Program uses artificial intelligence to track thousands of humpback whales through photographs – KFSK

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)

A new whale identification program uses artificial intelligence to identify humpbacks by their flukes. Through photographs shared by whale watchers, the citizens science program Happy Whale has recorded thousands of whales that travel to and from Alaska.

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

The tail fins or flukes are unique to each whale. The black and white pattern on the underside are precise identifiers.

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 the program, Happy Whale, as 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 man power 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 data set 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, you know, its pretty cool.

The program has documented about 30,000 humpbacks in the North Pacific, which Cheeseman expects is about 70 percent 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 resighted there, which we get a notification when that resighting 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 percent of humpbacks in Southeast Alaska are in the database already. But thats just the start of Happy Whale as 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, May 18 at the Wright Auditorium.

Originally posted here:
Program uses artificial intelligence to track thousands of humpback whales through photographs - KFSK

Artificial Intelligence in Transportation Market to Witness Huge Growth by 2029 |Continental, Magna, Bosch The Daily Vale – The Daily Vale

Artificial Intelligence in Transportation Market research report is the new statistical data source added by Research Cognizance.

Artificial Intelligence in Transportation Market is growing at a High CAGR during the forecast period 2022-2029. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.

Artificial Intelligence in Transportation Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share, and contact information are shared in this report analysis.

Get the PDF Sample Copy (Including FULL TOC, Graphs, and Tables) of this report @:

https://researchcognizance.com/sample-report

Top Key Players Profiled in this report are:

Continental, Magna, Bosch, Valeo, ZF, Scania, Paccar, Volvo, Daimler, Nvidia, Alphabet, Intel, Microsoft

The key questions answered in this report:

Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Artificial Intelligence in Transportation market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Artificial Intelligence in Transportation markets trajectory between forecast periods.

Get up to 30% Discount on this Premium Report @:

https://researchcognizance.com/discount

Regions Covered in the Global Artificial Intelligence in Transportation Market Report 2022: The Middle East and Africa (GCC Countries and Egypt) North America (the United States, Mexico, and Canada) South America (Brazil etc.) Europe (Turkey, Germany, Russia UK, Italy, France, etc.) Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)

The cost analysis of the Global Artificial Intelligence in Transportation Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

The report provides insights on the following pointers:

Market Penetration: Comprehensive information on the product portfolios of the top players in the Artificial Intelligence in Transportation market.

Product Development/Innovation: Detailed insights on the upcoming technologies, R&D activities, and product launches in the market.

Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market.

Market Development: Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies.

Market Diversification: Exhaustive information about new products, untapped geographies, recent developments, and investments in the Artificial Intelligence in Transportation market.

Buy Exclusive Report @:

https://researchcognizance.com/checkout

If you have any special requirements, please let us know and we will offer you the report as you want.

About Us:

Research Cognizance is an India-based market research Company, registered in Pune. Research Cognizance aims to provide meticulously researched insights into the market. We offer high-quality consulting services to our clients and help them understand prevailing market opportunities. Our database presents ample statistics and thoroughly analyzed explanations at an affordable price.

Contact Us:

Neil Thomas

116 West 23rd Street 4th Floor New York City, New York 10011

[emailprotected]

+1 7187154714

The rest is here:
Artificial Intelligence in Transportation Market to Witness Huge Growth by 2029 |Continental, Magna, Bosch The Daily Vale - The Daily Vale

Ukrainians put on masks and try to trick artificial intelligence – The Slovak Spectator

24. May 2022 at 17:11 IPremium content

The company employs women with children.

Font size:A-|A+Comments disabled

We live in an era of biometric revolution, a technology that allows the verification of a person's identity through fingerprints or facial recognition. We encounter it when unlocking a phone, safely accessing company premises or a bank account.

Since 2007 a Slovak company Innovatrics, founded by Jn Lunter Jr., has been involved in biometrics. On a global scale, the company is a strong player that has already verified the identity of more than a billion people.

Facial biometrics has significantly expanded the commercial capabilities of the technology, as cameras are cheaper and therefore used by more employees than fingerprint scanners.

When Innovatrics started, facial biometrics had a high error rate, but thanks to the development of neural networks and artificial intelligence, its reliability has significantly improved.

Recently, the company launched a project to improve the technology. For this purpose, the company has hired five Ukrainians and plans to employ more in the near future.

"We didn't want to be a charity, so we offered Ukrainians a job that would make sense for them, as well as for us," explains Jn Zborsk, a Communication Manager in the company.

The Innovatrics Ukrainian team's task is to create datasets to teach artificial intelligence to check liveness. In other words, software will identify whether the system is looking at a photo from a magazine, a living person, or a person wearing a mask during an identification check.

