Archive for the ‘Artificial Intelligence’ Category

Artificial intelligence in government is about people, not programming – Federal Times

U.S. government investment in artificial intelligence has grown significantly in the last few years, as evidenced by the additional funding for AI research in President Bidens 2023 fiscal budget.

With more than $2 billion allocated to the National Institute of Standards and Technology and the Department of Energy for AI research and development, it is clear there is a growing enthusiasm for the technology in government.

Driving the push to implement AI is the urgent need to address federal employee burnout. A recent study found that almost two-thirds of government employees are experiencing burnout, a much higher rate than seen in the private sector. Furthermore, almost half of respondents are considering leaving their government jobs within the next year due to increased burnout and stress.

One immediate solution to help this potential crisis is the responsible implementation of artificial intelligence. AI can mitigate the impact of burnout by removing repetitive and time-consuming tasks and streamlining processes, reducing the overall burden and repetitive nature of government work. However, effective AI investments demand more than just funding and technology.

Agencies must balance efforts to scale investment in AI while responding to the unique needs and challenges of the many diverse teams that make up the federal workforce.

Currently, there is a lack of cohesive guidance leading government efforts around AI. While organizations such as NIST have released basic AI framework for RMF, organizations without any AI experience may struggle to build the necessary foundation for a mature, agile AI posture. To lay the groundwork for a long-term AI strategy while generating short-term gains to support the federal workforce quickly, agencies must consider three guideline components of AI.

Dedicated funding for AI is only one component of an effective AI strategy. Before implementing new technologies, agencies must start with their existing processes beginning with their level of data maturity.

If an agency does not have enough historical data to analyze, or the data they do have is not organized, implementing AI can create extra work on the front end for federal workers who could find themselves sorting through inaccurate or incomplete data processed by AI. For example, in response to this challenge, the Department of Defense stood up the Chief Digital and Artificial Intelligence Office (CDAO) to lead the deployment of AI across DoD, including the Departments strategy and policy on data.

Once agencies realize a baseline of data maturity, they can pilot basic AI applications such as automating basic tasks, empowering agencies to gather high-quality data and provide analysis and insight around that data, providing the information needed to create a scalable AI roadmap that can integrate with other IT modernization technologies.

But having a roadmap alone is not enough to ensure that AI-driven technologies are useful for the federal workforce.

To implement AI that truly supports federal workers, agencies need to understand the main pain points and challenges facing federal employees. For most private enterprises looking to implement new technology, user experience surveys would be a core part of the pilot program to ensure an analytics-driven understanding of the technologys successes and gaps.

However, although employee input is a crucial part of the AI planning process, government surveys are often expensive and can take months or years to consolidate into actionable data.

One way to combat this difficulty is utilizing existing AI to inform AI investments. For example, instead of sending out a survey where the results may take months to receive, an AI dashboard may provide a real-time view of overall work showing what areas need more support or automate a simple response survey where employees can provide input.

Using relatively basic AI to evaluate implementation allows agencies to gain insight into the needs of the workforce more sustainably and effectively than surveys, showing IT leaders where to implement AI for the most impact.

Once agencies have an AI baseline and understand worker needs, the last step to implementing employee-focused AI is creating a robust AI-empowered employee experience program.

There are many ways that AI can help agencies with experience management, from automating timesheets to streamlining business decisions. When AI is designed with these improvements in mind, AIs tangible benefits support both broader organizational goals and the humans working to achieve them.

Scaling AI beyond pilot programs remains a challenge. One of the primary responsibilities of the CDAO is to develop processes for AI-enabled capabilities to be developed and fielded at scale across the defense space.

CDAO addressed this issue by selectively scaling only proven AI solutions for enterprise and joint use cases. Prioritizing proven solutions ensures that the AI they are implementing runs smoothly, is easy to use and most importantly is familiar to the workforce. As AI solutions become more sophisticated, agencies can continue to expand until they have a fully scalable AI network designed by and for humans.

