Mediaboss Marketing

Ameco Research Analysts says that the global market for artificial intelligence (AI) in aviation will reach a market size of more than US$ 6,900 Mn by 2028 rising at a CAGR of around 47% during the predicted years

Airports around the world took the brunt of the travel prohibitions and limitations imposed by the COVID-19 outbreak in a short period of time. Operations have been severely affected or fully suspended since international travel has been restricted to repatriation planes and cargo, and most airport halls are almost empty. Machine learning (ML), deep learning (DL), and other artificial intelligence (AI) approaches are beginning to influence the industry and have become fundamental methodologies in many research initiatives and new industrial advances. Machine learning becomes easier with access to vast volumes of data, allowing us to address previously inconceivable issues. The use of artificial intelligence (AI) in aviation can help with capacity, forecasting, air traffic management (ATM), delay management, the environment, connectivity, and safety.

The growing adoption of big data inArtificial Intelligence (AI) in Aviation Marketgrowth

Quick reactions to present and future market demands, better planning and tactically aligned decision making, and a clear understanding and monitoring of all important performance drivers significant to the airline industry are the ultimate benefits of big data analytics. When these advantages are correctly exploited, they contribute to lower operating costs, market-leading competitiveness, better customer service, higher profit margins, and more shareholder value.

Click here to get a Sample report copy https://www.amecoresearch.com/sample/276600

While a variety of variables have contributed to the expansion of AI in travel, one of the most important has been a shift in customer expectations. Because of the experiences provided by huge tech companies like Google, Amazon, Apple, and Facebook, travelers are increasingly demanding seamless and personalized experiences.

By leveraging artificial intelligence through virtual assistant technology that reads weather forecasts on demand, switches radio channels, and delivers situational information on demand, airline pilots can save time and increase efficiency. AI-based virtual assistants can manage repeated chores thanks to their capabilities. Additionally, Garmin offers AI audio panels that can be beneficial to pilots.

Artificial intelligence can also help to reduce overbooking. Based on past flyer information and historical passenger data, as well as changes in weather trends on a certain route, AI might be used to anticipate if a specific passenger will not show up or will transfer to another aircraft. The AI would then offer ground employees the most up-to-date passenger information. This technology could make it easier to remove passengers from planes that have already taken off.

Global Artificial Intelligence (AI) in Aviation Market Classification

Artificial intelligence (AI) in aviation market is divided into four categories: offering, technology, application, and regions. The offering segment can be split into hardware, software, and services. Based on offering, the software segment generated the largest revenue in 2020 while the services are likely to attain the fastest growth rate during the forecast period 2021 2028. Machine learning, context awareness computing, natural language processing, and computer vision are the categorizations of technology segments. The machine learning application acquired the most market share in 2020, whereas the natural language processing segment is expected to grow with a significant growth rate. Computer vision, a technique that utilizes cameras and machine learning algorithms to monitor complicated ground servicing activities, detect safety hazards in real-time, or sound alerts when a service takes longer than intended, is one of the emerging AI applications at airports. Gatwick Airport in London was one of the first major airports to use computer vision to speed up aircraft turnaround times and improve ground personnel safety. Based on application, the market is divided into virtual assistants, smart maintenance, manufacturing, training, surveillance, flight operations, and dynamic pricing. Among applications, the virtual assistant segment dominated the market with maximum share and is also likely to exhibit the quickest growth in the coming years.

Regional Stance

North America, Asia-Pacific, Latin America, Europe, and the Middle East & Africa (MEA) are the regional categorization of the global artificial intelligence (AI) in aviation market. North America generated the largest revenue in 2020 due to the early adoption of technologies and the presence of key players in the region. Meanwhile, the Asia-Pacific region is expected to witness the fastest growth rate in the coming years. China and Japan are the two largest contributors to AIs growth in the APAC aviation sector. The deployment of machine learning and natural language processing (NLP) technology for virtual assistance and training applications in the aviation sector is boosting AIs growth in this region. These countries have a high demand for AI technology to improve the efficiency of their aviation sectors.

Major Players

The key companies offered in the report include Airbus SE, Boeing, Garmin Ltd., Amazon, IBM Corporation, Intel Corporation, Lockheed Martin, Micron Technology, General Electric, MiSmart Maintenanceoft, Neurala, Inc. NVIDIA corporation, MINDTITAN, TAV Technologies, Samsung Electronics, Thales Group, and Xilinx.

