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Listen to Brutal Death Metal Made by a Neural Network
In a project called “Relentless Doppelganger,” a neural network is grinding out the blast beats, super-distorted guitars, and bellowing vocals of death metal.
The best part of all: it’s streaming its brutal creations 24 hours a day on YouTube — an intriguing and public example of AI that’s now able to generate convincing imitations of human art.
The death metal project, which they trained using tracks by death metal band Archspire, is the first that they’ve livestreamed instead of releasing as an album, and the change in format had everything to do with the quality of the neural network’s output.
Building Your First Neural Network
(Artificial) Neural Networks, so called because of their vague similarity to neural networks in the brain, are connections of artificial neurons.
Further, if the reader wants to run the models on their own (not a requirement, can follow along either way) we assume the user has properly installed all the needed libraries and is an intermediate Python user.
The model we seek to implement looks roughly like this: As you can see, this is a network with an input layer, a single hidden layer, and an output layer.
Instead of using 3 input neurons we use 4, instead of using 4 neurons in the hidden layer we use 8, and instead of using 2 output neurons we use 3.
In the Iris data set, this may mean we choose to use all of the data set’s popular features and include sepal length, sepal width, petal length, and petal width.
The number of neurons in the hidden layer(s) also will involve trial and error but there are some popular cheat sheets floating around for choosing number of neurons in a hidden layer, such as the following: where Nh is the number of neurons in the hidden layer, Ns is the number of samples in the training data, alpha is the scaling factor, Ni is the number of input neurons, and No is the number of output neurons.
This is a very baseline approach and the following article would be useful for a deeper dive on choosing number of layers and neurons in each layer, it’s a bit more nuanced than our broad example above.
The advanced user should employ more sophisticated methods for picking the number of neurons in the hidden layer: https://towardsdatascience.com/beginners-ask-how-many-hidden-layers-neurons-to-use-in-artificial-neural-networks-51466afa0d3e For the output layer, the number of neurons will vary based on our desired outcome.
This is obviously very powerful because it enables many of the complex machine learning and deep learning problems we are able to solve through multiclass classification.
The shape of the ReLU function looks like this: For a deeper dive into activation functions and to empower your choice each time you build your networks check out this great article.
input layer, 1 hidden layer, 1 output layer, 4 input neurons for the 4 features, 8 neurons in the hidden layer corresponding to the number of input neurons, output neurons, and size of the data set.
We first pick an arbitrary amount of epochs and set verbose to 1 as this allows us to see if our chosen number of epochs is too high, causing us to waste excess time training.
Two rival AI approaches combine to let machines learn about the world like a child
Over the decades since the inception of artificial intelligence, research in the field has fallen into two main camps.
The team, led by Josh Tenenbaum, a professor at MIT’s Center for Brains, Minds, and Machines, created a computer program called a neuro-symbolic concept learner (NS-CL) that learns about the world (albeit a simplified version) just as a child might—by looking around and talking.
Another neural network is trained on a series of text-based question-answer pairs about the scene, such as “Q: What’s the color of the sphere?” “A: Red.” This network learns to map the natural language questions to a simple program that can be run on a scene to produce an answer.
The NS-CL system is also programed to understand symbolic concepts in text such as “objects,” “object attributes,” and “spatial relationship.” That knowledge helps NS-CL answer new questions about a different scene—a type of feat that is far more challenging using a connectionist approach alone.
Itispossible to train just a neural network to answer questions about a scene by feeding in millions of examples as training data.But a human child doesn’t require such a vast amount of data in order to grasp what a new object is or how it relates to other objects.
- On 5. juli 2020
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