Archive for the ‘Artificial Intelligence’ Category

Artificial intelligence recruited to find clues about COVID-19 – Roll Call

U.S. health and technology specialists on Monday said they had launched a new collaborative venture to assemble a dataset of tens of thousands of scientific papers and literature on the coronavirus, which would then be analyzed by artificial intelligence programs to find patterns and answer questions raised by the World Health Organization about the pandemic.

The dataset includes 29,000 articles, including 13,000 full-text pieces of medical literature, which will be made available on a special website allowing data scientists and artificial intelligence programmers to propose tools and software code that can unearth insights from the articles, White House officials and experts told reporters in a conference call.

The venture came together after the White House Office of Science and Technology Policy issued a call to tech companies and research groups to figure out how artificial intelligence tools could be used to sift through thousands of research articles being published worldwide on the pandemic, said Lynn Parker, deputy chief technology officer at the White House office.

With data scientists and machine language experts mining the literature compilation known as COVID-19 Open Research Dataset, experts and White House officials expect to get help developing vaccines, forming new guidelines on how long social distancing should be maintained and other insights, Michael Kratsios, the U.S. chief technology officer said.

[White House: Limit gatherings to 10 people due to virus]

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Artificial intelligence recruited to find clues about COVID-19 - Roll Call

An Unexpected Ally in the War With Bacteria – The Atlantic

Using computers and machine learning to make sense of mountains of biomedical data is nothing new. But the team at the Massachusetts Institute of Technology, led by James Collins, who studies applications of systems biology to antibiotic resistance, and Regina Barzilay, an artificial-intelligence researcher, achieved success by developing a neural network that avoids scientists potentially limiting preconceptions about what to look for. Instead, the computer develops its own expertise.

Read: Antibiotic resistance is everyones problem

With this discovery platform, which has been made freely available, youre going to identify molecules that dont look like antibiotics youre used to seeing, Collins said. It really shows how you can use the emerging technology of deep learning in an innovative manner to discover new chemistries.

Ever since Alexander Fleming derived the first antibiotic from fungus, nature has been the font for our antibacterial drugs. But isolating, screening and synthesizing thousands of natural compounds for laboratory tests is extremely expensive and time-consuming.

To narrow the search, researchers have sought to understand how bacteria live and multiply, and then pursued compounds that attack those processes (such as by damaging bacterias cell walls, blocking their reproduction, or inhibiting their protein production). You start with the mechanisms, and then you reverse engineer the molecule, Barzilay said.

Even with the introduction of computer-assisted, high-throughput screening methods in the 1980s, however, progress in antibiotic development was virtually nonexistent in the decades that followed. Screening occasionally turned up drug candidates that were toxic to bacteria, but they were too similar to existing antibiotics to be effective against resistant bacteria. Pharmaceutical companies have since largely abandoned antibiotic development, despite the need, in favor of more lucrative drugs for chronic conditions.

Read: How antibiotic resistance could make common surgeries more dangerous

The new work by Barzilay, Collins, and their colleagues, however, takes a radically fresh, almost paradoxical approach to drug discovery: It ignores how the medicine works. Its an approach that can succeed only with the support of extremely powerful computing.

Behind the new antibiotic finding is a deep neural network, in which the nodes and connections of its learning architecture are inspired by the interconnected neurons in the brain. Neural networks, which are adept at recognizing patterns, are deployed across various industries and consumer technologies for uses such as image and speech recognition. Conventional computer programs might screen a library of molecules to find certain defined chemical structures, but neural networks can be trained to learn for themselves which structural signatures might be usefuland then find them.

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An Unexpected Ally in the War With Bacteria - The Atlantic

The Army Will Soon Be Able to Command Robot Tanks With Artificial Intelligence – The National Interest

(Washington, D.C.) The Army Research Laboratory is exploring new applications of AI designed to better enable forward operating robot tanks to acquire targets, discern and organize war-crucial information, surveil combat zones and even fire weapons when directed by a human.

