AI-induced Resurgence for the Travel Industry Amidst COVID-19

Presently, travelers have to toggle between multiple apps to gather each set of information. Leading travel app development companies in the industry …

The airline industry is probably the worst hit of all sectors due to COVID-19. As per figures released by the International Air Transport Association, a collective loss of US $84 billion will be inflicted on the industry in 2020. This is more than double the extent of loss (US$30 billion) that it suffered due to the global financial crisis in 2008.

Amidst an uncertain future, massive lay-offs, and carrier bankruptcies, experts place the turn-around time for recovery at 4-6 years, while some put it even further. In such an unpredictable air of gloom one thing is certain, the status quo has changed forever. The industry as a unit needs serious introspection combined with the power of technology to make travel contactless and safer than ever.

AI in the Aviation Industry – A Dire Need

Artificial Intelligence technology could be pivotal in transforming the face of travel. From the outset, AI rests on a solid footing of 4 key pillars namely:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Computer Vision

In the pre-covid era, there were numerous use cases of airlines using Artificial Intelligence. While it was predominantly used to optimize digital operations, the technology has to step out of its mold and offer a scope of work for AI in airport operations to become a reality. If initial signages are to be believed, the role of AI in the travel industry will be broadened to acquiesce travelers to new standards of safety.

percentage airlines with AI

There is no plan B. Over 100 million jobs have succumbed to covid-induced lay-offs, and the travel industry is likely to bear losses worth $1 trillion. It’s time to explore the applications of AI in the aviation industry.

AI in Aviation to Quell Future-Shock

The need for AI-driven customer experience in the travel industry in the post COVID world is huge. At the same time, it should not be looked upon as the panacea, but rather a pain-killer, to mitigate losses and welcome passengers back. The following are some of the realistic scenarios that are just as conceivable, as they are implementable when it comes to the future of AI in the aviation industry.

A point to note is that irrespective of the use case or the state of COVID19 driven economic condition, AI has found a permanent place in the aviation industry.

global AI in aviation market

Flying Optimized Routes

A lot of long-duration flights tend to have a mid-range landing spot, where often the passengers are required to undergo formal security procedures to check-in to a new flight. In formal terms, this is called a layover. The process is too discomforting from a traveler experience standpoint, forces human-human contact, and invariably increases the risk of community transmission.

Not to mention the fuel-refilling and the per capita resource consumption by passengers at the layover spot. One of the benefits of AI in the aviation industry in the post COVID world is that it can re-route and optimize long-duration flights. Till such time when the carriers reach full-capacity the shortest transit routes can be recommended by AI saving fuel and other capital-intensive resources.

Digitalized Check-ins

People are downright scared to get out of their homes let alone travel. For those mustering the fortitude to step foot on a plane, do so after ensuring the details about their boarding pass, baggage submission, weather updates, and flight status among other things. Presently, travelers have to toggle between multiple apps to gather each set of information. Leading travel app development companies in the industry are foraging ways through which AI helps in revamping the aviation industry.

Lufthansa, for instance, has provisioned for iterations to its mobile app so boarding passes could be stored digitally. An increasing number of pre-market trials suggest that smartphones could act as a one-stop-shop wallet storing necessary travel documentation. There could even be facial recognition to safeguard the app and ensure the best in class privacy. To roll the red carpet for an all-encompassing paperless travel experience, the International Air Transport Association (IATA) has initiated OneID, an identity management solution that will possibly incorporate AI-powered biometrics.

Baggage Assistance

Breakdown of customer complaint stats

** Other data includes complaints related to frequent flyers, smoking, tours credit, cargo problems, security, airport facilities, claims for bodily injury.

Baggage has always been a challenging area for the aviation sector. A challenge that is going to worsen in the COVID19 era. There is the consideration to be made for baggage deposits, wherein the luggage changes hands and multiplies possibilities of community transmission.

To tackle this, the airport concierge could innovate e-commerce apps operating to and fro between customer abodes and the airport. Empowering their architecture with RFID tags, and AI-enabled tracking systems, chances of not just baggage mishandling but also contact tracing can be mitigated in instances of virus transfer.

Not all of us would feel the safety net in trusting an unknown driver to take cost expensive items and dutifully deposit the same at airports. Therefore, for people hell-bent on doing things on their own, self-drop baggage lanes could save the day. In addition to reducing human dependency, they also cut short baggage processing times. Robots could be deployed in such lanes with AI-powered facial recognition software that would recognize the rightful owner of the items.