Why? The coronavirus pandemic has created a boom in technologies that enable companies to communicate with clients remotely. One such example is banks that allow the opening of an account from a living room.

Read more from the original source:
Ukrainians put on masks and try to trick artificial intelligence - The Slovak Spectator

Edgility, Inc. Launches EdgeAi, a Breakthrough Explainable and Open-Box Artificial Intelligence to Accelerate Adoption – TechDecisions

TAMPA, Fla.(BUSINESS WIRE)Edgility, Inc. launches EdgeAi as healthcares first operationally embedded AI with Explainability. EdgeAi exposes the internal mechanics of machine learning and deep learning systems in human-understandable terms. Trust in AI model predictions is paramount if care providers and administrators are to accept the decisions based on them. EdgeAi generates a standard label for transparency of the prediction model, leading to better AI adoption and open-box development.

EdgeAi natively incorporates Explainability so humans can understand the inputs and the results, said Justin Falk, Edgilitys Chief Technology Officer. In contrast to the traditional AI black-box, which even the developer rarely understands, EdgeAi surfaces the logic to the end-user.

As an early Edgility partner, University Hospitals (UH) in Cleveland is at the forefront of utilizing EdgeAi predictions and insight while revealing the data and logic that creates each forecast prediction. At UH, we utilize EdgeAi to predict discharges and expose the factors that influence the prediction visible to all end-users, said Sam Brown, UHs VP of Logistics and Systems Operation.

Built into Edgilitys Smart Operations and Orchestration platform, EdgeAi predictions are mapped to specific levers of action toward achieving a particular outcome.

UH is at the forefront of utilizing AI to build a culture of trust, said Cliff Megerian, MD, FACS, Chief Executive Officer of UH, Jane and Henry Meyer Chief Executive Officer Distinguished Chair. Paired with our strong strategic plan and operating model, we are achieving the advantages of being a unified health system, enabling future investments that advance care and help make UH the most trusted caregiver in Northeast Ohio.

EdgeAis predictions are translated into orchestrated actions and behaviors through Edgilitys Smart Operations Platform, making this a closed-loop intelligence and action engine. The future of problem-solving demands demystified algorithms, said Balaji Ramadoss, co-founder, and CEO of Edgility. EdgeAi enables health systems to construct their own AI factory to curate datasets and run thousands of learning cycles and algorithms.

Without a clear mapping to the next behavior, AI outputs are ineffective, said Peter Pronovost, MD, PhD, Chief Quality and Clinical Transformation Officer at UH. At UH, we utilize the EdgeAi prediction to hardwire behavior to fractal management models to drive accountability and action.

EdgeAi is built on Edgilitys Cognitive Platform and closes the AIaction loop in real-time. With over 2 million patients orchestrated just in 2021, the capability within EdgeAi will benefit millions more and transform patient care by bridging the current AI opacity.

EdgeAi is available for all existing clients utilizing Discharge as a Service, Intelligent Transfers as a Service, Hospital at Home Orchestration, and Staffing and Huddle Orchestration.

About Edgility, Inc.

Since 2016, Edgility has provided health systems with Smart Operations and Orchestration platforms. From care coordination and population management to quality of care to revenue leakage and emerging business opportunities, Edgilitys Operations Centers and EdgeAi orchestrate operational workflows and remove process fragmentation. Edgility partners with health systems to help tackle their most significant operational challenges. For more, visit edgilityhealth.com

University Hospitals / Cleveland, Ohio

Founded in 1866, University Hospitals serves the needs of patients through an integrated network of 23 hospitals (including five joint ventures), more than 50 health centers and outpatient facilities, and over 200 physician offices in 16 counties throughout northern Ohio. The systems flagship quaternary care, academic medical center, University Hospitals Cleveland Medical Center, is affiliated with Case Western Reserve University School of Medicine, Northeast Ohio Medical University, Oxford University and the Technion Israel Institute of Technology. The main campus also includes the UH Rainbow Babies & Childrens Hospital, ranked among the top childrens hospitals in the nation; UH MacDonald Womens Hospital, Ohios only hospital for women; and UH Seidman Cancer Center, part of the NCI-designated Case Comprehensive Cancer Center. UH is home to some of the most prestigious clinical and research programs in the nation, with more than 3,000 active clinical trials and research studies underway. UH Cleveland Medical Center is perennially among the highest performers in national ranking surveys, including Americas Best Hospitals from U.S. News & World Report. UH is also home to 19 Clinical Care Delivery and Research Institutes. UH is one of the largest employers in Northeast Ohio with more than 30,000 employees. Follow UH on LinkedIn, Facebook and Twitter. For more information, visit UHhospitals.org.

