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With wildfires becoming bigger and more destructive as the western part of the United States 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, chainsaws and aircraft.

The high-tech help could come from an area not normally associated with fighting wildfires: artificial intelligence (AI). And space.

Lockheed Martin Space, based in Jefferson County, is tapping decades of experience in managing satellites, exploring space and providing information to the US military to offer more accurate data quicker to ground crews.

It is talking to the US Forest Service, university researchers, and a Colorado state agency about how 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 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 organisations 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 0.06in of moisture, according to the National Weather Service.

Colorado had the highest number of fire-weather alerts in April than in 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 colour sensors to detect wildfires and provide the most up-to-date information possible to crews on the ground.

The on-board equipment is integrated with the Colorado Wildfire Information System, a database that provides images and details to local fire managers.

This aircraft is used to detect fires, help with perimeter mapping and give real-time situational awareness of fires. The Denver Post/TNS

Last year, we found almost 200 new fires that nobody knew anything about, said Bruce Dikken, unit chief for the agencys multi-mission aircraft programme.

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 aeroplanes began flying in 2014, Colorado was the only state with such a programme conveying the information in near real time, Dikken said.

Lockheed Martin representatives have been spending time in the air on the planes recently to see if its AI could 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 US Forest Services 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 pre-emptive 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 AI, whether in preventing fires or responding to them, is in 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 years. Lockheed Martin uses data from satellites managed by the European Space Agency that updates the information about every five days.

Lockheed is also working with Nvidia, a California tech 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 behaviour 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. The Denver Post/MediaNews Group/Tribune News Service

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Is artificial intelligence the next tool to fight wildfires? - The Star Online

International Joint Conferences on Artificial Intelligence is a non-profit corporation founded in California, in 1969 for scientific and educational purposes, including dissemination of information on Artificial Intelligence at conferences in which cutting-edge scientific results are presented and through dissemination of materials presented at these meetings in form of Proceedings, books, video recordings, and other educational materials. IJCAI consists of two divisions: the Conference Division and the AI Journal Division. IJCAI conferences present premier international gatherings of AI researchers and practitioners and they were held biennially in odd-numbered years since 1969.

Starting with 2016, IJCAI conferences are held annually.IJCAI-ECAI-22will be held in Vienna, Austria from July 23rd until July 29th, IJCAI-23 in Cape Town, South Africa, and IJCAI-PRICAI-24 in Shanghai, P.R. China.

IJCAI is governed by the Board of Trustees, with IJCAI Secretariat in charge of its operations.

IJCAI-21was held from August 19th until August 26th, 2021 in a virtual Montreal-themed reality. The Conference Committee thanks you all for participating.

Call for Proposals to Host IJCAI-ECAI-2026Call for IJCAI-22 Awards NominationsAI Hub launchedFunding Opportunities for Promoting AI Research Free Access to the AI journal

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Even as rapid improvements in artificial intelligence have led to speculation over significant changes in the health care landscape, the adoption of AI in health care has been minimal. A 2020 survey by Brookings, for example, found that less than 1 percent of job postings in health care required AI-related skills.

The Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), a research center within the MIT Schwarzman College of Computing, recently hosted the MITxMGB AI Cures Conference in an effort to accelerate the adoption of clinical AI tools by creating new opportunities for collaboration between researchers and physicians focused on improving care for diverse patient populations.

Once virtual, the AI Cures Conference returned to in-person attendance at MITs Samberg Conference Center on the morning of April 25, welcoming over 300 attendees primarily made up of researchers and physicians from MIT and Mass General Brigham (MGB).

MIT President L. Rafael Reif began the event by welcoming attendees and speaking to the transformative capacity of artificial intelligence and its ability to detect, in a dark river of swirling data, the brilliant patterns of meaning that we could never see otherwise. MGBs president and CEO Anne Klibanski followed up by lauding the joint partnership between the two institutions and noting that the collaboration could have a real impact on patients lives and help to eliminate some of the barriers to information-sharing.

Domestically, about $20 million in subcontract work currently takes place between MIT and MGB. MGBs chief academic officer and AI Cures co-chair Ravi Thadhani thinks that five times that amount would be necessary in order to do more transformative work. We could certainly be doing more, Thadhani said. The conference just scratched the surface of a relationship between a leading university and a leading health-care system.

MIT Professor and AI Cures Co-Chair Regina Barzilay echoed similar sentiments during the conference. If were going to take 30 years to take all the algorithms and translate them into patient care, well be losing patient lives, she said. I hope the main impact of this conference is finding a way to translate it into a clinical setting to benefit patients.

