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DUBLIN, March 25, 2021 /PRNewswire/ -- The "Artificial Intelligence in Military Market by Offering (Software, Hardware, Services), Technology (Machine Learning, Computer vision), Application, Installation Type, Platform, Region - Global Forecast to 2025" report has been added to ResearchAndMarkets.com's offering.

The Artificial Intelligence in military market is estimated at USD 6.3 billion in 2020 and is projected to reach USD 11.6 billion by 2025, at a CAGR of 13.1% during the forecast period.

The Artificial Intelligence in Military market includes major players such as BAE Systems Plc. (UK), Northrop Grumman Corporation (US), Raytheon Technologies Corporation (US), Lockheed Martin Corporation (US), Thales Group (US), L3Harris Technologies, Inc. (US), Rafael Advanced defense Systems (Israel), and IBM (US), among others. These players have spread their business across various countries includes North America, Europe, Asia Pacific, Middle East & Africa, and Latin America. COVID-19 has not affected the Ai in military market growth to some extent, and this varies from country to country. Industry experts believe that the pandemic has not affected the demand for Artificial Intelligence in the military market in defense applications.

Based on platform, the space segment of the Artificial Intelligence in military market is projected to grow at the highest CAGR during the forecast period

Based on platform, the space segment of the Artificial Intelligence in military market is projected to grow at the highest CAGR during the forecast period. The space AI segment comprises CubeSat and satellites. Artificial intelligence systems for space platforms include various satellite subsystems that form the backbone of different communication systems. The integration of AI with space platforms facilitates effective communication between spacecraft and ground stations.

Software segment of the Artificial Intelligence in Military market by offering is projected to witness the highest CAGR during the forecast period

Based on offering, the Software segment is projected to witness the highest CAGR during the forecast period. Technological advances in the field of AI have resulted in the development of advanced AI software and related software development kits. AI software incorporated in computer systems is responsible for carrying out complex operations. It synthesizes the data received from hardware systems and processes it in an AI system to generate an intelligent response. The software segment is projected to witness the highest CAGR owing to the significance of AI software in strengthening the IT framework to prevent incidents of a security breach.

The North American market is projected to contribute the largest share from 2020 to 2025 in the Artificial Intelligence in Military market

The US and Canada are key countries considered for market analysis in the North American region. This region is expected to lead the market from 2020 to 2025, owing to increased investments in AI technologies by countries in this region. This market is led by the US, which is increasingly investing in AI systems to maintain its combat superiority and overcome the risk of potential threats on computer networks. The US plans to increase its spending on AI in the military to gain a competitive edge over other countries.

The North American US is recognized as one of the key manufacturers, exporters, and users of AI systems worldwide and is known to have the strongest AI capabilities. Key manufacturers of Ai systems in the US include Lockheed Martin, Northrop Grumman, L3Harris Technologies, Inc., and Raytheon. The new defense strategy of the US indicates an increase in AI spending to include advanced capabilities in existing defense systems of the US Army to counter incoming threats.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights4.1 Attractive Growth Opportunities in AI in Military Market4.2 North America AI in Military Market, by Platform4.3 Asia-Pacific AI in Military Market, by Technology4.4 AI in Military Market, by Application4.5 AI in Military Market, by Region4.6 China: AI in Military Market, by Platform

5 Market Overview5.1 Introduction5.2 Market Dynamics5.2.1 Drivers5.2.1.1 Increased Government Spending on Defense to Improve AI Capabilities5.2.1.2 Development of Specialized AI Chips5.2.1.3 Growing Focus on Advanced C4Isr Capabilities5.2.1.4 Increasing Adoption of AI in Unmanned Vehicles5.2.1.5 Increasing Threats of Cyber Attacks5.2.2 Restraints5.2.2.1 Concerns Over Possibility of Errors in Complex Combat Situations5.2.2.2 Lack of Standards and Protocols for Use of AI in Military Applications5.2.3 Opportunities5.2.3.1 Incorporation of Quantum Computing in AI5.2.3.2 Increasing Adoption of AI in Predictive Maintenance in Military Platforms5.2.4 Challenges5.2.4.1 Absence of Backward Analysis5.2.4.2 Lack of Trained Personnel5.2.4.3 Sensitive Nature of Military Data5.3 Value Chain Analysis of AI in Military Market5.4 Trends/Disruption Impacting Customer Business5.4.1 Revenue Shift and New Revenue Pockets for AI in Military System Manufacturers5.5 Impact of COVID-19 on AI in Military Market5.6 Ranges and Scenarios5.7 Porter's Five Forces Analysis5.8 Regulatory Landscape5.9 Trade Analysis

