Astronomers have captured the aftermath of a dramatic gamma-ray burst, which was likely triggered by the death of an enormous star, and the brightest cosmic explosion ever detected.
While automated manufacturing is ubiquitous today, it was once a nascent field birthed by inventors such as Oliver Evans, who is credited with creating the first fully automated industrial process, in flour mill he built and gradually automated in the late 1700s. The processes for creating automated structures or machines are still very top-down, requiring humans, factories, or robots to do the assembling and making.
However, the way nature does assembly is ubiquitously bottom-up; animals and plants are self-assembled at a cellular level, relying on proteins to self-fold into target geometries that encode all the different functions that keep us ticking. For a more bio-inspired, bottom-up approach to assembly, then, human-architected materials need to do better on their own. Making them scalable, selective, and reprogrammable in a way that could mimic nature’s versatility means some teething problems, though.
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Ask a smart home device for the weather forecast, and it takes several seconds for the device to respond. One reason this latency occurs is because connected devices don’t have enough memory or power to store and run the enormous machine-learning models needed for the device to understand what a user is asking of it. The model is stored in a data center that may be hundreds of miles away, where the answer is computed and sent to the device.
MIT researchers have created a new method for computing directly on these devices, which drastically reduces this latency. Their technique shifts the memory-intensive steps of running a machine-learning model to a central server where components of the model are encoded onto light waves.
The waves are transmitted to a connected device using fiber optics, which enables tons of data to be sent lightning-fast through a network. The receiver then employs a simple optical device that rapidly performs computations using the parts of a model carried by those light waves.
Researchers from the Hefei Institutes of Physical Science (HFIPS) of the Chinese Academy of Sciences (CAS) have proposed a new artificial intelligence framework for target detection that provides a new solution for fast and high-precision real-time online target detection.
Relevant results were published in Expert Systems with Applications.
In recent years, deep learning theory has driven the rapid development of artificial intelligence technology. Object detection technology based on deep learning theory is also successful in many industrial applications. Current research focuses on improving the speed or accuracy of target detection and fails to take efficiency and accuracy into account. How to achieve fast and accurate object detection has become an important challenge in the field of artificial intelligence.
Field-effect transistors (FETs) are transistors in which the resistance of most of the electrical current can be controlled by a transverse electric field. Over the past decade or so, these devices have proved to be very valuable solutions for controlling the flow of current in semiconductors.
To further develop FETs, electronics engineers worldwide have recently been trying to reduce their size. While these down-scaling efforts have been found to increase the device’s speed and lower the power consumption, they are also associated with short-channel effects (i.e., unfavorable effects that occur when an FET’s channel length is approximately equal to the space charge regions of source and drain junctions within its substrate).
These undesirable effects, which include barrier lowering and velocity saturation, could be suppressed by using 2D semiconductor channels with high carrier mobilities and ultrathin high–k dielectrics (i.e., materials with high dielectric constants). Integrating 2D semiconductors with dielectrics with similar oxide thicknesses has been found to be highly challenging.
Harnessing The Power Of Science & Innovation For All — Dr. Anna Laura Ross, Ph.D., Unit Head for Emerging Technologies, Research Prioritization and Support, Science Division, WHO.
Dr. Anna Laura Ross, Ph.D. is the Unit Head for Emerging Technologies, Research Prioritization and Support, in the World Health Organization (WHO) Science Division (https://www.who.int/our-work/science-division), located in Geneva, as well as the Head of the WHO Science Council Secretariat.
The artificial intelligence speech translation system can decipher Hokkien, a spoken language.
Meta has created a new speech translator that can translate Hokkien, a predominantly oral language spoken in the diaspora of China and one of the national languages of Taiwan.
Meta.
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The new service will provide 350 Mbps internet, even while taking off and landing.
SpaceX just announced its new Starlink Aviation service, which will allow high-speed in-flight internet that will allow users to make voice calls and stream videogames, according to the company.
The new service promises speeds of up to 350 Mbps for airliners and is aimed mainly at private jet contractors. Earlier this year, SpaceX also announced a partnership with Hawaiian Airlines to bring free WiFi to its flights starting early next year.
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UCLA / Flexible Research Group.
The study was published today in the journal Science Robotics.
