GraphCore makes process visible
It can be difficult for those interested in the evolution of Artificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. but don’t have a background in related fields to wrap their minds around the abstract concepts surrounding it. Terms like convolutional Neural Networks are simplified abstract models of the human brain. Usually they have different layers and many nodes. Each layer receives input on which it carries out simple computations, and passes on the result to the next layer, by the final layer the answer to whatever problem will be produced. , Bayesian networks and Markov chains sound like almost esoteric-sounding ideas, but these are some of the machine techniques being used today for many useful applications we are beginning to take for granted, such as image and recognition, medical diagnostics and predictive text generation. But this obtuseness gets a little clearer when one is able to literally see the ‘big picture’ of how these An algorithm is a fixed set of instructions for a computer. It can be very simple like "as long as the incoming number is smaller than 10, print "Hello World!". It can also be very complicated such as the algorithms behind self-driving cars. work from a visual point of view. Using a new processor technology designed for artificially intelligent systems, Bristol-based startup Graphcore used its Intelligent Processing Unit (IPU) to create these stunning images of what the algorithms in a machine model look like when they are in action.
Colorful graphs show magical
“Unlike a scalar CPU or a vector GPU, the Graphcore Intelligent Processing Unit (IPU) is a graph processor,” explained the company in a blog post . “A computer that is designed to manipulate graphs is the ideal target for the computational graph models that are created by machine frameworks.” These false-color images we see here are actually computational graphs. In mathematics, graphs are data structures that show the relationships between vertices, nodes, points as connected by edges, arcs and lines, much like how a diagrammatic map of a human brain and the interconnections between its neurons and synapses might look. In this case, these computational graphs, mapped to an IPU, allow the essence of these models to be glimpsed at a glance, showing a complexity in the connections that are reminiscent of the scans of a human brain, perhaps even recalling a microscopic view of some strange cellular or amoeboid structure […]