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Using algorithms, neural networks can recognize hidden patterns and correlations in raw data and continuously learn and improve over time.

FREMONT, CA: Artificial neural networks are modeled on biological neural networks in the brain. The brain is created out of cells called neurons, which send signals to each other through connections. Neurons transmit electrical signals to other neurons, and they receive from other neurons. An artificial neuron simulates how a biological neuron behaves by adding together the values of the inputs it gets. Each of its inputs can be managed by multiplying it by some weighting factor.

Each neuron is connected to other neurons through these synaptic connections, whose values are quantified, and this weight values better the signals propagating through the network. The training process comprises adjusting these weight values so that the final output of the network offers the right answer. The easiest version of an artificial neural network has three layers of neurons.

Training with human-labeled data constitutes what is called supervised learning because humans supervise it. Supervised systems power today’s deep learning systems, and it is here that human biases in the pre-labeled data can prejudice the network. There are two types of machine learning. Unsupervised learning simply offers the network unlabeled data and asks it to find patterns and clusters in items independently. Humans come in after the fact to provide some names to the clusters the network has identified.

Unsupervised learning can be coupled with supervised learning to pre-train a network that is then trained with labeled data, significantly mitigating training time with supervised learning alone. The third category of machine learning is called reinforcement learning. Reinforcement learning is usually leveraged in games. Instead of being offered external data of winning and losing games, the system created this data by playing again and improving each time.

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