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Interactive online platforms have become an integral part of our daily lives. While user-generated content, free from traditional editorial constraints, has spurred vibrant online communications, improved business processes and expanded access to information, it has also raised complex questions regarding how to moderate harmful online content. As the volume of user-generated content continues to grow, it has become increasingly difficult for internet and social media companies to keep pace with the moderation needs of the information posted on their platforms. Content moderation measures supported by artificial intelligence (AI) have emerged as important tools to address this challenge.

Whether you are managing a social media platform or an e-commerce site, minimizing harmful content is critical to the user experience. Such harmful content can include everything from posts promoting violence to child abuse. In fact, the range and scope of potential harmful content has proven too broad for human moderators to comprehensively review. AI systems, designed to mirror the way humans think and process information, may be able to improve the speed and accuracy of this process. AI technology can take large data sets and teach machines to identify patterns or make predictions about certain inputs. Ultimately, this capability allows computers to recognize and filter certain words or images with more efficiency than humans can process this information. As an added benefit, this reduces or could potentially eliminate the need for human moderators to be directly exposed to harmful content.

While AI systems are promising, they are not without their own set of challenges. By some estimates, there are 2.5 quintillion bytes of data created each day. As such, while AI offers a way to more efficiently process large amounts of data, the volume of content at issue is now so vast that it has become critical that AI models perform with both speed and accuracy. And achieving optimal accuracy requires an AI model to not only be based on accurate data and imagery but also be able to appreciate nuances in the content reviewed to distinguish satire from disinformation. Further, questions have been raised regarding whether these models remove the inevitable biases of human content moderators, or if the AI models themselves actually introduce, entrench or amplify biases against certain types of users. One study, for example, found that AI models trained to process hate speech online were 1.5 times more likely to identify tweets as offensive or hateful when written by African-American users.

This tension demonstrates the difficult balance between designing models to address human inefficiencies and root out human error in content moderation while ensuring new systematic issues are not introduced into the models themselves. In fact, U.S. policymakers have conducted numerous hearings and floated legislative proposals to address concerns regarding bias within AI systems and the unintentional discrimination that could result from using such systems.

AI systems undeniably offer online platforms enhanced capabilities to effectively moderate user-generated content, but they present their own set of challenges that must be considered as these systems are designed and deployed as moderation tools.

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Everything in Moderation: Artificial Intelligence and Social Media Content Review - JD Supra

NEW YORK, March 25, 2021 /PRNewswire/ -- Fact.MR's report on global autonomous farm equipment market projects a steady growth of over 10% CAGR through 2021 to reach valuation of US$ 150 Bn by 2031. Penetration of Artificial Intelligence (AI) and advanced technology in farming sector to spike the demand of the market in coming years. Fact.MR's study projects fully and partially autonomous tractors to gain traction by 2031.

The latest edition of autonomous farm equipment market by Fact.MR finds that manufacturers are investing more in R&D activities to capitalize on upcoming opportunities, spurring the sales. Significant development in technology and incorporation of AI in the fully automated tractors and harvesters is attracting the suppliers to invest more in the similar technology to improve the efficiency

With the rising demand of autonomous tractors equipped with auto-steering and auto data collecting systems from the customers, manufacturers are riveted to invest more in R&D activities to incorporate AI. Hence fostering the sales.

"Heightened investments by manufacturers in research and development for advanced technology such as drones and fully automated tractors is stimulating growth of autonomous farm equipment," says a Fact.MR analyst.

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Key Takeaways

Competitive Landscape

Some of the key players profiled by Fact.MR supplying autonomous farm equipment include Bobcat, AGCO Corporation, John Deere, Case IH, Yanmar, D Robotix, Agribotix, ADAMA Agricultural Solution Ltd., Mahindra, Rowbot, and Clearpath Robotics among others. According to the study, the market is highly competitive and quality product innovation is the key strategy adopted by prominent players.

For instance, in December 2020, Raven Industries announced the launch of its first commercially-available Driverless Ag Technology: AutoCart. This autonomous grain cart solution is available in the market since the 2021 harvest season. The new product launch showcased the company's position as the technology leader in the agriculture by bridging the gap from human-operated to fully-autonomous platform.

In similar fashion, Yanmar Agribusiness Co. Ltd., in February 2021, upgraded its autonomous tractor with multi-frequency antenna to improve its performance capabilities. This upgraded autonomous tractors series will be available in the market from 1st April 2021, capable of full or partial autonomous operation, providing customer with the improved results for their farming needs.

