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DUBLIN, March 20, 2020 /PRNewswire/ -- The "Global Artificial Intelligence for Defense - Market and Technology Forecast to 2028" report has been added to ResearchAndMarkets.com's offering.

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This report examines, analyzes, and predicts the evolution of AI technologies, markets, and outlays (expenditures) over the next 8 years, 2020-2028 in the AI for the defense industry. It also examines AI markets geographically, focusing on the top 95% of global markets, in the United States, Europe, and Asia.

In particular, this report provides an in-depth analysis of the following:

Key Topics Covered

1 Introduction1.1 Objective1.2 Market Definition1.3 Methodology1.4 Events Based Forecast Scenario1.5 Who Will Benefit from This Report?1.5.1 Business Leaders & Business Developers1.5.2 Aerospace & Defense Manufacturers1.5.3 Policy Makers, Analysts and Planners1.5.4 Tech Companies1.6 Language

2 Executive Summary2.1 Global Artificial Intelligence for Defense - Trends and Insights2.2 Major Findings2.3 Major Conclusions2.4 Important Tables and Graphs

3 Current and Future Technology Overview of Global Artificial Intelligence for Defense3.1 Introduction3.2 Types of Artificial Intelligence for Defense3.3 Current Technologies3.4 Future Technologies

4 Current and Future Market Overview of the Global Artificial Intelligence for Defense4.1 Introduction4.2 Number of AI-Powered Autonomous Systems - Per Region4.2.1 Americas4.2.2 Europe4.2.3 Asia4.2.4 Middle East4.2.5 Africa4.3 Current Markets4.3.1 North America4.3.2 Europe4.3.3 Asia4.3.4 Middle East4.3.5 Rest of the World4.4 Future Markets4.5 How to Reach Scale4.5.1 Challenges Involved in Scaling4.5.2 Strategy for Scaling

5 Market Analysis5.1 Introduction5.1.1 Industry Chain Structure5.1.2 Support for Local Industry5.1.3 Policy5.1.4 Drivers5.1.5 Inhibitors5.1.6 Opportunities5.1.7 Challenges5.2 Porter's 5 Forces Analysis5.2.1 Competitive Rivalry5.2.2 Supplier Power5.2.3 Threat of Substitution5.2.4 Threat of New Entry5.3 Macro Environment5.3.1 Macroeconomic Factors5.3.2 Political5.3.3 Economic5.3.4 Social5.3.5 Technological5.4 Forecast Factors

6 Global Artificial Intelligence for Defense by Region to 20286.1 Introduction6.2 Global Artificial Intelligence for Defense Market by Region Overview6.2.1 Americas - in Artificial Intelligence for Defense Market6.2.2 Europe - in Artificial Intelligence for Defense Market6.2.3 Asia - in Artificial Intelligence for Defense Market6.2.4 Middle East - in Artificial Intelligence for Defense Market6.2.5 Africa - in Artificial Intelligence for Defense Market

7 Global Artificial Intelligence for Defense by Technology to 20287.1 Introduction7.2 Global Artificial Intelligence for Defense by Technology Overview7.2.1 Global Artificial Intelligence for Defense by Technology - Integrated Solutions7.2.2 Global Artificial Intelligence for Defense by Technology - Data Analysis7.2.3 Global Artificial Intelligence for Defense by Technology - Platform7.2.4 Global Artificial Intelligence for Defense by Technology - Interface7.2.5 Global Artificial Intelligence for Defense by Technology - Hardware

8 Global Artificial Intelligence for Defense by Application to 20288.1 Introduction8.2 Global Artificial Intelligence for Defense Market by Application Overview8.2.1 Global Artificial Intelligence for Defense Market by Application - Intelligence, Surveillance and Reconnaissance (ISR)8.2.2 Global Artificial Intelligence for Defense Market by Application - Search and Rescue8.2.3 Global Artificial Intelligence for Defense Market by Application - Combat8.2.4 Global Artificial Intelligence for Defense Market by Application - Transportation8.2.5 Global Artificial Intelligence for Defense Market by Application - Explosive Ordnance Disposal8.2.6 Global Artificial Intelligence for Defense Market by Application - Mine Clearance8.2.7 Global Artificial Intelligence for Defense Market by Application - Firefighting8.2.8 Global Artificial Intelligence for Defense Market by Application - Others

