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Dec 25, 2019

Brain Connections: Neuromorphic Devices Emulate the Brain’s Hardware

Posted by in categories: engineering, information science, robotics/AI

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.

Dec 25, 2019

Tencent details how its MOBA-playing AI system beats 99.81% of human opponents

Posted by in categories: entertainment, robotics/AI

Researchers at Tencent recently detailed an AI system capable of defeating teams of professionals in Honor of Kings, a MOBA game with a large fanbase.

Dec 25, 2019

A new deep learning model for EEG-based emotion recognition

Posted by in category: robotics/AI

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.

Dec 25, 2019

New engine tech could get us to Mars faster

Posted by in category: space travel

NASA wants to send humans to Mars one day, but do we have the engines to get us there?

Dec 25, 2019

The complicated ethics of genetic engineering

Posted by in categories: bioengineering, ethics, genetics

With new technology to edit genes, scientists are now working on things that once seemed impossible. But what are the boundaries? See the full 60 Minutes interview with Church, here: https://cbsn.ws/34ZhuTs

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Dec 25, 2019

Amazing Facts About Our Gut Microbiome

Posted by in category: biotech/medical

Over 35,000 bacterial species live in your gut!

Dec 25, 2019

To Make Robots Perform Better, Make Them Constantly Fear Death

Posted by in category: robotics/AI

So to increase productivity, neuroscientists propose giving IAs self preservation sense.

Robots that fear death 😂😅🤣.


Finally, robots that are actually lifelike.

Dec 24, 2019

Best News From 2019

Posted by in category: futurism

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Dec 24, 2019

Research on Application of Artificial Intelligence in Computer Network Technology

Posted by in categories: information science, internet, robotics/AI

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.

Dec 24, 2019

Evolutionary Changes in Brain Potentially Make us More Prone to Anxiety

Posted by in categories: biotech/medical, evolution, genetics, neuroscience

Neurochemicals such as serotonin and dopamine play crucial roles in cognitive and emotional functions of our brain. Vesicular monoamine transporter 1 (VMAT1) is one of the genes responsible for transporting neurotransmitters and regulating neuronal signaling. A research team led by Tohoku University has reconstructed ancestral VMAT1 proteins, revealing the functional changes in neurotransmitter uptake of VMAT1 throughout the course of human evolution.

Human bodies are made up of millions of cells. Each individual contains a specific set of instruction of codes that make up all of a living thing’s genetic material. These instructions are known as genomes. PhD candidate Daiki Sato and Professor Masakado Kawata of the Graduate School of Life Sciences at Tohoku University, and two of the authors involved in the current study, previously discovered VMAT1 to be one of the genes that had evolved throughout human lineage.

VMAT 1 contains two human-specific mutations, or where the genomes changed, with the change being represented as 130Glu to 130Gly and from 136Asn to 136Thr. Previous studies have shown that having the new 130Gly/136Thr variant decreases the uptake of neurotransmitters and is associated with higher depression and/or anxiety. In this study, Sato, Kawata and their colleagues revealed the evolutionary changes in neurotransmitter uptake of VMAT1 by reconstructing ancestral VMAT1 proteins. First they applied a fluorescent substrate to visualize and quantify the neurotransmitter uptake of each genotype. The ancestral (130Glu/136Asn) VMAT1 protein exhibited an increased uptake of neurotransmitters compared to a derived (130Gly/136Thr) genotype. Given that the derived (130Gly/136Thr) genotype is shown to be associated with depression and/or anxiety in modern human populations. “This results of our study reveal that our ancestors may have been able to withstand higher levels of anxiety or depression,” noted the authors.

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