Neural Network Poetry Is So Bad We Think It’s Written by Humans

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

Publicado en Ciencia y programación

Russian Students Dominate at the Computer Programming Olympics

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

Publicado en Ciencia y programación

Mathematics Content Writer (Spanish) – Job vacancy

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:

http://www.nagwa.com/en/uk/eng/careers/759175967878/

Publicado en Ciencia y programación

Jean Sammet, Co-Designer of a Pioneering Computer Language, Dies at 89

Software engineer Jean E. Sammet, who co-designed the Common Business Oriented Language (COBOL) and was elected the first female president of the ACM in 1974, passed away on May 20 at the age of 89. Sammet achieved a level of prominence in computing beyond most women of her generation, and she once said her ambition was “to put every person in communication with the computer,” according to University of Maryland professor Ben Shneiderman. The Computer History Museum’s Dag Spicer says Sammet’s book, “Programming Languages: History and Fundamentals,” published in 1969, “was, and remains, a classic” in the field. COBOL remains an essential element in the mainframes underlying corporate and government agency operations worldwide. Sammet worked with five other programmers designing COBOL over a period of two weeks, and the language enabled innovative techniques for describing and representing data in computer code. Sammet later worked to inject more engineering discipline into the language.

More info here: The New York Times, by Steve Lohr

Publicado en Ciencia y programación

An Algorithm Summarizes Lengthy Text Surprisingly Well

Researchers at Salesforce have developed an algorithm that applies machine-learning techniques to accurately and coherently condense lengthy textual documents, technology which could impact fields such as law, medicine, and scientific research. The algorithm blends various strategies, including supervised learning, by being fed summary examples, while also applying an artificial attention mechanism to the text it is receiving and generating. The process ensures the system will not return too many repetitive strands of text, which has been an issue for other summarization programs. In addition, the system conducts experiments to produce its own summaries via reinforcement learning. Northwestern University professor Kristian Hammond lauds the Salesforce algorithm, but says it also illustrates the limits of solely relying on statistical machine learning. “We need a little bit of semantics and a little bit of syntactic knowledge in these systems in order for them to be fluid and fluent,” Hammond says.

More info here: Technology Review

Publicado en Ciencia y programación

Flexible tactile sensor lets robots feel

Researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed a tactile sensor composed of silicon and carbon materials that can serve as a skin for robots, absorb shocks, and differentiate between various forms of touch. The researchers combined silicon and carbon nanotubes to produce a composite, which was paired with a medical-imaging technique called electrical impedance tomography. The team says the new material can distinguish between the location and the size of various forms by touch. In addition, it can withstand strong force, as well as function as a three-dimensional computer interface and tactile sensor. The researchers also note it can be reused even after partial damage to the sensor by filling and hardening the damaged region with composite. “This technology will contribute to the soft robot industry in the areas of robot skin and the field of wearable medical appliances,” says KAIST professor Jung Kim.

More info here:  EE Times Asia

Publicado en Ciencia y programación

Computers Learn to Cooperate Better Than Humans

Computers have for the first time trained themselves to cooperate in games in which the goal is to achieve the best possible outcome for all players. Brigham Young University professor Jacob Crandall and colleagues brought humans and computers together to play digital versions of chicken, prisoner’s dilemma, and a third collaborative game called “alternator.” Teams consisted of two people, two computers, or one human and one computer. Twenty-five different machine-learning algorithms were tested, but no one algorithm was capable of collaborating. The researchers then imbued communicative ability among the computers by adding 19 prewritten phrases to be sent back and forth between partners after each term. Over time, the computers had to learn the phrases’ definition in the context of the game. The S# algorithm learned to cooperate with its partner in a few turns, and the machine-only teams cooperated at a higher rate than humans by the end of the game.

More info here: Science – Jackie Snow

Publicado en Ciencia y programación