9 Forecast Global Artificial Intelligence for Defense by Offering to 20289.1 Introduction9.2 Global Artificial Intelligence for Defense by Offering Overview9.2.1 Global Artificial Intelligence for Defense - Machine Learning9.2.2 Global Artificial Intelligence for Defense - Speech Recognition9.2.3 Global Artificial Intelligence for Defense - Emotion Recognition9.2.4 Global Artificial Intelligence for Defense - Computer Vision9.2.5 Global Artificial Intelligence for Defense - AI Optimized Hardware9.2.6 Global Artificial Intelligence for Defense - Robotic Process Automation9.2.7 Global Artificial Intelligence for Defense - Cyber Defense9.2.8 Global Artificial Intelligence for Defense - Biometric

10 Global Artificial Intelligence for Defense by Type to 202810.1 Introduction10.2 Global Artificial Intelligence for Defense Market by Type Overview10.2.1 Global Artificial Intelligence for Defense - Reactive Machine10.2.2 Global Artificial Intelligence for Defense - Limited Memory10.2.3 Global Artificial Intelligence for Defense - Theory of Mind10.2.4 Global Artificial Intelligence for Defense - Self Aware AI10.2.5 Global Artificial Intelligence for Defense - Artificial Narrow Intelligence10.2.6 Global Artificial Intelligence for Defense - Artificial General Intelligence10.2.7 Global Artificial Intelligence for Defense - Artificial Super Intelligence

11 Global Artificial Intelligence for Defense by End-users to 202811.1 Introduction11.2 Global Artificial Intelligence for Defense by End-users Overview11.2.1 Global Artificial Intelligence for Defense by End-users - Army11.2.2 Global Artificial Intelligence for Defense by End-users - Air Force11.2.3 Global Artificial Intelligence for Defense by End-users - Navy11.2.4 Global Artificial Intelligence for Defense by End-users - Defense Department

12 Events Based Forecast for the Global Artificial Intelligence for Defense to 202812.1 Introduction12.2 Events Forecast Factors12.3 Event Forecast by Regions12.4 Event Forecast by Offering12.5 Event Forecast by Type12.6 Event Forecast by Application12.7 Event Forecast by Technology12.8 Event Forecast by End-user

13 Leading Companies in the Global Artificial Intelligence for Defense Market13.1 Airbus Defence and Space13.1.1 Company Profile13.1.2 Products & Services13.1.3 Segment Revenue13.1.4 Financial Info (Revenues, Profit Last 5 Years)13.1.5 Recent Contract Wins13.1.6 Recent Projects Completed13.1.7 Strategic Alliances13.1.8 Artificial Intelligence for Defense - Products & Services13.1.9 SWOT Analysis13.2 BAE Systems13.3 Boeing Co.13.4 Elbit Systems13.5 Inmarsat13.6 Israel Aerospace Industries (IAI)13.7 Leonardo13.8 Lockheed Martin13.9 Northrop Grumman Corp.13.10 Saab13.11 Other Companies of Interest13.11.1 Alphabet13.11.2 Amazon13.11.3 AMD13.11.4 Apple Inc.13.11.5 Baidu13.11.6 Deep Mind Technologies13.11.7 Facebook13.11.8 General Vision Inc.13.11.9 Intel13.11.10 Microsoft13.11.11 Aptiv13.11.12 Open AI13.11.13 Qualcomm13.11.14 Tesla13.11.15 Yandex

14 Conclusions and Recommendations14.1 Major Conclusions and Recommendations14.2 Fulfilling the Business Objectives

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Excerpt from:
Artificial Intelligence in the Global Defense Market, 2020-2028: Outlays Expenditures Over the Next 8 Years - Yahoo Finance

With businesses everywhere being disrupted by the coronavirus outbreak, it seems like a tough time to be an entrepreneur starting a new venture.

Yet the co-founders of the Richmond-based startup company Forestry Machine Learning say they are keeping a positive long-term outlook.

The startup specializes in helping clients implement a cutting-edge type of artificial intelligence called machine learning to improve their business strategies and operations, and the co-founders say they foresee demand only increasing for that service.

