We return to the question of terminology that we started this post with. Our feeling is that the term "artificial intelligence" has been used in so many ways that it is now confusing. People use AI to refer to all three approaches described above, plus others, and therefore has become almost meaningless. The term "machine learning" is a more narrowly defined term for machines that learn from data, including simple neural models such as ANNs and Deep Learning. We use the term "machine intelligence" to refer to machines that learn but are aligned with the Biological Neural Network approach. Although there still is much work ahead of us, we believe the Biological Neural Network approach is the fastest and most direct path to truly intelligent machines. This blog entry was modified on Thu Mar 24 2016 to clarify the timing of neural network research.
The majority of us know our age thanks to the birthday we celebrate each and every year. Yet there are poor orphans around the world who have their birth date shrouded in a mystery. Some of them would love to know when they were born in the first place. A new form of blood test may predict with 100% accuracy what the blood donor's chronological age is. AI is being used to test blood and find out the age of the patients who have had samples of their blood taken for analysis.
Magnetoencephalography (MEG) is a functional neuroimaging modality that records the magnetic fields induced by neuronal activity. It provides better temporal resolution than fMRI and is less affected by noise from intervening tissues than EEG. We propose a data driven, fully automated approach that extracts statistically independent MEG components and a convolutional neural network to discriminate the artifactual components from neuronal ones, without tedious manual labeling. Our custom, 10-layer Convolutional Neural Network (CNN) directly labels eye-blink artifacts. The spatial features the CNN learns are visualized using attention mapping, to reveal what it has learned and bolster confidence in the method's ability to generalize to unseen data.
In this video we build on last week Multilayer perceptrons to allow for more flexibility in the architecture! However, we need to be careful about the layer of abstraction we put in place in order to facilitate the work of the user who want to simply fit and predict. Here we make use of the following three concept: Network, Layer and Neuron. These three components will be composed together to make a fully connected feedforward neural network neural network. For those who don't know a fully connected feedforward neural network is defined as follows (From Wikipedia): "A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from its descendant: recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network."
This is a standard machine learning algorithm that uses supervised learning methods for classification, regression, and detection of outliers. They are used for protein classification, image segmentation and text categorization. A group of deep learning algorithms inspired by the nervous system. More precisely, they are inspired by a neuron organization in animal brains. They consist of units – artificial neurons that receive a signal from the previous layer, process it and send it to the next layer.