The airport operations staff must resolutely work towards increasing social distancing. One alternative for this emerged in the pre-pandemic era when JFK airport introduced Google Assistant’s interpreter mode. It supports 29 languages and will help international passengers with typical queries including airport navigation, luggage location, etc.

No doubt, AI is transforming the aviation industry in the post-COVID era. Another example of this would be in thermal imaging cameras. Made super efficient with passenger flow analytics and social distancing software, the cameras would scan body temperatures in real-time informing officials of doubtful cases that can be managed as per protocol.

AI-Fastened Security

One of the most cumbersome and inconvenient instances in the course to board a flight is security checks. All major airports mandate passengers to take off wearables and empty hand-bags so they can be thoroughly checked. Think we all can agree, the process is profoundly annoying. Not to mention the strict levels of distancing required to be maintained are not sustained when officials inspect travelers closely.

All this will be a thing of the past as Artificial Intelligence in aviation safety sees light at the end of the tunnel. State-of-the-art scanners would debut at the airports, infused with capabilities like X-Ray mapping, 3D image processing, and/or anomaly protection algorithms. Body scanners will be remodeled to incorporate AI technology.

AI-enabled automated target recognition algorithms synced into millimeter-wave scanners will make identifying rogue actors a click of the finger.

Digital Entertainment

Airport lounges see a significant number of people walk-in for entertainment/relaxation while waiting for the onboarding to commence. They are often empaneled with public computers and accessory booths used (and touched) by many. This needs to change. Carriers such as Delta Airlines are experimenting with a Parallel Reality experience that would facilitate multiple passengers, all simultaneously looking at the same screen, to view their respective flight information.

We have reason to believe that AI chatbot development is in full swing to complement the mass deployment of robots at airports. Chatbots in the airline industry will be fitted with facial recognition algorithms that would bring a wee bit of personal touch to machine-to-human interaction. Machines will be programmed to sing aloud the advantages of personal hygiene and sanitization. Lately, some of our partners have expressed interest in airline chatbot development. Such conversations are more than food for thought and if pursued with real purpose and a judicious budget, profitable advances can be made in a short period.

Robots for Product Delivery

Duty-free stores attract a lot of travelers thanks to unparalleled prices. But who said we need to risk public safety at such times for purposes of shopping. Store owners are realizing the significance of standardizing new norms to practice social distancing. For instance, Dubai Duty-Free while resuming operations posts the lockdown made customers use their concierge service to fill the cart.

Just so we leave nothing to the imagination, the carts were delivered to the customers by robots. The advantages of AI in the travel industry post Coronavirus are evident from such use cases.

In other locations, click-and-collect app models are establishing relatable grounds for business. Even before arriving at the airport, customers can order items waiting for them when they board-off the plane.

Final Thoughts

The inclusion of AI in the travel industry in the post COVID world is imminent. Agreeably, it will be a couple of years before the airports start bustling with the rush of people packed closed to each other and waiting for departures. A significant level of quid pro quo needs to be enacted for this distant, pun intended, reality to take a rebirth. Artificial Intelligence will take the mainstage in being the underpinning technology for all things automation.

Inclusion of AI in the travel industry will attract business-interest not limited to the airports, but branching well into the hospitality sector be it hotels, restaurants, or mobile food vans. With arguably the most talented technocrats under one roof, Appinventiv can be your technological partner.

Prateek Saxena
Prateek Saxena
Co-founder of Appinventiv
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8 Examples of Artificial Intelligence in our Everyday Lives

At the time, McCarthy only created the term to distinguish the AI field from cybernetics. However, AI is more popular than ever today due to: Increased …

The applications of artificial intelligence have grown over the past decade. Here are examples of artificial intelligence that we use in our everyday lives.

Main Examples of Artificial Intelligence Takeaways:

  • Artificial intelligence is an expansive branch of computer science that focuses on building smart machines.
  • American computer scientist John McCarthy coined the term artificial intelligence back in 1956.
  • Artificial intelligence and robotics are two entirely separate fields.
  • The four artificial intelligence types are reactive machines, limited memory, Theory of Mind, and self-aware.
  • Other subsets of AI include big data, machine learning, and natural language processing.
  • Artificial intelligenceexamples include Face ID, the search algorithm, and recommendation algorithm, among others.

The words artificial intelligence may seem like a far-off concept that has nothing to do with us. But the truth is that we encounter several examplesofartificial intelligence in our daily lives.

From Netflix‘s movie recommendation to Amazon‘s Alexa, we now rely on various AImodels without knowing it. In this post, we’ll consider eight examples of how we’re already using artificial intelligence.