This years event featured 25 speakers and two panels, with many of the speakers addressing the obstacles facing the mainstream deployment of AI in clinical settings, from fairness and clinical validation to regulatory hurdles and translation issues using AI tools.

On the speaker list, of note was the appearance of Amir Khan, a senior fellow from the U.S. Food and Drug Administration (FDA), who fielded a number of questions from curious researchers and clinicians on the FDAs ongoing efforts and challenges in regulating AI in health care.

The conference also covered many of the impressive advancements AI made in the past several years: Lecia Sequist, a lung cancer oncologist from MGB, spoke about her collaborative work with MGB radiologist Florian Fintelmann and Barzilay to develop an AI algorithm that could detect lung cancer up to six years in advance. MIT Professor Dina Katabi presented with MGBs doctors Ipsit Vahia and Aleksandar Videnovic on an AI device that could detect the presence of Parkinsons disease simply by monitoring a persons breathing patterns while asleep. It is an honor to collaborate with Professor Katabi, Videnovic said during the presentation.

MIT Assistant Professor Marzyeh Ghassemi, whose presentation concerned designing machine learning processes for more equitable health systems, found the longer-range perspectives shared by the speakers during the first panel on AI changing clinical science compelling.

What I really liked about that panel was the emphasis on how relevant technology and AI has become in clinical science, Ghassemi says. You heard some panel members [Eliezer Van Allen, Najat Khan, Isaac Kohane, Peter Szolovits] say that they used to be the only person at a conference from their university that was focused on AI and ML [machine learning], and now were in a space where we have a miniature conference with posters just with people from MIT.

The 88 posters accepted to AI Cures were on display for attendees to peruse during the lunch break. The presented research spanned different areas of focus from clinical AI and AI for biology to AI-powered systems and others.

I was really impressed with the breadth of work going on in this space, Collin Stultz, a professor at MIT, says. Stultz also spoke at AI Cures, focusing primarily on the risks of interpretability and explainability when using AI tools in a clinical setting, using cardiovascular care as an example of showing how algorithms could potentially mislead clinicians with grave consequences for patients.

There are a growing number of failures in this space where companies or algorithms strive to be the most accurate, but do not take into consideration how the clinician views the algorithm and their likelihood of using it, Stultz said. This is about what the patient deserves and how the clinician is able to explain and justify their decision-making to the patient.

Phil Sharp, MIT Institute Professor and chair of the advisory board for Jameel Clinic, found the conference energizing and thought that the in-person interactions were crucial to gaining insight and motivation, unmatched by many conferences that are still being hosted virtually.

The broad participation by students and leaders and members of the community indicate that theres an awareness that this is a tremendous opportunity and a tremendous need, Sharp says. He pointed out that AI and machine learning are being used to predict the structures of almost everything from protein structures to drug efficacy. It says to young people, watch out, there might be a machine revolution coming.

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Can artificial intelligence overcome the challenges of the health care system? - MIT News

The age of artificial intelligence (A.I.) is finally upon us. Consumer applications of A.I., in particular, have come a long way, leading to more accurate search results for online shoppers, allowing apps and websites to make more personalized recommendations, and enabling voice-activated digital assistants to better understand us.

As impressive as these uses of A.I. are, they only hint at how this game-changing technology will be applied in business. Because the goal of business A.I. is to help the companies that drive our global economy learn from their data to become vastly more resilient, adaptive, and innovative.

We all know there is tremendous potential value in data, which continues to grow exponentially. In fact, the world is creating 2.5 quintillion bytes of data every day (that's 2.5 followed by 18 zeros). To harness that potential, companies need A.I. to make sense of the data, and hybrid cloud computing platforms that can distribute it across organizations.

The economic opportunity behind these technologies is enormous, given that business is only about 10 percent of the way to realizing A.I.'s full potential. Fortunately, we are making steady progress, with the number of organizations poised to integrate A.I. into their business processes and workflows growing rapidly. A recent IBM study showed that more than a third of the companies surveyed were using some form of A.I. to save time and streamline operations.

Take the challenge of demographic shifts. A.I., in conjunction with hybrid cloud, is helping many companies automate certain routine business activities, and move people to higher-value work. In manufacturing, a factory floor operator can now rely on A.I. to detect defects that are invisible to the human eye. In health care, A.I.-enabled virtual agents can handle millions of calls at once. In the energy sector, autonomous robots can use cloud and A.I. to analyze data at the edge to improve equipment uptime and prevent power outages. Another example: IBM is helping McDonald's launch an automated order-taking drive-thru experience that benefits both customers and restaurant crews.

