Researchers at the universities of Toulouse and Paris-Saclay in France found they could differentiate between human and computerized Go players by analyzing the statistical characteristics of thousands of games played by people and algorithms. The researchers built databases of 8,000 games played by amateur humans, 8,000 played by the software Gnugo, 8,000 played by the software Fuego, and 50 games played by the software AlphaGo. Their analysis found network-based software forms more “communities”–signs the algorithms are creating varied and diverse strategies–than humans. The team also found statistical differences between the computer- and human-generated networks are much larger than the variability within each network. They suggest these differences could form the basis of a new type of Turing test. “We think our work indicates a path towards a better characterization and understanding of the differences between human and computer decision-making processes, which could be applied in many different areas,” says Paris-Saclay’s Olivier Giraud.
More info here: Phys.org, Lisa Zyga
Researchers at the Georgia Institute of Technology (Georgia Tech) have developed MERLIN, a computer-aided approach for streamlining the design process for origami-based structures. The researchers say MERLIN is a breakthrough that makes it easier for engineers and scientists to conceptualize new ideas graphically while generating the underlying mathematical data for building the structure. “With the new software, we can easily visualize and, most importantly, engineer the behavior of deployable, self-assembling, and adaptable origami systems,” says Georgia Tech professor Glaucio Paulino. The research involved building a computer model to simulate the interaction between the two facets of a folded sheet–how easily and how far the folds would bend, and how much the flat planes would deform during movement. MERLIN lets users simulate how origami structures will respond to compression forces at different angles. “The software also allows us to see where the energy is stored in the structure and better understand and predict how the objects will bend, twist, and snap,” Paulino says.
More info here: Georgia Tech Research Horizons
Researchers at the University of California, Los Angeles are constructing a device the California NanoSystems Institute’s Adam Stieg says is “inspired by the brain to generate the properties that enable the brain to do what it does.” The device is a mesh of highly interconnected silver nanowires that is self-configured out of random chemical and electrical processes. This network contains 1 billion artificial synapses for each square centimeter, and experiments found it can execute simple learning and logic operations, as well as filtering out unwanted noise from received signals. Instead of using software, the researchers leverage the network’s ability to distort an input signal in various ways, depending on where the output is quantified; this implies voice- or image-recognition applications. Another implication is the mesh could support reservoir computing, enabling users to select or mix outputs in such a manner that the result is a desired computation of the inputs.
More info here: Quanta Magazine, Andreas von Bubnoff
Researchers at Case Western Reserve University say they have developed a new machine-learning program that outperforms other methods in diagnosing Alzheimer’s disease before symptoms become too severe. The researchers say the program integrates a range of Alzheimer’s disease indicators, including mild cognitive impairment. In two successive stages, the algorithm selects the most relevant indicators to predict which patients have the disease. The researchers tested the algorithm using data from 149 patients collected via the Alzheimer’s Disease Neuroimaging Initiative. The team says they developed a Cascaded Multi-view Canonical Correlation Algorithm, which integrates measurements from magnetic resonance imaging scans, features of the hippocampus, glucose metabolism rates in the brain, proteomics, genomics, mild cognitive impairment, and other parameters. The algorithm selects the parameters that best distinguish between healthy and unhealthy patients, and then it selects from the unhealthy variables those that best distinguish who has mild cognitive impairment and who has Alzheimer’s disease.
More info here: The Daily (OH)
A neural network trained on poetry has attempted to write its own lines that mimic certain forms of verse. Its best efforts can convince people they are reading the words of a human poet. The poetic bot was developed by Jack Hopkins while he was a researcher at the University of Cambridge. It can be programmed to write in a particular rhythm or write poems on specific themes and can be endlessly tweaked to generate various forms. For example, Hopkins says it could write about Brexit in the style of a Greek epic or rewrite parts of Romeo and Juliet while mimicking a rapper. The AI poet was trained on more than 7 million words of 20th-century English poetry, most of it from poetry books found online. Hopkins also instructed the neural network to keep checking to make sure some of the words in each line relate to the selected theme.
More info here: New Scientist, Matt Reynolds
Russian and Chinese student teams won most of the top spots in the 41st annual ACM International Collegiate Programming Contest (ICPC) World Finals in May, and their lower-ranked U.S. counterparts attribute this disparity mainly to the fact the winners start learning computer programming much earlier. South Dakota School of Mines and Technology professor Larry Pyeatt says one factor in foreign programmers’ ascendancy has been cuts to U.S. computer science programs due to funding issues. Pyeatt also says on a trip to Russia earlier this year, he observed stark differences between U.S. and Russian education in the science, technology, engineering, and math (STEM) fields. “[Educators] start about four years earlier preparing [students] for STEM fields,” Pyeatt notes. The first-place prize went to a team from the St. Petersburg National Research University for Information Technologies, Mechanics and Optics, which solved 10 problems in the shortest time period.
More info here: Salon.com, Samuel Blackstone
Nagwa, an EdTech startup company in the field of mathematics education based in Windsor, UK, announces a new vacancy for a (native Spanish speaker) candidate with a PhD in mathematics.
The job title is “Mathematics Content Writer (Spanish)” and the salary bracket is £30,000–36,000. Full details about the job and a link for interested or potential applicants to apply is available at this link: