Category Archives: Artificial Intelligence

Artificial Intelligence/Machine Learning and the Future of National Security

AI is a once-in-a lifetime commercial and defense game changer

By Steve Blank

Hundreds of billions in public and private capital is being invested in AI and Machine Learning companies. The number of patents filed in 2021 is more than 30 times higher than in 2015 as companies and countries across the world have realized that AI and Machine Learning will be a major disruptor and potentially change the balance of military power.

Until recently, the hype exceeded reality. Today, however, advances in AI in several important areas (here, here, here, here and here) equal and even surpass human capabilities.

If you havent paid attention, nows the time.

AI and the DoD

The Department of Defense has thought that AI is such a foundational set of technologies that they started a dedicated organization -- the JAIC -- to enable and implement artificial intelligence across the Department. They provide the infrastructure, tools, and technical expertise for DoD users to successfully build and deploy their AI-accelerated projects.

Some specific defense-related AI applications are listed later in this document.

Were in the Middle of a Revolution

Imagine its 1950, and youre a visitor who traveled back in time from today. Your job is to explain the impact computers will have on business, defense and society to people who are using manual calculators and slide rules. You succeed in convincing one company and a government to adopt computers and learn to code much faster than their competitors /adversaries. And they figure out how they could digitally enable their business supply chain, customer interactions, etc. Think about the competitive edge theyd have by today in business or as a nation. Theyd steamroll everyone.

Thats where we are today with Artificial Intelligence and Machine Learning. These technologies will transform businesses and government agencies. Today, 100s of billions of dollars in private capital have been invested in 1,000s of AI startups. The U.S. Department of Defense has created a dedicated organization to ensure its deployment.

But What Is It?

Compared to the classic computing weve had for the last 75 years, AI has led to new types of applications, e.g. facial recognition; new types of algorithms, e.g. machine learning; new types of computer architectures, e.g. neural nets; new hardware, e.g. GPUs; new types of software developers, e.g. data scientists; all under the overarching theme of artificial intelligence. The sum of these feels like buzzword bingo. But they herald a sea change in what computers are capable of doing, how they do it, and what hardware and software is needed to do it.

This brief will attempt to describe all of it.

New Words to Define Old Things

One of the reasons the world of AI/ML is confusing is that its created its own language and vocabulary. It uses new words to define programming steps, job descriptions, development tools, etc. But once you understand how the new world maps onto the classic computing world, it starts to make sense. So first a short list of some key definitions.

AI/ML - a shorthand for Artificial Intelligence/Machine Learning

Artificial Intelligence (AI) - a catchall term used to describe Intelligent machines which can solve problems, make/suggest decisions and perform tasks that have traditionally required humans to do. AI is not a single thing, but a constellation of different technologies.

Machine Learning (ML) - a subfield of artificial intelligence. Humans combine data with algorithms (see here for a list) to train a model using that data. This trained model can then make predications on new data (is this picture a cat, a dog or a person?) or decision-making processes (like understanding text and images) without being explicitly programmed to do so.

Machine learning algorithms - computer programs that adjust themselves to perform better as they are exposed to more data.

The learning part of machine learning means these programs change how they process data over time. In other words, a machine-learning algorithm can adjust its own settings, given feedback on its previous performance in making predictions about a collection of data (images, text, etc.).

Deep Learning/Neural Nets a subfield of machine learning. Neural networks make up the backbone of deep learning. (The deep in deep learning refers to the depth of layers in a neural network.) Neural nets are effective at a variety of tasks (e.g., image classification, speech recognition). A deep learning neural net algorithm is given massive volumes of data, and a task to perform - such as classification. The resulting model is capable of solving complex tasks such as recognizing objects within an image and translating speech in real time. In reality, the neural net is a logical concept that gets mapped onto a physical set of specialized processors. See here.)

Data Science a new field of computer science. Broadly it encompasses data systems and processes aimed at maintaining data sets and deriving meaning out of them. In the context of AI, its the practice of people who are doing machine learning.

Data Scientists - responsible for extracting insights that help businesses make decisions. They explore and analyze data using machine learning platforms to create models about customers, processes, risks, or whatever theyre trying to predict.

Whats Different? Why is Machine Learning Possible Now?

To understand why AI/Machine Learning can do these things, lets compare them to computers before AI came on the scene. (Warning simplified examples below.)

Classic Computers

For the last 75 years computers (well call these classic computers) have both shrunk to pocket size (iPhones) and grown to the size of warehouses (cloud data centers), yet they all continued to operate essentially the same way.

Classic Computers - Programming

Classic computers are designed to do anything a human explicitly tells them to do. People (programmers) write software code (programming) to develop applications, thinking a priori about all the rules, logic and knowledge that need to be built in to an application so that it can deliver a specific result. These rules are explicitly coded into a program using a software language (Python, JavaScript, C#, Rust, ).

Classic Computers - Compiling

The code is then compiled using software to translate the programmers source code into a version that can be run on a target computer/browser/phone. For most of todays programs, the computer used to develop and compile the code does not have to be that much faster than the one that will run it.

Classic Computers - Running/Executing Programs

Once a program is coded and compiled, it can be deployed and run (executed) on a desktop computer, phone, in a browser window, a data center cluster, in special hardware, etc. Programs/applications can be games, social media, office applications, missile guidance systems, bitcoin mining, or even operating systems e.g. Linux, Windows, IOS. These programs run on the same type of classic computer architectures they were programmed in.

Classic Computers Software Updates, New Features

For programs written for classic computers, software developers receive bug reports, monitor for security breaches, and send out regular software updates that fix bugs, increase performance and at times add new features.

Classic Computers- Hardware

The CPUs (Central Processing Units) that write and run these Classic Computer applications all have the same basic design (architecture). The CPUs are designed to handle a wide range oftasks quickly in a serial fashion. These CPUs range from Intel X86 chips, and the ARM cores on Apple M1 SoC, to thez15 in IBM mainframes.

Machine Learning

In contrast to programming on classic computing with fixed rules, machine learning is just like it sounds we can train/teach a computer to learn by example by feeding it lots and lots of examples. (For images a rule of thumb is that a machine learning algorithm needs at least 5,000 labeled examples of each category in order to produce an AI model with decent performance.) Once it is trained, the computer runs on its own and can make predictions and/or complex decisions.

