A few scant equations can explain a variety of phenomena in our universe, over vast gulfs of space and time. Here’s a taste of just how powerful modern physics can be.
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 astrocyte is a different type of brain 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 computer systems inspired by the human brain 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.
Big data company Openairlines claims to be helping make flying more eco-friendly through in-depth data analysis, but it may be saving more money than fuel.
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.
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.
Continue reading “Can existing laws cope with the AI revolution?” »
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.
Continue reading “Programmable balloons pave the way for new shape-morphing devices” »
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.
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.
Continue reading “Scientists Fire Up a Commercially Available Desktop Quantum Computer” »
Engineers are turning to generative design algorithms to build components for NASA’s next-generation space suit—the first major update in decades.
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.
Continue reading “How Chinese tech giants are disrupting insurance industry with pooled funds” »
