interface for me. And I want you to essentially just respond, implement the logic of that program in your mind.'" "This is what I call neural software. Normal software is really brittle. 'Neural software' can't have bugs, only misunderstandings." "Large language models will be the software you interact with."

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A Neural Network Can Grow New Neurons Where It Is Confused? In 1994, Bernd Fritzke published A Growing Neural Gas Network Learns Topologies. He introduced a network that starts small, follows incoming data, and inserts new neurons where its error is highest. In the animation, the fog is the drifting data. The glowing nodes are neurons. The fibers are learned connections. The network grows into a living skeleton of the manifold.

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Does GPT understand the world? Here is what Ilya Sutskever, co-founder of OpenAI, says during a discussion with Jensen Huang, CEO of Nvidia: (1) When we train a large neural network to accurately predict the next word in lots of different texts from the internet, the AI is learning a world model. (2) On the surface, it may look like learning correlations in text, but it turns out that to 'just learn' statistical correlations in text, to compress information really well, what the neural network learns is some representation of the process that produced the text. (3) This text is a projection of the world...what the neural network is learning is aspects of the world, of people, of the human conditions, their hopes, dreams, motivations, their interactions...the situations we are in. The neural network learns a compressed, abstract, usable representation." Do you think learning representations = understanding? Are large language models simply stochastic parrots, or are they much more?

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