40 Artificial Intelligence Statistics You Need to Know

Want to know the state of artificial intelligence (AI) in some of the top industries in 2021?

We’ve curated and vetted 40 statistics about the emergence of AI, the ways it’s being used today, and expert projections for the future.

Top AI Statistics

  • The global AI market size is expected to hit $190 billion by 2025, with a CAGR of 36% from 2018 to 2025. (Source)
  • The global market size for AI is projected to grow by $58.3 billion in 2021 alone. (Source)
  • 86% of CEOs report that AI is currently implemented in their office in 2021. (Source)
  • 51% of e-commerce leaders have already integrated automation technologies in sales, marketing, and customer service. (Source)
  • 71% of marketers see AI as a useful tool in personalization. (Source)

AI in Healthcare

The healthcare industry has seen a surge in automated procedures due to the COVID-19 pandemic. Here are some stats that may surprise you.

  • In 2021, the healthcare segment of the AI industry is expected to reach $6.6 billion with a compound annual growth rate of 40%. (Source)
  • Yearly savings of $150 billion in the U.S. are expected from AI-powered medical applications by the year 2026. (Source)
  • According to 63% of medical professionals, artificial intelligence is making major contributions to the research of medical solutions in specialty care. (Source)
  • In 2022, 75% of automated interactions in the healthcare industry are expected to be successful, without having to switch to a human. (Source)

AI in Manufacturing

Throughout the world, manufacturers are using artificial intelligence for automated solutions. Here are some facts about the way manufacturing is adopting AI technology.

  • 51% of top manufacturers in Europe, 30% in Japan, and 28% in the U.S. are employing AI technology. (Source)
  • Almost 49% of automotive and manufacturing experts believe that AI is “absolutely critical for success,” while 44% report that AI technology is “highly important” to the production sector. (Source)
  • The worth of smart manufacturing is expected to reach $589.98 billion by 2028. (Source)
  • The generative design industry is expected to grow from $141 million in 2019 to $315 million in 2025. (Source)
  • According to Brian Matthews, Vice President of Platform Engineering at Autodesk, using a generative design is like renting 50,000 computers in the cloud. This means 50,000 days’ worth of engineering work can be done in a single day. (Source)

AI in Banking

The banking sector applies AI technology to recommendation systems for better customer experience and fraud detection and investigation. Here are some interesting statistics about AI in banking.

  • The universal expenditure on artificial intelligence is expected to grow from $50.1 billion in 2020 to $110 billion in 2024, and the banking sector is projected to be one of the main contributors. (Source)
  • 75% of banking professionals with more than $100 billion in assets are implementing AI-powered strategies for banking, compared to 46% with less than $100 billion in assets. (Source)
  • The global digital banking market size is expected to hit $1.61 trillion by 2027, up from $803.8 billion in 2018, with a CAGR of 8.9%. (Source)
  • 56% of the total AI product contribution in banking is fraud detection and risk management. Customer service and marketing make up 25%, and chatbots make up 13.5% of the total AI-powered solutions for the banking sector. (Source)

AI in Telecommunications

Artificial intelligence is rapidly expanding its presence in the telecommunications industry, led by AI-powered smartphones. Here are some stats that show how AI is taking hold in telecommunications.

  • It is expected that the AI telecom market will reach $13.45 billion in the year 2026. (Source)
  • By 2020, more than half of telecom service providers had integrated AI in their services in some form. Networking planning (70%) and performance management (64%) are the two use cases promising huge returns with AI-powered solutions in the telecom industry. (Source)

General Business Applications of AI

Next, here are some statistics that show how businesses are incorporating AI technology and what the future may be for AI-powered business solutions.