Contrary to much of the conversation around AI, people are the most essential component of successful artificial intelligence programs. For implementation to be successful, agencies need a human-centered AI mindset. Following these three guidelines to create human-centered AI creates space for the federal workforce to be exponentially more creative, productive and ultimately more effective in furthering agency missions, equipping leaders to elevate the full potential of their teams.

Dr. Allen Badeau is the chief technology officer for Empower AI, as well as the director of the Empower AI Center for Rapid Engagement and Agile Technology Exchange (CREATE) Lab.

This article is an Op-Ed and the opinions expressed are those of the author. If you would like to respond, or have an editorial of your own you would like to submit, please email Federal Times Senior Managing Editor Cary OReilly.

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Artificial intelligence in government is about people, not programming - Federal Times

Of God and Machines – The Atlantic

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Miracles can be perplexing at first, and artificial intelligence is a very new miracle. Were creating God, the former Google Chief Business Officer Mo Gawdat recently told an interviewer. Were summoning the demon, Elon Musk said a few years ago, in a talk at MIT. In Silicon Valley, good and evil can look much alike, but on the matter of artificial intelligence, the distinction hardly matters. Either way, an encounter with the superhuman is at hand.

Early artificial intelligence was simple: Computers that played checkers or chess, or that could figure out how to shop for groceries. But over the past few years, machine learningthe practice of teaching computers to adapt without explicit instructionshas made staggering advances in the subfield of Natural Language Processing, once every year or so. Even so, the full brunt of the technology has not arrived yet. You might hear about chatbots whose speech is indistinguishable from humans, or about documentary makers re-creating the voice of Anthony Bourdain, or about robots that can compose op-eds. But you probably dont use NLP in your everyday life.

Or rather: If you are using NLP in your everyday life, you might not always know. Unlike search or social media, whose arrivals the general public encountered and discussed and had opinions about, artificial intelligence remains esotericevery bit as important and transformative as the other great tech disruptions, but more obscure, tucked largely out of view.

Science fiction, and our own imagination, add to the confusion. We just cant help thinking of AI in terms of the technologies depicted in Ex Machina, Her, or Blade Runnerpeople-machines that remain pure fantasy. Then theres the distortion of Silicon Valley hype, the general fake-it-til-you-make-it atmosphere that gave the world WeWork and Theranos: People who want to sound cutting-edge end up calling any automated process artificial intelligence. And at the bottom of all of this bewilderment sits the mystery inherent to the technology itself, its direct thrust at the unfathomable. The most advanced NLP programs operate at a level that not even the engineers constructing them fully understand.

But the confusion surrounding the miracles of AI doesnt mean that the miracles arent happening. It just means that they wont look how anybody has imagined them. Arthur C. Clarke famously said that technology sufficiently advanced is indistinguishable from magic. Magic is coming, and its coming for all of us.

All technology is, in a sense, sorcery. A stone-chiseled ax is superhuman. No arithmetical genius can compete with a pocket calculator. Even the biggest music fan you know probably cant beat Shazam.

But the sorcery of artificial intelligence is different. When you develop a drug, or a new material, you may not understand exactly how it works, but you can isolate what substances you are dealing with, and you can test their effects. Nobody knows the cause-and-effect structure of NLP. Thats not a fault of the technology or the engineers. Its inherent to the abyss of deep learning.

I recently started fooling around with Sudowrite, a tool that uses the GPT-3 deep-learning language model to compose predictive text, but at a much more advanced scale than what you might find on your phone or laptop. Quickly, I figured out that I could copy-paste a passage by any writer into the programs input window and the program would continue writing, sensibly and lyrically. I tried Kafka. I tried Shakespeare. I tried some Romantic poets. The machine could write like any of them. In many cases, I could not distinguish between a computer-generated text and an authorial one.

A quotation from this story, as interpreted and summarized by Googles OpenAI software.

I was delighted at first, and then I was deflated. I was once a professor of Shakespeare; I had dedicated quite a chunk of my life to studying literary history. My knowledge of style and my ability to mimic it had been hard-earned. Now a computer could do all that, instantly and much better.

