PUBLIC MARKS with search autonomously

A long-term goal of Machine Learning research is to solve highly complex "intelligent" tasks, such as visual perception auditory perception, and language understanding. To reach that goal, the ML community must solve two problems: the Deep Learning Problem, and the Partition Function Problem. There is considerable theoretical and empirical evidence that complex tasks, such as invariant object recognition in vision, require "deep" architectures, composed of multiple layers of trainable non-linear modules. The Deep Learning Problem is related to the difficulty of training such deep architectures. Several methods have recently been proposed to train (or pre-train) deep architectures in an unsupervised fashion. Each layer of the deep architecture is composed of an encoder which computes a feature vector from the input, and a decoder which reconstructs the input from the features. A large number of such layers can be stacked and trained sequentially, thereby learning a deep hierarchy of features with increasing levels of abstraction. The training of each layer can be seen as shaping an energy landscape with low valleys around the training samples and high plateaus everywhere else. Forming these high plateaus constitute the so-called Partition Function problem. A particular class of methods for deep energy-based unsupervised learning will be described that solves the Partition Function problem by imposing sparsity constraints on the features. The method can learn multiple levels of sparse and overcomplete representations of data. When applied to natural image patches, the method produces hierarchies of filters similar to those found in the mammalian visual cortex. An application to category-level object recognition with invariance to pose and illumination will be described (with a live demo). Another application to vision-based navigation for off-road mobile robots will be described (with videos). The system autonomously learns to discriminate obstacles from traversable areas at long range.

Netsukuku is a mesh network or a P2P net system that generates and sustains itself autonomously. It is designed to handle an unlimited number of nodes with minimal CPU and memory resources. Thanks to this feature it can be easily used to build a worldwide distributed, anonymous and anarchical network, separated from the Internet, without the support of any servers, ISPs or authority controls. Keep in mind that it is a _physical network_, it isn't built upon any other existing net, therefore there must be computers linked _physically_ each other, then Netsukuku will build the routes.

Kliper is also being designed to operate completely autonomously, without the need for pilot control. "It's got to be capable of automatic flight," Thirkettle says. Some on the team hope Kliper would be able to travel to the Moon, "but I think there's a little bit of science fiction in that", he says.