What is Artificial Intelligence

Artificial intelligence is an expansive branch of computer science that focuses on building smartmachines. Thanks to AI, these machines can learn from experience, adjust to new inputs, and perform human-like tasks. For example, chess-playing computers and self-driving cars rely heavily on natural language processing and deep learning to function.

American computer scientist John McCarthy coined the term artificial intelligence back in 1956. At the time, McCarthy only created the term to distinguish the AI field from cybernetics.

However, AI is more popular than ever today due to:

  • Increased data volumes
  • Advancements in computing and storage
  • Advanced algorithms

Hollywood movies tend to depict artificial intelligence as a villainous technology that is destined to take over the world.

One example is the artificial superintelligence system, Skynet, from the film franchise Terminator. There’s also VIKI, an AI supercomputer from the movie I, Robot, who deemed that humans can’t be trusted with their own survival.

Holywood has also depicted AI as superintelligent robots, like in movies I Am Mother and Ex Machina.

However, the current AItechnologies are not as sinister — or quite as advanced. With that said, these depictions raise an essential question:

Are Robot Examples of Artificial Intelligence?

Examples of artificial intelligence image shows a white robot with big eyes
Alex Knight /

No, not exactly. Artificial intelligence and robotics are two entirely separate fields. Robotics is a technology branch that deals with physical robots — programmable machines designed to perform a series of tasks. On the other hand, AI involves developing programs to complete tasks that would otherwise require humanintelligence. However, the two fields can overlap to create artificially intelligent robots.

Most robots are not artificially intelligent. For example, industrialrobots are usually programmed to perform the same repetitive tasks. As a result, they typically have limited functionality.

However, introducing an AIalgorithm to an industrial robot can enable it to perform more complex tasks. For instance, it can use a path-finding algorithm to navigate around a warehouse autonomously.

To understand how that’s possible, we must address another question:

What are the Four Types of AI?

The four artificial intelligencetypes are reactive machines, limited memory, Theory of Mind, and self-aware. These AI types exist as a type of hierarchy, where the simplest level requires basic functioning, and the most advanced level is — well, all-knowing. Other subsets of AI include big data,machine learning, and natural language processing.

1. Reactive Machines

The simplest types of AIsystems are reactive. They can neither learn from experiences nor form memories. Instead, reactive machines react to some inputs with some output.

Examplesofartificial intelligence machines in this category include GooglesAlphaGo and IBM‘s chess-playing supercomputer, Deep Blue.

Showdown between Google DeepMind's AlphaGo AI and Lee Sedol.
Showdown between Google DeepMind’s AlphaGo AI and South Korea’s 9-dan professional Go player Lee Sedol. | Still Shot from AlphaGo – The Movie | Full Documentary

Deep Blue can identify chess pieces and knows how each of them moves. While the machine can choose the most optimal move from several possibilities, it can’t predict the opponent’s moves.

A reactive machine doesn’t rely on an internal concept of the world. Instead, it perceives the world directly and acts on what it sees.

2. Limited Memory

Limited memory refers to an AI‘s ability to store previous data and use it to make better predictions. In other words, these types of artificial intelligence can look at the recent past to make immediate decisions.

Note that limited memory is required to create every machine learning model. However, the model can get deployed as a reactive machine type.

The three significant examplesofartificial intelligence in this category are:

  1. Reinforcement Learning: Models that learn to make better predictions after several cycles of trial and error.
  2. Long Short-Term Memory (LSTMs): Models for predicting the next element in a sequence.
  3. Evolutionary Generative Adversarial Networks (E-GAN): The model produces a kind of growing thing that evolves.

A small, yellow self-driving bus deployed in Berlin, Germany last 2019.
In 2019, small, yellow self-driving buses were deployed in Berlin, making Germany’s capital city the first in the country to use autonomous vehicles on the roads. | falco/

Self-driving cars are limited memory AI that makes immediate decisions using data from the recent past.

For example, self-driving cars use sensors to identify steep roads, traffic signals, and civilians crossing the streets. The vehicles can then use this information to make better driving decisions and avoid accidents.

3. Theory of Mind

In Psychology, “theory of mind” refers to the ability to attribute mental state — beliefs, intent, desires, emotion, knowledge — to oneself and others. It’s the fundamental reason we can have social interactions.

Unfortunately, we’re yet to reach the Theory of Mindartificial intelligence type. Although voice assistants exhibit such capabilities, it’s still a one-way relationship.