A few weeks later, I woke up in the middle of the night with a realization: I had never seen the program use anachronistic words. I left my wife in bed and went to check some of the texts Id generated against a few cursory etymologies. My bleary-minded hunch was true: If you asked GPT-3 to continue, say, a Wordsworth poem, the computers vocabulary would never be one moment before or after appropriate usage for the poems era. This is a skill that no scholar alive has mastered. This computer program was, somehow, expert in hermeneutics: interpretation through grammatical construction and historical context, the struggle to elucidate the nexus of meaning in time.

The details of how this could be are utterly opaque. NLP programs operate based on what technologists call parameters: pieces of information that are derived from enormous data sets of written and spoken speech, and then processed by supercomputers that are worth more than most companies. GPT-3 uses 175 billion parameters. Its interpretive power is far beyond human understanding, far beyond what our little animal brains can comprehend. Machine learning has capacities that are real, but which transcend human understanding: the definition of magic.

This unfathomability poses a spiritual conundrum. But it also poses a philosophical and legal one. In an attempt to regulate AI, the European Union has proposed transparency requirements for all machine-learning algorithms. Eric Schmidt, the ex-CEO of Google, noted that such requirements would effectively end the development of the technology. The EUs plan requires that the system would be able to explain itself. But machine-learning systems cannot fully explain how they make their decisions, he said at a 2021 summit. You use this technology to think through what you cant; thats the whole point. Inscrutability is an industrial by-product of the process.

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My little avenue of literary exploration is my own, and neither particularly central nor relevant to the unfolding power of artificial intelligence (although I can see, off the top of my head, that the tech I used will utterly transform education, journalism, film, advertising, and publishing). NLP has made its first strides into visual arts tooDall-E 2 has now created a limitless digital museum of AI-generated images drawn from nothing more than prompts.

Others have headed into deeper waters. Schmidt recently proposed a possible version of our AI future in a conversation with this magazines executive editor, Adrienne LaFrance: If you imagine a child born today, you give the child a baby toy or a bear, and that bear is AI-enabled, he said. And every year the child gets a better toy. Every year the bear gets smarter, and in a decade, the child and the bear who are best friends are watching television and the bear says, I dont really like this television show. And the kid says, Yeah, I agree with you.

Schmidts vision does not yet exist. But in late 2020, Microsoft received a patent for chatbots that bring back the dead, using inputs from images, voice data, social media posts, electronic messages, written letters, etc. to create or modify a special index in the theme of the specific persons personality. Soon after, a company called Project December released a version of just such a personality matrix. It created bots such as William, which speaks like Shakespeare, and Samantha, a rather bland female companion. But it also allowed mourners to re-create dead loved ones. An article in the San Francisco Chronicle told the story of Joshua Barbeau, who created a bot of his deceased fiance, Jessica Pereira. Their conversation started like this:

Joshua: Technically, Im not really talking to you

Jessica: Huh?

Joshua: Youre a ghost.

Jessica: *looks at herself* How?

Joshua: Magic.

Jessica: I dont like magic. Where am I?

Joshua: Im Not sure. The internet? Where does it feel like you are?

Jessica: *smiles* Everywhere and nowhere. *cups her head with her hands* I am trying to work that out. How are you, honey?

Barbeaus conversation with Jessica continued for several months. His experience of Project December was far from perfectthere were glitches, there was nonsense, the bots architecture decayedbut Barbeau really felt like he was encountering some kind of emanation of his dead fiance. The technology, in other words, came to occupy a place formerly reserved for mediums, priests, and con artists. It may not be the first intelligent machine, Jason Rohrer, the designer of Project December, has said, but it kind of feels like its the first machine with a soul.

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What we are doing is teaching computers to play every language game that we can identify. We can teach them to talk like Shakespeare, or like the dead. We can teach them to grow up alongside our children. We can certainly teach them to sell products better than we can now. Eventually, we may teach them how to be friends to the friendless, or doctors to those without care.