Just as traditional programming has three steps - first coding a program, next compiling it and then running it - machine learning also has three steps: training (teaching), pruning and inference (predicting by itself.)

Machine Learning - Training

Unlike programing classic computers with explicit rules, training is the process of teaching a computer to perform a task e.g. recognize faces, signals, understand text, etc. (Now you know why you're asked to click on images of traffic lights, cross walks, stop signs, and buses or type the text of scanned image in ReCaptcha.) Humans provide massive volumes of training data (the more data, the better the models performance) and select the appropriate algorithm to find the best optimized outcome.

(See the detailed machine learning pipeline later in this section for the gory details.)

By running an algorithm selected by a data scientist on a set of training data, the Machine Learning system generates the rules embedded in a trained model. The system learns from examples (training data), rather than being explicitly programmed. (See the Types of Machine Learning section for more detail.) This self-correction is pretty cool. An input to a neural net results in a guess about what that input is. The neural net then takes its guess and compares it to a ground-truth about the data, effectively asking an expert Did I get this right? The difference between the networks guess and the ground truth is itserror. The network measures that error, and walks the error back over its model, adjusting weights to the extent that they contributed to the error.)

Just to make the point again: The algorithms combined with the training data - not external human computer programmers - create the rules that the AI uses. The resulting model is capable of solving complex tasks such as recognizing objects its never seen before, translating text or speech, or controlling a drone swarm.

(Instead of building a model from scratch you can now buy, for common machine learning tasks, pretrained models from others and here, much like chip designers buying IP Cores.)

Machine Learning Training - Hardware

Training a machine learning model is a very computationally intensive task. AI hardware must be able to perform thousands of multiplications and additions in a mathematical process called matrix multiplication. It requires specialized chips to run fast. (See the AI hardware section for details.)

Machine Learning - Simplification via pruning, quantization, distillation

Just like classic computer code needs to be compiled and optimized before it is deployed on its target hardware, the machine learning models are simplified and modified(pruned) touse less computingpower, energy, and memory before theyre deployed to run on their hardware.

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Artificial Intelligence/Machine Learning and the Future of National Security - smallwarsjournal

NEW YORK--(BUSINESS WIRE)--Eigen Technologies (Eigen), the global intelligent document processing (IDP) provider, is proud to announce that the company has been named on the fourth annual Forbes AI 50 list 2022 for North America. Produced in partnership with Sequoia Capital, this list recognizes the standout privately held companies in North America that are making the most interesting and impactful uses of AI.

In selecting honorees for this years list, Forbes evaluated hundreds of submissions, handpicking the top 50 most compelling companies. These are the businesses that are leading in the development and use of AI technology. With its focus on no-code, easy to use AI-powered IDP software with a small data approach, Eigen is a standout example of the type of business that embodies these qualities.

Dr. Lewis Z. Liu, Co-Founder & CEO, Eigen Technologies said:

Eigen has always been focused on taking cutting-edge technology and applying it to solve real world business problems, so we are absolutely thrilled to be recognized by Forbes as one of the most impactful AI businesses. We have won many awards over the years but being listed among these AI innovators is particularly special as it recognizes the very qualities that we seek to live by at Eigen. IDP technology, such as ours, is at the forefront of the next revolution in how organizations make use of the 80-90% of their data that is currently trapped and unusable. We pioneered the small data approach that is essential to turning this information into structured usable data and as a result were seeing fantastic traction in the market. We see this award as a recognition of our pioneering work that shows were on the right path as we scale.

About Eigen Technologies

Eigen is an intelligent document processing (IDP) company that enables its clients to quickly and precisely extract answers from their documents, so they can better manage risk, scale operations, automate processes and navigate dynamic regulatory environments.

Eigens customizable, no-code AI-powered platform uses machine learning to automate the extraction of answers from documents and can be applied to a wide variety of use cases. It understands context and delivers better accuracy on far fewer training documents, while protecting the security of clients data.

Our clients include some of the best-known and respected names in finance, insurance, law and professional services, including Goldman Sachs, ING, BlackRock, Aviva and Allen & Overy. Almost half of all global systemically important banks (G-SIBs) use Eigen to overcome their document and data challenges. Eigen is backed by Goldman Sachs, Temasek, Lakestar, Dawn Capital, ING Ventures, Anthemis and the Sony Innovation Fund by IGV.

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Eigen Technologies Named to Forbes AI 50 List of Top Artificial Intelligence Companies of 2022 - Business Wire

Technology for MSME: The shift towards more efficient technology solutions from the good old websites and emails, in the name of digital adoption, is apparent among MSMEs that have shied away from evolving technologies for a long time. The shift has largely been visible because of better affordability due to growing on-demand, or pay as you go or what is called the software-as-a-service (SaaS) ecosystem in India liberating small businesses from the cost conundrum to some extent.

As India observes the National Technology Day on Wednesday to commemorate its entry into the elite club of countries having nuclear weapons with the Pokhran nuclear tests in 1998, it is also the day to remember the countrys achievement in science and innovation. While a large number of MSMEs are yet to fully benefit from the technology revolution, some of them have certainly been warming up to the new age solutions such as artificial intelligence (AI) and using it also for better growth.

The implementation of AI is across multiple use cases. For instance, Delhi-based long-haul logistics services provider JCCI Logistics has deployed AI and internet of things (IoT) solutions to manage its fleet of around 150 trucks. The company, launched in 2004, uses on-demand fleet management software for GPS tracking of vehicles, fuel management, driver analytics, and route planning.

Vehicles need to run as much as possible and thats what matters. Before deploying this solution in 2020, our monthly cumulative running was around 8,000 to 10,000 kilometres. It has increased by around 20 per cent now. The jump, I think, is primarily because of the on-board diagnostics (OBD) device that you can fit in a vehicle to get data related to fuel consumption, drivers driving behaviour, whether there is unnecessary hard acceleration or not, etc., Sachin Jain, Founder, JCCI Logistics told Financial Express Online.

OBD is essentially a machine learning (ML) and internet of things (IoT) based device that gets signals from different sensors in a vehicle and conveys them to the users dashboard with the help of the software.

JCCI Logistics have been among post-Covid adopters of deep technology solutions as the pandemic perhaps necessitated the use of software and digital for sustenance.