For example, you could yell angrily at Google Maps to take you in another direction. However, it’ll neither show concern for your distress nor offer emotional support. Instead, the map application will return the same traffic report and ETA.

An AIsystem with Theory of Mind would understand that humans have thoughts, feelings, and expectations for how to be treated. That way, it can adjust its response accordingly.

4. Self-Awareness

The final step of AI development is to build self-aware machines — that can form representations of themselves. It’s an extension and advancement of the Theory of MindAI.

A self-aware machine has human-level consciousness, with the ability to think, desire, and understand its feelings. At the moment, these types of artificial intelligence only exist in movies and comic book pages. Self-aware machines do not exist.

Although self-aware machines are still decades away, several artificial intelligence examples already exist in our everyday lives.

What is Artificial Intelligence Used for Today?

Several examplesof artificial intelligence impact our lives today. These include FaceID on iPhones, the search algorithm on Google, and the recommendationalgorithm on Netflix. You’ll also find other examples of how AI is in use today on social media, digital assistants like Alexa, and ride-hailing apps such as Uber.

1. Face Detection and Recognition Technology

Virtual filters on Snapchat and the FaceID unlock on iPhones are two examplesofAI applications today. While the former uses face detection technology to identify any face, the latter relies on face recognition.

So, how does it work?

The TrueDepth camera on the Apple devices projects over 30,000 invisible dots to create a depth map of your face. It also captures an infrared image of the user’s face.

Apple FaceID technology reveal during the iPhone X launch in 2017.
Apple’s FaceID technology helps protect the information users store in their iPhone and iPad Pro. Face ID uses the TrueDepth camera and machine learning for a secure authentication solution. | Still shot from the iPhone X launch in 2017. | Apple

After that, a machine learning algorithm compares the scan of your face with what a previously enrolled facial data. That way, it can determine whether to unlock the device or not.

According to Apple, FaceID automatically adapts to changes in the user’s appearance. These include wearing cosmetic makeup, growing facial hair, or wearing hats, glasses, or contact lens.

The Cupertino-based tech giant also stated that the chance of fooling FaceID is one in a million.

2. Text Editor

Several text editors today rely on artificial intelligence to provide the best writing experience.

For example, document editors use an NLP algorithm to identify incorrect grammar usage and suggest corrections. Besides auto-correction, some writing tools also provide readability and plagiarism grades.

INK showing how its artificial intelligence system optimizes content for search.
INK is powered by a natural language processing technology that allows it to make intelligent SEO recommendations, helping writers and marketer make their content more relevant to their target audience. | INK |

However, editors such as INK took AI usage a bit further to provide specialized functions. It uses artificial intelligence to offer smart web content optimization recommendations.

Just recently, INK has released a study showing how its AI-powered writing platform can improve content relevance and help drive traffic to sites. You can read their full study here.

3. Social Media

Social media platforms such as Facebook, Twitter, and Instagram rely heavily on artificial intelligence for various tasks.

Currently, these social media platforms use AI to personalize what you see on your feeds. The model identifies users’ interests and recommends similar content to keep them engaged.

An image of a social media feed resembling Facebook.
Social media networks use artificial intelligence algorithms to personalize user feeds and filter unnecessary information like hate speech and posts inciting violence and discrimination. | 200degrees/

Also, researchers trained AImodels to recognize hate keywords, phrases, and symbols in different languages. That way, the algorithm can swiftly take down social media posts that contain hate speech.

Other examplesof artificial intelligence in social media include:

  • Emoji as part of predictive text
  • Facial recognition to automatically tag friends in photos
  • Smart filter to identify and remove spam messages
  • Smart replies for quickly responding to messages

Plans for social media platform involve using artificial intelligence to identify mental health problems. For example, an algorithm could analyze content posted and consumed to detect suicidal tendencies.

4. Chatbots

Getting queries directly from a customer representative can be very time-consuming. That’s where artificial intelligence comes in.

Computer scientists train chat robots or chatbots to impersonate the conversational styles of customer representatives using natural language processing.

An image showing Whatsapp layout design.
Chatbots are currently being used by many businesses to assist potential customers with their queries. | 200degrees/

Chatbots can now answer questions that require a detailed response in place of a specific yes or no answer. What’s more, the bots can learn from previous bad ratings to ensure maximum customer satisfaction.

As a result, machines now perform basic tasks such as answering FAQs or taking and tracking orders.

5. Recommendation Algorithm

Media streaming platforms such as Netflix, YouTube, and Spotify rely on a smart recommendation system that’s powered by AI.

