Times reporters traveled to Russia, Switzerland, California and Washington, D.C., talking to experts in the commercial tech, military and A.I. communities. Below are some key points and analysis, along with extras from the documentary.
“Killing in the Age of Algorithms” is a New York Times documentary examining the future of artificial intelligence and warfare.
Scientists have created thin films made from barium zirconium sulfide (BaZrS3) and confirmed that the materials have alluring electronic and optical properties predicted by theorists.
The films combine exceptionally strong light absorption with good charge transport—two qualities that make them ideal for applications such as photovoltaics and light-emitting diodes (LEDs).
In solar panels, for example, experimental results suggest that BaZrS3 films would be much more efficient at converting sunlight into electricity than traditional silicon-based materials with identical thicknesses, says lead researcher Hao Zeng, Ph.D., professor of physics in the University at Buffalo College of Arts and Sciences. This could lower solar energy costs, especially because the new films performed admirably even when they had imperfections. (Manufacturing nearly flawless materials is typically more expensive, Zeng explains.)
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.
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
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