Covid might have caused a faster switch to some AI/ML applications since the labor force was locked up. AI/ML provides a significant opportunity for reduction in input costs, particularly those of human capital. The advent of edge AI/ML will further hasten adoption, particularly as it gets married to IoT on small devices and sensors that are available at scale and used routinely by businesses of all sizes, Utkarsh Sinha, Managing Director at advisory firm Bexley Advisors told Financial Express Online.

Among the top sectors where the use of AI accelerated during the pandemic was restaurant as the pandemic precipitated the eateries into looking at ways that could help them optimize their processes right from sales to inventory management and more.

Kabir Suri who runs Azure Hospitality, which owns restaurant chains like Mamagoto, Dhaba, Speedy Chow, etc., has been using AI in the companys operations for the past five years while Covid only reinforced his commitment to AI for efficiency and growth. We have had a direct saving of 30 per cent in past five years along with getting customer insights due to AI that has led to an uptake in revenue as well. Five years back we had around 10 outlets and now have 60 across India, Suri told Financial Express Online.

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The company has an in-house AI solution that shows live sales, total transactions, menus, items sold, total consumption per restaurant, etc. The solution captures data from every restaurant throughout the day on a real time basis and consolidates it to show up for analysis on its dashboard. This becomes important for restaurants with chains to understand the consumer-behaviour pattern, the impact of different occasions on business like festivals such as Navratras in North particularly, Christmas in Goa, and some other festivals in South, said Suri.

Moreover, the AI solution at Azure Hospitality helps Suri control the HR module as well. You can look at your salary component, leaves, attendance, holidays, payslips, etc., through a single system every day whenever you want. Basically, AI helps you make better decisions as you grow bigger by minimizing the impact of any uncertainty, Suri added.

Another sector that depends heavily on technology and AI particularly is tourism for purposes ranging from travel booking via chatbots, flight forecasting in terms of the current best price and future prices, recommendations for hotel and cab booking based on travel-related searches, etc.

There is AI at every stage in tourism and aviation, Subhas Goyal, Founder and Chairman at B2B travel company STIC Travel told Financial Express Online. The company is the exclusive General Sales Agent (GSA) a sales representative of a company in a specific region or country for 11 international airlines in India including United Airlines, Air China, Croatia Airlines etc.

STIC has been using for the past five years AI-based Microsoft Dynamics CRM to manage customer relationships, track sales leads, marketing, etc., and streamline administrative processes in sales and marketing. The company is now also implementing a chatbot assistant to answer customer queries on its platform. Goyal noted the standard queries around bookings, holiday searches can be answered by the AI bot while for further details and feedback, there would be manual intervention.

Post-Covid, more MSMEs had started to use primary technology tools at least such as social media, online service aggregators, company websites etc. According to a Crisil survey of around 540 micro and small units released in April this year, over 65 per cent respondents adopted or upgraded their use of online aggregators, social media platforms, and company websites. Among sectors, manufacturing reported higher adoption with 71 per cent respondents adopting or upgrading their use of digital platforms in comparison to 66 per cent respondents in the services sector.

Good technology is invisible. AI/ML will soon form a fundamental layer in all operations and interactions for small businesses. As technology offerings scale, it will soon be easier to get good AI to do certain tasks than to get a human to do it. The impact of this on labor force utilization will be significant, added Sinha.

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National Technology Day: How artificial intelligence is helping MSMEs to optimize processes, accelerate growth - The Financial Express

Op-ed: Aaron Yeardley, Carbon Reduction Engineer, Tunley Engineering

The fourth industrial revolution (Industry 4.0) is already happening, and its transforming the way manufacturing operations are carried out. Industry 4.0 is a product of the digital era as automation and data exchange in manufacturing technologies shift the central industrial control system to a smart setup that bridges the physical and digital world, addressed via the Internet of Things (IoT).

Industry 4.0 is creating cyber-physical systems that can network a production process enabling value creation and real-time optimisation. The main factor driving the revolution is the advances in artificial intelligence (AI) and machine learning. The complex algorithms involved in AI use the data collected from cyber-physical systems, resulting in smart manufacturing.

The impact that Industry 4.0 will have on manufacturing will be astronomical as operations can be automatically optimised to produce increased profit margins. However, the use of AI and smart manufacturing can also benefit the environment. The technologies used to optimise profits can also be used to produce insights into a companys carbon footprint and accelerate its sustainability. Some of these methods are available to help companies reduce their GHG emissions now. Other methods have the potential to reduce global GHG emissions in the future.

Scope 3 emissions are the emissions from a companys supply chain, both upstream and downstream activities. This means scope 3 covers all of a companys GHG emission sources except those that are directly created by the company and those created from using electricity. It comes as no surprise that on average Scope 3 emissions are 5.5 times greater than the combined amount from Scope 1 and Scope 2. Therefore, companies should ensure all three scopes are quantitated in their GHG emissions baseline.

However, in comparison to Scope 1 and Scope 2 emissions, Scope 3 emissions are difficult to measure and calculate. This is because of a lack of transparency in supply chains, lack of connections with suppliers, and complex industrial standards that provide misleading information. The major issues concerning Scope 3 emissions are as follows:

AI-based tools can help establish baseline Scope 3 emissions for companies as they are used to model an entire supply chain. The tools can quickly and efficiently sort through large volumes of data collected from sensors. If a company deploys enough sensors across the whole area of operations, it can identify sources of emissions and even detect methane plumes.

A digital twin is an AI model that works as a digital representation of a physical piece of equipment or an entire system. A digital twin can help the industry optimise energy management by using the AI surrogate models to better monitor and distribute energy resources and provide forecasts to allow for better preparation. A digital twin will optimise many sources of data and bring them onto a dashboard so that users can visualise it in real-time. For example, a case study in the Nanyang Technological University used digital twins across 200 campus buildings over five years and managed to save 31% in energy and 9,600 tCO2e. The research used IESs ICL technology to plan, operate, and manage campus facilities to minimise energy consumption.

Digital twins can be used as virtual replicas of building systems, industrial processes, vehicles, and many other opportunities. The virtual environment enables more testing and iterations so that everything can be optimised to its best performance. This means digital twins can be used to optimise building management making smart strategies that are based on carbon reduction.