For the first time the Army will deploy manned tanks that are capable of controlling robotic vehicles able to adapt to the environment and act semi-independently. Manned vehicles will control a number of combat vehicles, not small ones but large ones. In the future we are going to be incorporating robotic systems that are larger, more like the size of a tanks, Dr. Brandon Perelman, Scientist and Engineer, Army Research Laboratory, Combat Capabilities Development Command, Army Futures Command, told Warrior in an interview, Aberdeen Proving Ground, Md.

The concept is aligned with ongoing research into new generations of AI being engineered to not only gather and organize information for human decision makers but also advance networking between humans and machines. Drawing upon advanced algorithms, computer technology can organize, and disseminate otherwise dis-aggregated pools of data in seconds -- or even milliseconds. AI-empowered sensors can bounce incoming images, video or data off a seemingly limitless existing database to assess comparisons, differences and perform near real-time analytics.

At the speed of the most advanced computer processing, various AI systems can simultaneously organize and share information, perform analyses and solve certain problems otherwise impossible for human address within any kind of comparable timeframe. At the same time, there are many key attributes, faculties and problem solving abilities unique to human cognition. The optimal approach is, according to Perelman, to simultaneously leverage the best of both.

We will use the power of human intelligence and the speed of AI to get novel interactions, Perelman added.

This blending, or synthesis of attributes between mind and machine is expected to evolve quickly in coming years, increasingly giving warzone commanders combat-sensitive information much faster and more efficiently. For instance, a forward operating robotic wingman vehicle could identify a target that might otherwise escape detection, and instantly analyze the data in relation to terrain, navigational details, previous missions in the area or a database of known threats.

You have an AI system that is not better than a human but different than a human. It might be faster and it might be more efficient at processing certain kinds of data. It will deal with threats in concert with human teammates that are completely different than the way we do things today, Perelman said.

With these goals in mind, the ARL is now working on mock up interfaces intended to go into the services emerging family of Next Generation Combat Vehicles. Smaller robots such as IED-clearing PackBots have been in existence for more than a decade; many of them have integrated software packages enabling various levels of semi-autonomy, able to increasingly perform a range of tasks without needing human intervention. Current ARL efforts now venture way beyond these advances to engineer much greater levels of autonomy and also engineer larger robots themselves such as those the size of tanks.

Army Research Lab Mock Up of Next-Gen Combat Vehicle AI-Enabled System

Bringing this kind of manned-unmanned teaming to fruition introduces new strategic and tactical nuances to combat, enabling war commanders a wider and more immediate sphere of options.

Commanders will be able to view a target through vehicle sensor packages, or if there is an aided target recognition technology or some kind of AI to spot targets, they might see battlespace target icons pop up on the map indicating the location of that target, Perelman said.

AI-oriented autonomous platforms can greatly shorten sensor-to-shooter time and enable war commanders to quickly respond to, and attack, fast emerging moving targets or incoming enemy fire.

Everything that a soldier does today. Shooting, moving, communicating.. Will be different in the future because you do not just have human to human teammates, you have humans working with AI-teammates, Perelman said.

Enabling robots to understand and properly analyze humans is yet another challenging element of this complex equation. When you have two humans, they know when the other is cold and tired, but when you bring in an AI system you dont necessarily have that shared understanding, Perelman said.

Various kinds of advanced autonomy, naturally, already exists, such as self-guiding aerial drones and the Navys emerging ghost fleet of coordinated unmanned surface vessels operating in tandem. Most kinds of air and sea autonomous vehicles confront fewer operational challenges when compared to ground autonomy. Ground warfare is of course known to incorporate many fast-changing variables, terrain and maneuvering enemy forces - at times to a greater degree than air and sea conditions - fostering a need for even more advanced algorithms in some cases. Nevertheless, the concepts and developmental trajectory between air, land and ground autonomy have distinct similarities; they are engineered to operate as part of a coordinated group of platforms able to share sensor information, gather targeting data and forward-position weapons -- all while remaining networked with human decision makers.