Contacts

Heather Holland

Edgility, Inc

heather@edgility.iowww.edgilityhealth.com(813) 431-5588

Read more from the original source:
Edgility, Inc. Launches EdgeAi, a Breakthrough Explainable and Open-Box Artificial Intelligence to Accelerate Adoption - TechDecisions

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

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.

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

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

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.

Here is the original post:
Artificial intelligence drives the way to net-zero emissions - Sustainability Magazine

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

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.

Invalid username/password.

Please check your email to confirm and complete your registration.

Use the form below to reset your password. When you've submitted your account email, we will send an email with a reset code.

Previous

Next

Continued here:
Artificial intelligence tapped to fight Western wildfires - Portland Press Herald - Press Herald

Adoption of AI/ML: How artificial intelligence is scaling up the education industry – The Financial Express

With the advancement of technology, artificial intelligence and machine learning (AI/ML) is on the verge of becoming an integral part of every industry, and education is no exception. With AI being enabled, learning can be customised for students. In the last few years, due to the emergence of machine learning, data has been treated as a prime knowledge resource and it is valued. Simultaneously, tech-based industry has upped the demand for AI/ML rapidly, therefore more students are taking up the course due to good career opportunities, Rajesh Khanna, professor, president, NIIT University, said.

Besides courses, it is has been observed that the such technology is being leveraged by ed-tech platforms as an business strategy enhancing tool. Starting from career counselling to exam proctoring, ed-techs have utilised AI/ML to accelerate the accuracy and productivity. There are multiple options when it comes to career counselling and opportunities. AI/ML can provide a good fit option for students when the right algorithm is taken into consideration, Rohan Pasari, CEO, Cialfo, said.

Further, AI/ML is used to conduct various tests online, so much so that it being now believed that it puts a seal to the authenticity of the process, as it gives the ability to remotely invigilate the test. On photographs taken at an interval, an AI algorithm is run to analyse the accuracy and authenticity of the examination. In CY21, Mettle conducted 20 million assessments across the globe on the online platform, out of which 16 million were remotely invigilated, Siddhartha Gupta, CEO, Mercer Mettle, a tech-based exam assessment platform, said.

According to Rackspaces AI/ML Annual Research Report 2022, AI/ML has been considered as the top two most important strategic technologies, along with cybersecurity. The report shows that up to 72% of respondents have noted AI/ML as part of their business strategy, IT strategy or both. Initially, the kind of industries which would have benefited from AI/ML were the financial market based companies. But the time has come that heavy machinery is now opening up to AI and ML to figure out and address the problems in a more distinct and accurate manner, Khanna added.

Although penetration of tech-based education can upskill the students, it is believed that enabling technologies is associated with various challenges and risk factors. The ministry report on school education 2020-21 revealed that post-pandemic, the dropout rate of students increased to 8.9% from 2.6% as the main reason being closure of schools and irregular online classes. In places like rural India, accessibility has always been a point of contention, which has also resulted in a digital divide. Whenever a new technology is enabled, the magnification of inequality also takes place. Places where devices and connectivity are not strongly available, they will definitely suffer. But the gap has to be filled by non-government orgnisation (NGOs) and government intervention by providing ways to resolve the issues, Khanna said.

Read Also: Union Education Minister, Shri Dharmendra Pradhan chairs meeting on formulation of HECI

Read the original here:
Adoption of AI/ML: How artificial intelligence is scaling up the education industry - The Financial Express

Global Artificial Intelligence in Drug Discovery Market Research Report to 2026 – AI Cloud to Create a Streamlined and Automated Approach in Drug…

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Drug Discovery Market Research Report by Offering (Services and Software), Application, End User, Region (Americas, Asia-Pacific, and Europe, Middle East & Africa) - Global Forecast to 2026 - Cumulative Impact of COVID-19" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence in Drug Discovery Market size was estimated at USD 566.22 million in 2020, USD 701.05 million in 2021, and is projected to grow at a Compound Annual Growth Rate (CAGR) of 25.06% to reach USD 2,166.65 million by 2026.

Competitive Strategic Window:

The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:

The FPNV Positioning Matrix evaluates and categorizes the vendors in the Artificial Intelligence in Drug Discovery Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:

The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

The report provides insights on the following pointers:

1. Market Penetration: Provides comprehensive information on the market offered by the key players

2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets

3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments

4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players

5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:

1. What is the market size and forecast of the Global Artificial Intelligence in Drug Discovery Market?

2. What are the inhibiting factors and impact of COVID-19 shaping the Global Artificial Intelligence in Drug Discovery Market during the forecast period?

3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Artificial Intelligence in Drug Discovery Market?

4. What is the competitive strategic window for opportunities in the Global Artificial Intelligence in Drug Discovery Market?