Predictive maintenance of machines and equipment used in industry is now becoming common practice because it saves companies costs in performing scheduled maintenance, or costs in fixing broken equipment. The AI-based tool uses machine learning to learn how historical sensor data maps to historical maintenance records. Once a machine learning algorithm is trained using the historical data, it can successfully predict when maintenance is required based on live sensor readings in a plant. Predictive maintenance accurately models the wear and tear of machinery that is currently in use.

The best part of predictive maintenance is that it does not require additional costs for extra monitoring. Algorithms have been created that provide accurate predictions based on operational telemetry data that is already available. Predictive maintenance combined with other AI-based methods such as maintenance time estimation and maintenance task scheduling can be used to create an optimal maintenance workflow for industrial processes. Conversely, improving current maintenance regimes which often contribute to unplanned downtime, quality defects and accidents is appealing for everybody.

An optimal maintenance schedule produced from predictive maintenance prevents work that often is not required. Carbon savings will be made via the controlled deployment of spare parts, less travel for people to come to the site, and less hot shooting of spare parts. Intervening with maintenance only when required and not a moment too late will save on the use of electricity, efficiency (by preventing declining performance) and human labour. Additionally, systems can employ predictive maintenance on pipes that are liable to spring leaks, to minimise the direct release of GHGs such as HFCs and natural gas. Thus, it has huge potential for carbon savings.

Research has shown that underpinning the scheduling of maintenance activities on predictive maintenance and maintenance time estimation can produce an optimal maintenance scheduling (Yeardley, Ejeh, Allen, Brown, & Cordiner, 2021). The work optimised the scheduling by minimising costs based on plant layout, downtime, and labour constraints. However, scheduling can also be planned by optimising the schedule concerning carbon emissions. In this situation, maintenance activities can be performed so that fewer journeys are made and GHG emissions are saved.

The internet of things (IoT) is the digital industrial control system, a network of physical objects that are connected over the internet by sensors, software and other technologies that exchange data with each thing. In time, the implementation of the IoT will be worldwide and every single production process and supply chain will be available as a virtual image.

Open access to a worldwide implementation of the IoT has the potential to provide a truly circular economy. Product designers can use the information available from the IoT and create value from other peoples waste. Theoretically, we could establish a work where manufacturing processes are all linked so that there is zero extracted raw materials, zero waste disposed and net-zero emissions.

Currently, the world has developed manufacturing processes one at a time, not interconnected value chains across industries. It may be a long time until the IoT creates the worldwide virtual image required, but once it has the technology is powerful enough to address losses from each process and exchange material between connected companies. Both materials and energy consumption can be shared to lower CO2 emissions drastically. It may take decades, but the IoT provides the technology to create a circular economy.

ConclusionAI has enormous potential to benefit the environment and drive the world to net-zero. The current portfolio of research being conducted at the Alan Turning Institute (UKs national centre for data science) includes projects that explore how machine learning can be part of the solution to climate change. For example, an electricity control room algorithm is being developed to provide decision support and ensure energy security for a decarbonised system. The national grids electricity planning is improved by forecasting the electricity demand and optimising the schedule. Further, Industry 4.0 can plan for the impact that global warming and decarbonisation strategies have on our lives.

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Artificial intelligence drives the way to net-zero emissions - Sustainability Magazine

DENVER With wildfires becoming bigger and more destructive as the West dries out and heats up, agencies and officials tasked with preventing and battling the blazes could soon have a new tool to add to their arsenal of prescribed burns, pick axes, chain saws and aircraft.

The high-tech help could come by way of an area not normally associated with fighting wildfires: artificial intelligence. And space.

Lockheed Martin Space, based in Jefferson County, is tapping decades of experience of managing satellites, exploring space and providing information for the U.S. military to offer more accurate data quicker to ground crews. They are talking to the U.S. Forest Service, university researchers and a Colorado state agency about how their their technology could help.

By generating more timely information about on-the-ground conditions and running computer programs to process massive amounts of data, Lockheed Martin representatives say they can map fire perimeters in minutes rather than the hours it can take now. They say the artificial intelligence, or AI, and machine learning the company has applied to military use can enhance predictions about a fires direction and speed.

The scenario that wildland fire operators and commanders work in is very similar to that of the organizations and folks who defend our homeland and allies. Its a dynamic environment across multiple activities and responsibilities, said Dan Lordan, senior manager for AI integration at Lockheed Martins Artificial Intelligence Center.

Lockheed Martin aims to use its technology developed over years in other areas to reduce the time it takes to gather information and make decisions about wildfires, said Rich Carter, business development director for Lockheed Martin Spaces Mission Solutions.

The quicker you can react, hopefully then you can contain the fire faster and protect peoples properties and lives, Carter said.

The concept of a regular fire season has all but vanished as drought and warmer temperatures make Western lands ripe for ignition. At the end of December, the Marshall fire burned 991 homes and killed two people in Boulder County. The Denver area just experienced its third driest-ever April with only 0.06 of an inch of moisture, according to the National Weather Service.

Colorado had the highest number of fire-weather alerts in April than any other April in the past 15 years. Crews have quickly contained wind-driven fires that forced evacuations along the Front Range and on the Eastern Plains. But six families in Monte Vista lost their homes in April when a fire burned part of the southern Colorado town.

Since 2014, the Colorado Division of Fire Prevention and Control has flown planes equipped with infrared and color sensors to detect wildfires and provide the most up-to-date information possible to crews on the ground. The onboard equipment is integrated with the Colorado Wildfire Information System, a database that provides images and details to local fire managers.

Last year we found almost 200 new fires that nobody knew anything about, said Bruce Dikken, unit chief for the agencys multi-mission aircraft program. I dont know if any of those 200 fires would have become big fires. I know they didnt become big fires because we found them.

When the two Pilatus PC-12 airplanes began flying in 2014, Colorado was the only state with such a program conveying the information in near real time, Dikken said. Lockheed Martin representatives have spent time in the air on the planes recently to see if its AI can speed up the process.

We dont find every single fire that we fly over and it can certainly be faster if we could employ some kind of technology that might, for instance, automatically draw the fire perimeter, Dikken said. Right now, its very much a manual process.

Something like the 2020 Cameron Peak fire, which at 208,663 acres is Colorados largest wildfire, could take hours to map, Dikken said.

And often the people on the planes are tracking several fires at the same time. Dikken said the faster they can collect and process the data on a fires perimeter, the faster they can move to the next fire. If it takes a couple of hours to map a fire, what I drew at the beginning may be a little bit different now, he said.