First, the system collects data on users’ interests and behavior using various online activities. After that, machine learning and deep learning algorithms analyze the data to predict preferences.

That’s why you’ll always find movies that you’re likely to watch on Netflix’s recommendation. And you won’t have to search any further.

6. Search Algorithm

Search algorithms ensure that the top results on the search engine result page (SERP) have the answers to our queries. But how does this happen?

Search companies usually include some type of quality control algorithm to recognize high-quality content. It then provides a list of search results that best answer the query and offers the best user experience.

Google search
Search engines like Google is powered by multiple algorithms that help it match people’s queries with the best answers available online. | Google

Since search engines are made entirely of codes, they rely on natural language processing (NLP) technology to understand queries.

Last year, Google announced Bidirectional Encoder Representations from Transformers(BERT), an NLP pre-training technique. Now, the technology powers almost all English-based query on Google Search.

7. Digital Assistants

In October 2011, Apple’s Siri became the first digital assistant to be standard on a smartphone. However, voice assistants have come a long way since then.

Today, Google Assistant incorporates advanced NLP and ML to become well-versed in human language. Not only does it understand complex commands, but it also provides satisfactory outputs.

OK Google voice command hovering over an iPhone device.
Google Assistant is one of the most popular digital assistants available today. | Kaufdex/

Also, digital assistants now have adaptive capabilities for analyzing user preferences, habits, and schedules. That way, they can organize and plan actions such as reminders, prompts, and schedules.

8. Smart Home Devices

Various smart home devices now use AIapplications to conserve energy.

For example, smart thermostats such as Nest use our daily habits and heating/cooling preferences to adjust home temperatures. Likewise, smart refrigerators can create shopping lists based on what’s absent on the fridge’s shelves.

The way we use artificial intelligence at home is still evolving. More AIsolutions now analyze human behavior and function accordingly.

Wrapping Up: Other Examples of Artificial Intelligence

We encounter AI daily, whether you’re surfing the internet or listening to music on Spotify.

Other examplesofartificial intelligence are visible in smart email apps, e-commerce, smart keyboard apps, as well as banking and finance. Artificial intelligence now plays a significant role in our decisions and lifestyle.

The media may have portrayed AI as a competition to human workers or a concept that’ll eventually take over the world. But that’s not the case.

Instead, artificial intelligence is helping humans become more productive and helping us live a better life.

Read More: The Best Artificial Intelligence Books you Need to Read Today

Can AI-driven tech help bring back tourism travel?

Industry experts believe data science and AI technology will be a key tool in the revival of travel, with electronic passports and IDs, boarding passes, …
  • Artificial intelligence (AI) will rapidly spread to almost every aspect of the travel and the hospitality industry post-pandemic
  • AI can be used for a plethora of uses, including fleet and operations management, disruption management, customer service and retention, autonomous machines and services, recommendation engines, price forecasting, and many more.

The global restrictions imposed since March this year due to Covid-19 has brought international travel to a screeching halt. It has caused a 22% fall in international tourist arrivals during the first quarter of 2020 and could further decline by 60-80% over the whole year.

Travel will be back — inevitably. But the way we travel will undergo a dramatic transformation.

Although technology may have already changed how travelers plan, book, and embark on their journeys drastically over the past decade, at this juncture, data science and artificial intelligence (AI) is expected to play a major role in helping the industry resuming back to the new normal. Before the pandemic, AI and other forms of machine learning were just beginning to infiltrate the travel sector. Their biggest advantage is the ability to personalize experiences and streamline services based on customer data.

Singapore, for example, is hoping that AI — through local start-ups — can help the city-state’s tourism sector bring back visitors safely, as ongoing border restrictions and lower consumer appetite for international flights have changed travel as an industry.

So what could traveling look like in the wake of the pandemic?

Naturally, companies would explore solutions that allow travelers to maintain social distancing norms as much as possible.

Industry experts believe data science and AI technology will be a key tool in the revival of travel, with electronic passports and IDs, boarding passes, medical screening, and robot cleaners being deployed widely to limit physical contact between people and surfaces.

If AI is utilized to its full capability, the travel industry could be completely revolutionized, as it would be able to change aspects of customer service, assist in passenger identification while traveling, and personalize a traveler’s experience through curated recommendations.

One great example is the adoption of AI concierge by numerous hotel chains including Hilton Worldwide’s Connie — the first true AI-powered concierge bot since 2016.