PaLM, Googles latest foray into NLP, has 540 billion parameters. According to the engineers who built it, it can summarize text, reason through math problems, use logic in a way thats not dissimilar from the way you and I do. These engineers also have no idea why it can do these things. Meanwhile, Google has also developed a system called Player of Games, which can be used with any game at allgames like Go, exercises in pure logic that computers have long been good at, but also games like poker, where each party has different information. This next generation of AI can toggle back and forth between brute computation and human qualities such as coordination, competition, and motivation. It is becoming an idealized solver of all manner of real-world problems previously considered far too complicated for machines: congestion planning, customer service, anything involving people in systems. These are the extremely early green shoots of an entire future tech ecosystem: The technology that contemporary NLP derives from was only published in 2017.

And if AI harnesses the power promised by quantum computing, everything Im describing here would be the first dulcet breezes of a hurricane. Ersatz humans are going to be one of the least interesting aspects of the new technology. This is not an inhuman intelligence but an inhuman capacity for digital intelligence. An artificial general intelligence will probably look more like a whole series of exponentially improving tools than a single thing. It will be a whole series of increasingly powerful and semi-invisible assistants, a whole series of increasingly powerful and semi-invisible surveillance states, a whole series of increasingly powerful and semi-invisible weapons systems. The world would change; we shouldnt expect it to change in any kind of way that you would recognize.

Our AI future will be weird and sublime and perhaps we wont even notice it happening to us. The paragraph above was composed by GPT-3. I wrote up to And if AI harnesses the power promised by quantum computing; machines did the rest.

Technology is moving into realms that were considered, for millennia, divine mysteries. AI is transforming writing and artthe divine mystery of creativity. It is bringing back the deadthe divine mystery of resurrection. It is moving closer to imitations of consciousnessthe divine mystery of reason. It is piercing the heart of how language works between peoplethe divine mystery of ethical relation.

All this is happening at a raw moment in spiritual life. The decline of religion in America is a sociological fact: Religious identification has been in precipitous decline for decades. Silicon Valley has offered two replacements: the theory of the simulation, which postulates that we are all living inside a giant computational matrix, and of the singularity, in which the imminent arrival of a computational consciousness will reconfigure the essence of our humanity.

Like all new faiths, the tech religions cannibalize their predecessors. The simulation is little more than digital Calvinism, with an omnipotent divinity that preordains the future. The singularity is digital messianism, as found in various strains of Judeo-Christian eschatologya pretty basic onscreen Revelation. Both visions are fundamentally apocalyptic. Stephen Hawking once said that the development of full artificial intelligence could spell the end of the human race. Experts in AI, even the men and women building it, commonly describe the technology as an existential threat.

But we are shockingly bad at predicting the long-term effects of technology. (Remember when everybody believed that the internet was going to improve the quality of information in the world?) So perhaps, in the case of artificial intelligence, fear is as misplaced as that earlier optimism was.

AI is not the beginning of the world, nor the end. Its a continuation. The imagination tends to be utopian or dystopian, but the future is humanan extension of what we already are. My own experience of using AI has been like standing in a river with two currents running in opposite directions at the same time: Alongside a vertiginous sense of power is a sense of humiliating disillusionment. This is some of the most advanced technology any human being has ever used. But of 415 published AI tools developed to combat COVID with globally shared information and the best resources available, not one was fit for clinical use, a recent study found; basic errors in the training data rendered them useless. In 2015, the image-recognition algorithm used by Google Photos, outside of the intention of its engineers, identified Black people as gorillas. The training sets were monstrously flawed, biased as AI very often is. Artificial intelligence doesnt do what you want it to do. It does what you tell it to do. It doesnt see who you think you are. It sees what you do. The gods of AI demand pure offerings. Bad data in, bad data out, as they say, and our species contains a great deal of bad data.

Artificial intelligence is returning us, through the most advanced technology, to somewhere primitive, original: an encounter with the permanent incompleteness of consciousness. Religions all have their approaches to magictransubstantiation for Catholics, the lost temple for the Jews. Even in the most scientific cultures, there is always the beyond. The acropolis in Athens was a fortress of wisdom, a redoubt of knowledge and the power it bringsthrough agriculture, through military victory, through the control of nature. But if you wanted the inchoate truth, you had to travel the road to Delphi.

