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In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to solving the central artificial intelligence problem-making computers as intelligent as people, or strong AI.[1] To call a problem AI-complete reflects an attitude that it would not be solved by a simple algorithm.

An artificial neural network (ANN), usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase.

"Vicarious is developing machine learning software based on the computational principles of the human brain. Our first technology is a visual perception system that interprets the contents of photographs and videos in a manner similar to humans. Powering this technology is a new computational paradigm we call the Recursive Cortical Network ™"

"Loom is a language and environment for constructing intelligent applications. The heart of Loom is a knowledge representation system that is used to provide deductive support for the declarative portion of the Loom language. Declarative knowledge in Loom consists of definitions, rules, facts, and default rules. A deductive engine called a classifier utilizes forward-chaining, semantic unification and object-oriented truth maintainance technologies in order to compile the declarative knowledge into a network designed to efficiently support on-line deductive query processing."

my6sense's super duper AI (artificial intelligence) technology learns what is most important to you based on your consumption habits, serving you as a digital sense, or as we call it 'Digital Intuition'. There is nothing you need to do other than simply use the service like you would any other reader/aggregator. No preferences need to be set. No surveys to fill out. No blood samples needed. The m6s service 'senses' your interactions with every message; whether you click on it, ignore it or share it. Just use the service naturally, and the Digital Intuition magic will come your way – connecting you with your most important content on your iPhone.

Called the journal of record for the AI community, AI Magazine helps AAAI members stay abreast of significant new research and literature across the entire field of artificial intelligence. This quarterly publication features timely, feature-length articles, consistently crafted to be clear enough to permit specialists to review work outside their particular area of expertise.

The journal serves as a forum favoring the dissemination of interdependent and interdisciplinary research involved in information retrieval, database, human-machine communication and artificial intelligence. The recent developments in these fields call for cross-discipline collaborations and perspectives. The I3 journal intends to promote inter-disciplinarity working at the development of common terminology, concepts, typologies, methods and ontologies and aiming at the identification of a unified perspective on research issues. The journal is disseminated both as a traditional paper publication (published by Cépaduès http://www.cepadues.com/) and as an electronic journal on the web (http://www.journal-i3.org/).

The Oycib infrastructure is based in the ethnographic observation model based agents called e-Xploration. The infrastructure allows the analysis, interpretation and visualization of profiles and digital practices. Furthermore, we propose work in the context-awareness to enhance the collaboration and cooperation among people and groups.

"Scene recognition is one of the hallmark tasks of computer vision, allowing defining a context for object recognition. Here we introduce a new scene-centric database called Places, with 205 scene categories and 2.5 millions of images with a category label. Using convolutional neural network (CNN), we learn deep scene features for scene recognition tasks, and establish new state-of-the-art performances on scene-centric benchmarks. Here we provide the Places Database and the trained CNNs for academic research and education purposes."

"Collection of Interactive Machine Learning Examples We call them "seeds". Each seed is a machine learning example you can start playing with. Explore, learn and grow them into whatever you like. Powered by CoLab"

"How Montreal became the world's leading AI and deep learning hub"

'From Copernicus Big Data to Extreme Earth Analytics Rapidly increasing volumes of diverse data from distributed sources create challenges for extracting valuable knowledge and commercial value from data. This calls for novel methods, approaches and engineering paradigms in analytics and data management. As the success will require not only efficient data processing and management, but also sufficient computing capacity and connectivity, a coordinated action with all related areas is necessary and will contribute to a European leadership in these areas.'

"The future is multi-model Call the right model at the right time with the world's most powerful AI model router."

"We have entered a new era of artificial intelligence, where we can create intelligent avatars made from the digital profiles of our behaviors online. We call them Personal Artificial Intelligence, or PAI. Project PAI is founded on the belief that every person in the world should have their own digital avatar, one that speaks, thinks, and behaves just like their self."