6 Industry Trends6.1 Introduction6.2 Key Technological Trends in AI in Military Market6.2.1 Need of Quantum AI for Computation of Machine Learning Algorithms6.2.2 5G Networking for Faster Data Transfer6.2.3 Internet of Battlefield Things (Iobt)6.2.4 Blockchain6.2.5 Advanced Analytics6.2.6 Big Data Analytics6.2.7 Artificial Neural Network6.3 Use Case Analysis: AI in Military Market6.3.1 Deployment of a Pictorial Training Tool to Improve Battlefield First-AId Skills from Charles River Analytics6.3.2 C3 AI Readiness: Use of AI Predictive Maintenance in Us AIr Force6.3.3 Have Raider: Deployed to Demonstrate Manned-Unmanned Teaming6.4 Trade Analysis6.5 Impact of Megatrends6.6 Innovation & Patent Registrations

7 AI in Military Market, by Offering7.1 Introduction7.2 Hardware7.2.1 Processor7.2.1.1 Development of Specialized Chips Pave Way for Wider Application of AI in Military7.2.2 Memory7.2.2.1 High Bandwidth Parallel File Systems Increase Efficiency and Throughput of Memory Devices7.2.3 Network7.2.3.1 5G Network Improves Connection Capabilities7.3 Software7.3.1 AI Solutions7.3.1.1 Securonix (Us), IBM (Us), Darktrace (Uk): Major Companies Developing AI Solutions7.3.1.1.1 Cloud7.3.1.1.2 On-Premise7.3.2 AI Platforms7.3.2.1 Demand for Intelligent Applications and Learning Algorithms on the Rise7.4 Services7.4.1 Deployment & Integration7.4.1.1 Used to Create and Deploy Custom Text Analytics7.4.2 Upgrades & Maintenance7.4.2.1 Use of Predictive Maintenance Tools Boosts Segment Growth7.4.3 Software Support7.4.3.1 Periodic Upgradation to Improve Capabilities Drives Software Support Segment7.4.4 Others

8 AI in Military Market, by Application8.1 Introduction8.2 Warfare Platforms8.2.1 Rise of AI in Ew Platforms Boost Segment Growth8.3 Cybersecurity8.3.1 Increasing Cyber-Attacks and Need for Security Drive Segment8.4 Logistics & Transportation8.4.1 Increasing Tactical and Strategic Military Operations Fuel Segment Growth8.5 Surveillance & Situational Awareness8.5.1 Efficiency in Gathering Actionable Intelligence Drives Segment8.6 Command & Control8.6.1 Improve Ability to Gather Data for Better Decision Making8.7 Battlefield Healthcare8.7.1 Segment Driven by New Capabilities That Reduce Battlefield Causalities8.8 Simulation & Training8.8.1 Increasing Investments in Simulation & Training Sector Drive Segment Growth8.9 Threat Monitoring8.9.1 Adoption of AI in UAVs to Assist in Threat Monitoring on the Rise8.10 Information Processing8.10.1 Processing Huge Volume of Data to Gather Valuable Insights Boosts Segment Growth8.11 Others8.11.1 Need to Decrease Downtime Drives Others Segment

9 AI in Military Market, by Technology9.1 Introduction9.2 Machine Learning9.2.1 Deep Learning9.2.1.1 Deep Learning Increasingly Used in Facial Recognition9.2.2 Supervised Learning9.2.2.1 Classification and Regression: Major Segments of Supervised Learning9.2.3 Unsupervised Learning9.2.3.1 Unsupervised Learning Integral to Identifying Patterns in Critical Data9.2.4 Reinforcement Learning9.2.4.1 Reinforcement Learning Used for Autonomous Decision Making in Military Applications9.2.5 Generative Adversarial Learning9.2.5.1 Surveillance and Situational Awareness Applications Widely Use Generative Adversarial Learning9.2.6 Others9.3 Natural Language Processing9.3.1 High Demand for Programming of Computers to Process Natural Language Data9.4 Context-Aware Computing9.4.1 Used for Improvement of Rf Signals and Situational Awareness9.5 Computer Vision9.5.1 Investments in Development of High-Resolution 3D Geospatial Information Systems Boost Segment9.6 Intelligent Virtual Agent9.6.1 Demand for Virtual Identities for Recruitment, Cyber Defense, and Training9.7 Others9.7.1 Increase in Adoption of Speech Recognition and Emotional Recognition