FEW SIGNIFICANT POINTS FROM TABLE OF CONTENT:

Global Artificial Intelligence (AI) in Aviation Market Research Report 2021-2028

CHAPTER 1. Industry Overview of Artificial Intelligence (AI) in Aviation MarketCHAPTER 2. Research ApproachCHAPTER 3. Market Dynamics And Competition AnalysisCHAPTER 4. Manufacturing Plant AnalysisCHAPTER 5. Artificial Intelligence (AI) in Aviation Market By OfferingCHAPTER 6. Artificial Intelligence (AI) in Aviation Market By TechnologyCHAPTER 7. Artificial Intelligence (AI) in Aviation Market By ApplicationCHAPTER 8. North America Artificial Intelligence (AI) in Aviation Market By CountryCHAPTER 9. Europe Artificial Intelligence (AI) in Aviation Market By CountryCHAPTER 10. Asia Pacific Artificial Intelligence (AI) in Aviation Market By CountryCHAPTER 11. Latin America Artificial Intelligence (AI) in Aviation Market By CountryCHAPTER 12. Middle East & Africa Artificial Intelligence (AI) in Aviation Market By CountryCHAPTER 13. Player Analysis Of Artificial Intelligence (AI) in Aviation MarketCHAPTER 14. Company Profile

For Premium Report copy-https://www.amecoresearch.com/buy/276600

If any have question please feel free to contact at[emailprotected]&[emailprotected]

Contact No:+ 1 407 915 4157

See the original post here:
Artificial Intelligence (AI) in Aviation Market Sales and Revenue Report 2021 2030 The Daily Vale - The Daily Vale

Old Timer may be the oldest known humpback, first sighted Lynn Canal, Southeast Alaska 1972. Also sighted as PWF-NP1117 and HIHWNMS-2017-2-25WWG01A01 (Photo by Jim Nahmens, courtesy of Happy Whale)

Its a special moment, watching a gigantic humpback going for a deep dive. The whales back arches and the tail swings up, disappearing below the surface like the pointed toes of an Olympic diver.

The black-and-white patterns on the underside of a whales tail fins, or flukes, are unique. Now a citizen science program called Happy Whale uses artificial intelligence to quickly identify humpbacks from those patterns.

Through photographs shared by whale watchers, Happy Whale has recorded thousands of whales that travel to and from Alaska.

Like facial recognition, we can tell who it is, said Ted Cheeseman, an expedition scientist who has studied whales all over the world, including in Antarctica. He co-founded Happy Whaleas a way to track humpbacks, a species thats known to travel thousands of miles.

Its helping to answer a lot of questions about their individual behavior.

Who does the whale hang out with? Does the whale have a calf? Cheeseman said. What is the larger story here such that we can build family relationships and so on, tell more of the story of the individual. To me, thats a huge part of this.

The difference between this photo ID program and others in the past is the manpower needed. Happy Whale uses an automated computer program to ID the photos instead of people doing it by hand. Just one full-time and two part-time employees run the database and confirm the results.

The program started in 2015 but took years to test and fine tune. Now, whale watchers can share their fluke photos and locations to the online database, which has identified 68,000 humpbacks worldwide.

The program started with 18,000 whale photos that had been previously identified by hand. Cheeseman says Happy Whale is more efficient.

Somebody gives me a dataset of a thousand photos, it used to be that that would be an hour per photo, Cheeseman said. The actual matching time is now insignificant. If someone gives me a thousand photos I can tell them the next day that, Oh, 700 of them are these known whales and these 300, those are probably new.'

The program has documented about 30,000 humpbacks in the North Pacific, which Cheeseman expects is about 70% of the population.

Participants are rewarded for their work. They usually get an initial response within a few days to a week and get notices when their whale is spotted again.

Dennis Rogers, a long-time whale watching guide in Petersburg, has uploaded over 5,500 photos to the program.

Its very interesting just to see the migrations, Rogers said. Some of these whales go to Hawaii for the winter, and theyre re-sighted there, which we get a notification when that re-sighting happens. Some of our whales go to Mexico. Its real interesting, some of our whales go to Mexico one year and to Hawaii the next year.

Rogers encourages his clients to send in their photos as well. He says other tracking systems, including satellite tags, can fall off whales within days.

This is purely un-invasive and gives a great amount of information over time. Some of our whales, weve been tracking close to 40 years, Rogers said.

The program has found some unusual migrations in Alaskas individual whales, said Scott Roberge, a board member for Petersburgs Marine Mammal Center.

Theyve followed one from Alaska to Hawaii to Japan back to Alaska, Roberge said. Made the loop of the North Pacific.

Roberge also contributes photographs and enjoys getting the feedback.