A fragment of humanity is about to leap forward massively, and to transform itself massively as it leaps. Another fragment will remain, and look much the same as it always has: thinking meat in an inconceivable universe, hungry for meaning, gripped by fascination. The machines will leap, and the humans will look. They will answer, and we will question. The glory of what they can do will push us closer and closer to the divine. They will do things we never thought possible, and sooner than we think. They will give answers that we ourselves could never have provided. But they will also reveal that our understanding, no matter how great, is always and forever negligible. Our role is not to answer but to question, and to let our questioning run headlong, reckless, into the inarticulate.

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Of God and Machines - The Atlantic

Artificial intelligence thinks the Aspen area looks like this – The Aspen Times

Aspen is known for its world-class skiing, sky-high real-estate prices and breath-taking mountain views. The town has been known to conjure artistic inspiration, as well; its the town where Stevie Nicks reportedly wrote the hit Landslide, and a place John Denver called home for many years.

According to Swift Luxe, there are approximately 1.5 million visitors to Aspen each year who come to take in the beauty of the area.

While its practically impossible to capture Aspen and the surrounding areas beauty in an image, an AI program tried. The images below were created using a program calledDream Studio beta, a more rapid and accessible version ofStable Diffusion, a text-to-image modelthat was released to the public last month.

When this artificial intelligence text-to-image application thinks of Aspen, it thinks of vast mountain ranges.

Aspen, ColoradoStable Diffusion

Aspen, ColoradoStable Diffusion

Aspen, ColoradoStable Diffusion

Aspen, ColoradoStable Diffusion

Aspen, ColoradoStable Diffusion

Aspen, ColoradoStable Diffusion

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This is pretty close if you ask us.

Maroon BellsStable Diffusion

Maroon BellsStable Diffusion

Maroon BellsStable Diffusion

Maroon BellsStable Diffusion

Maroon BellsStable Diffusion

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Aspen Real EstateStable Diffusion

Aspen Real EstateStable Diffusion

Aspen Real EstateStable Diffusion

Aspen Real EstateStable Diffusion

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Snowmass VillageStable Diffusion

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Close, very close.

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Artificial intelligence thinks the Aspen area looks like this - The Aspen Times

Artificial Intelligence Tool May Help in Early Diagnosis of… – Fragile X News Today

A new tool that uses artificial intelligence (AI) to analyze healthcare records may aid in the early diagnosis of fragile X syndrome, a new study reports.

By incorporating a combination of co-occurring conditions, an AI-assisted pre-screening tool was developed and validated to identify potential cases at least 5 years earlier than the time of clinical diagnosis, the researchers wrote.

The scientists said their AI tool was used successfully to analyze a healthcare database in the U.S. state of Wisconsin. Moving forward, this artificial intelligence-based program could be used across other healthcare databases to better and potentially much sooner identify individuals who may be affected by fragile X.

Our AI-assisted pre-screening approach can facilitate and accelerate the clinical diagnosis of [fragile X syndrome] and decrease the duration of the diagnostic odyssey and degree of stress experienced by patients and their families, the team wrote.

One concern in the move forward is that the AI tool thus far was only used in healthcare systems that were predominately comprised of white patients. More testing and validation is needed in other racial and ethnic patient populations, the researchers said.

The study, Advancing artificial intelligence-assisted pre-screening for fragile X syndrome, was published inBMC Medical Informatics and Decision Making.

Fragile X syndrome can manifest very differently from person to person, which makes diagnosing the genetic disorder a challenge. Studies have shown a marked gap between the estimated prevalence of fragile X and the actual number of people diagnosed suggesting that as many as 70% of people affected by fragile X syndrome have not been properly diagnosed.

To bridge that gap, a team led by scientists at the University of Wisconsin-Madison created an artificial intelligence tool aimed at better diagnosing fragile X syndrome. This tool is applied to data that is routinely collected in electronic healthcare records (EHRs).