10 AI in Military Market, by Platform10.1 Introduction10.2 AIrborne10.3 Land10.4 Naval10.5 Space

11 AI in Military Market, by Installation Type11.1 Introduction11.2 New Installation11.2.1 Growing Defense Expenditure on AI-Powered Tools and Systems Boosts New Installation Segment11.3 Upgradation11.3.1 Demand for Enhanced Military Capabilities Drives Upgradation of Hardware Components and Software Modules

12 Regional Analysis12.1 Introduction12.2 AI in Military Market: Three Global Scenarios12.3 North America12.4 Europe12.5 Asia-Pacific12.6 Middle East & Africa12.7 Latin America

13 Competitive Landscape13.1 Introduction13.2 Ranking Analysis of Key Market Players, 201913.3 Share of Key Market Players, 201913.4 Revenue Analysis of Top 5 Market Players, 201913.5 Company Evaluation Quadrant13.5.1 Star13.5.2 Emerging Leader13.5.3 Pervasive13.5.4 Participant13.5.5 AI in Military Market Competitive Leadership Mapping (SME)13.5.5.1 Progressive Companies13.5.5.2 Responsive Companies13.5.5.3 Starting Blocks13.5.5.4 Dynamic Companies13.6 Competitive Scenario13.6.1 Market Evaluation Framework13.6.2 New Product Launches and Developments13.6.3 Contracts13.6.4 Acquisitions/Partnerships/Joint Ventures/Agreements/Expansions

14 Company Profiles14.1 Introduction14.2 Key Players14.2.1 Lockheed Martin Corporation14.2.2 The Boeing Company14.2.3 General Dynamics Corporation14.2.4 Rafael Advanced Defense Systems Ltd.14.2.5 Northrop Grumman Corporation14.2.6 Thales Group14.2.7 Raytheon Technologies Corporation14.2.8 Bae Systems plc14.2.9 International Business Machines Corp. (Ibm)14.2.10 Charles River Analytics14.2.11 Caci International Inc.14.2.12 Shield AI14.2.13 Science Applications International Corp. (Saic)14.2.14 Saab Ab14.2.15 Nvidia Corporation14.2.16 Leonardo S.P.A (Leonardo)14.2.17 Soar Technologies Inc.14.2.18 L3Harris Technologies, Inc.14.2.19 Rheinmetall Ag14.2.20 Sparkcognition Inc14.2.21 Leidos Holdings Inc. (Leidos)14.2.22 Safran Sa14.2.23 Honeywell International Inc.14.2.24 Darktrace Limited14.2.25 Sz Dji Technology

15 Appendix15.1 Discussion Guide

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The U.S. National Security Commission on Artificial Intelligence recently made the news when its members warned that America faces a national security crisis due to insufficient investment in artificial intelligence and emerging technologies. Commission Vice Chair Robert Work argued we dont feel this is the time for incremental budgets This will be expensive and requires significant change in the mindset at the national, and agency, and Cabinet levels. Commission Chair Eric Schmidt extended those worries by saying China is catching the US and competition with China will increase.

This is not the first time the country has worried about the economic and national security ramifications of new technologies. In the aftermath of World War II, the United States, Soviet Union, China, France, Germany, Japan, the United Kingdom, and others were concerned about the risk of war and the ethical aspects of nuclear weapons, chemical agents, and biological warfare. Despite vastly different worldviews, national interests, and systems of government, their leaders reached a number of agreements and treaties to constrain certain behaviors, and define the rules of war. There were treaties regarding nuclear arms control, conventional weapons, biological and chemical weapons, outer space, landmines, civilian protection, and the humane treatment of POWs.

The goal through these agreements was to provide greater stability and predictability in international affairs, introduce widely-held humanitarian and ethical norms into the conduct of war, and reduce the risks of misunderstandings that might spark unintended conflict or uncontrollable escalation. By talking with adversaries and negotiating agreements, the hope was that the world could avoid the tragedies of large-scale conflagrations, now with unimaginably destructive weapons, that might cost millions of lives and disrupt the entire globe.

With the rise of artificial intelligence, supercomputing, and data analytics, the world today is at a crucial turning point in the national security and the conduct of war. Sometimes known as the AI triad, these characteristics and other weapons systems, such as hypersonics, are accelerating both the speed with which warfare is waged, and the speed with which warfare can escalate. Called hyperwar by Amir Husain and one of us (John R. Allen), this new form of warfare will feature levels of autonomy, including the potential for lethal autonomous weapons without humans being in the loop on decision-making.

