This paper tackles the age-old question of derivative free optimization in neural networks. This paper introduces AdaSwarm, a novel derivative-free optimizer to have similar or better performance to Adam but without "gradients". To support the AdaSwarm, a novel Particle Swarm Optimization Exponentially weighted Momentum PSO (EM-PSO), a derivative-free optimizer, is also proposed which tackles constrained and unconstrained single objective optimization problems and looks at applying the proposed momentum particle swarm optimization on benchmark test functions, engineering optimization problems and habitability scores for exoplanets which show speed and convergence of the technique. The EM-PSO is extended by approximating the gradient of a function at any point using the parameters of the particle swarm optimization. This is a novel technique to simulate gradient descent, an extremely popular method in the back-propagation algorithm, using the approximated gradients from the particle swarm optimization parameters. Mathematical proofs of gradient approximation by EM-PSO, thereby bypassing the gradient computation, are presented. The AdaSwarm is compared with various optimizers and the theory and algorithmic performance are supported by promising results.

A preliminary test in only eight volunteers suggests the first coronavirus vaccine to be tested in people seems to be safe and can stimulate an immune response against the virus. Antibodies generated by the volunteers were able to stop the virus from replicating in human cells in the laboratory and the levels of antibodies in their blood were similar to those previously detected in recovered covid-19 patients. Tal Zaks of Moderna, the US firm developing the vaccine, said that if the next stages go well, it could be widely available by the end of this year or early next year. The US stock market was up sharply today following the announcement. However, it remains to be seen if such speedy testing and manufacturing of a vaccine is really possible – no vaccine has ever been produced in less than five years. Meanwhile, a trial of another vaccine, developed by researchers at the University of Oxford found it wasn't able to stop six rhesus macaque monkeys from becoming infected with the ...

High throughput genetic analysis is a tool that allows researchers to analyze a lot of DNA data in a short period of time. The work is commonly done in a lab with scientific instruments. Larry Purcell uses it to evaluate thousands of agricultural test plots at once. He does it from a distance of 100 feet -- straight up. Using an off-the-shelf aerial drone, Purcell can identify those soybean plants that have the genetic make-up, or genotype, for high rates of nitrogen fixation.

Researchers have developed a new machine learning algorithm that can determine whether ancient excrement was deposited by a human or a dog. Bones and artifacts are great, but ancient poop can offer archaeologists tremendous insights, too -- insights into dietary patterns, parasite evolution and more. The only problem is that it can be hard to identify the owner of really old feces. Specifically, scientists have trouble differentiating between ancient human and dog feces. Dogs have been hanging out around humans for thousands of years.

Not so long ago, the concept of a fully automated store seemed something of a curiosity. Now, in the midst of the COVID-19 pandemic, the idea of relying on computers and robotics, and checking out groceries by simply picking them off the shelf doesn't seem so peculiar after all. Part of my research involves looking at how we deal with complex artificial intelligence (AI) systems that can learn and make decisions without any human involvement, and how these types of AI technologies challenge our current understanding of the law and its application. How should we govern these systems that are sometimes called disruptive, and at other times labeled transformative? I am particularly interested in whether -- and how -- AI technologies amplify the social injustice that exists in society.

For many of us, our microwaves and dishwashers aren't the first thing that come to mind when trying to glean health information, beyond that we should (maybe) lay off the Hot Pockets and empty the dishes in a timely way. But we may soon be rethinking that, thanks to new research from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). The system, called "Sapple," analyzes in-home appliance usage to better understand our health patterns, using just radio signals and a smart electricity meter. Taking information from two in-home sensors, the new machine learning model examines use of everyday items like microwaves, stoves, and even hair dryers, and can detect where and when a particular appliance is being used. For example, for an elderly person living alone, learning appliance usage patterns could help their health-care professionals understand their ability to perform various activities of daily living, with the goal of eventually helping advise on healthy patterns.

The US Air Force (USAF) has launched a competition to design the artificially intelligent software, called Skyborg, that would control its planned fleet of loyal wingman unmanned air vehicles (UAV). The service intends to grant indefinite delivery/indefinite quantity contracts worth $400 million per awardee to develop the software and related hardware, it says in a request for proposals released on 15 May. The USAF is looking for technical and cost proposals from companies by 15 June 2020 and intends to award multiple companies contracts, though it may award just one contract or no contracts, based on proposals. Skyborg would be artificially intelligent software used to control the flight path, weapons and sensors of large numbers of UAVs. Automating flight control, in particular via artificial intelligence, is seen as necessary to allow a single person, perhaps a backseat pilot in a fighter aircraft, to command multiple UAVs at once.

However, we know there's still a lot to do – this work does not yet represent a product that could be implemented in routine clinical practice. While our model can make better predictions than clinical experts, there are many other factors to consider for such systems to be impactful in a clinical setting. While the model was trained and evaluated on a population representative of the largest eye hospital in Europe, additional work would be needed to evaluate performance in the context of very different demographics. A recent study examining the use of a different AI system in a clinical setting highlighted just some of the sociotechnical issues for such systems in practice. Another difficult point to contend with is that any prediction system will have a certain rate of false positives: that is, when a patient is found to have a condition, or predicted to develop one, that they don't actually have.

In July 1848, L'illustration, a French weekly, printed the first photo to appear alongside a story. It depicted Parisian barricades set up during the city's June Days uprising. Nearly two centuries later, photojournalism has bestowed libraries with legions of archival pictures that tell stories of our past. But without a methodical approach to curate them, these historical images could get lost in endless mounds of data. That's why the Library of Congress in Washington, D.C. is undergoing an experiment. Researchers are using specialized algorithms to extract historic images from newspapers.

One expects a Green Beret medic to readily respond to calls for help, so it's not that surprising that Luke Sciulli packed his bags in early April and left Pittsburgh for New York City, an epicenter for the COVID-19 pandemic, to volunteer in a field hospital. Sciulli, a senior research analyst in the School of Computer Science's Auton Lab, explains his motivations more humbly: his house had burned down and he was living in a camping trailer. When he heard that former Special Forces medics and medical personnel were opening an ad hoc hospital in New York, he figured, why not? Whatever his motivation, Sciulli began work April 16 at the NewYork-Presbyterian Ryan F. Larkin Field Hospital, named for a Navy SEAL and medic who took his own life three years ago after suffering traumatic brain injury. Located in an indoor soccer stadium at Columbia University, the temporary hospital served as a step-down unit for COVID-19 patients, providing a place for the recovering patients to convalesce a few more days before heading home.