Lordan said Lockheed Martin engineers who have flown with the state crews, using the video and images gathered on the flights, have been able to produce fire maps in as little as 15 minutes.

The company has talked to the state about possibly carrying an additional computer that could help crunch all that information and transmit the map of the fire while still in flight to crews on the ground, Dikken said. The agency is waiting to hear the results of Lockheed Martins experiences aboard the aircraft and how the AI might help the state, he added.

Actionable intelligence

The company is also talking to researchers at the U.S. Forest Service Missoula Fire Sciences Laboratory in Montana. Mark Finney, a research forester, said its early in discussions with Lockheed Martin.

They have a strong interest in applying their skills and capabilities to the wildland fire problem, and I think that would be welcome, Finney said.

The lab in Missoula has been involved in fire research since 1960 and developed most of the fire-management tools used for operations and planning, Finney said. Were pretty well situated to understand where new things and capabilities might be of use in the future and some of these things certainly might be.

However, Lockheed Martin is focused on technology and thats not really been where the most effective use of our efforts would be, Finney said.

Prevention and mitigation and preemptive kind of management activities are where the great opportunities are to change the trajectory were on, Finney said. Improving reactive management is unlikely to yield huge benefits because the underlying source of the problem is the fuel structure across large landscapes as well as climate change.

Logging and prescribed burns, or fires started under controlled conditions, are some of the management practices used to get rid of fuel sources or create a more diverse landscape. But those methods have sometimes met resistance, Finney said.

As bad as the Cameron Peak fire was, Finney said the prescribed burns the Arapaho and Roosevelt National Forests did through the years blunted the blazes intensity and changed the flames movement in spots.

Unfortunately, they hadnt had time to finish their planned work, Finney said.

Lordan said the value of artificial intelligence, whether in preventing fires or responding to a fire, is producing accurate and timely information for fire managers, what he called actionable intelligence.

One example, Lordan said, is information gathered and managed by federal agencies on the types and conditions of vegetation across the country. He said updates are done every two to three two years. Lockheed Martin uses data from satellites managed by the European Space Agency that updates the information about every five days.

Lockheed is working with Nvidia, a California software company, to produce a digital simulation of a wildfire based on an areas topography, condition of the vegetation, wind and weather to help forecast where and how it will burn. After the fact, the companies used the information about the Cameron Peak fire, plugging in the more timely satellite data on fuel conditions, and generated a video simulation that Lordan said was similar to the actual fires behavior and movement.

While appreciating the help technology provides, both Dikken with the state of Colorado and Finney with the Forest Service said there will always be a need for ground-truthing by people.

Applying AI to fighting wildfires isnt about taking people out of the loop, Lockheed Martin spokesman Chip Eschenfelder said. Somebody will always be in the loop, but people currently in the loop are besieged by so much data they cant sort through it fast enough. Thats where this is coming from.

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Artificial intelligence tapped to fight Western wildfires - Portland Press Herald - Press Herald

During the first few months of a babys life, it is usually difficult to tell the exact sex or age by sight alone.

A machine learning model showed that it is possible to identify both indicators, the sex and the age of a baby, based on its temperament, thanks to artificial intelligence.

A babys propensity to show fear, smile or laugh can tell a lot about their age or gender, in the eyes of artificial intelligence.

This was demonstrated by a research team led by professionals from Washington State University and the University of Idaho, United States, through a study in which a machine learning model was used to analyze temperament data in 4,438 babies, in an attempt to classify babies by gender and age.

Working with this many babies is an almost intractable task for just one research laboratory. However, obtaining this sample was possible thanks to the collection of reliable information from different scientific sources, to collect child behavior data collected between 2006 and 2019.

The data with which we worked in this research come from a questionnaire that applies a temperament measurement scale, in which parents recorded the frequency of 191 different behaviors that their children from 3 to 12 months of age showed during one week. These behaviors can be categorized into 14 different dimensions of temperament, such as smiling, activity level, anger or frustration, and fear. Overall, the sample collects data from 2,298 boys and 2,093 girls.

Working with this background, an AI model capable of using these indicators to identify the age and sex of a baby was developed. Previously, research has already been carried out that, in an isolated way, investigates the relationship between temperament and the age or sex of the baby. This research, on the other hand, is the first to combine these elements during the analysis.

During the first 48 weeks of a babys life, it was easier for algorithms to decipher a babys age than its gender. However, after that period, the system was able to specify their classification, which suggests that gender differences are accentuated from that age.

It is at least suggestive of a picture in which temperament begins to differentiate by gender in a more powerful way around the age of one year, commented Maria Gartstein, lead author of the study and professor of psychology at Washington State University.

For purposes of this study, research co-author Erich Seamon of the University of Idaho Institute for Modeling, Collaboration and Innovation used machine learning algorithms to classify babies as male or female under three age ranges: 0- 24 weeks of age, 24-48 weeks of age, and older than 48 weeks, based on their scores for the 14 dimensions of temperament.

Accuracy rates increased with age, from a low of 38% for age group one to 57% for age group three.

It was a good opportunity to do a kind of demonstration study using these machine learning techniques that require really large data sets and are not very common in socio-emotional development researchGartstein commented. It gave us the opportunity for the first time to really consider the extent to which gender differences are informed by childhood age.

Although the results of this research are subject to the multiple influences that may affect its study sample, the work presented is consistent with previous research that already indicated that the effects of socialization begin to become visible around one year of age.

Read more here:

They present artificial intelligence capable of predicting the age and sex of a baby based on its temperament - InTallaght

Artificial Intelligence in Medical Imaging Market gives a detailed study of the industry, replete with a spec sheet, market segmentation, and other information, in Data Bridge Market Research. The study analyses the potential and present market scenario for the projected period of 2022-2028, offering data and updates on the worldwide Artificial Intelligence in Medical Imaging markets major segments. An influential Artificial Intelligence in Medical Imaging market report is a window to the industry which explains what market definition, classifications, applications, engagements and market trends are. By keeping end users at the centre point, a team of researchers, forecasters, analysts and industry experts work exhaustively to formulate this market research report. The market is supposed to grow during the forecast period due to growing demand at the end user level. According to this market report, new highs will take place in the Artificial Intelligence in Medical Imaging market. Artificial Intelligence in Medical Imaging market survey report offers the best professional in-depth study on the current state for the industry.

