Artificial Intelligence, Machine Learning, Neural Networks and Deep Learning are currently very popular buzzwords and they are often used in the same context. However, there are clear differences between them. Thus, I would like to briefly refer to their definitions and associations.
The term Artificial Intelligence was coined in 1956 by Marvin Minsky, John McCarthy and other scientists during a workshop at Dartmouth College.
AI serves as an umbrella for a machine technology in order to provide intelligence and it covers a wide range of methods, logics, procedures and algorithms. This is a very indefinite description though since even the term “intelligence” is generally difficult to define.
As a rule, these are computer programs which imitate certain human “cognitive abilities”, e.g. the learning of connections or specific problem solutions. The AI research focus is on the neuro-linguistic programming (NLP), learning, knowledge processing and planning. In addition to computer science and mathematics, neurosciences, sociology, philosophy, communication science and linguistics are also involved in AI research.
Machine learning is a sub-discipline of artificial intelligence and refers to statistical techniques by which machines perform on the basis of learned interrelationships. On the basis of data gathered or collected, algorithms (a sequence of defined steps to achieve a goal) are independently “learned” by computers without being programmed by a human being. A variety of algorithms and methods are e.g. Support Vector Machines, Decision Tree, Random Forrest, Logistic Regression, Bayesian Networks, and-neural networks.
Neural networks are computer models for machine learning, which are based on the structure and functioning of the biological brain. An artificial neuron processes a plurality of input signals, and in turn, when the sum of the input signals exceeds a certain threshold value, it sends signals to further adjacent neurons.
In this case, a single neuron can only have very simple computations like classifications. If, however, a large number of neurons are connected in different architectures, more and more complex situations can be learned and corresponding operations can then be carried out.
Simple neural networks include an input layer to which the input signals (e.g., image information) are applied. An output layer that contains neurons responsible for different results and a hidden layer through which input and output layers are linked. If a network architecture contains more than one hidden layer then one speaks about Deep-Neural-Networks or…
Deep learning is the area of machine learning that will change our lives most over the next few years.
This is a neural network that has more than one hidden layer. Current architectures used in practice, comprise up to one hundred layers with tens of thousands of neurons. These networks can be used to solve very complex tasks, e.g. Image recognition, speech recognition, machine translation and, and, and…
In the following posts of this blog I will add more details regarding the specific topologies and their application areas…stay tuned.