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This Brain-Inspired AI Self-Drives With Just 19 Neurons

#artificialintelligence

Recently, a team of researchers from MIT, Institute of Science and Technology Austria (IST Austria) and Technische Universität Wien (TU Wien) developed an AI system by combining brain-inspired neural computation principles and scalable deep learning architectures. The AI system is basically a brain-inspired intelligent agent that learns to control an autonomous vehicle directly from its camera inputs. The researchers discovered that a single algorithm with 19 control neurons, connecting 32 encapsulated input features to outputs by 253 synapses, learns to map high-dimensional inputs into steering commands. One of the interesting facts of this research is that the AI agent is inspired by the neural computations known to happen in biological brains in order to achieve a remarkable degree of controllability. They took the inspiration from animals as small as the roundworms.


Global Big Data Conference

#artificialintelligence

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.


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.


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.


What is artificial intelligence (AI)? – IAM Network

#artificialintelligence

Artificial intelligence (AI) is a difficult term to define because experts continue to argue about its definition. We'll get into those arguments later, but for now, think of AI as the technology through which computers execute tasks that would normally require human intellect. Humans and animals have a natural intellect, but computers and other intelligent agents have artificial intelligence that engineers and scientists design.AI differs from machine learning and deep learning, though the topics are related. Machine learning is a subcategory within AI in which a machine learns and performs functions it wasn't specifically programmed to do (using what some argue to be logic). Deep learning is a subcategory of machine learning that allows machines to analyze multi-layer algorithms or neural networks.


What is artificial intelligence (AI)?

#artificialintelligence

Artificial intelligence (AI) is a difficult term to define because experts continue to argue about its definition. We'll get into those arguments later, but for now, think of AI as the technology through which computers execute tasks that would normally require human intellect. Humans and animals have a natural intellect, but computers and other intelligent agents have artificial intelligence that engineers and scientists design. AI differs from machine learning and deep learning, though the topics are related. Machine learning is a subcategory within AI in which a machine learns and performs functions it wasn't specifically programmed to do (using what some argue to be logic).