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Category: information science

Neurons, specialized cells that transmit nerve impulses, have long been known to be a vital element for the functioning of the human brain. Over the past century, however, neuroscience research has given rise to the false belief that neurons are the only cells that can process and learn information. This misconception or ‘neurocomputing dogma’ is far from true.

An is a different type of cell that has recently been found to do a lot more than merely fill up spaces between neurons, as researchers believed for over a century. Studies are finding that these cells also play key roles in brain functions, including learning and central pattern generation (CPG), which is the basis for critical rhythmic behaviors such as breathing and walking.

Although astrocytes are now known to underlie numerous brain functions, most existing inspired by the only target the structure and function of neurons. Aware of this gap in existing literature, researchers at Rutgers University are developing brain-inspired algorithms that also account for and replicate the functions of astrocytes. In a paper pre-published on arXiv and set to be presented at the ICONS 2020 Conference in July, they introduce a neuromorphic central pattern generator (CPG) modulated by artificial astrocytes that successfully entrained several rhythmic walking behaviors in their in-house robots.

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Dimensionality reduction is an unsupervised learning technique.

Nevertheless, it can be used as a data transform pre-processing step for machine learning algorithms on classification and regression predictive modeling datasets with supervised learning algorithms.

There are many dimensionality reduction algorithms to choose from and no single best algorithm for all cases. Instead, it is a good idea to explore a range of dimensionality reduction algorithms and different configurations for each algorithm.

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Say something Eric Klien.

Given the increasing proliferation of AI, I recently carried out a systematic review of AI-driven regulatory gaps. My review sampled the academic literature on AI in the hard and social sciences and found fifty existing or future regulatory gaps caused by this technology’s applications and methods in the United States. Drawing on an adapted version of Lyria Bennett-Moses’s framework, I then characterized each regulatory gap according to one of four categories: novelty, obsolescence, targeting, and uncertainty.

Significantly, of the regulatory gaps identified, only 12 percent represent novel challenges that compel government action through the creation or adaptation of regulation. By contrast, another 20 percent of the gaps are cases in which AI has made or will make regulations obsolete. A quarter of the gaps are problems of targeting, in which regulations are either inappropriately applied to AI or miss cases in which they should be applied. The largest group of regulatory gaps are ones of uncertainty in which a new technology is difficult to classify, causing a lack of clarity about the application of existing regulations.

Novelty. In cases of novel regulatory gaps, a technology creates behavior that requires bespoke government action. Of the identified cases, 12 percent are novel. This includes, for example, the Food and Drug Administration’s (FDA) standard for certifying the safety of high-risk medical devices which is applicable to healthcare algorithms, also called black-box medicine.

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Balloon shaping isn’t just for kids anymore. A team of researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) has designed materials that can control and mold a balloon into pre-programmed shapes. The system uses kirigami sheets—thin sheets of material with periodic cuts—embedded into an inflatable device. As the balloon expands, the cuts in the kirigami sheet guide the growth, permitting expansion in some places and constricting it in others. The researchers were able to control the expansion not only globally to make large-scale shapes, but locally to generate small features.

The team also developed an inverse design strategy, an algorithm that finds the optimum design for the kirigami inflatable device that will mimic a target shape upon inflation.

“This work provides a new platform for shape-morphing devices that could support the design of innovative medical tools, actuators and reconfigurable structures,” said Katia Bertoldi, the William and Ami Kuan Danoff Professor of Applied Mechanics at SEAS and senior author of the study.

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Text is backward. Clocks run counterclockwise. Cars drive on the wrong side of the road. Right hands become left hands.

Intrigued by how reflection changes images in subtle and not-so-subtle ways, a team of Cornell researchers used artificial intelligence to investigate what sets originals apart from their reflections. Their algorithms learned to pick up on unexpected clues such as hair parts, gaze direction and, surprisingly, beards – findings with implications for training machine learning models and detecting faked images.

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Scientists suggest a desktop quantum computer based on nuclear magnetic resonance (NMR) could soon be on its way to a classroom near you. Although the device might not be suited to handle large quantum applications, the makers say it could help students learn about quantum computing.

SpinQ Chief Scientist Prof. Bei Zeng from University of Guelph, announced the SpinQ Gemini, a two-qubit desktop quantum computer, at the industry session of the Quantum Information Processing (QIP2020) conference, which is held recently in Shenzhen, China. It is the first time that a desktop quantum computer is commercially available, according to the researchers.

SpinQ Gemini is built by the state-of-the-art technology of permanent magnets, providing 1T magnetic field, running at room temperature, and maintenance free. It demonstrates quantum algorithms such as Deutsch’s algorithm and Grover’s algorithm for teaching quantum computing to university and high school students, also provides advanced models for quantum circuit design and control sequence design for researchers.

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However, the situation has been improving as Chinese tech giants including e-commerce company Alibaba, search engine Baidu, on-demand delivery company Meituan Dianping, ride-hailing operator Didi Chuxing and smartphone maker Xiaomi now offer more affordable health care plans via mutual aid platforms, which operate as a collective claim-sharing mechanism.

China’s online mutual aid platforms are disrupting old school insurance companies by leveraging big data and internet finance technologies to offer low cost medical coverage.

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Updated mathematical techniques that can distinguish between two types of ‘non-Gaussian curve’ could make it easier for researchers to study the nature of quantum entanglement.

Quantum entanglement is perhaps one of the most intriguing phenomena known to physics. It describes how the fates of multiple particles can become entwined, even when separated by vast distances. Importantly, the probability distributions needed to define the quantum states of these particles deviate from the bell-shaped, or ‘Gaussian’ curves which underly many natural processes. Non-Gaussian curves don’t apply to quantum systems alone, however. They can also be composed of mixtures of regular Gaussian curves, producing difficulties for physicists studying quantum entanglement. In new research published in EPJ D, Shao-Hua Xiang and colleagues at Huaihua University in China propose a solution to this problem. They suggest an updated set of equations that allows physicists to easily check whether or not a non-Gaussian state is genuinely quantum.

As physicists make more discoveries about the nature of quantum entanglement, they are rapidly making progress towards advanced applications in the fields of quantum communication and computation. The approach taken in this study could prove to speed up the pace of these advances. Xiang and colleagues acknowledge that while all previous efforts to distinguish between both types of non-Gaussian curve have had some success, their choices of Gaussian curves as a starting point have so far meant that no one approach has yet proven to be completely effective. Based on the argument that there can’t be any truly reliable Gaussian reference for any genuinely quantum non-Gaussian state, the researchers present a new theoretical framework.

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