5. What are the technology trends and regulatory frameworks in the Global Artificial Intelligence in Drug Discovery Market?

6. What is the market share of the leading vendors in the Global Artificial Intelligence in Drug Discovery Market?

7. What modes and strategic moves are considered suitable for entering the Global Artificial Intelligence in Drug Discovery Market?

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Companies Mentioned

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

See the original post here:
Global Artificial Intelligence in Drug Discovery Market Research Report to 2026 - AI Cloud to Create a Streamlined and Automated Approach in Drug...

Alcatel-Lucent Enterprise enhances its Asset Tracking solution with Artificial Intelligence capabilities and push-button alerts – Macau Business

With enabled instant location of Bluetooth Low Energy (BLE) tags connected to individuals and critical equipment, to supply real-time and historic contact tracing, with increased precision accuracy and additional new features.

PARIS, FRANCE News Direct 10 May 2022 Alcatel-Lucent Enterprise, a leading provider of network, communications and cloud solutions tailored to customers industries is placing AI (Artificial Intelligence) and ML (Machine Learning) at the heart of its technology development. ALEs enhanced OmniAccess Stellar Asset Tracking solution now offers new customisable push-button alerts and an AI/ML powered real-time location algorithm for environments that require improved accuracy compared with standard tools.

Designed to quickly locate assets or individuals, use analytics to optimise workflows, and simplify the ability to provide contact tracing, Alcatel-Lucent Enterprise Asset Tracking is set to deliver an enriched user experience with finer location precision thanks to its AI and Machine Learning capabilities.

Further enhancements of the solution include equipping BLE tags with a new alert button, to notify users of activity at the touch of a button, or by sending automated notifications from an indoor geofenced area and immediately share vital information in real-time.

This solution holds powerful potential for the healthcare industry, for use cases such as calling medical staff for assistance, locating and assessing the availability of critical equipment, and improving safety of patients and staff.

The alert button function is also fully programmable for use case flexibility and enables configuration for button press request action, with real-time location, extending its value beyond the healthcare sector to be used to enhance campus security for staff and students in schools or enable security personnel to call for assistance in a variety of indoor environments.

Asset tracking users can also receive alerts via a range of media, making sure information is delivered to the right person, or group, at the right time, through the most convenient channel.

Notifications are sent instantly to the Alcatel-Lucent OmniVista Cirrus Asset Manager and distributed via Android push notification to the OmniAccess Stellar Asset Tracking app, web push to desktop or mobile device, email, SMS, Rainbow and other third-party systems, such as IQ Messenger. This message server includes additional notification media such as Alcatel-Lucent desktop, DECT and WLAN phones, nurse call systems, etc.

Daniel Faurlin, Business Line Manager, Network Business Division at Alcatel-Lucent Enterprise, comments:

Our OmniAccess Stellar Asset Tracking solution has proved an essential tool for our customers and they can now track, locate and monitor the usage patterns of their assets with even greater accuracy and efficiency. Although contact tracing and asset tracking came to light most prominently during the health crisis, its ability to improve performance across numerous industries extends beyond the turbulence of the pandemic and can be harnessed to bring operations into the digital age.

As ALE continues to enrich its offer under the traditional CAPEX model, it has also expanded to a new hybrid Network as a Service offering, combining both CAPEX & OPEX options.

In line with customer requirements, ALE plans to add asset tracking and contact tracing capabilities to its Network-as-a-Service offer. The company also provides a pay-as-you-grow model for businesses looking to ramp up their digital transformation with a manageable predictable monthly fee and the opportunity to benefit from the latest technology updates with a reduced initial investment.

Our aim is always to make accessing high-performance and data-rich solutions as easy as possible for our customers. As we continue to innovate and enhance our solutions, so too will we develop new models to make digital transformation universally accessible with options for every business and industry, adds Nolwenn Simon, Product Line Manager Network Value added solutions, Alcatel-Lucent Enterprise.

Alcatel-Lucent Enterprise delivers the customised technology experiences enterprises need to make everything connect.

ALE provides digital-age networking, communications and cloud solutions with services tailored to ensure customers success, with flexible business models in the cloud, on premises, and hybrid. All solutions have built-in security and limited environmental impact.

Over 100 years of innovation have made Alcatel-Lucent Enterprise a trusted advisor to more than a million customers all over the world.

With headquarters in France and 3,400 business partners worldwide, Alcatel-Lucent Enterprise achieves an effective global reach with a local focus.

al-enterprise.com | LinkedIn | Twitter | Facebook | Instagram

#AlcatelLucentEnterprise

The issuer is solely responsible for the content of this announcement.

View original post here:
Alcatel-Lucent Enterprise enhances its Asset Tracking solution with Artificial Intelligence capabilities and push-button alerts - Macau Business