Standing at two feet high and interacting with guests during check-in — Connie can provide succinct information to guests on local attractions and places to visit. Being AI-driven with self-learning ability, it can also learn and adapt and respond to each guest on a personalized basis.

Chatbot technology is another big strand of AI, and not surprisingly many travel brands have already launched their own versions in the past year or so. Skyscanner is just one example, creating an intelligent bot to help consumers find flights in Facebook Messenger.

Another immediate and visible change in the travel industry will be a shift to touchless travel from airport curbside to hotel check-in. Automation across the entire travel industry will become the new norm. From a visa application center, an airplane, to sightseeing at a destination, people would look for an assurance that all touchpoints during a journey have adopted the health and hygiene aspect.

With physical fingerprint and hand scanners phased out, its use will become more widespread and m0re touchless options will come into play including contactless fingerprint, as well as iris and face recognition.

All in all, the travel and hospitality industry transformation will morph into an experience-driven and asset-light business, as wide adoption of AI will usher a new-age customer experience and set a benchmark for other industries to emulate.

Dashveenjit Kaur| @DashveenjitK

Dashveen writes for Tech Wire Asia and TechHQ, providing research-based commentary on the exciting world of technology in business. Previously, she reported on the ground of Malaysia’s fast-paced political arena and stock market.

The Future Of Work Now: AutoML At 84.51°And Kroger

A relatively new technology called “automated machine learning, or “AutoML,” is shaking up the world of data science. It’s making professional data …

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon. Steve Miller of Singapore Management University and I are collaborating on these stories.

84.51° building

84.51° headquarters, Cincinnati


Data science and machine learning developers are among the hottest jobs in the world right now. In 2012 I and my co-author DJ Patil (who went on to become the first Chief Data Scientist of the United States government) wrote an article about data scientists that was subtitled “Sexiest Job of the 21st Century.” Data science has only become more important since then as AI and machine learning have proliferated throughout organizations.

Data scientists work with AI every day in the sense that they are developers of AI applications. But many of them are now also working with AI in another way as well: their work is being automated. Some of it, anyway. A relatively new technology called “automated machine learning, or “AutoML,” is shaking up the world of data science. It’s making professional data scientists more productive by automating aspects of their work, and enabling the emergence of “citizen data scientists” who may not have graduate degrees in quantitative fields, but can still develop effective machine learning models using AutoML.

84.51°—an organization named after the longitude of Cincinnati, where it is based—is the dedicated analytics and data science group for the supermarket giant Kroger. It collects and analyzes longitudinal data—observations over time—so the name is appropriate if unusual. In 2015, Kroger purchased a majority of dunnhumbyUSA to create a new, wholly owned business, 84.51°. Now it serves only Kroger and its large network of supplier partners.

84.51° Projects and Automated Machine Learning

The website of 84.51° provides a few revealing numerical facts that convey the enormous size and scope of their data science efforts:

· 1250 consumer packaged goods partners

· 60 million households

· 1 billion personalized offers delivered to customers last year

· Over 10 petabytes of customer data analyzed

· 3 billion customer shopping baskets analyzed

· 138 different machine learning models in production.

Many of the group’s predictive models are used every day by Kroger. For example, the sales forecasting application creates forecasts for each item in each of more than 2500 stores for each of the subsequent 14 days. In most companies, these types of sales forecasting models are updated rarely or never, but sales forecasting for Kroger is dynamic. These forecast models are updated on a nightly basis based on the most recent data. Using another 84.51° capability, “Kroger Precision Marketing” analyzes the relationships between media exposure and store sales. It uses customer purchase data to make brand advertising more addressable, actionable, and accountable. Over the past three years media campaigns for over 1000 brands have been orchestrated using the results of this data science-driven analysis.

Dealing with such vast amounts of data and large numbers of models would be challenging without some degree of automation. Several years ago 84.51° began a project called “Embedded Machine Learning.” Its objective was to increase the productivity and effectiveness of machine learning through automation in conjunction with a more standardized work process and a standard tool. The tool chosen was an automated machine learning system called DataRobot (I am an advisor to the company). It automates many steps in the machine learning process, including data preparation, feature engineering (deciding what features or variables to include in the model), trying out many different machine learning algorithms to see which ones provide the best predictions, and generating the programming code (or automatically producing an application program interface, or API) to implement the model.

It’s not uncommon for professional data scientists to distrust AutoML or disbelieve that it can create effective models. At 84.51°, some experienced data scientists were concerned that they would be moving to a world in which their deep and hard-earned knowledge of algorithms and methods would have no currency. The company’s leaders emphasized that the new tools would empower people to do their work more efficiently. Over time, this proved to be the case, and there is little or no pushback from the experienced data scientists about the use of the DataRobot tool.