Artificial intelligence in medical imaging market is expected to gain market growth in the forecast period of 2021 to 2028. Data Bridge Market Research analyses the market to reach at an estimated value of USD 1,579.33 million and grow at a CAGR of 4.11% in the above-mentioned forecast period. Increased numbers of diagnostic procedures drives the artificial intelligence in medical imaging market.

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Top Key Players Included in This Report:

BenevolentAI, OrCam, Babylon, Freenome Inc., Clarify Health Solutions, BioXcel Therapeutics, Ada Health GmbH, GNS Healthcare, Zebra Medical Vision Inc., Qventus Inc, IDx Technologies Inc., K Health, Prognos, Medopad Ltd., Viz.ai Inc., Voxel Technology, Renalytix AI plc, Beijing Pushing Technology Co. Ltd., PAIGE, mPulse Mobile, Suki AI Inc., BERG LLC, Zealth Inc., OWKIN INC., and Your.MD Ltd. UK

The Study Is Segmented By Following:

By Technology (Deep Learning, Computer Vision, NLP, Others), Offering (Hardware, Software, Services), Deployment Type (On-Premise, Cloud), Application (X-Ray, CT, MRI, Ultrasound, Molecular Imaging), Clinical Applications (Breast, Lung, Neurology, Cardiovascular, Liver, Prostate, Colon, Musculoskeletal, Others), End-User (Hospitals, Clinics, Research Laboratories, Others)

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Geographic Segment Covered in the Report:

The Artificial Intelligence in Medical Imaging report provides information on the market area, which is divided into sub-regions and countries / regions. In addition to the market share in each country and sub-region, this chapter in this report also contains information on profit opportunities. This chapter of the report mentions the market share and growth rate for each region, country and sub-region during the estimated period.

The study has covered and anatomized the eventuality of Worldwide Artificial Intelligence in Medical Imaging Industry and provides statistics and information on market dynamics, growth factors, crucial challenges, major motorists & conditions, openings and Forecast 2028. Businesses can confidently rely on the information mentioned in the reliable Artificial Intelligence in Medical Imaging Market analysis report as it is derived only from the valuable and genuine resources. This market research report delivers comprehensive analysis of the market structure along with the estimations of the various segments and sub-segments of the market. The transformation in market landscape is analyzed in the finest Artificial Intelligence in Medical Imaging marketing report which is mainly observed due to the moves of key players or brands which include developments, product launches, joint ventures, mergers and acquisitions that in turn change the view of the global face of the industry.

The Report Answers Questions Similar as:

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Which are the products/ parts/ operations/ areas to invest in over the cast period in the Artificial Intelligence in Medical Imaging Industry?

What are the inhibiting factors and impact of COVID-19 shaping the Artificial Intelligence in Medical Imaging Market during the cast period?

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Whats the request size and Forecast of the Artificial Intelligence in Medical Imaging Market?

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The report has been prepared using the latest methods and tools for primary and secondary research. Our analysts rely on government documents, white papers, press releases, reliable investor information, financial and quarterly reports, and public and private interviews to gather data and information about the market in which they operate.

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Artificial Intelligence in Medical Imaging Market In Depth Analysis Along with Statistics and Forecast 2022-2029 Queen Anne and Mangolia News - Queen...

Grand View Research, Inc. Market Research And Consulting

According to new report available with Grand View Research, the global artificial intelligence in healthcare industry is anticipated to grow owing to increasing demand for personalized medicine and reducing care expenses, from 2022 to 2030.

Artificial Intelligence In Healthcare Industry Overview

The global artificial intelligence in healthcare market size was valued at USD 10.4 billion in 2021 and is expected to reach USD 208.2 billion by 2030, registering a CAGR of 38.4% over a forecast period.

The growing datasets of patient health-related digital information, increasing demand for personalized medicine, and the rising demand for reducing care expenses are some of the major driving forces of the market growth. The growing global geriatric population, changing lifestyles, rising prevalence of chronic diseases has contributed to the surge in demand for diagnosing and improved understanding of diseases in their initial stages. Artificial Intelligence (AI) and machine learning (ML) algorithms are being widely adopted and integrated into healthcare systems to accurately predict diseases in their early stage based on historical health datasets.

Gather more insights about the market drivers, restrains and growth of the Global Artificial Intelligence In Healthcare Market

Furthermore, deep learning technologies,predictive analytics, content analytics, and Natural Language Processing (NLP) tools are enabling care professionals to diagnose patients underlying health conditions at an earlier stage. The Covid-19 pandemic positively influenced the demand for AI technologies and unearthed the potential held by these advanced technologies. Healthcare systems widely adopted these technologies in the rapid diagnosis and detection of different virus strains and utilized personalized information in improving the management of the outbreak. AI/ML algorithms were utilized in the diagnosis sector wherein these technologically driven modules were trained with datasets of chest CT images, symptoms, pathological findings, and exposure history to diagnose Covid-19 positive patients rapidly and accurately.

Moreover, the growing shortage of healthcare workforce drove the adoption of AI/ML technologies. Therefore, AI algorithms can be trained to analyze patient health information which further supports care providers in quickly diagnosing the condition and devising an accurate treatment regime. Supportive government initiatives, the rising number of mergers and acquisitions and technological collaborations, and the ongoing Covid-19 pandemic had a significant role in boosting the growth of the market and accelerating the adoption rate of AI in healthcare. AI/ML algorithms are being widely implemented in rapid and accurate diagnosis of medical conditions after the initial implementation in the detection of Covid-19 positive patients using personalized patient information and data consolidation.

As per an NCBI study in 2020, AI-based algorithms accurately detected 68% of COVID-19 positive cases in a dataset of twenty-five patients which were diagnosed as negative cases by care professionals. The implementation of AI/ML technologies in enhancing patient care, reducing machine downtime, and minimizing care expenses are some of the driving forces of artificial intelligence (AI) in healthcare market growth. Owing to the Covid-19 pandemic, AI-based technologies witnessed a significant boost in adoption and are set to experience a dramatic growth trajectory. Healthcare functions such as diagnostics, patient management, medication management, claims management, workflow management, integration of machines, and cybersecurity saw a remarkable surge in the integration of AI/ML technologies.