You can take risks you would never do with a manned platform. A robotic system with weapons does not need to account for crew protection, Perelman said.

Interestingly, the Army Research Lab current efforts with human-machine interface are reinforced by an interesting 2015 essay in the International Journal of Advanced Research in Artificial Intelligence, which points to networking, command and control and an ability to integrate with existing technologies as key to drone-human warfare.

They (drones) should effectively interact with manned components of the systems and operate within existing command and control infrastructures, to be integral parts of the system, in Military Robotics: Latest Trends and Spatial Grasp Solutions, by Peter Simon Sapaty - Institute of Mathematical Machines and Systems, National Academy of Sciences.

Increased use of networked drone warfare not only lowers risks to soldiers but also brings the decided advantage of being able to operate in more of a dis-aggregated, or less condensed formation, with each drone and soldier system operating as a node in a larger integrated network. Dispersed forces can not only enable longer-range connectivity and improved attack options but also reduce force vulnerability to enemy fire by virtue of being less aggregated.

Despite the diversity of sizes, shapes, and orientations, they (drones and humans) should all be capable of operating in distributed, often large, physical spaces, thus falling into the category of distributed systems, Sapaty writes in the essay.

Also of great significance, Army thinkers explain, is that greater integration of drone attack assets can streamline a mission, thereby lessening the amount of soldiers needed for certain high-risk operations.

When you are calling in artillery or air support, there is a minimum distance from where you are able to do that as a human being. You dont have the same restrictions with robotic systems, so it allows you to take certain risks, Perelman.

A paper in an Army University Press publication explains how drones can expand the battlefield. By utilizing drone systems for combatfewer warfighters are needed for a given mission, and the efficacy of each warfighter is greater. Next, advocates credit autonomous weapons systems with expanding the battlefield, allowing combat to reach into areas that were previously inaccessible, the essay states. (Amitai Etzioni, Phd, Oren Etzioni, Phd)

This article by Kris Osborn originally appeared in WarriorMaven in 2020.

Kris Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army - Acquisition, Logistics& Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel and The History Channel. He also has a Masters Degree in Comparative Literature from Columbia University.

Image: Reuters

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The Army Will Soon Be Able to Command Robot Tanks With Artificial Intelligence - The National Interest

Artificial Intelligence and RPA: Keys to Digital Transformation – Datamation

Register for this live video webcast - Friday, March 27 at 10 AM PT Ask the expert - get your AI/RPA questions answered by an industry expert.

One of the keys to digital transformation that most fashionable term is creating a management structure in which everything is accountable to data analytics. In a related trend, robotic process automation (RPA) helps automate a company's work flow and business processes. At its most optimum, RPA is driven by an AI-based analytics platform.

These key emerging technologies are the focus on this webinar. By attending, you will learn:

To provide insight into digital transformation, RPA and AI, I'll speak with Amir Orad, CEO, Sisense

Titl

Register for this live video webcast - Friday, March 27 at 10 AM PT

Bring your questions to this live video webcast well answer as many as we can.

Amir Orad, CEO, Sisense

James Maguire, Managing Editor, Datamation moderator

Bring your questions to this live video webcast well answer as many as we can.

Register for this live video webcast - Friday, March 27 at 10 AM PT

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Artificial Intelligence and RPA: Keys to Digital Transformation - Datamation

Artificial Intelligence as a Service Market Growth by Top Companies, Trends by Types and Application, Forecast to 2026 – Bandera County Courier

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Artificial Intelligence as a Service Market was valued at USD 1.58 Billion in 2018 and is projected to reach USD 32.5 Billion by 2026, growing at a CAGR of 45.7% from 2019 to 2026.

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Artificial Intelligence as a Service Market Growth by Top Companies, Trends by Types and Application, Forecast to 2026 - Bandera County Courier