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Category: robotics/AI

A new bipartisan #congressionalreport calls for the #DefenseDepartment to get a lot more serious about the race to acquire #artificialintelligence and #autonomouscapabilities, modeling efforts to become dominant in these spheres after the “Manhattan Project” initiative to test and develop nuclear weapons in the 1940s.

On Tuesday, the House Armed Services Committee released the results of a yearlong review, co-led by Reps. Seth Moulton, D-Mass., and Jim Banks, R-Ind., aimed at assessing #U.S. #militarycapabilities and preparedness to meet current threats. The 87-page #Future of Defense Task Force Report contains some expected findings — #China and #Russia are identified as the top security threats to the U.S. and modernization is described as an urgent need — but there are surprising points of emphasis.

A bipartisan congressional report calls for the DoD to get more serious about the race to acquire artificial intelligence and autonomous capabilities, modeling efforts to become dominant in these spheres after the “Manhattan Project” initiative to test and develop nuclear weapons in the 1940s.

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Researchers at the Allen Institute for Artificial Intelligence (AI2) have created a machine learning algorithm that can produce images using only text captions as its guide. The results are somewhat terrifying… but if you can look past the nightmare fuel, this creation represents an important step forward in the study of AI and imaging.

Unlike some of the genuinely mind-blowing machine learning algorithms we’ve shared in the past—see here, here, and here —this creation is more of a proof-of-concept experiment. The idea was to take a well-established computer vision model that can caption photos based on what it “sees” in the image, and reverse it: producing an AI that can generate images from captions, instead of the other way around.

This is a fascinating area of study and, as MIT Technology Review points out, it shows in real terms how limited these computer vision algorithms really are. While even a small child can do both of these things readily—describe an image in words, or conjure a mental picture of an image based on those words—when the Allen Institute researchers tried to generate a photo from a text caption using a model called LXMERT, it generated nonsense in return.

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autonomous drone delivery

DroneUp and NATO Allied Command Transformation performed an experiment to prove a new and innovative way of resupplying soldiers on the battlefield. The experiment proved that autonomous drone delivery works.

“DroneUp recently partnered with North Atlantic Treaty Organization Allied Command Transformation, Joint Force Development Directorate, Operational Experimentation branch in an experiment designed to determine if autonomous delivery of a specified payload to identified recipients under field conditions could be proven viable,” says a press release.

The experiment took place on September 21, 2020 in Lawrenceville, VA and included Pale Horse

autonomous drone delivery

Weapons Institute, Daniel Defense, Ultimate Training Munitions (UTM), and WeaponLogic. In summary, here’s how the autonomous drone delivery system test worked: soldiers running out of ammunition hit a button (which can be attached to their hat or clothing.) That button signals a drone to fly to that individual soldier and drop a payload – which can be unique to that individual. Then the drone returns home for the next mission.

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Frost & Sullivan’s recent analysis, Data Science Impacting the Pharmaceutical Industry, finds that data science tools are promising technologies transforming drug discovery costs, speed, and efficiency. When combined with other emerging tech areas, artificial intelligence (AI) technologies move…

Pharmaceutical companies and hospitals are adopting data science rapidly, and its application is going to be established in all branches of healthcare

SANTA CLARA, Calif., Sept. 29, 2020 /PRNewswire/ — Frost & Sullivan’s recent analysis, Data Science Impacting the Pharmaceutical Industry, finds that data science tools are promising technologies transforming drug discovery costs, speed, and efficiency. When combined with other emerging tech areas, artificial intelligence (AI) technologies move to the next phase of advancements. Hence, they are expected to witness adoption by pharma and biotech companies in the next four to five years. Further, with the COVID-19 pandemic, AI and machine learning (ML) can be used for drug research and clinical trials against the coronavirus to screen large databases and perform docking studies to identify existing potential drugs or design new drugs using advanced learning algorithms.

For further information on this analysis, please visit: http://frost.ly/4l2.

“Applying data science tools in healthcare, especially for drug discovery, has a huge potential to systematically change the entire existing practices and methods,” said Aarthi Janakiraman, Technical Insights Research Manager at Frost & Sullivan. “Additionally, pharmaceutical companies and hospitals are adopting this system rapidly, and its application is going to be established in all branches of healthcare.”

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In a major strategy shift, Sberbank, the most popular Russian lender, wants to build its own ecosystem going far beyond the world of finance and to be known not just as a bank, but also as a tech company.

During its first major online event, which was held on Thursday, Sberbank – now rebranded as Sber – presented a range of services and gadgets signaling it wants to go deeper into the tech sector. For example, the bank presented a family of “emotional” virtual assistants, called ‘Salute’, which will be incorporated into all of Sberbank’s devices and mobile apps.

There are three assistants in the Salute family, called Sber, Joy, and Athena. Unlike Apple’s Siri or Amazon’s Alexa, the company is betting on the “emotional” features of the virtual assistants, as each has its own “temper,” allowing users to choose the one they find most suitable.

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