The growing penetration of Artificial Intelligence technologies in healthcare applications led to key market participants focusing on product innovation and technological collaborations to expand their product portfolio and meet the growing demands during the pandemic. For instance, in May 2020, MIT-IBM Watson AI Lab promoted the implementation of AI technologies in research projects studying the health and economic consequences of the pandemic. Similarly, Qventus introduced AI-based patient flow automation systems covering critical resource control, hospitalization duration optimization, Covid-19 scenario planner, and ICU capacity creation in its healthcare facilities.

Artificial Intelligence In Healthcare Market Segmentation

Based on the Component Insights, the market is segmented into Software Solutions, Hardware, and Services.

Based on the Application Insights, the market is segmented into Robot-Assisted Surgery, Virtual Assistants, Administrative Workflow Assistants, Connected Machines, Diagnosis, Clinical Trials, Fraud Detection, Cybersecurity, and Dosage Error Reduction.

Based on the Regional Insights, the market is segmented into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.

Market Share Insights

Key Companies Profile:

Increasing investments in R&D, innovative product developments and launches, the rising number of technological collaborations, and service differentiation are the key strategies players are focusing on to gain a competitive edge in the market.

Some of the prominent players in the artificial intelligence in healthcare market include,

Order a free sample PDF of the Artificial Intelligence In Healthcare Market Intelligence Study, published by Grand View Research.

About Grand View Research

Grand View Research is a full-time market research and consulting company registered in San Francisco, California. The company fully offers market reports, both customized and syndicates, based on intense data analysis. It also offers consulting services to business communities and academic institutions and helps them understand the global and business scenario to a significant extent. The company operates across multitude of domains such as Chemicals, Materials, Food and Beverages, Consumer Goods, Healthcare, and Information Technology to offer consulting services.

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To view the original version on ABNewswire visit: Artificial Intelligence in Healthcare Market To Witness An Explosive CAGR of 38.4% Till 2030, Driven By Rising Datasets of Patient Health-Related Digital Information | Grand View Research, Inc.

Excerpt from:

Artificial Intelligence in Healthcare Market To Witness An Explosive CAGR of 38.4% Till 2030, Driven By R - Benzinga

Artificial Intelligence in Healthcare market analysis report highlights the idea of high level analysis of major market segments and recognition of opportunities. Market analysis and market segmentation has been reviewed here in terms of markets, geographic scope, years considered for the study, currency and pricing, research methodology, primary interviews with key opinion leaders, DBMR market position grid, DBMR market challenge matrix, secondary sources, and assumptions. This market report carries out comprehensive analysis of company profiles of key market players that offers a competitive landscape. Artificial Intelligence in Healthcare market document exhibits important product developments and tracks recent acquisitions, mergers and research in the healthcare industry by the top market players.

The market study and analysis of the large scale Artificial Intelligence in Healthcare report lends a hand to figure out types of consumers, their views about the product, their buying intentions and their ideas for advancement of a product. To attain knowledge of all the above factors, this transparent, extensive and supreme market report is generated. And for the same, the report also describes all the major topics of the market research analysis that includes market definition, market segmentation, competitive analysis, major developments in the market, and excellent research methodology. This Artificial Intelligence in Healthcare market research report has been formed with the vigilant efforts of innovative, enthusiastic, knowledgeable and experienced team of analysts, researchers, industry experts, and forecasters.

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Leading Key Players Operating in the Artificial Intelligence in Healthcare Market Includes:NVIDIA Corporation (US), Intel Corporation (US), IBM (US), Google LLC (US), Microsoft (US), General Vision Inc. (US), Johnson & Johnson Services, Inc. (US), Siemens Healthcare GmbH (Germany), Medtronic (Ireland), CloudMedx Inc. (US)

Market Analysis and Insights: Global Artificial Intelligence in Healthcare Market:

This Artificial Intelligence in Healthcare market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Healthcare market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Browse Full Report Along With Facts and Figures @ https://www.databridgemarketresearch.com/reports/global-artificial-intelligence-in-healthcare-market?AZ

Market Analysis and Insights: Global Artificial Intelligence in Healthcare Market:

This Artificial Intelligence in Healthcare market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Healthcare market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Artificial Intelligence in Healthcare Market, By Region:

Global Artificial Intelligence in Healthcare market is analyzed and market size insights and trends are provided by country, product as referenced above.

The countries covered in the Artificial Intelligence in Healthcare market report are the U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.

North America dominates the Artificial Intelligence in Healthcare market because of the rise in the cases of arrhythmic diseases, favorable reimbursement policies for patients, high demand for advanced treatment methods and developed healthcare infrastructure in the region. Asia-Pacific is estimated to grow in the forecast period due to the high prevalence of cardiovascular diseases, increase in adoption of advanced digital devices, large population and launch of new innovative products.

Table of Contents

Global Artificial Intelligence in Healthcare Market Size, status and Forecast to 2029

1 Market summary2 Manufacturers Profile3 Global Artificial Intelligence in Healthcare Sales, Overall Revenue, Market Share and Competition by Manufacturer4 Global Artificial Intelligence in Healthcare market analysis by numerous Regions5 North America Artificial Intelligence in Healthcare by Countries6 Europe Artificial Intelligence in Healthcare by Countries7 Asia-Pacific Artificial Intelligence in Healthcare by Countries8 South America Artificial Intelligence in Healthcare by Countries9 Middle east and Africas Artificial Intelligence in Healthcare by Countries10 Global Artificial Intelligence in Healthcare Market phase by varieties11 Global Artificial Intelligence in Healthcare Market phase by Applications12 Artificial Intelligence in Healthcare Market Forecast13 Sales Channel, Distributors, Traders and Dealers14 Analysis Findings and Conclusion15 Appendix

Check Complete Table of Contents with List of Table and Figures @ https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-healthcare-market&AZ

What are the market opportunities, market risks, and market overviews of the Artificial Intelligence in Healthcare Market?

Who are the distributors, traders, and merchants in the Artificial Intelligence in Healthcare Market?

What is the analysis of sales, income, and prices of the leading manufacturers in the Artificial Intelligence in Healthcare Market?

What are the Artificial Intelligence in Healthcare market opportunities and threats faced by the global Artificial Intelligence in Healthcare Market vendors?

What are the main factors driving the worldwide Artificial Intelligence in Healthcare Industry?

