Facebook Shut Down An Artificial Intelligence Program That Developed Its Own Language

  Deep learning uses neural networks to learn tasks that contain one or more hidden layers.  What is the nature of deep learning?  Is deep learning predictable?  More importantly, what are the consequences of deep learning in autonomous machines?  The link below, about an experiment at Facebook that took some unexpected turns, is a very interesting article that feeds into perceptions on either the benevolent or malevolent of artificial intelligence (AI).  Implementing AI raises questions of whether machine learning should be supervised by humans, partially supervised, or be completely autonomous.

http://www.msn.com/en-us/news/technology/facebook-shut-down-an-artificial-intelligence-program-that-developed-its-own-language/ar-AAp1wzQ?li=AA4Zoy&ocid=spartanntp

The Annual Technology Vectors brief has been published by the AFCEA International Technology Committee

  The Armed Forces Communications & Electronics Association (AFCEA) International Technology Committee has released an update of its annual presentation on current technology trends.

The briefing provides insights and expertise on emerging technology hot topics most relevant to Federal technology leaders and why these technologies require further scrutiny.

The technology vectors are featured in a concise knowledge base format and includes points of contact for questions and additional information.

Vector topics include elements and sub-elements surrounding cloud computing, smart/additive manufacturing, big data analytics, Apache Hadoop & Apache NiFi, advanced cybersecurity, quantum computing, and mobility/wireless communications.

The advanced cybersecurity areas include cyber supply chain anti-counterfeit measures, light-weight encryption for use in IoT devices, micro-segmentation protection capabilities in data centers, and artificial intelligence (AI) insertion for machine-to-machine security.

Requests for downloads of the presentation can be made at:

http://www.afcea.org/signal/resources/linkreq.cfm?id=114