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d-18686House OversightOther

Technical discussion of neural networks and quantum brain theory, no actionable political or financial leads

The passage is purely scientific exposition about AI and quantum brain hypotheses, containing no names, transactions, dates, or allegations involving powerful actors. It offers no investigative value. Describes how artificial neural networks are trained via backpropagation. Mentions Stuart Hameroff's quantum consciousness theory involving tubulin. Contains no references to officials, agencies, fin

Date
November 11, 2025
Source
House Oversight
Reference
House Oversight #015812
Pages
1
Persons
0
Integrity
No Hash Available

Summary

The passage is purely scientific exposition about AI and quantum brain hypotheses, containing no names, transactions, dates, or allegations involving powerful actors. It offers no investigative value. Describes how artificial neural networks are trained via backpropagation. Mentions Stuart Hameroff's quantum consciousness theory involving tubulin. Contains no references to officials, agencies, fin

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quantum-consciousnessneurosciencehouse-oversightartificial-intelligence

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Text extracted via OCR from the original document. May contain errors from the scanning process.
122 Are the Androids Dreaming Yet? inhibitors and activators like in real synapses. An individual neuron will fire if the sum of all the connections multiplied by the weights reaches a certain pre-determined threshold. A neural network does not run a program in the conventional sense, and must be trained through experience rather like a human brain. The training process allows the weights in the network table to be adjusted to give the correct result. But, unlike the brain, you can read the weights and even save them to a disk. The neural network tables start with random settings. You show the network the letter ‘A’ and adjust the weights in the tables until it gives a positive answer: ‘It’s an A. Repeat the process with the other letters until the network correctly distinguishes them. As you do this a computer algorithm constantly adjusts the weighting tables using a method called ‘back propagation. At the end of the training process you can show the network some new input and see how it does. For example, a letter ‘A that is in a slightly different font to anything in the training set. Trained neural networks can perform complex tasks such as recognizing faces or making clinical diagnoses, and they can be allowed to modify their weighting tables as they work so they learn from experience in a similar way to a human brain. Strong AI proponents believe making a thinking machine is just a matter of building a really large, fast neural network and working out how to train it efficiently. Quantum Brains Conventional wisdom says each brain cell is a single processing unit making an on-off decision — fire, or don't fire - depending on the state of its neighbors. But, Stuart Hameroff, Professor of Anesthesiology at the University of Arizona, thinks neurons are not the fundamental information-processing unit in the brain. He suggests that this accolade should go to tubulin. Tubulin is a small, versatile protein that self- assembles into filaments rather like the way buckyballs - a magnetic children’s toy — can be arranged. There are two types of tubulin molecule: a and B. They slot together and wrap around to form a micro tube about 25nm in diameter. Tubulin micro tubes do several important things in the body. They form the skeleton of neurons and give them structure. They are involved in guiding neurons as they grow towards each other to form new connections, and they also operate in the nucleus of a cell to unzip

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