What are the Top Players in Artificial Intelligence in Healthcare industry?

What is the analysis of sales, income, and prices by type, application of the Artificial Intelligence in Healthcare market?

What is regional sales, income, and price analysis for Artificial Intelligence in Healthcare Market?

Research Methodology: GlobalLiquid Chromatography Devices Market

Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please request an analyst call or can drop down your enquiry.

The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Global versus Regional and Vendor Share Analysis.

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Data Bridge Market Researchhas presented itself as an unconventional, neoteric market research and consulting company with an unprecedented level of resilience and integrated approaches.We are committed to finding the best market opportunities and promoting effective information for your business to thrive in the market.Data Bridge Market Research provides appropriate solutions to complex business challenges and initiates an effortless decision-making process.

Data Bridge strives to create satisfied customers who rely on our services and rely on our hard work with certainty.Getpersonalizationanddiscounton the report by emailingsopan.gedam@databridgemarketresearch.com.Were happy with our glorious 99.9% customer satisfaction rating.

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Artificial Intelligence in Healthcare Market Global Analysis, Opportunities and Forecast to 2029 Queen Anne and Mangolia News - Queen Anne and...

This market report contains market insights and analysis for this industry which are backed up by SWOT analysis. This market report examines market drivers, market restraints, challenges, opportunities, and key developments in this industry. This report provides with complete knowledge and information of rapidly changing market landscape, what is already there in the market, future trends or market expectations, the competitive environment, and competitor strategies that aids in planning your own strategies with which you can outshine the competitors. This market report has been designed by keeping in mind the customer requirements which assist them in increasing their return on investment (ROI).

Moreover, this research report highlights numerous industry verticals such as company profile, contact details of manufacturer, product specifications, geographical scope, production value, market structures, recent developments, revenue analysis, market shares and possible sales volume of the company. To gain actionable market insights to build sustainable and money-spinning business strategies with ease, This market research report is a great option. This report provides top to bottom analysis and estimation of various market related factors that plays key role in better decision making. This report describes CAGR (compound annual growth rate) values and its fluctuations for the specific forecast period.

Data Bridge Market Research analyses theartificial intelligence in aviation marketwill exhibit a CAGR of 46.3% for the forecast period of 2022-2029 and is likely to reach the USD 9,995.84 million by 2029.

Get Sample Report in PDF Version @https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-artificial-intelligence-in-aviation-market

Top Players Analysed in the Report are:

Some of the major players operating in the artificial intelligence in aviation market are IBM, Microsoft, Amazon Web Services, Inc., Airbus S.A.S., Xilinx, NVIDIA Corporation, Intel Corporation, General Electric, Micron Technology, Inc., Garmin Ltd., Lockheed Martin Corporation, SAMSUNG, Thales Group, MINDTITAN, Mitsubishi Electric Corporation, OMRON Corporation, TAV Technologies and IRIS Automation, among others.

Porters five forces model in the report provides insights into the competitive rivalry, supplier and buyer positions in the market and opportunities for the new entrants in the global Artificial Intelligence in Aviation market over the period. Further, Growth Matrix gave in the report brings an insight into the investment areas that existing or new market players can consider.

Research Methodology

A) Primary Research

Our primary research involves extensive interviews and analysis of the opinions provided by the primary respondents. The primary research starts with identifying and approaching the primary respondents, the primary respondents are approached include

Key Opinion Leaders Internal and External subject matter experts Professionals and participants from the industry

Our primary research respondents typically include

Executives working with leading companies in the market under review Product/brand/marketing managers CXO level executives Regional/zonal/ country managers Vice President level executives.B) Secondary Research

Secondary research involves extensive exploring through the secondary sources of information available in both the public domain and paid sources. Each research study is based on over 500 hours of secondary research accompanied by primary research. The information obtained through the secondary sources is validated through the crosscheck on various data sources.

Read Detailed Index of full Research Study @https://www.databridgemarketresearch.com/reports/global-artificial-intelligence-in-aviation-market

Who Will Get Advantage of This Report?

The prime aim of theGlobal Artificial Intelligence in Aviation Marketis to provide industry investors, private equity companies, company leaders and stakeholders with complete information to help them make well-versed strategic decisions associated to the chances in the Concealed Door Closer market throughout the world.

The secondary sources of the data typically include

Company reports and publications Government/institutional publications Trade and associations journals Databases such as WTO, OECD, World Bank, and among others Websites and publications by research agencies

Key Market Segmentation:

On the basis of technology, the artificial intelligence in aviation has been segmented into computer vision, machine learning, context awareness computing and natural language processing. Machine learning is further segmented into deep learning, supervised learning, unsupervised learning, reinforcement learning and semi-supervised learning.

Based on offering, the artificial intelligence in aviation market has been segmented into hardware, software and services. Hardware is further segmented into processors, memory and networks. Software is further segmented into AI solutions and AI platforms. Services are further segmented into deployment and integration and support and maintenance.

On the basis of application, the artificial intelligence in aviation market has been segmented into dynamic pricing, virtual assistants, flight operations, smart maintenance, manufacturing, surveillance, training and other applications. Manufacturing is further segmented into material movement, predictive maintenance and machinery inspection, production planning, quality control and reclamation.

Key Elements that the report acknowledges:

Market size and growth rate during the forecast period. Key factors driving this market Key market trends cracking up the growth of this market Challenges to market growth Key vendors of this market Detailed SWOT analysis Opportunities and threats face by the existing vendors in this global market Trending factors influencing the market in the geographical regions Strategic initiatives focusing on the leading vendors PEST analysis of the market in the five major regions

To check the complete Table of Content click here: @https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-aviation-market

About Data Bridge Market Research, Private Ltd

Data Bridge Market ResearchPvtLtdis a multinational management consulting firm with offices in India and Canada. As an innovative and neoteric market analysis and advisory company with unmatched durability level and advanced approaches. We are committed to uncover the best consumer prospects and to foster useful knowledge for your company to succeed in the market.

Data Bridge Market Research has over 500 analysts working in different industries. We have catered more than 40% of the fortune 500 companies globally and have a network of more than 5000+ clientele around the globe. Our coverage of industries includes

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Global Artificial Intelligence in Aviation Market Size, Share And Trends Analysis Report By Product Types, And Applications Forecast Queen Anne and...