Its incredible to get that information and to get the email that says, Oh, the whale that you took a picture of last summer was just found in Hawaii, and it just had a baby, he said.

Cheeseman believes that over 95% of humpbacks in Southeast Alaska are in the database already. But the program is expanding. Cheeseman hopes to automate dorsal fin recognition within the year, which would allow them to identify and track orcas and other species a lot faster.

Cheeseman gave a presentation in Petersburg on May 18 at the Wright Auditorium.

See the original post here:
Citizen science program uses photos and artificial intelligence to track thousands of humpback whales - KTOO

SANTA CLARA, Calif.--(BUSINESS WIRE)--Ayar Labs, the leader in chip-to-chip optical connectivity, is developing with NVIDIA groundbreaking artificial intelligence (AI) infrastructure based on optical I/O technology to meet future demands of AI and high performance computing (HPC) workloads. The collaboration will focus on integrating Ayar Labs technology to develop scale-out architectures enabled by high-bandwidth, low-latency and ultra-low-power optical-based interconnects for future NVIDIA products. Together, the companies plan to accelerate the development and adoption of optical I/O technology to support the explosive growth of AI and machine learning (ML) applications and data volumes.

Meeting Future Performance and Power Requirements with Optical I/O

Optical I/O uniquely changes the performance and power trajectories of system designs by enabling compute, memory and networking ASICs to communicate with dramatically increased bandwidth, at lower latency, over longer distances and at a fraction of the power of existing electrical I/O solutions. The technology is also foundational to enabling emerging heterogeneous compute systems, disaggregated/pooled designs, and unified memory architectures that are critical to accelerating future data center innovation.

Todays state-of-the-art AI/ML training architectures are limited by current copper-based compute-to-compute interconnects to build scale-out systems for tomorrows requirements, said Charles Wuischpard, CEO of Ayar Labs. Our work with NVIDIA to develop next-generation solutions based on optical I/O provides the foundation for the next leap in AI capabilities to address the worlds most sophisticated problems.

Delivering the Next Million-X Speedup for AI with Optical Interconnect

Over the past decade, NVIDIA-accelerated computing has delivered a million-X speedup in AI, said Rob Ober, Chief Platform Architect for Data Center Products at NVIDIA. The next million-X will require new, advanced technologies like optical I/O to support the bandwidth, power and scale requirements of future AI and ML workloads and system architectures.

As AI model sizes continue to grow, by 2023 NVIDIA believes that models will have 100 trillion or more connections a 600X increase from 2021 exceeding the technical capabilities of existing platforms. Traditional electrical-based interconnects will reach their bandwidth limits, driving lower application performance, higher latency and increased power consumption. New interconnect solutions and system architectures are needed to address the scale, performance and power demands of the next generation of AI. Ayar Labs collaboration with NVIDIA is focused on addressing these future challenges by developing next-generation architectures with optical I/O.

To learn more about Ayar Labs chip-to-chip optical technology, please visit: https://ayarlabs.com/

About Ayar Labs

Ayar Labs is disrupting the traditional performance, cost, and efficiency curves of the semiconductor and computing industries by driving a 1000x improvement in interconnect bandwidth density at 10x lower power. Ayar Labs patented approach uses industry standard cost-effective silicon processing techniques to develop high speed, high density, low power optical-based interconnect chiplets and lasers to replace traditional electrical based I/O. The company was founded in 2015 and is funded by a number of domestic and international venture capital firms as well as strategic investors. For more information, visit http://www.ayarlabs.com.

Read this article:
Ayar Labs to Accelerate Development and Application of Optical Interconnects in Artificial Intelligence/Machine Learning Architectures with NVIDIA -...

CONCORD, Mass., May 24, 2022 /PRNewswire/ --GRAY Associatestoday announces its continued commitment to accelerating higher education's future and growth strategy through innovative new software tools and expanded data-informed academic program evaluation support.

Gray's sophisticated new Predict Program Size offering has launched, joining the PES+ (Program Evaluation System) Software to empower academic program professionals to make smart data-informed program planning decisions. Powered by artificial intelligence that encompasses the latest in machine learning, Predict Program Size maximizes outcomes by accurately estimating the potential size of current and new programs for an institution, identifying programs that will increase enrollment and revenue, and reducing the risk of new program failures.

This new machine learning-based approach provides universities a way to predict enrollments and graduations and to make better decisions about the use of their valuable resources. "We have always been committed to our higher education community. I am proud of the advanced academic program planning tools and features being developed for our PES+ subscribers. These advancements not only include Predict Program Size but also Predict Margins for Economics, and a new Program Portfolio Management dashboard." Robert Gray Atkins, Gray's CEO.

