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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)

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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.

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

Before the turn of the millennium, a cybercriminal was a lone wolf, a hacker, who felt the urge to expose the lacunae in a computer network or operating system. Financial gain was not the criterion for a cyberattack. However, the technological advancements following the millennium bug bred a new generation of cybercriminals.

Today, cybercriminals are no longer lone wolves; they operate in highly skilled criminal rings with access to shared data, tools, expertise, and malicious artificial intelligence (AI). By weaponizing AI and turning it for malicious purposes, they can increase the scale and mount a wide range of cyberattacks. Recent studies confirm the weaponization of AI: according to a Forrester report, 77 per cent of business leaders surveyed across the world expect that weaponized AI will lead to a rise in the scale of cyberattacks.

Weaponized AI is taking many forms

Adversarial AI, which takes advantage of an AI models inherent trait of learning, has come to the fore and is posing new threats. Adversarial AI through malicious inputs can disrupt a single device or an entire group of devices that are using an AI model. AI malware, which hides deep within a seemingly innocent application to avoid detection, uses AI models to detect if it has reached a specific target. Then there are AI-powered botnets that harness the power of AI to adapt faster than a cybersecurity team can react. As cybercriminals evolve in their attacks, using weaponized or malicious AI, the existing defences would often be lacking in identifying these adversaries.

Alert fatigue is real

While cyberattacks become more elaborate and sophisticated, the tools needed to fight them are becoming more complex and at the same time, it is becoming increasingly difficult to find people with the right skills. In an IBM Resilient and Ponemon study, 75 per cent of the respondents said they were facing moderately to high difficulty in hiring and retaining skilled cybersecurity personnel. The fact that in cybersecurity roles, skills need to evolve continuously in line with the threat landscape further compounds the talent challenge. AI harvests information to help security analysts work faster and more efficiently. Security analysts can apply AI to train computers in the language of security using techniques like natural language processing.

Another impediment for companies is timely insights that can help arrive at the best conclusions and business choices. However, they often struggle to synthesize the required insights as the context becomes more complicated. Simply put, they are unable to access enough data in time. In a study by cybersecurity firm, Fidelis, 83 per cent of surveyed companies admitted they could not even process half the alerts they received daily. Moreover, companies encounter roadblocks in their response as cyberattacks are now happening at faster speeds. Significantly, studies prove that the longer it takes to address a data breach, the more expensive it becomes to remediate.

To scale at a similar pace of cyberattacks and combat the advanced tools and malware, companies need to augment traditional programming, which merely averts known patterns or threats, by combining their cybersecurity operations with AI.

AI-powered Behavioural Analysis can be a trusted advisor

With cognitive systems that learn and reason from their interactions with humans and augment rule-based programming, AI will learn more and more. With AI and analytics, companies can improve threat detection time and accuracy. They can use predictive analytics to identify network anomalies, detect malware and analyze user behaviour patterns to determine risky users within the company, and potentially thwart fraud and insider threats. By applying AI to behavioural biometrics, they can identify the users better based on their keyboard strokes, mouse movements, or use of mobile devices. This enhances cybersecurity besides fostering an improved and seamless user experience. One of the areas organizations need to focus on while applying AI is to take a closer look at their models as well as their partners AI security tools to ensure they are trustworthy, and that AI bias does not affect security outcomes. Close monitoring of the algorithms and the input data coupled with training for security teams in diverse facets of a problem is critical in keeping AI bias under check.

Another important aspect is that AI can enable the contextualization of data insights and machine learning logic to help companies prioritize the most important threat alerts. AI and analytics allow security orchestration to automatically block threats, correct problems, respond to attacks and automate low-level alerts based on prior examples.

Productized AI to stop weaponized AI

Companies can rely on good AI to create models that could potentially tackle Adversarial AI and AI-powered attacks. AI models can be hardened to make them more robust against malicious inputs from Adversarial AI. Companies can look at using AI in detectors to go beyond rule-based security, reasoning, and automation, to enhance the effectiveness of their security operations. Good AI outcomes cannot be built overnight they would need similar rigour and training akin to any other good product development processes. The approach of building customized and automated AI to identify attacks might sound like a silver bullet but companies should recognize the fallacy of this approach; AI is a security asset and in conjunction with their security teams and traditional programming, it can help them fight in the evolving cyber battlefield. One aspect is for sure AI would tip the balance in this cyberwar what needs to be seen is to see how companies productize and befriend AI to help them tip the balance in their favour.

Views expressed above are the author's own.

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Artificial Intelligence: Your friend in the fight against cyberattacks - The Times of India Blog