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Artificial Intelligence - Research Areas
The domain of artificial intelligence is huge in breadth and width.
While proceeding, we consider the broadly common and prospering research areas in the domain of AI −
Later, it turned out that the machine requires more knowledge, complex knowledge representation, and complicated algorithms for handling mundane tasks.
This is the reason why AI work is more prospering in the Expert Tasks domain now, as the expert task domain needs expert knowledge without common sense, which can be easier to represent and handle.
JAIR is published byAI Access Foundation,a nonprofit public charity whose purpose is to facilitate the dissemination of scientific results in artificial intelligence.
How artificial intelligence is changing wildlife research
Machine learning will not only usher in the automation of monitoring endangered species from camera trap data but also the automation of spotting poachers, something Morris describes as being right on the cusp of practicality: “You can’t put people everywhere but you can put cameras in a lot of places.” The potential for computer vision to help the planet encompasses everything from analyzing aerial imagery in arctic and savannah landscapes to monitor large animals to tracking forest recovery and loss from satellite imagery, to even monitoring plastic pollution using AI drones.
Dear Colleague Letter: EArly-concept Grants for Exploratory Research on Artificial Intelligence (AI) and Society - Supported Jointly with the Partnership on AI
November 15, 2018 Dear Colleagues: The National Science Foundation's (NSF) Directorates for Computer and Information Science and Engineering (CISE) and Social, Behavioral and Economic Sciences (SBE) together with the Partnership on AI (PAI) wish to notify the community of their interest in supporting EArly-concept Grants for Exploratory Research (EAGERs) to understand the social challenges arising from AI technology and enable scientific contributions to overcome them.
The last 20 years have seen rapid advances in machine learning, pattern recognition, planning, effective decision making, natural language processing, and machine vision.
They are yielding increasingly diverse and large-scale applications deployed in settings subject to unanticipated challenges with complex social effects.
With increases in the scale and diversity of deployments comes the need to better understand AI in the open world, including unforeseen circumstances and social impacts, and to craft approaches to AI that consider these from the start.
Vital directions include developing principles for safe, robust, and trustworthy AI (including shared responsibilities between humans and AI systems);
That roadmapping activity aims to build community consensus around grand challenges in AI and an associated long-term and interdisciplinary research agenda.
- On Saturday, April 4, 2020
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