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Flying high with AI: Alaska Airlines uses artificial intelligence to save time, fuel and money



How Alaska Airlines executed the perfect artificial intelligence use case. The company has saved 480,000 gallons of fuel in six months and reduced 4,600 tons of carbon emissions, all from using AI.

Alaska Airlines plane touching down

Image: Eliyahu Yosef Parypa/Shutterstock

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Given the near 85% fail rate in corporate artificial intelligence projects, it was a pleasure to visit with Alaska Airlines, which launched a highly successful AI system that is helping flight dispatchers. I visited with Alaska to see what the “secret sauce” was that made its AI project a success. Here are some tips to help your company execute AI as well as Alaska Airlines has. 

SEE: Hiring Kit: Video Game Programmer (TechRepublic Premium)

Don’t over-sell to your executive management

Initially, the idea of overhauling flight operations control existed in concept only. “Since the idea was highly conceptual, we didn’t want to oversell it to management,” said Pasha Saleh, flight operations strategy and innovation director for Alaska Airlines. “Instead, we got Airspace Intelligence, our AI vendor, to visit our network centers so they could observe the problems and build that into their development process. This was well before the trial period, about 2.5 years ago.”

Saleh said it was only after several trials of the AI system that his team felt ready to present a concrete business use case to management. “During that presentation, the opportunity immediately clicked,” Saleh said. “They could tell this was an industry-changing platform.” 

Define a compelling business use case

Alaska cut its teeth on having to innovate flight plans and operations in harsh arctic conditions, so it was almost a natural step for Alaska to become an innovator in advancing flight operations with artificial intelligence.

SEE: Digital transformation: A CXO’s guide (free PDF) (TechRepublic)

“I could see a host of opportunities to improve the legacy system across the airline industry that could propel the industry into the future,” Saleh said. “The first is dynamic mapping. Our Flyways system was built to offer a fully dynamic, real-time ‘4D’ map with relevant information in one, easy-to-understand screen. The information presented includes FAA data feeds, turbulence reports and weather reports, which are all visible on a single, highly detailed map. This allows decision-makers to quickly assess the airspace. The fourth dimension is time, with the novel ability to scroll forward eight-plus hours into the future, helping to identify potential issues with weather or congestion.”

“We saved 480,000 gallons of fuel in six months and reduced 4,600 tons of carbon emissions.” Pasha Saleh, flight operations strategy and innovation director for Alaska Airlines

The Alaska Flyways system also has built-in monitoring and predictive abilities. The system looks at all scheduled and active flights across the U.S., scanning air traffic systemically rather than focusing on a single flight. It continuously and autonomously evaluates the operational safety, air-traffic-control compliance and efficiency of an airline’s planned and active flights. The predictive modeling is what allows Flyways to “look into the future,” helping inform how the U.S. airspace will evolve in terms of weather, traffic constraints, airspace closures and more.

SEE: 9 questions to ask when auditing your AI systems (TechRepublic)

“Finally the system presents recommendations,” Saleh said. “When it finds a better route around an issue like weather or turbulence, or simply a more efficient route, Flyways provides actionable recommendations to flight dispatchers. These alerts pop up onto the computer screen, and the dispatcher decides whether to accept and implement the recommended solution. In sum: The operations personnel always make the final call. Flyways is constantly learning from this.”

Get staff involved—and supportive

Saleh recalled the early days when autopilot was first introduced. “There was fear it would replace pilots,” he said. “Obviously, that wasn’t the case, and autopilot has allowed pilots to focus on more things of value. It was our hope that Flyways would likewise empower our dispatchers to do the same.”

SEE: Graphs, quantum computing and their future roles in analytics (TechRepublic) 

One step Alaska took was to immediately engage its dispatchers in the design and operation of the Flyways system. Dispatchers tested the platform for a six-month trial period and provided feedback for enhancing it. This was followed by on-site, one-on-one training and learning sessions with the Airspace Intelligence team. “The platform also has a chat feature, so our dispatchers could share their suggestions with the Airspace Intelligence team in real time,” Saleh said. “Dispatchers could have an idea, and within days, the feature would be live. And because Flyways uses AI, it also learned from our dispatchers, and got better because of it.”

Define the relationship between human and machine

While Flyways can speed times to decisions on route planning and other flight operations issues, humans will always have the role in route planning, and will always be the final decision-makers. “This is a tool that enhances, rather than replaces, our operations,” Saleh said. Because flight dispatchers were so integrally involved with the project’s development and testing, they understood its fit as a tool and how it could enhance their work.

Capture business value

“With the end result, I would say satisfaction is an understatement,” Saleh said. “We’re all blown away by the efficiency and predictability of the platform. But what’s more, is that we’re seeing an incredible look into the future of more sustainable air travel.

“One of the coolest features to us is that this tool embeds efficiency and sustainability into our operation, which will go a long way in helping us meet our goal of net zero carbon emissions by 2040. We saved 480,000 gallons of fuel in six months and reduced 4,600 tons of carbon emissions. This was at a time when travel was down because of the pandemic. … We anticipate Flyways will soon become the de facto system for all airlines. But it sure has been cool being the first airline in the world to do this!”