  • 60% of B2B sales organizations will embrace data-driven selling instead of selling based on experience and intuition by 2025. (Source)
  • 87% of the companies using AI are adopting it for improved email marketing. (Source)
  • 61% of marketing professionals are interested in AI-powered sales forecasting. (Source)
  • Netflix has saved $1 billion by adopting an AI algorithm for its recommendation engine. (Source)
  • In 2020, the U.S. government announced it will spend more than $1 billion for AI and quantum information science research labs. (Source).
  • The chatbot market size is expected to reach $2.48 billion by the year 2028. (Source)
  • 78% of companies state that they have already implemented AI solutions for better customer experience. (Source)
  • 80% of business and marketing professionals are using chatbots for effective dealing with the customers. (Source)
  • It is predicted that by 2025, 75% of customer interaction will use AI-enabled systems instead of human interaction. (Source)
  • The self-driving car industry is estimated at $56.21 billion in 2021. It is expected to reach $220.44 billion by 2026, with a CAGR of 36.47%. (Source)
  • In 2025, it is projected that 8 million vehicles with AI technology will be shipped to customers. (Source)
  • Volkswagen and Ford are in a joint pact of automated vehicle manufacturing called Argo AI. Volkswagen has invested $2.6 billion in Argo AI. (Source)
  • The global drone market is expected to expand and reach $8.52 billion by 2027, given the growing demand by public and government sectors. (Source)
  • DJI, the leading name in the global drone market, has total funding of $1.1 billion. (Source)
  • 8% of the American population is a drone owner, and 15% have used a drone. (Source)
  • The smart assistant industry is forecasted to reach $44.26 billion by the year 2027. (Source)
  • Google Assistant, with an accuracy of 98% on navigation questions, is the most effective virtual assistant overall. (Source)
  • The number of digital voice assistants in use will reach 8 billion by 2023. This is an increase from 2.5 billion in late 2018. (Source)
  • 30% of jobs are expected to be automated by 2030. (Source)
  • The size of the industrial automation market will expand to nearly $300 billion by 2026. (Source)

Final Thoughts

These statistics show the contributions AI and machine learning are making in every field, from medical to manufacturing. AI is being actively integrated into every domain and is the key to the growth of every business.

What is Computer Vision and How is it Changing the World?

It is no secret that computer vision is rapidly changing our lives.

Images and videos are an integral part of our everyday lives in countless ways – medical procedures, e-commerce, security, technological interaction and many other fields related to daily activities. Did you know that on Facebook alone, around 350 million images are uploaded every day? And that over 500 hours of video are uploaded to YouTube every single minute?

Hardware and software advances now allow computers to review, analyze and provide meaningful outcomes from images and videos. Nowadays, computers are nearing the replication of the human vision and even surpass it in some respects.

The economic impact of computer vision is growing rapidly. The global computer vision market was valued at 10.6 Billion US$ in 2019 and it is expected to grow at 7.6% compound annual growth rate between 2020-2027.

But what is computer vision?

  • Computer vision is a field of computer science that enables computers to replicate the complexity of human vision.
  • Computer vision is all about extraction of useful information from images and videos.
  • Recent advances in artificial intelligence, deep learning and neural networks have enabled rapid adoption of computer vision and new breakthroughs are happening every day.
  • Computer vision includes and enables various tasks, such as detection, pattern recognition, classification, segmentation, and others.

Different types of algorithms are used for image analysis in computer vision:

  • Image classification algorithms – Automatically providing a specific label to an image which describes the image’s content.
  • Object detection algorithms – while classification algorithms explain what object is visible in the image, object detection algorithms provide information about the object’s location in any given image. These algorithms have endless functionalities.
  • Segmentation algorithms reveal which pixels correspond with each object, thus make images easier to analyze, providing additional data about the images and their objects. There are some types of segmentation : semantic segmentation marks all pixels that belongs to a certain type of object with the same classification, while instance segmentation classifies various objects of the same type differently. Region based segmentation uses the edges in the image and the change in spatial characteristic in the image, and many more.

Computer vision is used to solve many problems and affects almost every aspect of our daily routines. Here are some examples:

  • Face recognition, for example, has a wide range of applications – building a virtual makeover system, supporting law enforcement (detection and recognition of criminals), increasing security, tagging people in images etc.
  • In the medical world, it helps to diagnose a patient faster and more accurately. Tumor detection is based on tissue images, blood flow monitoring, etc. Automatic navigation inside the body helps during medical procedures – and all these procedures rely more and more on computer vision.
  • In the transportation world, computer vision is the basis of autonomous vehicles, that depend on it. For a car to drive automatically, a clear understanding of the world around it is required and it is mostly based on images and their correct interpretation.
  • In the aerial field, when implemented in drones for example, computer vision helps to navigate, to lock on a target, avoid any obstacles and track changes over time.
  • E-commerce has also moved towards digitalization and automated technologies that rely on computer vision. For example, applications that recognize clothing items, clothes suggestions and demonstration of how clothes and accessories will look on you.
  • Augmented and mixed reality relies on computer vision capabilities. These applications require elements such as depth and dimensions to place virtual objects in the physical world, these are computed from the image sequences using the various computer vision techniques.

As the world entered a new decade, we can expect to see exciting new innovations and practices that will be based on computer vision.

Milli Peled, VP Marketing

Matlab, Python and How to Best Combine Between Them

vision elements matlab vs python

As data scientists and computer vision specialists, the most prominent tools we use are Matlab and Python. In the following blog post, I’d like to share with you some thoughts and best practices regarding the combination of these two important tools.