Their aim was to identify data in such EHRs that could predict the diagnosis of fragile X, even before the disorder itself is formally diagnosed.

The pre-screening model is not intended to be a replacement for genetic testing, but it can serve as a tool to automatically alert physicians about the presence of multiple [fragile X syndrome]-related phenotypes in the patients medical records, the scientists wrote.

By prompting the physician to further evaluate such individuals and refer them for genetic testing and counseling, our approach could accelerate the diagnostic process and be instrumental in identifying un-diagnosed individuals in the population and addressing their health conditions, the team wrote.

To create the tool, the team used EHR data collected from 1979 to 2018 served by the Marshfield Clinic Health System in Wisconsin.

From the data, the team identified 55 people who had been diagnosed with fragile X syndrome at an age of 10 or older. The scientists also used data from 5,500 people without a fragile X diagnosis, who were similar to the patients in terms of age and sex.

For all of these patients, the researchers extracted data from five years before the formal diagnosis of fragile X syndrome, or the equivalent ages for controls.

All data used in this study are directly collected in a medical setting and are in fact real world data from actual patients, providing further proof of [the AI tools] potential utility in real world clinical applications, the team noted.

With these data in hand, the researchers then trained their artificial intelligence algorithm using a mathematical strategy called random forest. Conceptually, the AI tool uses a set of mathematical rules to look for patterns in the diagnostic codes that could differentiate between people with or without fragile X syndrome.

To test the utility of the trained algorithm, the scientists tested it on data collected from UW Health, a separate healthcare system in Wisconsin.

Our next step, reported here for the first time, was to evaluate the performance of this model in a new unseen dataset, i.e., an external validation study, they wrote.

In this dataset, the team identified data for 52 fragile X cases and 5,200 people without the disorder, matched for sex and age.

To test the tools accuracy, the researchers calculated a statistical measurement called the area under the receiver operating characteristic curve, or AUROC. This is basically a measurement of how well a test can tell the difference between two groups i.e., fragile X or not. AUROC values can range from 0.5 to 1, with higher values suggesting better ability to discriminate.

In the original Marshfield dataset, the AUROC for the AI tool was 0.798. In the UW Health analysis, it was 0.795.

The AUROCs of the predictive models created and evaluated using the Marshfield cases and the UW Health cases were almost identical (0.798 vs. 0.795), representing the high level of reproducibility of results in different health care systems, the scientists wrote.

Our AI-assisted pre-screening tool could significantly improve the diagnostic process and could provide substantial benefits for patients, families and the health care system, they concluded.

A noted limitation of this analysis was that nearly 90% of patients in both healthcare systems were white. The researchers highlighted a need to further validate this model in other populations, especially those of non-European ancestry.

Additional studies on larger populations will provide more precise information on the performance of the model, they wrote.

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Artificial Intelligence Tool May Help in Early Diagnosis of... - Fragile X News Today

How Artificial Intelligence Is Influencing the Future of Work in the Airline Industry – Skift Travel News

Airlines aiming for total revenue optimization need intelligent solutions. While artificial intelligence and deep learning algorithms promise better forecasting capabilities, those systems can only truly shine when coupled with the flexibility and human touch of a data analyst.

FLYR

Commercial airlines and other travel and transportation leaders are facing significant challenges in managing pricing, demand, and logistics in todays volatile environment. This summers travel disruptions have laid bare the potential for intermittent hiccups in post-pandemic operations to have drastic effects on customer satisfaction and revenue opportunities.

With travelers patience wearing thin, airlines need to reinforce their people, processes, and technologies. By building artificial intelligence (AI) solutions into processes across their organizations, airlines can leverage their data, analysts, and revenue management opportunities to take advantage of new business fundamentals in this changing environment.

Artificial intelligence isnt replacing the airline data analysts job, said Alex Mans, founder and CEO of FLYR Labs, a technology company driving commercial optimization for airlines. But it is changing their role and hopefully unlocking their potential to drive revenue and improve operational efficiencies.

