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I have an idea for an RNN which has no separate internal memory state only an output.
But there is a gate in which tells the neural network whether the output will be acted out in the physical world or it will be an internal thought.
think this would be quite realistic because human's either talk or think in an internal monologue, but don't do both.
think there would be social pressure involved when talking to someone to keep the gate open.
EmoNet: Emotional neural network automatically categorises feelings
A neural network called EmoNet has been designed to automatically categorise the feelings of an individual.
‘Craving,’ ‘sexual desire,’ and ‘horror’ were able to be determined with a high confidence, while the AI struggled more with ‘confusion,’ ‘awe,’ and ‘surprise’ which were considered more abstract emotions.
Potential applications for such technology is endless, but it’s easy to imagine it one day being of particular benefit in helping to detect mental health conditions and offer a potentially life-saving intervention.
A separate paper that was published earlier this month claimed that emotional recognition by AI can’t be trusted, based on a review of 1,000 other studies.
Neural network, a computer program that operates in a manner inspired by the natural neural network in the brain.
If the total of all the weighted inputs received by a particular neuron surpasses a certain threshold value, the neuron will send a signal to each neuron to which it is connected in the next layer.
First, a network can be equipped with a feedback mechanism, known as a back-propagation algorithm, that enables it to adjust the connection weights back through the network, training it in response to representative examples.
Second, recurrent neural networks can be developed, involving signals that proceed in both directions as well as within and between layers, and these networks are capable of vastly more complicated patterns of association.
(In fact, for large networks it can be extremely difficult to follow exactly how an output was determined.) Training neural networks typically involves supervised learning, where each training example contains the values of both the input data and the desired output.
In contrast, certain neural networks are trained through unsupervised learning, in which a network is presented with a collection of input data and given the goal of discovering patterns—without being told what specifically to look for.
- On 14. april 2021
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