It is an interesting time to be launching a company, said David Der, the startups CEO. Co-founder Brian Forrester is chief revenue officer.

Overall, I am optimistic, Der said. Sure, there might be some setbacks nobody is really taking in-person meetings right now but a lot of the value we can deliver can be done virtually anyway.

Our sales strategy remains the same, he said. We are still prospecting and in business development stages, full speed ahead.

Machine learning is a subset of artificial intelligence that involves using computer algorithms to quickly analyze large amounts of data and learn from it. The tools can be used to make better predictions about how people and systems behave.

The Forestry part of the companys name is a nod to lingo within the artificial intelligence industry.

Machine learning, artificial intelligence, and the larger ecosystem around that, is really just coming of age, said Forrester, who is also co-founder of Workshop Digital, a Richmond-based digital marketing firm where he continues to work.

For the last three or four years, we have had access to more data than we have ever had before, Forrester said. Computing power has caught up to be able to process that. A lot of the companies I work with over 100 companies across the U.S. and Canada are still trying to figure out how to leverage that data to inform business strategy, reduce risk and increase profitability.

Machine learning can be used to improve financial forecasting, cybersecurity and fraud prevention, among other things, said Der, who brings to the startup a background in computer science.

Der was among a group of co-founders of Notch, a technology consulting company founded in Richmond in 2014 that specialized in data engineering and machine learning. In late 2017, Notch was acquired by financial services giant Capital One Financial Corp.

Der said he left Capital One in December after a two-year commitment and started working on creating the new business.

Entrepreneurship is really a passion of mine, Der said. In a way, we are picking up the torch where Notch left off two years ago. I also want to bring to the table my experience now from the financial services industry.

While machine learning can be utilized by many organizations, Der said the startup is targeting three primary industries: financial services, health care and digital marketing.

The goal of machine learning in digital marketing is to deliver the right message to the right person through the right medium at the right time, Der said.

Forrester brings deep experience in digital marketing through his company, Digital Workshop.

I have spent 11 years building a company, and we have been fairly successful, Forrester said. My role in this company [Forestry] is to build our sales and marketing strategy as we grow and follow Davids lead.

Will Loving and Scott Walker, both with Richmond-based Consult360, also are investing partners in the startup.

Forrester said he has experience navigating a startup during a time of economic disruption.

I dont think the problems that machine learning is trying to solve are going to go away just because of this, he said, referring to the coronavirus disruptions. In fact, they are more pervasive now than ever. Leveraging more computing power to tackle bigger problems is not going to go away.

Excerpt from:
Startup Spotlight: Forestry Machine Learning wants to help clients use artificial intelligence to improve business - Richmond.com

Is AI core to growing your business?

Artificial intelligence technology is powering big changes across all industries, but its tough to separate out the companies with truly transformative applications from marketing hype. Thats why Forbes is compiling a list of promising startups that are emerging as leaders in this space.

Is AI at the heart of what your company does, not just a driver for an auxiliary business or way to improve an existing product? We want to hear from you.

Nominations are now open for the second annual Forbes AI list, which seeks to highlight private companies that are applying artificial intelligence to solve problems in innovative ways.

Forbes, in partnership with Sequoia Capital and Meritech Capital, will evaluate hundreds of companies based on metrics including revenue, growth and valuation, with a panel of experts weighing in on how innovative and mission-critical each companys use of AI is (versus buzzwords thrown onto a slide-deck).

We welcome any U.S.-based private company to apply by filling out this form by Friday, April 10. The number of nominations wont influence our selection, so stick to just one per company, please.

We look forward to hearing from you!

Read more from the original source:
Is Your Company Using Artificial Intelligence To Transform An Industry? Nominations For The Forbes 2020 AI 50 List Are Now Open - Forbes

The "Innovations in Artificial Intelligence, Predictive Analytics, and BIM" report has been added to ResearchAndMarkets.com's offering.

This edition of IT, Computing and Communications (ITCC) TechVision Opportunity Engine (TOE) provides a snapshot of the emerging ICT led innovations in artificial intelligence, predictive analytics, and building information modelling. This issue focuses on the application of information and communication technologies in alleviating the challenges faced across industry sectors in areas such as retail, agriculture, construction, healthcare, and industrial sectors.

