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This is happening in the city of Tianjin, about an hour’s drive south of Beijing, within a gleaming office building that belongs to iFlytek, one of China’s rapidly rising artificial-intelligence companies. Beyond guarded gates, inside a glitzy showroom, the US president is on a large TV screen heaping praise on the Chinese company. It’s Trump’s voice and face, but the recording is, of course, fake—a cheeky demonstration of the cutting-edge AI technology iFlytek is developing.

Jiang Tao chuckles and leads the way to some other examples of iFlytek’s technology. Throughout the tour, Tao, one of the company’s cofounders, uses another remarkable innovation: a hand-held device that converts his words from Mandarin into English almost instantly. At one point he speaks into the machine, and then grins as it translates: “I find that my device solves the communication problem.”

IFlytek’s translator shows off AI capabilities that rival those found anywhere in the world. But it also highlights a big hole in China’s plan, unveiled in 2017, to be the world leader in AI by 2030. The algorithms inside were developed by iFlytek, but the hardware—the microchips that bring those algorithms to life—was designed and made elsewhere. While China manufactures most of the world’s electronic gadgets, it has failed, time and again, to master the production of these tiny, impossibly intricate silicon structures. Its dependence on foreign integrated circuits could potentially cripple its AI ambitions.

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Unilever, the multinational consumer goods manufacturer, uses artificial intelligence and machine learning to help with recruiting and onboarding of new employees. The algorithms help to sift through CVs and even conduct and analyze video interviews.

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Investigators from the Neurodegenerative Diseases Research Group at the University of Extremadura are studying signaling mediated by a pathway known as planar cell polarity (PCP), which regulates the coordinated orientation of cells during organogenesis, the process of organ formation in living organisms. This pathway has been highly conserved on the evolutionary scale, and one of its key functions in vertebrates is the regulation of the coordinated positioning of centrioles/ciliary basal cells inside cells.

This signaling pathway was discovered initially in the fruit fly genus Drosophila, although the majority of the pathway components have been retained in humans. It has likewise been observed that certain pathologies such as hydrocephaly, infertility and some kinds of cancers are associated with defective functioning of this signaling.

Under the auspices of the project EPICENTR within the Spanish national research plan, whose objective is to study the planar polarisation of centrioles in epithelial , the UEx researchers have now published the first results of their investigation in the journal Development. These results are related to the polarised positioning mechanism of centrioles in Drosophila and its correlation with actin.

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Facial recognition technology is being tested by businesses and governments for everything from policing to employee timesheets. Even more granular results are on their way, promise the companies behind the technology: Automatic emotion recognition could soon help robots understand humans better, or detect road rage in car drivers.

But experts are warning that the facial-recognition algorithms that attempt to interpret facial expressions could be based on uncertain science. The claims are a part of AI Now Institute’s annual report, a nonprofit that studies the impact of AI on society. The report also includes recommendations for the regulation of AI and greater transparency in the industry.

“The problem is now AI is being applied in a lot of social contexts. Anthropology, psychology, and philosophy are all incredibly relevant, but this is not the training of people who come from a technical [computer science] background.” says Kate Crawford, co-founder of AI Now, distinguished research professor at NYU and principal researcher at Microsoft Research. “Essentially the narrowing of AI has produced a kind of guileless acceptance of particular strands of psychological literature that have been shown to be suspect.”

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The promise of quantum computing brings with it some mind-blowing potential, but it also carries a new set of risks, scientists are warning.

Specifically, the enormous power of the tech could be used to crack the best cyber security we currently have in place.

A new report on the “progress and prospects” of quantum computing put together by the National Academies of Sciences, Engineering, and Medicine (NASEM) in the US says that work should start now on putting together algorithms to beat the bad guys.

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Over the past few years, classical convolutional neural networks (cCNNs) have led to remarkable advances in computer vision. Many of these algorithms can now categorize objects in good quality images with high accuracy.

However, in real-world applications, such as autonomous driving or robotics, imaging data rarely includes pictures taken under ideal lighting conditions. Often, the images that CNNs would need to process feature occluded objects, motion distortion, or low signal to noise ratios (SNRs), either as a result of poor image quality or low light levels.

Although cCNNs have also been successfully used to de-noise images and enhance their quality, these networks cannot combine information from multiple frames or video sequences and are hence easily outperformed by humans on low quality images. Till S. Hartmann, a neuroscience researcher at Harvard Medical School, has recently carried out a study that addresses these limitations, introducing a new CNN approach for analyzing noisy images.

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Despite growing excitement around the transformative potential of quantum computing, leaders in many industries are still unfamiliar with the technology that’s likely to prove more disruptive than Artificial Intelligence and blockchain. This ignorance seems particularly acute in industries that deal with physical systems and commodities. In an informal survey of two dozen executives in transportation, logistics, construction and energy, only eight had heard of quantum computing and only two could explain how it works.

In many ways this lack of awareness is understandable. Quantum computing’s value to our digital infrastructure is obvious, but its value to our physical infrastructure is perhaps less evident. Yet, the explosion of power and speed that quantum computers will unleash could indeed have a profound impact on physical systems like our transportation and utility networks. For companies, municipalities and nation states to stay competitive and capture the full benefit of the quantum revolution, leaders must start thinking about how quantum computing can improve our infrastructure.

Unlike classical computers, in which a bit of information can be either a zero or a one, quantum computers are able to take advantage of a third state through a phenomenon known as superposition. Superposition, which is a property of physics at the quantum scale, allows a quantum bit or qubit to be a zero, a one or a zero and a one simultaneously. The result is an astronomical increase in computational capacity over existing transistor-based hardware. Google, for example, has found that its quantum machines can run some algorithms 100 million times faster than conventional processors.

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