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Digital Identity Verification Spends to Surge by 2026



The amount business spends on digital identity verification processes is forecast to nearly double over the next five years, data from Juniper Research suggests.

The processes, which include selfie scans, address checks and knowledge-based authentication will generate a $9.4 billion spend in 2021 but grow to $16.7 billion in 2026. COVID-19 is a main reason for the surge, as more companies were forced to digitally onboard users in socially distanced times. Like many online behaviors, the pandemic accelerated already present trends more than it created new ones.

The business climate is at the point where seamless digital onboarding is now table stakes. That can be a challenge for companies quickly forced to become more digital. They are faced with the need to provide a low-friction yet highly secure experience that incorporates such complex processes as artificial intelligence and behavioral analytics.

In 2026 the banking and financial services sectors will account for more than 60 percent of digital identity verification spend.

Digital-only banks have shown that fully digital KYC can work and is very engaging for the user, therefore the pressure is on for traditional banks to deploy new identity verification services,” co-author Vladimir Surovkin said. “Managing this transition quickly, and getting the user convenience/security balance right will determine overall success.”

The number individual identity checks performed is expected to more than double from 45 billion in 2021 to 92 billion in 2026. In addition to financial services, mobile network operation and online gambling are two other ripe areas, the report states.

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Artificial Intelligence

Nvidia’s Canvas AI painting tool instantly turns blobs into realistic landscapes



AI has been filling in the gaps for illustrators and photographers for years now — literally, it intelligently fills gaps with visual content. But the latest tools are aimed at letting an AI give artists a hand from the earliest, blank-canvas stages of a piece. Nvidia’s new Canvas tool lets the creator rough in a landscape like paint-by-numbers blobs, then fills it in with convincingly photorealistic (if not quite gallery-ready) content.

Each distinct color represents a different type of feature: mountains, water, grass, ruins, etc. When colors are blobbed onto the canvas, the crude sketch is passed to a generative adversarial network. GANs essentially pass content back and forth between a creator AI that tries to make (in this case) a realistic image and a detector AI that evaluates how realistic that image is. These work together to make what they think is a fairly realistic depiction of what’s been suggested.

It’s pretty much a more user-friendly version of the prototype GauGAN (get it?) shown at CVPR in 2019. This one is much smoother around the edges, produces better imagery, and can run on any Windows computer with a decent Nvidia graphics card.

This method has been used to create very realistic faces, animals and landscapes, though there’s usually some kind of “tell” that a human can spot. But the Canvas app isn’t trying to make something indistinguishable from reality — as concept artist Jama Jurabaev explains in the video below, it’s more about being able to experiment freely with imagery more detailed than a doodle.

For instance, if you want to have a moldering ruin in a field with a river off to one side, a quick pencil sketch can only tell you so much about what the final piece might look like. What if you have it one way in your head, and then two hours of painting and coloring later you realize that because the sun is setting on the left side of the painting, it makes the shadows awkward in the foreground?

If instead you just scribbled these features into Canvas, you might see that this was the case right away, and move on to the next idea. There are even ways to quickly change the time of day, palette, and other high-level parameters so they can quickly be evaluated as options.

Animation of an artist sketching while an AI interprets his strokes as photorealistic features.

Image Credits: Nvidia

“I’m not afraid of blank canvas any more,” said Jurabaev. “I’m not afraid to make very big changes, because I know there’s always AI helping me out with details… I can put all my effort into the creative side of things, and I’ll let Canvas handle the rest.”

It’s very like Google’s Chimera Painter, if you remember that particular nightmare fuel, in which an almost identical process was used to create fantastic animals. Instead of snow, rock and bushes, it had hind leg, fur, teeth and so on, which made it rather more complicated to use and easy to go wrong with.

Image Credits: Devin Coldewey / Google

Still, it may be better than the alternative, for certainly an amateur like myself could never draw even the weird tube-like animals that resulted from basic blob painting.

Unlike the Chimera Creator, however, this app is run locally, and requires a beefy Nvidia video card to do it. GPUs have long been the hardware of choice for machine learning applications, and something like a real-time GAN definitely needs a chunky one. You can download the app for free here.

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How one founder realized satellite internet didn’t have to be fast or expensive to be useful



It’s hard to understand just how steeply the cost of launching and operating satellites has dropped, particularly since the introduction of lower cost launch services from a number of commercial players, and the maturation of the smartphone supply chain. Swarm co-founder and CEO realized just how much the cost curve had changed when she and her co-founder Ben Longmeir realized that they could outfit tiny satellites Longmeir had created as a kind of space lover’s hobby with the equipment needed to provide low-bandwidth connectivity to low-powered devices around the world.

In this week’s episode of Found, Sara walks us through how she went from an engineering career that included stints at NASA’s Jet Propulsion Laboratory and Google, to building Swarm as a first-time founder and CEO. We covered a range of topics including how Sara and Ben decided who would be CEO, what it’s like leading a small but growing team, and how to evaluate your decisions as a founder, and commit to a course of action to move forward.

Sara was extremely candid with us about her experience as a founder and CEO, and this is definitely one of our most open and honest conversations to date.

