In 1918, the American chemist Irving Langmuir published a paper examining the behavior of gas molecules sticking to a solid surface. Guided by the results of careful experiments, as well as his theory that solids offer discrete sites for the gas molecules to fill, he worked out a series of equations that describe how much gas will stick, given the pressure. Now, about a hundred years later, an "AI scientist" developed by researchers at IBM Research, Samsung AI, and the University of Maryland, Baltimore County (UMBC) has reproduced a key part of Langmuir's Nobel Prize-winning work. The system--artificial intelligence (AI) functioning as a scientist--also rediscovered Kepler's third law of planetary motion, which can calculate the time it takes one space object to orbit another given the distance separating them, and produced a good approximation of Einstein's relativistic time-dilation law, which shows that time slows down for fast-moving objects. A paper describing the results is published in Nature Communications on April 12.

Cyber Threat Intelligence (CTI) is information describing threat vectors, vulnerabilities, and attacks and is often used as training data for AI-based cyber defense systems such as Cybersecurity Knowledge Graphs (CKG). There is a strong need to develop community-accessible datasets to train existing AI-based cybersecurity pipelines to efficiently and accurately extract meaningful insights from CTI. We have created an initial unstructured CTI corpus from a variety of open sources that we are using to train and test cybersecurity entity models using the spaCy framework and exploring self-learning methods to automatically recognize cybersecurity entities. We also describe methods to apply cybersecurity domain entity linking with existing world knowledge from Wikidata. Our future work will survey and test spaCy NLP tools and create methods for continuous integration of new information extracted from text.

The meetup will take place in Baltimore and will start at 6 pm with 30 minutes of networking -- join us for beer & pizza -- followed by a program featuring Philip Resnik speaking on the state of NLP -- on mainstream and emerging natural language understanding techniques -- and mapped to startup and AI research opportunities.

MIAMI – Two people were killed and 11 others wounded Sunday when a video game tournament competitor went on a shooting rampage before turning the gun on himself in the northern Florida city of Jacksonville, local police said. Sheriff Mike Williams named the suspect of the shooting at a Madden 19 American football eSports tournament as 24-year-old David Katz from Baltimore, Maryland. "There were three deceased individuals at the scene, one of those being the suspect, who took his own life," Williams told reporters. He said local fire and rescue transported nine victims -- seven of whom had gunshot wounds -- to local hospitals, while another two people who were shot took their own transportation to hospital. Williams said Katz was a competitor in the eSports tournament and used "at least one handgun" to carry out the shooting.

Computer programs can, in some cases, predict chemical toxicity as well as tests done on rats and other animals.Credit: Coneyl Jay/SPL Machine-learning software trained on masses of chemical-safety data is so good at predicting some kinds of toxicity that it now rivals -- and sometimes outperforms -- expensive animal studies, researchers report. Computer models could replace some standard safety studies conducted on millions of animals each year, such as dropping compounds into rabbits' eyes to check if they are irritants, or feeding chemicals to rats to work out lethal doses, says Thomas Hartung, a toxicologist at Johns Hopkins University in Baltimore, Maryland. "The power of big data means we can produce a tool more predictive than many animal tests." In a paper published in Toxicological Sciences1 on 11 July, Hartung's team reports that its algorithm can accurately predict toxicity for tens of thousands of chemicals -- a range much broader than other published models achieve -- across nine kinds of test, from inhalation damage to harm to aquatic ecosystems. The paper "draws attention to the new possibilities of big data", says Bennard van Ravenzwaay, a toxicologist at the chemicals firm BASF in Ludwigshafen, Germany.

Last week I attended the 2018 INFORMS Conference on Business Analytics & Operations Research in Baltimore, MD. Among the activities of the conference, several teams of researchers from universities and industries were competing in challenges to show how their work is influencing the world. I had the honor to be among the finalist teams for the 2018 Syngenta Crop Challenge in Analytics. The Syngenta Crop Challenge in Analytics was established in 2015 with funding provided by prize winnings awarded to Syngenta in connection with its receipt of the 2015 Franz Edelman Award for Achievement in Operations Research and the Management Sciences. This year's Challenge asked participants to develop a quantitative framework for predicting corn hybrids performance in new, untested locations.