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 / Unsplash.com

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/Pixabay.com

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 | inkforall.com

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/Pixabay.com

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/Pixabay.com

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/Pixabay.com

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

AI can be used in all retirement plans if focused properly – WPS panelists

“For this moment, we use alternative data as an input into our decision-making process and not yet to replace” human judgment, APG’s Ms. Halme said.

All retirement plans — regardless of size — can apply artificial intelligence to their portfolios in some way, but robust governance and operational capabilities must be in place.

Speaking Wednesday at the Pensions & Investments WorldPensionSummit on a panel addressing how the integration of data will revolutionize investing, panelists said the size of a retirement plan does not matter when it comes to making use of data and technology.

“It’s not about small schemes or big schemes — it depends on what you’re trying to achieve with AI,” said Charles Wu, deputy CIO and general manager, defined contribution investments at the A$44 billion ($31.1 billion) State Super, Sydney. “Probably where it’s going to save (smaller plans) a fair bit of cost will be on the administration side and operational side. The hurdle to apply this on investment, it is a little bit higher.”

Terhi Halme, senior sustainability specialist at APG, said the manager, along with other participating managers and asset owners, enlisted the help of an external technology firm as it worked on its sustainable development investments asset owner platform. The platform helps institutional firms invest alongside the United Nation’s sustainable development goals using data and standardization. “So you can also look elsewhere for the resources, and it doesn’t necessarily require that you build in all of the capabilities and the data internally,” Ms. Halme said.

Over the long run, the hope is that small plans “will get even more benefit, because they don’t have the resources, they don’t have the analysts and internal head count,” said Ashby Monk, executive director and research director at Stanford University’s Global Projects Center and co-founder and chairman of Long Game.

However, plans must ensure they have robust governance structures in place to embed AI and use alternative data in their portfolios, panelists said.

Plan executives must also think about where they want to embed a new data-driven capability.

The process will be similar to that about a decade ago, when plan executives began considering the insourcing of investment management, Mr. Monk said.

“We need to really think about what are the safe spaces to onboard AI. Recognize that our industry is not very good at failing, and yet there is no innovation without failure. … That’s hard in our world where we have prudent person rules and fiduciary duties. We’re not designed for that,” Mr. Monk said.

Plans should find safe areas to experiment with AI, he said. “The first place to apply AI is not to try to generate alpha. It would be, let’s think about our operational footprint, see if we can unleash AI to take a bunch of unstructured data and make it structured … to do better passive investing.” Once there is comfort around the role AI can take in an investment process, then look into how it can be integrated “in attempts to beat the market,” Mr. Monk said.

Panelists added that the use of AI will bring about a number of advantages for plan executives, including the ability to better hold investment managers to account and to help inform decisions.

But panelists were split as to whether investment managers should be worried about machines taking their jobs.

“For this moment, we use alternative data as an input into our decision-making process and not yet to replace” human judgment, APG’s Ms. Halme said.

State Super’s Mr. Wu agreed. “I think we’re far from it,” with human judgment needed throughout the investment process.

“I’m actually really positive with the development, because it actually unlocks the time of doing some mundane work” and you can do something of higher cognitive value, he said. “It allows you time to think.”

Mr. Monk, however, said investment executives should polish their resumes.

“It’s not just AI — tech is going to transform our industry. We really haven’t been beaten by software the way other industries have. … We kind of hide our (intellectual property) in boxes unlike other open-source industries, so we haven’t had that tech revolution yet to really transform our operations. And it’s coming.”

That trend has been exacerbated by the coronavirus pandemic, he said.

“There’s gonna be a lot of freedom time, which is code for certain people may not be required,” Mr. Monk said.

Gaining the enhanced “inferential insights” that come with using AI “will really play out to empower long-term investors,” he added.

But human beings “will always have a comparative advantage in human relationships — so as long as this is a business where we need to have conversations, exchange ideas, discover things, we will always need to be in the loop, even if the robots 50 years from now are far better than us at capital allocation and risk management,” Mr. Monk said.

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

84.51°

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 Sales Market 2020 Size, Share, Forecast to 2026 …

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Takeaways in Brief:

1. This information report proceeds with unearthing the various growth propellants that harness optimum growth in global Military Artificial Intelligence (AI) And Cybernetics Sales market.

2. The report is also committed to adequately gauge for ample threats and challenges that collectively drive high end rise in global Military Artificial Intelligence (AI) And Cybernetics Sales market.

3. Finally, this report also tilts towards identifying offbeat market opportunities even amidst the odds and catastrophes to ensure tremendous transformation in global Military Artificial Intelligence (AI) And Cybernetics Sales market.

4. The report ensures ample information flow on new technological milestones, novel product expansion schemes as well as pipeline activities and investments likelihood.

5. Additional details on regulatory developments and funding policies besides COVID-19 management schemes are also included in the report.

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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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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.

Top Companies:

General Dynamics

Lockheed Martin Corporation

Northrop Grumman Corporation

BAE system

Boeing

Blue Bear

Charles River Analytics

IBM

Leidos

Raytheon

SparkCognition

SAIC

Soar Tech

Thales Group

Browse Full Report @ https://www.orbisresearch.com/reports/index/global-military-artificial-intelligence-ai-and-cybernetics-market-2020-2026-with-breakdown-data-of-capacity-sales-revenue-price-cost-and-gross-profit

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:

Land

Air

Naval

Space

By Application:

Cybersecurity

Warfare platform

Surveillance

Logistics and transportation

Autonomous weapons and targeting system

Battlefield healthcare

Simulation

The growth of business is dependent on its various segments and the report covers all the possible segments of the business. The report gives you a detailed view of the various market segments based on type, application, geography and other relevant features. The report will help the readers understand the behavior pattern of the consumers towards a product category or the overall market. Region wise segmentation is an integral part of the market report and is done in this market report. The report gives a detailed study about the various regional segments of the market along with an overview of the largest market contributors. The report will give you a summary of business opportunities and revenue prospects over the forecast period and corresponding growth driving factors. For organizations or businesses looking for growth opportunity by undergoing changes that would positively impact the business, segmentation helps in understanding the market dynamics. The Military Artificial Intelligence (AI) And Cybernetics’ report will cover the main region and share information about the market size and value in the particular region. The report also have similar information for other regional segmentation.

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