The initial focus for AutoML at 84.51° was to improve the productivity of data scientists. But the group has also used the automated tools to expand the number of people who can use and apply machine learning. 84.51° has been growing its data science function to meet rapidly expanding demand for modeling and analytics to solve complex business problems. It is a challenge to find well-trained data scientists. So 84.51° employs AutoML to make it possible for those without traditional data science training to create machine learning models. 84.51° now regularly hires “Insights Specialists”—people who don’t have as much experience with machine learning, but who are skilled at communicating and presenting results, and who have high business acumen. Aided by AutoML, a substantial number of activities within traditional model development such as use case identification and exploratory analyses can now also be done by these Insights Specialists. The data scientists with more statistical and machine learning experience can focus their time on the aspects of machine learning that requires their deeper expertise, and also to spend more time training and consulting with others having less experience.

Two Data Scientists and their Reaction to AutoML

Alex Gutman and Nina Lerner are senior data scientists at 84.51°. Gutman, formerly a data scientist across Cincinnati at Procter & Gamble, is a “Lead Data Scientist” and was instrumental in introducing AutoML to 84.51°. He trained many 84.51° employees in the use of DataRobot, and now runs predictions for the optimal item assortments in particular Kroger stores.

Gutman was one of the data scientists who was initially intimidated by AutoML; he felt threatened by the automation and by the tool’s capabilities. But when he became head trainer of DataRobot, the more he learned, the better he felt about it. However, he still started his two-day training sessions by saying, “You might feel intimidated by this.”

He saw the primary benefit of AutoML as increasing his productivity:

“It used to take days and weeks to transform raw data into an algorithm-ready dataset and build a model—now it’s a few hours or at most a couple of days. That frees up my time to think deeper about the problem I am trying to solve with machine learning—what we call solution engineering.”

The automation capabilities also help him give rapid feedback to his internal customers. “This helps me find new features or supplemental data assets to improve prediction accuracy, and gets results more quickly to show to the decision-maker to see if they are on track.”

The DataRobot system uses a “leader board” that ranks the alternative models it generates in terms of their degree of ability to predict the data. Even with this automated model ranking, Gutman says there is still an important role for the data scientist. “If you want to interpret the model you need to have some insight into how it works. You need to be able to explain it to the decision-maker.”

Nina Lerner is a Director of Data Science at 84.51° and is responsible for developing new data assets to enable data scientists to more accurately predict and understand consumer behavior. She also oversees the data governance of behavioral segmentations across the business. She was an early adopter of AutoML and has helped to migrate multiple users over to the technology.

Lerner has a graduate degree from Columbia University in quantitative analytics. She was trained to take pride in the process of building analytic models and in using them to successfully predict and categorize outcomes—“We built them with our own hands,” she said. Consequently, AutoML was initially very threatening to her. “You no longer needed all of your training and time investment for model creation. It was intimidating and scary for that reason.”

She quickly embraced the technology, however, and became a strong advocate for AutoML. She said:

“It was such a game-changer. Previously, I would sometimes spend two months building a model, choosing between XG Boost, Random Forest, Ridge Regression [different algorithm types], and other model types. And now, within two days, I can explore many more methods than those.”

Like Alex Gutman, she had plenty of things to do with the time she saved. “It freed me up to spend time on what made the difference in the models. I could craft more thoughtful features, add new features, and define the problem better.” She loves the new focus and says she thinks the areas of the problem she addresses now have more value to the business.

DataRobot is always adding new algorithms to its platform and Lerner acknowledges that she does not always know the details of these new approaches. However, if a new method is identified by the tool as promising, Lerner is able to understand the formulas for the models she digs into as result of her academic training. She can use all of her quantitative data modelling knowledge to assess the quality of the model, understand why the machine learning scores come out the way they do, and use diagnostic techniques to ensure the model is sound.

For both of these data scientists, AutoML gives them more time to think deeply about the problem they are solving and to explore more alternatives. They admit that there are some people in their organization who use DataRobot in a more black-box way that eliminates the need to understand anything. They both emphasize they do not espouse or endorse the approach of: “I have a dataset, let me try it in DataRobot and see what happens.”