ITCC TOE's mission is to investigate emerging wireless communication and computing technology areas including 3G, 4G, Wi-Fi, Bluetooth, Big Data, cloud computing, augmented reality, virtual reality, artificial intelligence, virtualization and the Internet of Things and their new applications; unearth new products and service offerings; highlight trends in the wireless networking, data management, and computing spaces; provide updates on technology funding; evaluate intellectual property; follow technology transfer and solution deployment/integration; track development of standards and software; and report on legislative and policy issues and many more.

The Information & Communication Technology cluster provides global industry analysis, technology competitive analysis, and insights into game-changing technologies in wireless communication and computing space. Innovations in ICT have deeply permeated various applications and markets.

These innovations have a profound impact on a range of business functions for computing, communications, business intelligence, data processing, information security, workflow automation, quality of service (QoS) measurements, simulations, customer relationship management, knowledge management functions and many more. The global teams of industry experts continuously monitor technology areas such as Big Data, cloud computing, communication services, mobile and wireless communication space, IT applications & services, network security, and unified communications markets. In addition, we also closely look at vertical markets and connected industries to provide a holistic view of the ICT Industry.

Key Topics Covered:

Innovations in Artificial Intelligence, Predictive Analytics, and BIM

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/kmqkj0

View source version on businesswire.com: https://www.businesswire.com/news/home/20200320005350/en/

Contacts

ResearchAndMarkets.comLaura Wood, Senior Press Managerpress@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470For U.S./CAN Toll Free Call 1-800-526-8630For GMT Office Hours Call +353-1-416-8900

See the rest here:
Innovations in Artificial Intelligence, Predictive Analytics, and BIM (2019) - ResearchAndMarkets.com - Yahoo Finance

The traditional treatment planning process takes days to create an optimized radiation therapy delivery plan, but new artificial intelligence (AI) technologies are helping speed this process. In some cases autonomous AI-generated plans can be generated in a couple minutes.

"AI can help treatment planners and dosimetrists by saving a lot of time doing simpler, repetitive tasks that you do again and again, now the AI can do that for you and you can spend your time onthose more challenging, difficult cases and you can do a better job there," explained Steve Jiang, Ph.D., director of the medical artificial intelligence and automation lab and vice-chair of the Department of Radiation Oncology, University of Texas Southwestern.

Varian and RaySearch have both developed machine-learning technologies to automate treatment plans.

"The fully automated system takes in the patient imaging and the target defined by the physician, and out on the other end comes a fully deliverable therapy plan," said Kevin Moore, Ph.D., DABR, deputy director of medical physics and associate professor, University of California San Diego, who is using the Varian AI TSP software.

He said UCSD began using the software in tandem with traditional treatment planning to ensure the plans were as good as human created plans. After a human-made plan was completed, he said they ran the AI and it took 5-20 minutes to complete a plans start to finish depending on the complexity. "The comparisons were very good," Moore said. The site is now running the AI treatment plans first and and the human planner looks at it to see if it can be further optimized.

The system has helped with speed and efficacy at UCSD Moore said, and the site has now treated well over 1,000 patients with its AI-assisted planning.

Machine learning was incorporated into the RaySearch 8B TPS in 2018 and began it began to beused clinicallyin 2019. The system is trained to take the treatment planning computed tomography (CT) scan and automatically segment the anatomy and auto contour to help speed the planning process.

Princess Margaret Cancer Center, part of the University Health Network in Toronto, Canada, was an early adopter of AI-based treatment planning in 2019. The center conducted a study where it used the RaySearch machine learning treatment plan system to automatically generate plans along side traditional human made plans. The radiation oncologist then compared the two plans to decide if they are acceptable and which is their favorite plan.

"The automated treatment planning system works by training the algorithm with curated sets of similar treatment plans and it is able to detect the patients who are most similar to a novel patient and create a new treatment plan for that patient with no user interaction, beyond pressing the play button," explained Leigh Conroy, Ph.D., physics resident, at Princess Margaret Cancer Center, who has been working on the AI implementation.

She said the machine-generated plan can be modified and optimized by the human planners before being sent to the therapy system. The center began using the system clinically last summer.

Some plans are easier to create than others, so Conroy said the AI system might be used to help free up treatment planners to work on more complex cases, such as head and neck.

