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Advancing Artificial Intelligence Research - Liwaiwai

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As part of a new collaboration to advance and support AI research, the MIT Stephen A. Schwarzman College of Computing and the Defense Science and Technology Agency in Singapore are awarding funding to 13 projects led by researchers within the college that target one or more of the following themes: trustworthy AI, enhancing human cognition in complex environments, and AI for everyone. The 13 research projects selected are highlighted below. Emerging machine learning technology has the potential to significantly help with and even fully automate many tasks that have confidently been entrusted only to humans so far. Leveraging recent advances in realistic graphics rendering, data modeling, and inference, Madry's team is building a radically new toolbox to fuel streamlined development and deployment of trustworthy machine learning solutions. In natural language technologies, most languages in the world are not richly annotated.


Advancing artificial intelligence research

#artificialintelligence

The broad applicability of artificial intelligence in today's society necessitates the need to develop and deploy technologies that can build trust in emerging areas, counter asymmetric threats, and adapt to the ever-changing needs of complex environments. As part of a new collaboration to advance and support AI research, the MIT Stephen A. Schwarzman College of Computing and the Defense Science and Technology Agency in Singapore are awarding funding to 13 projects led by researchers within the college that target one or more of the following themes: trustworthy AI, enhancing human cognition in complex environments, and AI for everyone. The 13 research projects selected are highlighted below. Emerging machine learning technology has the potential to significantly help with and even fully automate many tasks that have confidently been entrusted only to humans so far. Leveraging recent advances in realistic graphics rendering, data modeling, and inference, Madry's team is building a radically new toolbox to fuel streamlined development and deployment of trustworthy machine learning solutions.


Advancing artificial intelligence research

#artificialintelligence

The broad applicability of artificial intelligence in today's society necessitates the need to develop and deploy technologies that can build trust in emerging areas, counter asymmetric threats, and adapt to the ever-changing needs of complex environments. As part of a new collaboration to advance and support AI research, the MIT Stephen A. Schwarzman College of Computing and the Defense Science and Technology Agency in Singapore are awarding funding to 13 projects led by researchers within the college that target one or more of the following themes: trustworthy AI, enhancing human cognition in complex environments, and AI for everyone. The 13 research projects selected are highlighted below. Emerging machine learning technology has the potential to significantly help with and even fully automate many tasks that have confidently been entrusted only to humans so far. Leveraging recent advances in realistic graphics rendering, data modeling, and inference, Madry's team is building a radically new toolbox to fuel streamlined development and deployment of trustworthy machine learning solutions.


HAMLET: A platform to simplify AI research and development

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Machine learning (ML) algorithms have proved to be highly valuable computational tools for tackling a variety of real-world problems, including image, audio and text classification tasks. Computer scientists worldwide are developing more of these algorithms every day; thus, keeping track of them and quickly finding or accessing those introduced in the past is becoming increasingly challenging. With this in mind, researchers at Purdue University and University of Cincinnati recently created HAMLET, a platform that could help computer scientists and developers to browse through existing machine learning models and train or evaluate their own algorithms, thus aiding their research and development efforts. This platform, presented in a paper pre-published on arXiv, could ultimately democratize machine learning models developed around the world, allowing research teams to share their models with each other. "Organizing and keeping track of the machine learning algorithms and datasets has always been a major challenge for us, as well for as many other researchers in the field," Ahmad Esmaeili, one of the researchers who carried out the study, told TechXplore.


Artificial Intelligence Drives the Archaeological Discoveries of Stone Age

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Machine learning model and Neural Networks helps in extracting archaic information about human civilization. Archaeology is the gateway to our past. It describes events which shaped the world how it is today and the transition that led humans from animal-hunter to a knowledgeable-mosaic. In archaeology, Stone Age holds the key relevance. It establishes the patterns of human behavior and helps in identifying the transitions that hurled humans to the path of development.


2021 Trends in Artificial Intelligence and Machine Learning: The ModelOps Movement - insideBIGDATA

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Everything Artificial Intelligence has ever been, hopes to be, or currently is to the enterprise has been encapsulated in a single emergent concept, a hybrid term, simultaneously detailing exactly where it is today, and just where it's headed in the coming year. The ModelOps notion is so emblematic of AI because it gives credence to its full breadth (from machine learning to its knowledge base), which Gartner indicates involves rules, agents, knowledge graphs, and more. ModelOps is about more than simply operationalizing and governing AI models. Moreover, it involves doing so onsite while leveraging the advantages of the cloud and, when it comes to AI's machine learning prowess, with a range of approaches rooted in supervised, unsupervised, and even reinforcement learning. Implicit to these capabilities is the need to position machine learning models at the edge, supersede their traditional training data limitations (and methods), and imbibe everything from streaming to static data for a predictive exactness based on the most current data possible.


Animal Cognition Induces Common Sense in Artificial Intelligence Agents

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Reinforcement learning models are trained, using a similar concept by animal researchers to train animals. For a very long period, artificial intelligence agents were trained on machine learning models to perform tasks that are usually done by humans. The neural networks of machine learning models are designed and trained in such a format that they perform the tasks without any human intervention or supervision. However, ever since its inception, the researchers and scientists are curious to induce cognitive abilities into artificial intelligence agents. For a decade, despite the experiments designed to train the artificial neural network by utilizing the human cognitive ability for adopting common sense, the researchers were unable to reach into a reasonable conclusion. The researchers were resorting to behavioral science and neuroscience earlier to induce common sense into the artificial intelligence agents.


Global Big Data Conference

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Machine learning model and Neural Networks helps in extracting archaic information about human civilization. Archaeology is the gateway to our past. It describes events which shaped the world how it is today and the transition that led humans from animal-hunter to a knowledgeable-mosaic. In archaeology, Stone Age holds the key relevance. It establishes the patterns of human behavior and helps in identifying the transitions that hurled humans to the path of development.


Artificial Intelligence Books for Beginners

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Artificial Intelligence (AI) has taken the world by storm. Almost every industry across the globe is incorporating AI for a variety of applications and use cases. Some of its wide range of applications includes process automation, predictive analysis, fraud detection, improving customer experience, etc. To learn more about AI and it's concepts, you can start by reading the Top Artificial Intelligence Books for self-learning. AI is being foreseen as the future of technological and economic development.


AAAI 2021 Spring Symposia

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The Machine Learning for Mobile Robot Navigation in the Wild Symposium will consist of invited talks, technical presentations, spotlight posters, robot demonstrations, industry spotlights, breakout sessions, and interactive panel discussions. All contributions should be submitted electronically via AAAI EasyChair site.