Sickle Cell Therapy With CRISPR Gene Editing Shows Promise : Shots — Health News NPR tells the exclusive, behind-the-scenes story of the first person with a genetic disorder to be treated in the United States with the revolutionary gene-editing technique CRISPR.
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.
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.
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 machine learning (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.
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
Watch Full Episodes of “60 Minutes” HERE: http://cbsn.ws/1Qkjo1F
Get more “60 Minutes” from “60 Minutes: Overtime” HERE: http://cbsn.ws/1KG3sdr
Relive past episodies and interviews with “60 Rewind” HERE: http://cbsn.ws/1PlZiGI
Follow “60 Minutes” on Instagram HERE: http://bit.ly/23Xv8Ry
Like “60 Minutes” on Facebook HERE: http://on.fb.me/1Xb1Dao
Follow “60 Minutes” on Twitter HERE: http://bit.ly/1KxUsqX
Follow “60 Minutes” on Google+ HERE: http://bit.ly/1KxUvmG
Continue reading “The complicated ethics of genetic engineering” »
So to increase productivity, neuroscientists propose giving IAs self preservation sense.
Robots that fear death 😂😅🤣.
Finally, robots that are actually lifelike.
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.