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More Valuable Insights on Autonomous farm equipment Market

Fact.MR, in its latest report, offers a detailed segmentation of the global autonomous farm equipment market. The study divulges essential insights on the autonomous farm equipment market on the basis of product (tractors, harvesters, UAVs, and others), and region (North America, Latin America, Europe, East Asia, South East Asia & Oceania, and MEA)

Key Questions Covered in the Report

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Explore Fact.MR's Coverage on the Automotive Domain

Trailer Terminal Tractor Market: Fact.MR's latest coverage on global trailer terminal tractor market gives a detailed analysis on the prominent growth dynamics, including possible drivers, opportunities and new entrants, expected to prevail across the landscape for the upcoming decade. A detailed insight regarding key geographies and prominent manufacturers has been embedded in this report.

Farm Tires Market: The latest report by Fact.MR on global farm tires market offers an unbiased analysis on the key drivers, trends and opportunities expected to prevail across prominent segments and key geographies for the forthcoming assessment period. Additionally, details about prominent manufacturers and their revenue shares have also been incorporated.

Autonomous Emergency Braking System (AEBS) Market: Fact.MR, in its new offering, provides a detailed segmentation on the global autonomous emergency breaking system market, presenting historical demand data and forecast statistics. The study divulges compelling insights on the flame retardant polyester resins market on the basis of product type, technology type, vehicle type, and regional outlook.

About Fact.MR

Market research and consulting agency with a difference! That's why 80% of Fortune 1,000 companies trust us for making their most critical decisions. We have offices in US and Dublin, whereas our global headquarter is in Dubai. While our experienced consultants employ the latest technologies to extract hard-to-find insights, we believe our USP is the trust clients have on our expertise. Spanning a wide range from automotive & industry 4.0 to healthcare & retail, our coverage is expansive, but we ensure even the most niche categories are analyzed. Reach out to us with your goals, and we'll be an able research partner.

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Artificial Intelligence in Autonomous Farm Equipment an Exciting Prospect: Fact.MR - PRNewswire

In the production work of the future, autonomous systems will support people through data-based analyzes and intelligent solution patterns for value-adding activities.Experts from the two Fraunhofer institutes IPA and IAO in Stuttgart will show their guests at the International Open Lab Day on April 16, 2021 the benefits that artificial intelligence (AI) can bring.

In the future, AI could identify potential for optimization, make fitters work easier or set up personnel deployment plans: These are just a few examples of how AI can help make production more resilient and flexible. However, this does not mean that the human being becomes an assistant to the algorithms, but remains the director of the processes, works efficiently in an adaptive environment and no longer has to take on so many control tasks. Digital technologies help to make production work more human-centered. In the Future Work Lab, we show the potential of cognitive assistance systems and participatory implementation methods for use in operational production systems, says Simon Schumacher, project manager of the Future Work Lab at the Fraunhofer Institute for Manufacturing Engineering and Automation IPA.

Together with his colleagues from the Fraunhofer Institute for Industrial Engineering and Organization IAO, the scientist has set up over 50 demonstrators in the Future Work Lab that cover the entire spectrum of future industrial work environments. On April 16, 2021, the two institutes invite you to the virtual International Open Lab Day from 9 a.m. (CET). The research team guides its guests live through the exhibition and shows a selection of new AI demonstrators:

AI in Process Optimization:A portable, flexible data acquisition tool automatically reveals optimization potential in production.For this purpose, a modular sensor system was combined with AI-based algorithms for process recognition.

AI in Assembly:To support human workers, object recognition with the help of augmented reality glasses is increasingly being used in assembly.For this, the corresponding software must be trained in advance with a large amount of high-quality data.Thanks to AI, this training is automated.

AI in PersonnelPlanning :A flexible deployment of personnel enables short-term shift adjustments, contains time buffers for crisis or boom phases and offers employees attractive working hours.AI supports tailor-made planning.

Trustworthy AI:Wherever people are affected by decisions made by AI, it is important to make their internal functioning and decision-making comprehensible.This transparency can help to increase the acceptance of AI.

You can register for the International Open Lab Day at this link.

For more information: http://www.ipa.fraunhofer.de

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View Larger Image Joseph Sanford, M.D., and Adria Abella Villafranca use code they have written to test the OAK-D camera, equipped with spatial artificial intelligence. Image by Karmen Robinson

March 25, 2021 | The University of Arkansas for Medical Sciences (UAMS) Institute of Digital Health & Innovation (IDHI) was one of 13 university teams selected as a North American regional finalist in the OpenCV Artificial Intelligence Competition.

Joseph Sanford, M.D.

There were nearly 1,400 entries worldwide in this competition, with 210 teams chosen as finalists across six geographic regions.