Both Alex Gutman and Nina Lerner have to present their results to Kroger. In doing so they make heavy use of a feature in DataRobot called “prediction explanations.” It identifies the key features in the chosen machine learning model, and their direction of influence. “It might tell them,” Gutman said, “why someone would redeem this coupon, or not.” Lerner agreed, “We share interpretable output, not the model itself, with our Kroger stakeholders. We tell them why households got a particular score, why scores changed since the last model, and what features drove the prediction.”

Working with Insights Specialists

Nina Lerner has worked with Insights Specialists on making use of AutoML. She trained one such person, for example, to use the DataRobot system and follow their machine learning process. She commented that there was more handholding involved than in working with those with strong statistical backgrounds. But while more guidance on her part is required, Insights Specialists tend to have strong capabilities for linking the model results to business needs, and they take on more of the effort to provide informative explanations to Kroger stakeholders. They describe what business value the data is providing, create business relevant stories to explain the AutoML models, and know what questions a client might ask.

Alex Gutman has less experience in working jointly with Insights Specialists on projects. But he had these types of employees as students in his training classes. There he noticed in modeling competitions (giving the class a dataset, and seeing who got the best result) those who “beat the leaderboard”—found a better model than the one automatically selected by the AutoML technology—were likely to be in the Insights role. Rather than trying the latest Python program, their approach was to really understand the variables that predict the outcome. One Insights Specialist, for example, combined household income with house value to create an affordability measure that was a good predictor of buying behavior. Lerner added, “Subject matter engineering always adds the most value.”

The Future of Data Scientists

Neither Gutman nor Lerner is particularly concerned that data science will be entirely automated by AutoML. “It’s just another tool in the toolbox,” Lerner commented, noting that she has observed quantitative analysts in the past who felt threatened by the previous generations of statistical packages like SAS and SPSS.

Alex Gutman says that after teaching AutoML tools to many 84.51° employees, he thinks there will always be a need for consulting from data scientists like himself and Lerner who understand what’s happening beneath all the automation. “As powerful as AutoML is,” he adds, “it doesn’t do much to shorten the entire pipeline of solving a problem with machine learning. You still have to spend a lot of time defining the problem, and gathering and curating the data to address it. AutoML has just shifted the focus.”

Nina Lerner concluded with some reflections on her overall career in data science:

“I’ve approached my career in terms of adaptability and being willing to change with the technology. I can’t let the world pass me by. If automation is here, I need to be an early adopter or be left in the dust. I have to stay at the forefront of the technology. I could have become an expert on Random Forest [a particular modeling technique], which someone once advised me to do. But I wouldn’t be as successful if I had. I’ve had more personal career growth because I can do a lot of things in the field and I move quickly to embrace new approaches.”

Military Artificial Intelligence (AI) And Cybernetics Market 2020-2026, With Breakdown Data of …

The research report on the topic of Military Artificial Intelligence (AI) And Cybernetics‘, gives a comprehensive study of various factors of the Military …

The research report on the topic of Military Artificial Intelligence (AI) And Cybernetics’, gives a comprehensive study of various factors of the Military Artificial Intelligence (AI) And Cybernetics’ market. The market report is created and written keeping in consideration various important factors. The reports are written after an in depth market study and analysis. It testifies the constant growth in the Military Artificial Intelligence (AI) And Cybernetics’ market, in spite of the current unsteady market scenario in terms of revenue.

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Top Companies:

General Dynamics

Lockheed Martin Corporation

Northrop Grumman Corporation

BAE system


Blue Bear

Charles River Analytics






Soar Tech

Thales Group

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In the recent years, the Military Artificial Intelligence (AI) And Cybernetics’ market has seen growth to USD XXX million and is predicted to grow more during the coming year. The report gives a detailed summary of the market trends, shares and patterns of revenue growth and the market value. The market research document on Military Artificial Intelligence (AI) And Cybernetics’ is written post extensive research and findings based on it. The report is structured and is well written by industry experts. The report covers important information about various vendors, manufacturers, research papers and many similar important facts and features. The report gives an in-depth study of producers that are supplying the market. With the help of this report knowledge about the market and its key players can be gained for those wanting to enter the market.

Furthermore, the report shall give you a detailed list of competitive analysis and it would give you a detailed report on the various market strategies, models and growth pattern in terms of revenue of the competitors. Market segmentation, forecast and other factors of the business which gives a qualitative and quantitative view of the market. Anyone thinking of investing in a new business, needs to have a look at the Military Artificial Intelligence (AI) And Cybernetics’ market report to judge and understand the business dynamics at the same time get competitor analysis.

By Types:





By Application:


Warfare platform


Logistics and transportation

Autonomous weapons and targeting system

Battlefield healthcare


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