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A site that posts the latest AI news, Deep Learning, and Data Science news on a daily basis. Unlike some other sites that post high level, academic presentations and large amounts of data, our site features articles for beginners and veteran researchers in the field. Our titles contain video lectures, labs, and in-depth analyses in order to help you stay on top of the most interesting research and developments in AI. We post one article every single day of the week, 24/7. We also post with a blog, and some resources articles to help you with other cool tools and technology. Our goal is to make this site as easy to navigate as possible.GPT-2
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The main aim of this project is to collect insights and experiences of professional developers working with AI technologies, in an effort to better understand the state of current AI technologies, current challenges, and upcoming trends in the field. This contribution is part of Microsoft’s IoT Revolution initiative to promote collaboration with groups that are leading the way in creating a connected world through automation and artificial intelligence (AI). Additionally, this contribution includes contributions from Autonomic computing, Cognitive systems, Cyber security, Datatech, Data science, Machine learning, Morisonnet, Neural networks and Vision.
The challenge will last for a full month and you will be given a set of constraints such as the type of problem you need to solve or the software you will be working with, as well as some parameters that you can tweak to come up with an solution that suits you best. This will be a short and sweet challenge that we hope you will enjoy!
Problem statement: Develop an AI that can understand, use, and explain a linked list of sentences. The AI should be able to compare different described actions (like “Add two numbers”) and decide how to add the numbers in a given order.
A node in a node network such as a child chain or nephew core might have a parent who has attributes such as “Intelligence” and “Wisdom.” A simple uncle core may have an attribute called “Mem or Knowledge” that is specified to say something about the memory of the node. A more advanced uncle core may have additional attributes, such as “Connections” that indicate how the uncle core is connected to other nodes in the network.
The desired output from the AI should be a node structure, which can be a vector, a set, a tree, or a and mapping (via the and operation). If the AI returns a and mapping, that means the AI was satisfied with the presented node structure and can cease all operations. Otherwise, the AI should call stopIteration and provide further input to allow the AI to improve on the provided input. For example, the node structure might be Smith@ABC and the output could be Smith+ABC, so the AI builds a connection between the two nodes called I_Smith and returns I_ABC.
The AI should be able to decide if two node structures are the same or different, for instance, if they have the same parent attribute and the same attribute on the same level. This is accomplished by first deciding if n*(n-1) equals 2*(n-2). If n equals 2, then there is no way to tell them apart. Continue until every node in the output is the same or different.
For every node structure, find a mapping from its name to its type, such as “Object” for a linked list node or “Constant” for a number. For instance, for the above linked list node SCALE_4_4_32, find the mapping for SCALE_4_4_32, then the mapping for 4, then 3, 2, and 1, respectively. For nodes with unknown attributes, determine if the node has any attributes at all, for instance, for the node mentioned above, I_SCALE_4_4_32, find I_SCALE, I_32, and I_32, respectively, then for every unknown node name, return I_ for I_not_I, stopIteration, and determine the type of node from the type system.
For every unknown attribute, determine the name of the unknown attribute, for instance, I_not_I for the unknown attribute I_not_I, stopIteration, determine the name of the attribute, for instance, attribute for attribute, stopIteration, determine if the attribute is present in the node structure, for instance, SCALE_4_4_32 for SCALE_4_4_32, stopIteration, and determine if the node has the unknown attribute, for instance, I_not_I for I_not_I, stopIteration, and determine for the I_not_I type system, stopIteration.
For every unknown node, determine if the node has any attributes, for instance, SCALE_4_4_32 for SCALE_4_4_32, stopIteration, and determine if the attribute is present in the node structure, for instance, SCALE_4_4_32 for SCALE_4_4_32, stopIteration, and determine if the node has the unknown attribute, for instance, for the I_not_I type system, stopIteration, and stopIteration determines if the unknown node has the desired output, for instance, SCALE for the desired output.
For every unknown node, determine if the node has any attributes, for instance, SCALE_4_4_32 for SCALE, stopIteration, and determine if the attribute is present in the node structure, for instance, SCALE_
Note: this text is taken from https://www.machinewrites.com/gpt-2-generated-artificial-intelligence-article-208/.