Airline data can be difficult to parse, but artificial intelligence makes it easier. Its impossible for humans to manually process all the information airlines are collecting from digital sources, but deep learning neural networks can provide effective forecasts that give analysts the confidence to make better decisions.

Historically, the leading forecasting type has been linear, regression-based models, where analysts look at very concentrated year-over-year patterns, Mans said. The problem is that theres just not enough data on any single flight for a given point in time to drive accurate forecasts in a volatile environment. Legacy systems are really bad at determining whether booking one seat on a given flight has a meaningful impact on the outcome.

To power deep learning algorithms, airlines feed neural networks vast amounts of historical data such as bookings, searches, events, promotions, and competitive prices resulting in forecasts that keep analysts better informed on revenue and load-factor performance into the future.

They can compare how the actual performance builds against the forecast data as the departure date approaches, Mans said. The real value comes when, with a platform such as ours, analysts come to trust the forecast and can start using it to inform strategic decisions instead of viewing it as a loose guideline.

According to Mans, artificial intelligence should be thought of as an airline data analysts smart sidekick its not replacing the analysts job, but rather enhancing the analysts ability to make coordinated operational decisions in areas where automation alone is insufficient.

In the past, analysts have not had accurate forecasts, so most of their decisions were based on instinct, Mans said. On top of that, they havent had good user interfaces for consuming that data. We generate much better forecasts, enable smarter workflows, and provide a dedicated user interface where analysts can easily access and filter the data and then use the resulting information. With better load and revenue forecasts at any level of granularity across the network, they can do amazing things.

For example, if an analyst sees a cluster of flights months into the future with a 99-percent forecasted load factor, they can alert colleagues in charge of scheduling and suggest adding capacity. At most airlines, where functions across the organizations are typically siloed, this kind of cross-functional collaboration between commercial teams isnt common.

All it takes to start breaking down those silos is for other teams to have access to the same information that the revenue management team has access to, Mans said. At the end of the day, different departments are trying to achieve the same results: maximize revenue and contain costs.

In addition to playing gatekeeper of that information, the analysts role will evolve to support a variety of critical functions across the organization.

For one thing, they can look around corners that the data itself cant see, Mans said. The analyst might know that a schedule change is coming, but unless that information is passed to our system, we have no awareness. Artificial intelligence doesnt know everything. Another thing to note is that optimizing for maximum revenue isnt always the goal. An airline entering a new market may want to follow a non-revenue-optimal strategy focused on market control or market share, so the analyst is needed to fine-tune that strategy. Or consider promotions every airline runs tons of promotions throughout the year, whether tied to their credit card program, certain destinations, or other variables that require the analyst to actively work with our platform and their marketing team to achieve the best results.

For airlines to weather the storm of todays unprecedented industry disruptions, dynamic pricing powered by deep learning algorithms is essential. FLYR was built to provide a singular platform that helps airlines manage data, break past data silos with consistently accurate forecasts that can be accessed by anybody in the organization, and achieve total revenue management across all of their products.

Our job is to help airlines effectively price everything they want to sell, including ancillaries like seat selection, extra baggage, priority boarding, and other upsells, Mans said. FLYRs operating system provides a vertically integrated SaaS platform across data management, forecasting, pricing, automation, business intelligence, and reporting which includes simulation and scenario evaluation while also removing constraints within e-commerce and fulfillment thanks to acquisitions such as Newshore so airlines can get stuff done faster and more efficiently. Thats what were building as a company, and thats the future of work within this industry.

Join us on October 19th at 11:00 a.m. ET for a webinar featuring FLYR founder and CEO Alex Mans, How Artificial Intelligence Is Reshaping the Travel Business. Register today

For more information on how FLYR is helping airlines achieve total revenue optimization, check out their latest whitepaper with IATA.

This content was created collaboratively by FLYR and Skifts branded content studio, SkiftX.

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How Artificial Intelligence Is Influencing the Future of Work in the Airline Industry - Skift Travel News