Tag Archives: machine-learning

AI Robot Presents TV News In China

China’s state news agency Xinhua this week introduced the newest members of its newsroom: AI anchors who will reporttirelessly” all day every day, from anywhere in the country. Chinese viewers were greeted with a digital version of a regular Xinhua news anchor named Qiu Hao. The anchor, wearing a red tie and pin-striped suit, nods his head in emphasis, blinking and raising his eyebrows slightly.

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Not only can I accompany you 24 hours a day, 365 days a year. I can be endlessly copied and present at different scenes to bring you the news,” he says. Xinhua also presented an English-speaking AI, based on another presenter, who adds: “The development of the media industry calls for continuous innovation and deep integration with the international advanced technologies … I look forward to bringing you brand new news experiences.”

Developed by Xinhua and the Chinese search engine, Sogou, the anchors were developed through machine learning to simulate the voice, facial movements, and gestures of real-life broadcasters, to present a “a lifelike image instead of a cold robot,” according to Xinhua.

Source: https://www.theguardian.com/

New Materials For New Processors

Computers used to take up entire rooms. Today, a two-pound laptop can slide effortlessly into a backpack. But that wouldn’t have been possible without the creation of new, smaller processors — which are only possible with the innovation of new materials. But how do materials scientists actually invent new materials? Through experimentation, explains Sanket Deshmukh, an assistant professor in the chemical engineering department of Virginia Tech whose team’s recently published computational research might vastly improve the efficiency and costs savings of the material design process.

Deshmukh’s lab, the Computational Design of Hybrid Materials lab, is devoted to understanding and simulating the ways molecules move and interact — crucial to creating a new material. In recent years, materials scientists have employed machine learning, a powerful subset of artificial intelligence, to accelerate the discovery of new materials through computer simulations. Deshmukh and his team have recently published research in the Journal of Physical Chemistry Letters demonstrating a novel machine learning framework that trainson the fly,” meaning it instantaneously processes data and learns from it to accelerate the development of computational models. Traditionally the development of computational models are “carried out manually via trial-and-error approach, which is very expensive and inefficient, and is a labor-intensive task,” Deshmukh explained.

This novel framework not only uses the machine learning in a unique fashion for the first time,” Deshmukh said, “but it also dramatically accelerates the development of accurate computational models of materials.” “We train the machine learning model in a ‘reverse’ fashion by using the properties of a model obtained from molecular dynamics simulations as an input for the machine learning model, and using the input parameters used in molecular dynamics simulations as an output for the machine learning model,” said Karteek Bejagam, a post-doctoral researcher in Deshmukh’s lab and one of the lead authors of the study.

This new framework allows researchers to perform optimization of computational models, at unusually faster speed, until they reach the desired properties of a new material.

Source: https://vtnews.vt.edu/

How To Recreate Memories Of Faces From Brain Data

A new technique developed by neuroscientists at the University of Toronto can reconstruct images of what people perceive based on their brain activity. The technique developed by Dan Nemrodov, a postdoctoral fellow in Assistant Professor Adrian Nestor’s lab at U of T Scarborough, is able to digitally reconstruct images seen by test subjects based on electroencephalography (EEG) data.

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When we see something, our brain creates a mental percept, which is essentially a mental impression of that thing. We were able to capture this percept using EEG to get a direct illustration of what’s happening in the brain during this process,” says Nemrodov.

For the study, test subjects hooked up to EEG equipment were shown images of faces. Their brain activity was recorded and then used to digitally recreate the image in the subject’s mind using a technique based on machine learning algorithms. It’s not the first time researchers have been able to reconstruct images based on visual stimuli using neuroimaging techniques. The current method was pioneered by Nestor, who successfully reconstructed facial images from functional magnetic resonance imaging (fMRI) data in the past, but this is the first time EEG has been used.

Source: https://www.reuters.com/
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Human Internal Verbalizations Understood Instantly By Computers

MIT researchers have developed a computer interface that can transcribe words that the user verbalizes internally but does not actually speak aloud. The system consists of a wearable device and an associated computing system. Electrodes in the device pick up neuromuscular signals in the jaw and face that are triggered by internal verbalizations — saying wordsin your head” — but are undetectable to the human eye. The signals are fed to a machine-learning system that has been trained to correlate particular signals with particular words. The device also includes a pair of bone-conduction headphones, which transmit vibrations through the bones of the face to the inner ear. Because they don’t obstruct the ear canal, the headphones enable the system to convey information to the user without interrupting conversation or otherwise interfering with the user’s auditory experience.

The device is thus part of a complete silent-computing system that lets the user undetectably pose and receive answers to difficult computational problems. In one of the researchers’ experiments, for instance, subjects used the system to silently report opponents’ moves in a chess game and just as silently receive computer-recommended responses.

The motivation for this was to build an IA device — an intelligence-augmentation device,” says Arnav Kapur, a graduate student at the MIT Media Lab, who led the development of the new system. “Our idea was: Could we have a computing platform that’s more internal, that melds human and machine in some ways and that feels like an internal extension of our own cognition?” “We basically can’t live without our cellphones, our digital devices,” adds Pattie Maes, a professor of media arts and sciences and Kapur’s thesis advisor. “But at the moment, the use of those devices is very disruptive. If I want to look something up that’s relevant to a conversation I’m having, I have to find my phone and type in the passcode and open an app and type in some search keyword, and the whole thing requires that I completely shift attention from my environment and the people that I’m with to the phone itself. So, my students and I have for a very long time been experimenting with new form factors and new types of experience that enable people to still benefit from all the wonderful knowledge and services that these devices give us, but do it in a way that lets them remain in the present.”

Source: http://news.mit.edu/