It will affect both the nature and character of war and usher in new risks for humanity. As noted in ourrecent AI book Turning Point,this emerging reality could feature swarms of drones that may overwhelm aircraft carriers, cyberattacks on critical infrastructure, AI-guided nuclear weapons, and hypersonic missiles that automatically launch when satellite sensors detect ominous actions by adversaries. It may seem to be a dystopian future, but some of these capabilities are with us now. And to be clear, both of us, and more broadly the worlds liberal democracies, are struggling with the moral and ethical implications of fully autonomous, lethal weapon systems.

In this high-risk era, it is now time to negotiate global agreements governing the conduct of war during the early adoption and adaptation of AI and emerging technologies to the waging of war and to specific systems and weapons. It will be much easier to do this before AI capabilities are fully fielded and embedded in military planning. Similar to earlier treaties on nuclear, biological, and chemical weapons in the post-war period, these agreements should focus on several key principles:

The good news is there are some international entities that already are working on these issues. For example, the Global Partnership on Artificial Intelligence is a group of more than a dozen democratic nations that have agreed to support the responsible and human-centric development and use of AI in a manner consistent with human rights, fundamental freedoms, and our shared democratic values. This community of democracies is run by the Organization for Economic Cooperation and Development and features high-level convenings, research, and technical assistance.

That said, there are increasingly calls for the technologically advanced democracies to come together to aggregate their capacities, as well as leveraging their accumulated moral strength, to create the norms and ethical behaviors essential to governing the applications of AI and other technologies. Creating a reservoir of humanitarian commitment among the democracies will be vital to negotiating from a position of moral strength with the Chinese, Russians, and other authoritarian states whose views on the future of AI vary dramatically from ours.

In addition, the North Atlantic Treaty Organization, European Union, and other regional security alliances are undertaking consultations designed to create agreed-to norms and policies on AI and other new technologies. This includes effort to design ethical principles for AI that govern algorithmic development and deployment and provide guardrails for economic and military actions. For these agreements to be fully implemented though, they will need to have the active participation and support of China and Russia as well as other relevant states. For just as it was during the Cold War, logic should dictate that potential adversaries be at the negotiating table in the fashioning of these agreements. Otherwise, democratic countries will end up in a situation where they are self-constrained but adversaries are not.

It is essential for national leaders to build on international efforts and make sure key principles are incorporated into contemporary agreements. We need to reach treaties with allies and adversaries that provide reliable guidance for the use of technology in warfare, create rules on what is humane and morally acceptable, outline military conduct that is unacceptable, ensure effective compliance, and take steps that protect humanity. We are rapidly reaching the point where failure to take the necessary steps will render our societies unacceptably vulnerable, and subject the world to the Cold War specter of constant risk and the potential for unthinkable destruction. As advocated by the members of the National Security Commission, it is time for serious action regarding the future of AI. The stakes are too high otherwise.

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It is time to negotiate global treaties on artificial intelligence - Brookings Institution

Hui Xie,1,2 Qing Li,2,3 Ping-Feng Hu,4 Sen-Hua Zhu,5 Jian-Fang Zhang,6 Hong-Da Zhou,1 Hai-Bo Zhou4

1Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, Peoples Republic of China; 2Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Chenzhou, 423000, Peoples Republic of China; 3Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, Peoples Republic of China; 4Department of Radiology, The Second Peoples Hospital of Chenzhou City, Chenzhou, 423000, Peoples Republic of China; 5Beijing Linking Medical Technology Co., Ltd, Beijing, 100085, Peoples Republic of China; 6Department of Physical Examination, Disease Control and Prevention of Chenzhou, Chenzhou, 423000, Peoples Republic of China

Correspondence: Qing LiDepartment of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, 25 Renmin Street, Chenzhou, 423000, Peoples Republic of ChinaTel +86 19918761912Email xnxyliqing@163.com

Objective: The aim of this study was to explore the role of the AI system which was designed and developed based on the characteristics of COVID-19 CT images in the screening and evaluation of COVID-19.Methods: The research team adopted an improved U-shaped neural network to segment lungs and pneumonia lesions in CT images through multilayer convolution iterations. Then the appropriate 159 cases were selected to establish and train the model, and Dice loss function and Adam optimizer were used for network training with the initial learning rate of 0.001. Finally, 39 cases (29 positive and 10 negative) were selected for the comparative test. Experimental group: an attending physician a and an associate chief physician a read the CT images to diagnose COVID-19 with the help of the AI system. Control group: an attending physician b and an associate chief physician b did the diagnosis only by their experience, without the help of the AI system. The time spent by each doctor in the diagnosis and their diagnostic results were recorded. Paired t-test, univariate ANOVA, chi-squared test, receiver operating characteristic curves, and logistic regression analysis were used for the statistical analysis.Results: There was statistical significance in the time spent in the diagnosis of different groups (P< 0.05). For the group with the optimal diagnostic results, univariate and multivariate analyses both suggested no significant correlation for all variables, and thus it might be the assistance of the AI system, the epidemiological history and other factors that played an important role.Conclusion: The AI system developed by us, which was created due to COVID-19, had certain clinical practicability and was worth popularizing.

