Fine. “Rat hole”? No.
Let’s cut to the chase. Oticon calls their new technology “Deep neural network”. It’s branded.
I’ll leave that to others to understand what that means. Me? I go down the “rat hole” of simple definition. In which case, as a descriptor, “deep neural network” tells me nothing objective about the new hearing aid. It’s nonsense. You agree!! we agree!
Sheesh! when did Webster’s and common parlance become a “rat hole”?
Here’s from Wikepdia:
In biology[edit]
Animated confocal micrograph of part of a biological neural network in a mouse’s striatum
Main article: Neural network (biology)
In the context of biology, a neural network is a population of biological neurons chemically connected to each other by synapses. A given neuron can be connected to hundreds of thousands of synapses.[1] Each neuron sends and receives electrochemical signals called action potentials to its connected neighbors. A neuron can serve an excitatory role, amplifying and propagating signals it receives, or an inhibitory role, suppressing signals instead.[1]
Populations of interconnected neurons that are smaller than neural networks are called neural circuits. Very large interconnected networks are called large scale brain networks, and many of these together form brains and nervous systems.
Signals generated by neural networks in the brain eventually travel through the nervous system and across neuromuscular junctions to muscle cells, where they cause contraction and thereby motion.[2]
In machine learning[edit]
Main article: Neural network (machine learning)
Schematic of a simple feedforward artificial neural network
In the context of machine learning, a neural network is an artificial mathematical model used to approximate nonlinear functions. While early artificial neural networks were physical machines,[3] today they are almost always implemented in software.
Neurons in an artificial neural network are usually arranged into layers, with information passing from the first layer (the input layer) through one or more intermediate layers (hidden layers) to the final layer (the output layer).[4] The “signal” input to each neuron is a number, specifically a linear combination of the outputs of the connected neurons in the previous layer. The signal each neuron outputs is calculated from this number, according to its activation function. The behavior of the network depends on the strengths (or weights) of the connections between neurons. A network is trained by modifying these weights through empirical risk minimization or backpropagation in order to fit some preexisting dataset.[5]
Neural networks are used to solve problems in artificial intelligence, and have thereby found applications in many disciplines, including predictive modeling, adaptive control, facial recognition, handwriting recognition, general game playing, and generative AI.
