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Category: robotics/AI – Page 2,037

Human Brain-Like Functions Emerge in Neuromorphic Metallic Nanowire Network

An international joint research team led by NIMS succeeded in fabricating a neuromorphic network composed of numerous metallic nanowires. Using this network, the team was able to generate electrical characteristics similar to those associated with higher-order brain functions unique to humans, such as memorization, learning, forgetting, becoming alert and returning to calm. The team then clarified the mechanisms that induced these electrical characteristics.

The development of artificial intelligence (AI) techniques has been rapidly advancing in recent years and has begun impacting our lives in various ways. Although AI processes information in a manner similar to the human brain, the mechanisms by which human brains operate are still largely unknown. Fundamental brain components, such as neurons and the junctions between them (synapses), have been studied in detail. However, many questions concerning the brain as a collective whole need to be answered. For example, we still do not fully understand how the brain performs such functions as memorization, learning, and forgetting, and how the brain becomes alert and returns to calm. In addition, live brains are difficult to manipulate in experimental research. For these reasons, the brain remains a mysterious organ.

Brain Connections: Neuromorphic Devices Emulate the Brain’s Hardware

Nowadays, there is an imperative need for novel computational concepts to manage the enormous data volume produced by contemporary information technologies. The inherent capability of the brain to cope with these kinds of signals constitutes the most efficient computational paradigm for biomimicry.

Representing neuronal processing with software-based artificial neural networks is a popular approach with tremendous impacts on everyday life; a field commonly known as machine learning or artificial intelligence. This approach relies on executing algorithms that represent neural networks on a traditional von Neumann computer architecture.

An alternative approach is the direct emulation of the workings of the brain with actual electronic devices/circuits. This emulation of the brain at the hardware-based level is not only necessary for overcoming limitations of conventional silicon technology based on the traditional von Neumann architecture in terms of scaling and efficiency, but in understanding brain function through reverse engineering. This hardware-based approach constitutes the main scope of neuromorphic devices/computing.

A new deep learning model for EEG-based emotion recognition

Recent advances in machine learning have enabled the development of techniques to detect and recognize human emotions. Some of these techniques work by analyzing electroencephalography (EEG) signals, which are essentially recordings of the electrical activity of the brain collected from a person’s scalp.

Most EEG-based emotion classification methods introduced over the past decade or so employ traditional (ML) techniques such as support vector machine (SVM) models, as these models require fewer training samples and there is still a lack of large-scale EEG datasets. Recently, however, researchers have compiled and released several new datasets containing EEG brain recordings.

The release of these datasets opens up exciting new possibilities for EEG-based emotion recognition, as they could be used to train deep-learning models that achieve better performance than traditional ML techniques. Unfortunately, however, the low resolution of EEG signals contained in these datasets could make training deep-learning models rather difficult.

Research on Application of Artificial Intelligence in Computer Network Technology

This paper attempts to apply artificial intelligence (AI) to computer network technology and research on the application of AI in computing network technology.

With the continuous expansion of the application scope of computer network technology, various malicious attacks that exist in the Internet range have caused serious harm to computer users and network resources.

By studying the attack principle, analyzing the characteristics of the attack method, extracting feature data, establishing feature sets, and using the agent technology as the supporting technology, the simulation experiment is used to prove the improvement effect of the system in terms of false alarm rate, convergence speed, and false-negative rate, the rate reached 86.7%. The results show that this fast algorithm reduces the training time of the network, reduces the network size, improves the classification performance, and improves the intrusion detection rate.

How the ‘big 5’ bolstered their AI through acquisitions in 2019

Throughout 2019, tech companies have ramped up their efforts to secure the best AI talent and technology. Here, we take a look back at some of this activity, with a focus on the “big 5”: Facebook, Amazon, Apple, Microsoft, and (Alphabet’s) Google (FAAMG).

All the big tech firms secured AI talent and technology through acquisitions this year, including Facebook, Amazon, Apple, Microsoft, and Google.

Scientists mapped Titan’s awe-inspiring terrain for the first time

Navigating Titan, Saturn’s largest moon, is a challenge. Just getting close is hard enough — it’s hundreds of millions of miles away, after all. But let’s suppose either a robot or a human lands on the surface of the only other body in the Solar System known to have liquid on its surface. They’d need a map — and fortunately, NASA has one ready to go should the occasion ever arise.

In November 2019, scientists made the first ever map detailing the moon’s complicated — and terrifying — terrain. It reveals a moon filled with weird and wonderful geography, including dunes, liquid methane lakes, plains, labyrinthine canyons, and craters.

This is #10 on Inverse’s 20 wildest space discoveries of 2019.