The Computer Science Network

AI Magazine

The CSNET project, sponsored by the National Science Foundation, has as its goal the design and implementation of a computer communications network to provide services to computer science research groups in the United States. Experience with Arpanet has shown that access to a computer network can lead to significantly higher level of interaction between geographically dispersed researchers. In this regard, a goal of CSNET is to foster "universal connectivity', i.e., participation by every university, industrial, and government computer science research group in the United States. By December 1982, over eighty university, government, and industrial sites will be participating.


6 Helpful Machine Learning Tutorials and Courses to Grasp the Essentials

#artificialintelligence

Machine learning is the future of automation. Millions of tasks performed by humans on a daily basis will be eventually replaced by neural networks trained. Even now, machine learning algorithms shape your life. The job market is shifting to accommodate this new technology, and those who are capable of programming their own networks (or integrating with existing ones) are in high demand. There has never been a better time to dive into machine learning.



Capsule Networks: The New Deep Learning Network – Towards Data Science

#artificialintelligence

Convolutional Networks have been hugely successful in the field of deep learning and they are the primary reason why deep learning is so popular right now! They have been very successful, but they have drawbacks in their basic architecture, causing them to not work very well for some tasks. CNN's detect features in images and learn how to recognize objects with this information. Layers near the start detecting really simple features like edges and layers that are deeper can detect more complex features like eyes, noses, or an entire face. It then uses all of these features which it has learned, to make a final prediction.


[Report] Network science on belief system dynamics under logic constraints

Science

People tend to structure their beliefs in a way that appears consistent to them. But how do some beliefs within groups persist in the face of social pressure, whereas others change and, by changing, influence a cascade of other beliefs? Friedkin et al. developed a model that can describe complexes of attitudes in a group that interact and change (see the Perspective by Butts). Their model revealed how the changing views of the U.S. population on the existence of weapons of mass destruction in Iraq changed their views on whether the invasion by the United States was justified.