Then there is the massive challenge of cybersecurity. The inherent business value of data makes it a prime target for hackers. But with about a half-million unfilled cybersecurity jobs in the U.S. alone, security teams are stretched dangerously thin. Most data breaches today take an average of 287 days to detect and contain. That is clearly unacceptable. With A.I.'s ability to analyze threat information at scale, we can help reduce that timeline to a few days or even hours.

A.I. is not only making businesses smarter, stronger, and safer; it is also accelerating scientific discovery. A.I. can speed the ingestion of scientific papers and the extraction of knowledge by 1,000x compared with human experts. At the height of the global pandemic, IBM adapted our cloud-based A.I. platform to comb through thousands of scientific papers about the coronavirus. We then shared relevant data with fellow members of the Covid-19 High Performance Computing Consortium to speed up drug design.

As these use cases show, for business A.I. to be effective it must also be trustworthy and explainable. It is one thing to rely on an A.I. application to order dinner for us. It is quite another to have it drive a car or make potentially life-or-death recommendations about a course of medical treatment.

For this reason, technology companies must be clear about who trains their A.I. systems, what data is used in that training, and, most important, what went into their algorithm's recommendations. Developing responsible, ethical A.I. requires that we remove any potential for human bias to influence this process.

We must also recognize that the purpose of A.I. systems is to augment--not replace--human intelligence. Throughout history, the introduction of new technologies has led to sea changes in the way businesses create value while eliminating burdensome and repetitive tasks for humans. These include everything from windmills to the printing press to the steam engine and factory robotics. This is how progress happens. Artificial intelligence will create even greater progress, but only if it is deployed responsibly.

Businesses have the potential to usher in a new and unprecedented era of greater productivity, faster insights, better decision-making, and enhanced employee and customer experiences through the combination of A.I. and hybrid cloud. Given the enthusiasm of our clients for these transformative technologies, the business A.I. spring can't come soon enough.

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Why Artificial Intelligence Creates an Unprecedented Era of Opportunity in the Near Future - Inc.

Elementaryrecognized for achievements in machine vision and industrial quality inspections

NEW YORK, May 19, 2022 /PRNewswire/ --CB Insights today named Elementary to its annual AI 100 ranking, showcasing the 100 most promising private artificial intelligence companies in the world.

"This is the sixth year that CB Insights has recognized the most promising private artificial intelligence companies with the AI 100. This year's cohort spans 13 industries, working on everything from recycling plastic waste to improving hearing aids," said Brian Lee, Senior Vice President of CB Insights' Intelligence Unit."Last year's AI 100 companies had a remarkable run, raising more than $6 billion, including 20 mega-rounds worth more than $100 million each. We're excited to watch the companies on this year's list continue to grow and create products and services that meaningfully impact the world around them."

"Manufacturing and supply chain are being forced through the largest transformation we've seen in decades. The global supply chain shock, coupled with increased demand and a difficult labor market, make it imperative that manufacturers find autonomous solutions to automate processes, improve digital intelligence, and increase yield and volume," said Arye Barnehama, Chief Executive Officer and founder of Elementary. "At Elementary, we champion closed-loop quality. Our platform uses edge machine learning to inspect goods and protect production lines from defects. Using cloud technology, inspection data is analyzed for defects and root causes. These AI-driven, real-time insights are then pushed to the factory floor, closing the loop and avoiding defects through operational improvements."

Utilizing theCB Insights platform, the research team picked 100 private market vendors from a pool of over 7,000 companies, including applicants and nominees. They were chosen based on factors including R&D activity,proprietary Mosaic scores, market potential, business relationships, investor profile, news sentiment analysis, competitive landscape, team strength, and tech novelty. The research team also reviewed thousands ofAnalyst Briefings submitted by applicants.

Quick facts about the 2022 AI 100:

About ElementaryElementary delivers an easily scalable, flexible, securly connected machine vision platform that leverages the power of machine learning to open new use cases, provide insights, and close the loop on the manufacturing process. With Elementary Quality as a Service (QaaS), we deploy the inspection hardware, train the machine learning models, integrate with your automation equipment, and provide data analytics. From cameras, lighting and mounting to software and support, we are the single-source product experts, providing everything you need to increase detections, reduce defects and improve productivity.For more information, please visit:https://www.elementaryml.com/.

SOURCE Elementary

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Elementary Named to the 2022 CB Insights AI 100 List of Most Innovative Artificial Intelligence Startups - PR Newswire