We loved our time chatting with Sara, and we hope you love yours listening to the episode. And of course, we’d love if you can subscribe to Found in Apple Podcasts, on Spotify, on Google Podcasts or in your podcast app of choice. Please leave us a review and let us know what you think, or send us direct feedback either on Twitter or via email at And please join us again next week for our next featured founder.

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As clinical guidelines shift, heart disease screening startup pulls in $43M Series B



Cleerly Coronary, a company that uses A.I powered imaging to analyze heart scans, announced a $43 million Series B funding this week. The funding comes at a moment when it seems that a new way of screening for heart disease is on its way. 

Cleerly was started in 2017 by James K. Min a cardiologist, and the director of the Dalio Institute for Cardiac Imaging at New York Presbyterian Hospital/Weill Cornell Medical College. The company, which uses A.I to analyze detailed CT scans of the heart, has 60 employees, and has raised $54 million in total funding.

The Series B round was led by Vensana Capital, but also included LVR Health, New Leaf Venture Partners, DigiTx Partners, and Cigna Ventures. 

The startup’s aim is to provide analysis of detailed pictures of the human heart that have been examined by artificial intelligence. This analysis is based on images taken via Cardiac Computer Tomography Angiogram (CTA), a new, but rapidly growing manner of scanning for plaques. 

“We focus on the entire heart, so every artery, and its branches, and then atherosclerosis characterization and quantification,” says Min. “We look at all of the plaque buildup in the artery, [and] the walls of the artery, which historical and traditional methods that we’ve used in cardiology have never been able to do.”

Cleerly is a web application, and it requires that a CTA image specifically, which the A.I. is trained to analyze, is actually taken when patients go in for a checkup. 

When a patient goes in for a heart exam after experiencing a symptom like chest pain, there are a few ways they can be screened. They might undergo a stress test, an echocardiogram (ECG), or a coronary angiogram – a catheter and x-ray-based test. CTA is a newer form of imaging in which a scanner takes detailed images of the heart, which is illuminated with an injected dye. 

Cleerly’s platform is designed to analyze those CTA images in detail, but they’ve only recently become a first-line test (a go-to, in essence) when patients come in with suspected heart problems. The European Society of Cardiology updated guidelines to make CTA a first-line test in evaluating patients with chronic coronary disease. In the UK, it became a first-line test in the evaluation of patients with chest pain in 2016.

CTA is already used in the US, but guidelines may expand how often it’s actually used. A review on CTA published on the American College of Cardiology website notes that it shows “extraordinary potential.” 

There’s movement on the insurance side, too. In 2020, United Healthcare announced the company will now reimburse for CTA scans when they’re ordered to examine low-to medium risk patients with chest pain. Reimbursement qualification is obviously a huge boon to broader adoption.

CTA imaging might not be great for people who already have stents in their hearts, or, says Min, those who are just in for a routine checkup (there is low-dose radiation associated with a CTA scan). Rather, Cleerly will focus on patients who have shown symptoms or are already at high risk for heart disease. 

The CDC estimates that currently 18.2 million adults currently have coronary artery heart disease (the most common kind), and that 47 percent of Americans have one of the three most prominent risk factors for the disease: high blood pressure, high cholesterol, or a smoking habit. 

These shifts (and anticipated shifts) in guidelines suggest that a lot more of these high-risk patients may be getting CTA scans in the future, and Cleerly has been working on mining additional information from them in several large-scale clinical trials.

There are plenty of different risk factors that contribute to heart disease, but the most basic understanding is that heart attacks happen when plaques build up in the arteries, which narrows the arteries and constricts the flow of blood. Clinical trials have suggested that the types of plaques inside the body may contain information about how risky certain blockages are compared to others beyond just much of the artery they block. 

A trial on 25,251 patients found that, indeed, the percentage of construction in the arteries increases the risk of heart attack. But the type of plaque in those arteries identified high-risk patients better than other measures. Patients who went on to have sudden heart attacks, for example, tended to have higher levels of fibrofatty or necrotic core plaque in their hearts. 

These results do suggest that it’s worth knowing a bit more detail about plaque in the heart. Note that Min is an author of this study, but it was also conducted at 13 different medical centers. 

As with all A.I based diagnostic tools the big question is: How well does it actually recognize features within a scan? 

At the moment FDA documents emphasize that it is not meant to supplant a trained medical professional who can interpret the results of a scan. But tests have suggested it fares pretty well. 

A June 2021 study compared Cleerly’s A.I analysis of CTA scans to that of three expert readers, and found that the A.I had a diagnostic accuracy of about 99.7 percent when evaluating patients who had severe narrowing in their arteries. Three of nine study authors hold equity in Cleerly. 

With this most recent round of funding, Min says he aims to pursue more commercial partnerships and scale up to meet the existing demand. “We have sort of stayed under the radar, but we came above the radar because now I think we’re prepared to fulfill demand,” he says. 

Still, the product itself will continue to be tested and refined. Cleerly is in the midst of seven performance indication studies that will evaluate just how well the software can spot the litany of plaques that can build up in the heart.

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