Keywords: CT, COVID-19, intelligent analysis, AI, helping role

This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License.By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

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Artificial intelligence in helping with COVID-19 | JIR - Dove Medical Press

VERONA, Italy--(BUSINESS WIRE)--Trueblue, after having announced the integration of its Artificial Intelligence Relationship Management with Microsoft Dynamics 365 and Power Platform, officially launches on the market:

AiDEA

Smart Customer Engagement

AiDEA is the new AI driven Omnichannel Customer Engagement suite. The foundation of the solution, represented by Artificial Intelligence , integrates and powers the operational and analytical functionalities based on Microsoft Dynamics 365 and Power Platform, for a holistic and integrated experience, with the goal of revolutionizing the working model of Pharma & Life Science markets, simplifying omni-channel engagement through intuitive and conversational interaction.

Two fundamental components guide the change, whose union had not yet materialized in the reference market: the concrete integration of Big Data in the perspective of Multichannel Management and the use of Artificial Intelligence functionalities and algorithms. The latter is an element that can no longer be postponed from an IT point of view, as it is necessary to drive Customer Engagement processes to satisfy company objectives from both a strategic and an operational point of view.

These elements require a structural change in the approach of organizations and tools, as a generic Customer Relationship Management system is no longer sufficient. It is in fact necessary to adopt specific Smart Omnichannel Customer Engagement solutions, fully enabled in terms of Artificial Intelligence, to have, in a quick, simple and intuitive way, precise indications about one's own customers.

As part of this transition in fact, Pharma companies such as Angelini Pharma, Alfasigma and others are taking this direction with strength and determination with the aim of innovating and achieving their business results faster.

"Artificial Intelligence represents a tremendous opportunity to increase our effectiveness and we want to provide this competitive advantage to our employees thanks to AiDEA" said Pierluigi Antonelli, CEO of Angelini Pharma "After a long and thorough analysis, we identified Trueblue and Microsoft as the best partners to advance our Customer Engagement capabilities by delivering an innovative digital CRM solution that transforms strategy into action.

Trueblue, which has always been at the center of technological and digital innovation for the pharmaceutical industry, thanks to the integration with Microsoft introduces with AiDEA a new paradigm in which Artificial Intelligence is the backbone and key factor of the evolutionary process.

"Through this integration, Trueblue will help companies in the industry accelerate their growth and find new ways to drive Digital Innovation through a wide range of solutions that will enable them to simplify the use of AI in their daily activities," said Marco Bonesini CEO of Trueblue

In todays reality of accelerated digital transformation processes, pharma & life science companies rely on proactive solutions such as AIDEA, integrated with Dynamics 365 and Power Platform, to enable effective omnichannel strategies said Elena Bonfiglioli, Managing Director, HealthCare and Life Sciences, EMEA Regional Lead.

Discover more About Trueblue

See more here:
Trueblue Designs the Future of Artificial Intelligence and Analytics for Healthcare With Aidea Integrated With Microsoft Dynamics 365 - Business Wire

NORFOLK, Va. - News 3 is taking action for your health!

March is National Colorectal Cancer Awareness Month. Be aware - the Hampton Roads area was identified as a "hot spot" for colon cancer deaths, according to the American Cancer Society.

In Norfolk, a trial is currently underway, adding the tool of artificial intelligence in the quest for prevention.

As the video indicates, AI technology is like an extra set of eyes; it highlights areas of interest when a patient undergoes a colonoscopy.

Early detection means better outcomes, and the addition of artificial intelligence in the screening appears to be an asset for both doctor and patient.

Dr. David Johnson calls it a game changer.

Weve used it for a year. We find it incredibly helpful in our practice and now in the trial, we see an increment in even among experts detecting polyps. We can do better. There were 3,000 cases in 2020 of colon cancer, 53,000 deaths. Barbara, we have to do better, and we can," Dr. Johnson said.

Related: Local woman shares story of colon cancer, warns others to get screened

The American Cancer Society recommends that you start screening at the age of 45 if you are at normal risk.

If you are at a higher risk, talk to your primary care provider.

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Detecting colon cancer early with artificial intelligence - wtkr.com - wtkr.com