By today computer can outperform humans in a lot of tasks. Especially within the field of well described mathematical problems even a standard calculator can solve problems in fractions of the time a human would need. Nevertheless, there are many everyday situations which can be better handled by humans compared to technology. A perfect example is represented by the task to recognize a face.
In order to approach the ability a human shows with regard to tasks such as face recognition researcher put intensive work into the field of artificial neural networks (ANN) which is a sub-sector of artificial intelligence (AI).
The following is determined to give a short introduction of how the human brain functions as it is the foundation of all research towards artificial neural networks.
The brain consists out of a network built of approximately 100 billion neurons. Every neuron has an internal or membrane potential which changes with incoming signals from other neurons. At the time a certain threshold value inside the neuron is reached it sends out a spike. A spike is a short and sudden increase in voltage. Thereafter the spike spreads through the axon towards synapses which build the connection to following neurons. A single neuron is connected to roughly 10,000 synapses that influence the internal potential and therefore the firing of a spike. Using the mentioned setup, the human brain is capable of solving visual tasks in very short time and remarkably energy efficient.
Having the human brain as model, ANN have the potential to disrupt almost every industry. According to KC Cheug (2018) ANN creates algorithms that can be utilized in modeling complex patterns and, in turn, helps the user in the decision-making process.
I am convinced that ANN will be one of the leading technologies in the upcoming future as it taps into fields which were yet not approachable for technology, namely assessing feelings, interpreting gestures etc..
S. Herculano-Houzel. “The Human Brain in Numbers: A Linearly Scaled-up Primate Brain.” In: Frontiers in Human Neuroscience 3 (Nov. 2009), p. 31. issn: 662-5161. doi: 10.3389/neuro.09.031.2009. url: http://www.ncbi.nlm.nih. gov/pmc/articles/PMC2776484/.
I. Perrett, E. T. Rolls, and W. Caan. “Visual neurones responsive to faces in the monkey temporal cortex.” In: Experimental brain research 47.3 (1982), pp. 329–342.
