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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.


Plan2Explore: active model-building for self-supervised visual reinforcement learning

AIHub

To operate successfully in unstructured open-world environments, autonomous intelligent agents need to solve many different tasks and learn new tasks quickly. Reinforcement learning has enabled artificial agents to solve complex tasks both in simulation and real world. However, it requires collecting large amounts of experience in the environment, and the agent learns only that particular task, much like a student memorizing a lecture without understanding. Self-supervised reinforcement learning has emerged as an alternative, where the agent only follows an intrinsic objective that is independent of any individual task, analogously to unsupervised representation learning. After experimenting with the environment without supervision, the agent builds an understanding of the environment, which enables it to adapt to specific downstream tasks more efficiently.


Group Search Optimization for Applications in Structural Design - Programmer Books

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Civil engineering structures such as buildings, bridges, stadiums, and offshore structures play an import role in our daily life. However, constructing these structures requires lots of budget. Thus, how to cost-efficiently design structures satisfying all required design constraints is an important factor to structural engineers. Traditionally, mathematical gradient-based optimal techniques have been applied to the design of optimal structures. While, many practical engineering optimal problems are very complex and hard to solve by traditional method.


2020 Intro to Agent-Based Modeling Simulation AI in NetLogo

#artificialintelligence

Description When people talk about artificial intelligence, they usually talk about machine learning. Most people have not heard about Agent-Based modeling AI . Agent-Based modeling is much simpler than machine learning. You basically just let agents interact in an environment and watch for any emergent behavior. You practically do not have to have any math background and you are able to create amazing things.


Researchers design virtual environment to spur development of helpful home robots

#artificialintelligence

Without much prior experience, kids can recognize other people's intentions and come up with plans to help them achieve their goals, even in novel scenarios. That's why researchers at MIT, Nvidia, and ETH Zurich developed Watch-And-Help (WAH), a challenge in which embodied AI agents need to understand goals by watching a demonstration of a human performing a task and coordinating with the human to solve the task as quickly as possible. The concept of embodied AI draws on embodied cognition, the theory that many features of psychology -- human or otherwise -- are shaped by aspects of the entire body of an organism. By applying this logic to AI, researchers hope to improve the performance of AI systems like chatbots, robots, autonomous vehicles, and even smart speakers that interact with their environments, people, and other AI. A truly embodied robot could check to see whether a door is locked, for instance, or retrieve a smartphone that's ringing in an upstairs bedroom.


Natural Language Misunderstanding

Communications of the ACM

In today's world, it is nearly impossible to avoid voice-controlled digital assistants. From the interactive intelligent agents used by corporations, government agencies, and even personal devices, automated speech recognition (ASR) systems, combined with machine learning (ML) technology, increasingly are being used as an input modality that allows humans to interact with machines, ostensibly via the most common and simplest way possible: by speaking in a natural, conversational voice. Yet as a study published in May 2020 by researchers from Stanford University indicated, the accuracy level of ASR systems from Google, Facebook, Microsoft, and others vary widely depending on the speaker's race. While this study only focused on the differing accuracy levels for a small sample of African American and white speakers, it points to a larger concern about ASR accuracy and phonological awareness, including the ability to discern and understand accents, tonalities, rhythmic variations, and speech patterns that may differ from the voices used to initially train voice-activated chatbots, virtual assistants, and other voice-enabled systems. The Stanford study, which was published in the journal Proceedings of the National Academy of Sciences, measured the error rates of ASR technology from Amazon, Apple, Google, IBM, and Microsoft, by comparing the system's performance in understanding identical phrases (taken from pre-recorded interviews across two datasets) spoken by 73 black and 42 white speakers, then comparing the average word error rate (WER) for black and white speakers.



Is Artificial Intelligence Closer to Common Sense?

#artificialintelligence

Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.


Is Artificial Intelligence Closer to Common Sense?

#artificialintelligence

Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.


Why and how to build autonomous systems - AI for Business

#artificialintelligence

Automated control systems were one of the most disruptive applications of industrial technology in the 20th century. The ability to control workflows and processes based on specific inputs and outputs streamlined even the most complex manufacturing processes. These systems, however, need specific parameters and, in some cases, require extensive human oversight and planning to ensure optimal execution. Innovations in AI training methodologies are pushing past these limitations to produce the next wave of disruption to industrial technology: autonomous systems. Autonomous machines do more than address the limitations of automated systems, however.