TheRayStation 8B TPS software can be trained to automate treatment planning as well as organ segmentation, either using the clinics own data or by pre-trained models provided by RaySearch.

RaySearch is developing several other machine learning applications, including target volume estimation and large-scale data extraction and analysis.

Varian received FDA clearance for its Ethos AI-driven radiation therapy system in February 2020. It is an adaptive intelligence solution that uses AI in the treatment system to take the onboard cone beam CT imaging and compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Tumors change position and size during a course of radiotherapy treatments.Changes in anatomical positioning that move tumors outside TPS margins also occur due to weight loss, position of the uterus, how full the bladder is, and intestinal gas, explained David Sjostrom, Ph.D., deputy chief physicist, Herlev Hospital, Department of Oncology, Division of Radiotherapy, Herlev, Denmark. Hehadthe first clinical experience treating patients with the Varian Ethos system in September 2019.

"Normally it would take days to modify a treatment plan, and you don't do that online with the patient on the table. What we have done up until today was treating within the margins of the plan, or maybe had a study where we had different selections of plans, but it is still not the optimal way of doing it," Sjostrom said.

"So you would have a plan for a small bladder, a medium sized bladder and a large bladder, but what if it is anything in between? With the AI drive workflow, we now get results where we don't need to edit anything and that is the beauty," he explained.

Sjostrom said the system takes a couple minutes to create the plan and then it can be compared to the original plan and you can tell the system which one you want to use, or you can modify one of the plans. However, Sjostrom said so far they have picked the AI generated plan without modification. He said they have averaged a 40 percent reduction in the target margins using the AI-based system.

In early 2018, Mirada Medical received U.S. Food and Drug Administration (FDA) clearance for the worlds first AI-powered auto-contouring solution. The DLCExpert software automatically contours of computed tomography (CT) scans based on next-generation deep learning contouring (DLC) and is now in use in multiple academic medical centers worldwide.

The DLCExpert deep learning software automatically identifies organs, segments and auto-contours them as the first step in creating radiation oncology treatment plans without any human intervention. The files created by the software are vendor neutral and can be imported into any vendors treatment planning system.

Siris Medical also gained FDA clearance in 2018 for its AI-driven, real-time editing of plan contours in its PlanMD decision support software. As the user draws the contours of the target treatment area, the software lets the user see the result of their efforts in real time, without re-optimizing or replanning, which can lead to significant time savings. A newer version of the software was released in January 2019.

The PlanMD software is a complement to Siris other AI product, QuickMatch, which automatically pulls up prior cases from its archive that are most similar to the current one. Once the QuickMatch algorithm has been properly trained, it can reduce the time required for treatment planning by up to 70 percent, according to the vendor.

As radiation oncology moves towarduse of magnetic resonance imaging (MRI) radiotherapy treatment systems because of the better soft tissue delineation and ability to provide real-time imaging during treatment, AI will play a role to help eliminate the need for CT scans. CT is the basis for all current treatment planning because the radiation absorption of various tissues can be calculated from the Hounsfield units of the image grayscales that makeup the image. So all plans, even MR-guided Linacs, require CT imaging for treatment planning.

However, AI can create MRI-derived CT-like image reconstructions for treatment planning and eliminate the need for CT exams, Jiang explained. He said this can help reduce costs and save hospital resources. Eliminating CT scans also can help speed the planning process and reduce patient wait times for treatment, he said.

VIDEO: Artificial Intelligence Driven Adaptive Radiotherapy System Begins Treating Patients Interview with David Sjostrom, Ph.D.

VIDEO: Artificial Intelligence Automatic Contouring and Segmentation For Radiotherapy

New Treatment Planning System Technologies

VIDEO: Real-world Implementation of Deep Learning for Treatment Planning Interview with Kevin Moore, Ph.D.

VIDEO: Varian Showcases Latest Developments at ASTRO 2019

Top Technology Trends at ASTRO 2018

Varian Receives FDA 510(k) Clearance for Ethos Therapy

VIDEO: The Impact of Artificial Intelligence on Radiation Therapy Interview with Steve Jiang, Ph.D.

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Artificial Intelligence Greatly Speeds Radiation Therapy Treatment Planning - Imaging Technology News