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It seems like everyone is talking about artificial intelligence at the moment, and theres good reason for that. We are seeing its revolutionary impact across just about every industry:

In healthcare, where its used to track pandemics and develop vaccines.

In banking and finance, where it detects fraudulent transactions and enables more accurate assessments of lending risks.

In security, where it prevents cyberattacks and data breaches.

In biotechnology, where it augments advances made in fields such as gene editing, promising to help eradicate diseases and put an end to food shortages.

In retail, where it predicts what customers are likely to buy, and puts them in front of them at the time theyre ready to pull the trigger.

What You Need To Know Before You Start Working With Artificial Intelligence

I firmly believe that the true value of AI estimated to be worth $13 trillion to the global economy by 2030 will be realized due to it being accessible to businesses of all shapes and sizes, not just multinational corporations. A vast and eclectic ecosystem of cloud-based, as-a-service platforms reduces the need for expensive infrastructure investments and also means that niche solutions exist to help automate solutions in every industry.

But whether youre simply looking to use AI-augmented marketing tools or to implement machine learning and real-time data analytics from top to bottom of your organization, there are some important points to consider first. The cost of deploying AI may have fallen dramatically in the last decade, but it still requires an investment of time and money, and going into it half-cocked simply because it seems like everyone else is doing it, and you have a fear of missing out can be a recipe for an expensive disaster.

Strategy First

The first principle is to start with a strategy. Simply put, this means understanding what you are trying to achieve. AI technologies are tools that are deployed tactically to achieve strategic objectives. Your strategy should be in line with your business objectives are you aiming for growth? Improving customer retention or lifetime value? Or to reduce overheads involved with design, manufacturing, distribution, or after-sales service? Once you know what you want to achieve, then you can start looking for AI technologies such as machine learning, computer vision, or natural language processing - that can help you get the job done. I like to start by thinking of the key questions a business needs to answer to be able to hit its targets. Who wants to buy our products or services, or how can we improve the value customers get from dealing with us? Remember, always fit technology to a problem, rather than problems to the technology!

What data do I need?

Once you know what your problems are, start thinking about the information you need to answer questions and get them solved. Data might be internal, such as records of customer transactions and interactions, or external, such as information on demographic trends, behavioral data from social media, or publicly available government data. Data can also be structured neat, tidy data that fits into spreadsheets such as statistical data or website clickstream data, or unstructured messy but potentially highly valuable data such as images, videos, speech recordings, or written text. The most advanced AI projects often work with real-time streaming data. This gives us up-to-the minute insights that can be acted on immediately.

What infrastructure do I need?

Building AI infrastructure doesnt necessarily mean creating algorithms from scratch, big data storage solutions, and a complicated systems architecture process. Cloud providers give companies of any size access to pay-as-you-go storage and AI compute solutions, as well as consulting expertise to get them up and running. Nevertheless, its still important to understand the range of services and solutions available in your market. Will a public cloud provider give you everything you need? Particularly if youre interested in working with very sensitive personal data, you may need to consider on-premises or hybrid infrastructure at some level, which gives you more direct control over your information.

What governance issues will I face?

Working with data brings legal as well as moral and ethical obligations. Legislation is tightening around companies involved with collecting and processing personal information from their customers or the wider public, a good example of this is the European Union GDPR, introduced in 2018. The law (and others like it, such as the California Consumer Privacy Act) oblige businesses that collect personal data to operate within a robust legal framework or face harsh financial penalties. Governance also encompasses the ethical and moral questions that need to be addressed when applying technology in ways that might affect peoples lives. In the information age, trust is essential if customers dont trust you with their data, your plans are thwarted before you even start. This means you have to be able to demonstrate that everything youre doing is governed by a strong code of ethics.

What skills will I need?

There's no getting away from it; we are in the middle of an AI skills crisis. What that means is that industry is coming up with ideas for using AI quicker than colleges and universities can churn out graduates with the skills to bring these ideas to life. People with AI engineering talents are hot property on the jobs market, and their salaries reflect that. But AI doesnt build itself (quiet) yet, so you're going to need human skills. They can be acquired either by hiring them in (which, as mentioned, can be expensive) or by upskilling existing workforces. Another option is to partner with outside agencies, such as consultants. The approach you choose will depend to a large extent on the scale of your AI ambitions and the resources you have available.

Do you have a data-driven culture?

To some extent, this is all about attitude. What is the attitude, at all levels, towards technology, data, and AI-driven innovation in your organization? In a data-driven business culture, everyone from the boardroom to the shop floor understands the advantages that can be achieved by putting data at the heart of operations and decision-making. This certainly isnt true of all organizations. Some not-exactly-helpful attitudes that are still prevalent in business include "We aren't ready to be an AI company," "AI is too expensive or complicated," "We know our business better than a machine ever will, or Our customers arent interested in us becoming an AI company. There may be good reasons for all of these attitudes, but too often, they are grounded in a fear of the unknown or an unwillingness to move away from a methodology thats been successful in the past - even when its clearly becoming less successful as the world becomes increasingly digitized. The fact is, you can never know enough about your customers. You can never stop looking for ways to drive efficiency across your operations. And you can never stop making your products smarter and more useful. For almost any business, AI is the key to making these things happen.

Of course, this article only scratches the surface of what you need to know before you start working with AI. But all of these topics (and many more) are covered in-depth in the new edition of my book, Data Strategy: How to Profit From A World of Big Data, Analytics And Artificial Intelligence.

For more, subscribe to my newsletter and follow me on Twitter, LinkedIn, and YouTube. And dont forget to check out my website.

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What You Need To Know Before You Start Working With Artificial Intelligence - Forbes

(TNS) 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 way of an area not normally associated with fighting wildfires: artificial intelligence. And space.

Lockheed Martin Space, based in Jefferson County, Colo., 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.

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 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 2020Cameron Peakfire, 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.

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.

2022 MediaNews Group, Inc. Distributed by Tribune Content Agency, LLC.

See more here:
How Artificial Intelligence Can Help Fight Fires in the West - Governing