The proliferation of robotic warships could make naval warfare safer for human beings. But it also could have the unintended effect of reducing the threshold for military action. Recent events in the Strait of Hormuz underscore that danger. In the summer of 2019 U.S. and Iranian forces each shot down a surveillance drone belonging to the other side, escalating tensions that began with U.S. president Donald Trump's decision to withdraw the United States from the 2015 deal limiting Iran's nuclear program. "The immediate danger from militarized artificial intelligence isn't hordes of killer robots, nor the exponential pace of a new arms race," Evan Karlik, a U.S. Navy lieutenant commander, wrote for Nikkei Asian Review.

Sitting in a U.N. committee meeting in Geneva earlier this year in a session on Lethal Autonomous Weapons Systems (aka killer robots), I was shocked to hear the American delegates claim that AI-powered automated warfare could be safe and reliable. Not only are they wrong, but their thinking endangers us all. It's the same logic that led to the Cold War, the nuclear arms race and the Doomsday Clock. I quit my job at a young, promising tech company in January in protest precisely because I was concerned about how the Pentagon might use AI in warfare and how the business I was part of might contribute to it. I have seen close up the perils of this unreliable but powerful technology, and I have since joined the International Committee for Robot Arms Control (ICRAC) and the Campaign to Stop Killer Robotsto make sure that AI is used responsibly, even in cases of war.

An organization that won the Nobel Prize in 2017 for its work to eliminate nuclear weapons is sounding the alarm about the possibility of artificial intelligence leading to unintended wars. Beatrice Fihn, executive director of the International Campaign to Abolish Nuclear Weapons, is worried that hackers could breach A.I. technologies that are used in nuclear programs or that they could use A.I. to dupe countries into launching attacks. For example, deepfakes, or realistic-looking computer-altered videos, may be used to "create a perceived threat that might not be there," she warns, prompting governments to overreact. Fihn told Fortune that she wants to convene a meeting in the fall with nuclear weapons experts and some of the leading companies in A.I. and cybersecurity. Participants in the off-the-record event, she said, would produce a document that her group would use to inform governments and others about the danger.

String similarity models are vital for record linkage, entity resolution, and search. In this work, we present STANCE --a learned model for computing the similarity of two strings. Our approach encodes the characters of each string, aligns the encodings using Sinkhorn Iteration (alignment is posed as an instance of optimal transport) and scores the alignment with a convolutional neural network. We evaluate STANCE's ability to detect whether two strings can refer to the same entity--a task we term alias detection. We construct five new alias detection datasets (and make them publicly available). We show that STANCE or one of its variants outperforms both state-of-the-art and classic, parameter-free similarity models on four of the five datasets. We also demonstrate STANCE's ability to improve downstream tasks by applying it to an instance of cross-document coreference and show that it leads to a 2.8 point improvement in B^3 F1 over the previous state-of-the-art approach.

Future space-based telescopes will leverage starshades as components that can be independently positioned. Starshades will adjust the light coming in from exoplanet host stars and enhance the direct imaging of exoplanets and other phenomena. In this context, scheduling of space-based telescope observations is subject to a large number of dynamic constraints, including target observability, fuel, and target priorities. We present an application of genetic algorithm (GA) scheduling on this problem that not only takes physical constraints into account, but also considers direct human suggestions on schedules. By allowing direct suggestions on schedules, this type of heuristic can capture the scheduling preferences and expertise of stakeholders without the need to always formally codify such objectives. Additionally, this approach allows schedules to be constructed from existing ones when scenarios change; for example, this capability allows for optimization without the need to recompute schedules from scratch after changes such as new discoveries or new targets of opportunity. We developed a specific graph-traversal-based framework upon which to apply GA for telescope scheduling, and use it to demonstrate the convergence behavior of a particular implementation of GA. From this work, difficulties with regards to assigning values to observational targets are also noted, and recommendations are made for different scenarios.

The UK is planning to harness AI to help determine the condition of roads and where investment should be prioritised. British drivers are well-accustomed to poor road conditions, especially potholes and the long delays in getting them fixed (one ingenious man has even come up with an innovative way of getting the council to fix them faster...) To be fair to councils, keeping all the roads in top condition is expensive. Factors like minimising disruption along busy routes, and planning diversions, must also be considered. Fortunately, AI is beginning to help automate this automotive dilemma. The Department for Transport (DfT) has awarded £2m in funding to a project using AI to examine the condition of roads, forming part of a wider £350 million funding package.

This article was originally published on the National Center for Supercomputing Applications website. In 2007, the Sloan Digital Sky Survey (SDSS) launched a citizen science campaign called Galaxy Zoo to enlist the public's help in classifying the hundreds of thousands of galaxy images captured by an optical telescope. Through this highly successful crowdsourcing effort, volunteers reviewed the images online to help determine whether each galaxy had a spiral or elliptical structure. Leveraging data generated by the Galaxy Zoo project, a team of scientists is now applying the power of artificial intelligence (AI) and high-performance supercomputers to accelerate efforts to analyze the increasingly massive datasets produced by ongoing and future cosmological surveys. In a new study, researchers from the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign and the Argonne Leadership Computing Facility (ALCF) at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a novel combination of deep learning methods to provide a highly accurate approach to classifying hundreds of millions of unlabeled galaxies.

To answer the question, "Where's Waldo?" readers need to look for a number of distinguishing features. Several characters may be spotted with a striped scarf, striped hat, round-rimmed glasses, or a cane, but only Waldo will have all of these features. As described July 22, 2019, in Nature Microbiology, a team led by scientists at the U.S. Department of Energy (DOE) Joint Genome Institute (JGI), a DOE Office of Science User Facility, developed an algorithm that a computer could use to conduct a similar type of search in microbial and metagenomic databases. In this case, the machine "learned" to identify a certain type of bacterial viruses or phages called inoviruses, which are filamentous viruses with small, single-stranded DNA genomes and a unique chronic infection cycle. "We're not sure why we systematically manage to miss them; maybe it's due to the way we currently isolate and extract viruses," said the study's lead author Simon Roux, a JGI research scientist in the Environmental Genomics group.

Dr. Agyeya Tripathi has completed his Ph.D. and holds a Masters degree in Business Administration and another Masters in Electronics and Communication. He is a national resource person for Financial Inclusion under National Rural Livelihood Mission, Ministry of Rural Development, Government of India. Dr. Agyeya Tripathi has completed his Ph.D. and holds a Masters degree in Business Administration and another Masters in Electronics and Communication.

It is 8.30am and Britain's most eminent scientist is taking a windswept stroll through Dorset's rolling hills. It seems hard to believe that James Lovelock – sprightly despite a walking stick and bristling with a fierce, bright-eyed intelligence – will turn 100 this week. But the man known for proposing one of the most visionary scientific theories of the last century starts the day just as he always does, with a brisk walk from his coastguard's cottage by the shores of Chesil Beach with his beloved wife, Sandy. That Lovelock is conscious of his own mortality is to be expected. But that he is also musing on the future of the Earth he will never live to see – one which involves cyborgs, no less – is, perhaps, rather more surprising.