The IDHI team, named the Little Rock-ies, received the nod of approval to conduct non-invasive risk assessments of patient airways, with the use of specialized cameras that are equipped with spatial artificial intelligence, commonly referred to as spatial AI. As a finalist, the team won 10 cameras to complete the project.

Respiratory illness has increased due to COVID-19, shining a light on the risks surrounding invasive airway procedures, said institute director Joseph Sanford, M.D. Using these spatial AI cameras, we are working on a solution that adapts existing assessments to better quantify difficult intubation risk, which can aid in selecting medications, equipment and techniques to safely secure a patients airway.

Team members (from top left) Catherine Shoults, Pablo Trevino, Lori Wong, M.D., and Adria Abella Villafranca.

The spatial AI component of the camera provides human-level perception and real-time video to determine the distance and depth of the space inside of a patients airway as well as other features of the head and neck anatomy. Anesthesiologists currently use exam techniques that subjectively estimate these risks, meaning the assessment could vary from physician to physician.

In contrast, the teams proposed method requires simply placing a camera in front of a patients open mouth, and the device will objectively calculate the measurements of the airway and provide an assessment score to determine the risks of invasive airway procedures, regardless of who the administering physician is.

Kevin Sexton, M.D.

As the team works to bring this new development to the forefront, one of the most significant factors of its success has been the contributions from UAMS students.

Computer vision and artificial intelligence are new frontiers for health care, and it isthrillingto have the opportunity to develop this expertise, said Catherine Shoults, a Ph.D. biomedical informatics student who also holds a masters degree in public health. As we continue to discuss these devices, we can continue to develop applications to advance health care.

Alongside Sanford and Shoults, members of the team include Kevin Sexton, M.D., Lori Wong, M.D., Michael Cruz, Pablo Trevino and Adria Abella Villafranca.

Regional winners will be announced July 26.

Originally posted here:
Institute of Digital Health & Innovation Team Chosen as a Finalist in Artificial Intelligence Competition - UAMS News

The US Defense Advanced Research Projects Agencys (DARPAs) Air Combat Evolution (ACE) programme tested team dogfighting between artificial intelligence-controlled fighters in a software simulation in February.

Scrimmage 1, run by Johns Hopkins Applied Physics Laboratory, tested artificial intelligence algorithms in simulated two-versus-one engagements: two friendly blue Lockheed Martin F-16s fighting as a team against an enemy red aircraft, DARPA said on 18 March.

The simulations follow the AlphaDogfight Trials conducted last August, a virtual demonstration of one-versus-one dogfighting with a short-range gun. The new scrimmages included long-range virtual missile engagements.

These new engagements represent an important step in building trust in the algorithms since they allow us to assess how the [artificially intelligent] agents handle clear avenue of fire restrictions set up to prevent fratricide, says Colonel Dan Javorsek, programme manager in DARPAs Strategic Technology Office. This is exceedingly important when operating with offensive weapons in a dynamic and confusing environment that includes a manned fighter, and also affords the opportunity to increase the complexity and teaming associated with manoeuvring two aircraft in relation to an adversary.

Ultimately, DARPA envisions loyal wingman unmanned air vehicles (UAVs) handling dogfightingautonomously while a human pilot would focus on higher-cognitive battle manager decisions. The agency is working to demonstrate that artificial intelligence-controlled UAVs can be trusted in battle.

To begin capturing this trust data, test pilots have flown several flights in an [Aero Vodochody] L-29 jet trainer at the University of Iowa Technology Institutes Operator Performance Laboratory, says DARPA. The two-seat jet is outfitted with sensors in the cockpit to measure pilot physiological responses, giving researchers clues as to whether the pilot is trusting the [artificial intelligence] or not.

For safety reasons, the L-29 is actually flown from the front cockpit seat by a human servo actuator, a human pilot that is reading flight instructions generated by an artificially intelligent program and then executing those manoeuvres. To the evaluator pilot in the backseat, it appears as if the [artificial intelligence program] is performing the aircraft manoeuvres, says DARPA.

Weve started looking at measurement techniques to see where the evaluation pilots head is pointing, as well as where their eyes are looking around the cockpit, Javorsek says. This enables us to see how much the pilot is checking on the autonomy by looking outside the window, and comparing that to how much time they spend on their battle management task.

DARPA plans to transition its dogfighting artificial intelligence algorithms from its simulations to subscale aircraft demonstration in late 2021.

The ACE programme will culminate in a full-scale, artificial intelligence-controlled L-39 jet flying in team dogfight trials in late 2023 and 2024. DARPA is developing aero performance models of the L-39 and contractor Calspan has begun modifying an initial aircraft.

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DARPA tests artificial intelligent dogfighting in two-versus-one simulations - Flightglobal