As higher education becomes more competitive, institutions embracecustomized data and analytics to help themmake better-informedprogram decisions. Gray's PES+software integrates local, regional, and national market data on educational programs, including data on student demand, competition, and employer needs.

Gray collects and analyzesdata from the Bureau of Labor Statistics, job postings, Google Searches, the American Community Survey, IPEDS, and more, so schools can use PES+ to score and rank thousands of programs and decide what actions to take.PES+ also calculates the revenue, cost, and margin of academic programs, courses, and sections to enable institutions to understand the markets and margins for their programs.

About Gray Associates

Gray helps colleges and universities make data-informed decisions about their academic programs. Gray's software integrates the best available data on academic program economics, student demand, employer needs, and competitive intensity for the precise market served by each institution. Faculty and administrative leaders use the software to score, rank, and evaluate programs in a collaborative process that builds consensus on programs to start, sunset, sustain, or grow. With Gray's tools and processes, institutions identify paths to increase enrollment, revenue, and efficiency, while investing in their mission and strengthening relationships among faculty and administrators.

Press Contact:Jackie LucasVera Voce Communication978-255-1159[emailprotected]

SOURCE Gray Associates

Originally posted here:
Artificial Intelligence Comes to Campus: Gray Associates Announces Predict Program Size Software That Improves Higher Education Decisions for Academic...

Federated, distributed, and few-shot learning can make consumers direct participants in Artificial Intelligence processes

NEW YORK, May 25, 2022 /PRNewswire/ -- Artificial Intelligence (AI) is all around us, but the processes of inference and learning that form the backbone of AI typically take place in big servers, far removed from consumers. New models are changing all that, according to ABI Research, a global technology intelligence firm, as the more recent frameworks of Federated Learning, Distributed Learning, and Few-shot Learning can be deployed directly on consumers' devices that have lower compute and smaller power budget, bringing AI to end users.

"This is the direction the market has increasingly been moving to, though it will take some time before the full benefits of these approaches become a reality, especially in the case of Few-Shot Learning, where a single individual smartphone would be able to learn from the data that it is itself collecting. This might well prove to be an attractive proposition for many, as it does not involve uploading data onto a cloud server, making for more secure and private data. In addition, devices can be highly personalized and localized as they can possess high situational awareness and better understanding of the local environments," explains David Lobina, Research Analyst at ABI Research.

ABI Research believes that it will take up to 10 years for such on-device learning and inference to be operative, and these will require adopting new technologies, such as neuromorphic chips. The shift will take place in more powerful consumer devices, such as autonomous vehicles and robots, before making its way into the likes of smartphones, wearables, and smart home devices. Big players such as Intel, NVIDIA, and Qualcomm have been working on these models in recent years, which in addition to neuromorphic chipset players such as BrainChip and GrAI Matter Labs, have provided chips that offer improved performance on a variety of training and inference tasks. The take-up is still small, but it can potentially disrupt the market.

"Indeed, these learning models have the potential to revolutionize a variety of sectors, most probably the fields of autonomous driving and the deployment of robots in public spaces, both of which are currently difficult to pull off, particularly in co-existence with other users," Lobina concludes. "Federated Learning, Distributed Learning, and Few-shot Learning reduces the reliance on cloud infrastructure, allowing AI implementers to create low latency, localized, and privacy preserving AI that can deliver much better user experience for end users."

These findings are from ABI Research's Federated, Distributed and Few-Shot Learning: From Servers to Devicesapplication analysis report.This report is part of the company'sAI and Machine Learningresearch service, which includes research, data, and ABI Insights. Application Analysisreports present in-depth analysis on key market trends and factors for a specific technology.

About ABI ResearchABI Research is a global technology intelligence firm delivering actionable research and strategic guidance to technology leaders, innovators, and decision makers around the world. Our research focuses on the transformative technologies that are dramatically reshaping industries, economies, and workforces today.

ABI Research

For more information about ABI Research's services, contact us at +1.516.624.2500 in the Americas, +44.203.326.0140 in Europe, +65.6592.0290 in Asia-Pacific, or visitwww.abiresearch.com.

Contact Info:

Global Deborah PetraraTel: +1.516.624.2558[emailprotected]

SOURCE ABI Research

Originally posted here:
More Than 2 Billion Shipments of Devices with Machine Learning will Bring On-Device Learning and Inference Directly to Consumers by 2027 - PR Newswire