In recent years, Matlab lost a lot of its prestige and Python became much more popular. Nevertheless, I still find many advantages working with Matlab. Its IDE (Integrated Development Environment) is extremely convenient and allows me to debug and dig into my code very efficiently, more than any Python IDE allows. Since debugging and digging is the main action an algorithm developer is doing, this feature is very important to me.

I also found Matlab more convenient in visualization especially in 3D and the built-in functions are very stable with great documentation (after all, you do pay for something…).

From my experience, the main advantage in Python is the huge diversity of implementations of state-of-the-art algorithms. With millions of developers in this open-source code, I can be sure that if I need some implementation, someone has already done it. This is especially true in deep learning.

So, just like everything in life, this is not simply “black and white”. Both Matlab and Python have pros and cons. That’s why I was very happy to find out that Matlab can run very easily any Python command and package. How easily? All I had to do is write Py. followed by any Python command I chose. No need for imports or reinstallation of packages and so actually in some ways it is easier to run Python from Matlab than any other IDE!

vision elements

So, if you think that both Matlab and Python are great tools, here is a great option to use them both, simultaneously.

For any questions on Computer vision or Artificial Intelligence development projects, please don’t hesitate to contact us via info@vision-elements.com

Asaf Shimshovitz, PhD

What is AI? 15 Common Questions, Answered

Artificial intelligence, also called AI, is revolutionizing nearly every sector of society. As time goes on, more companies and governments are implementing it into their processes, and it is helping us come up with solutions to some of our biggest problems.

The reason we see this takeover by AI is that we live in a data-driven world. Everything we do today relies on data, and the more data there is, the more patterns there are for AI systems to detect. This processing power is exactly what makes AI such a revolutionizing technology, as humans could never match the processing power and speed of AI.

So, what is AI?

Here are the answers to 15 common questions regarding this technology:

1. What is artificial intelligence?

vision elements AI

You might hear the term thrown around by engineers and scientists and it can seem like a complex and confusing subject. However, anyone can gain a solid understanding of what artificial intelligence is.

AI is the simulation of human intelligence in machines. These machines are often programmed to think like humans and mimic our actions, but they are far more advanced than we could ever be. With their ability to process massive amounts of data, they can quickly detect patterns and help make highly accurate predictions.

You have probably seen sci-fi movies depicting these conscious machines. But, in reality, they are the basis for many of our most advanced technologies and daily activities, such as self-driving cars, speech recognition systems, robotics and automation, recommendation systems, and medical imaging.

2. How does artificial intelligence work?

vision elements ai

Now that you know what artificial intelligence is, let’s look at how it works.

The term AI, in general, refers to algorithms designed to perform tasks by machines. Machine learning takes place when an AI system can learn to perform a task, given a known model representing the relevant reality. Machine learning has been used in practice for several decades, and usually requires a fair amount of data for training. In the last decade, more profound schemes evolved for the purpose of teaching machines to perform desired tasks based on even larger amounts of data – also knowns as Deep Learning. With deep learning, a neural network reprograms itself as it processes more data, which allows it to perform its designed task more accurately. Deep learning is the most powerful of the subsets where a machine learning application teaches itself to perform a specific task with increasing accuracy, but it requires no human intervention.

Machine learning uses simple neural networks. A neural network is a sort of replication of the human brain, consisting of a large grid of simple units, called neurons, used to process data. The network can make predictions with various degrees of confidence.

As for deep learning models, those rely on deep neural networks (abbreviated DNN) with multiple hidden layers, designed to eliminate the need of a human programmer to specify the model of the reality being studied. This entire process results in a highly refined and accurate model, all without human intervention.

3. Who invented artificial intelligence?

The evolution of artificial intelligence has been taking place for decades. While it is not a product invented by any one individual, there have been many big players in the field throughout history. British scientist Alan Turing is widely considered the father of artificial intelligence; in a 1950 article, he proposed the model for machine learning by suggesting that it would be more effective to create a simple computer and teach it than it would be to create a complex computer. Turing likened this model to raising a child.

Unfortunately, computers of the 1950’s did not have the computational power required for such an undertaking, though in 1956, computer scientists Allen Newell, Cliff Shaw, and Herbert Simon developed the first AI program, named Logic Theorist. This proof of concept for artificial intelligence helped many people realize the potential for, and unavoidable move towards, AI technologies.

4. How long has AI existed?

We can say that the invention of AI took place with that first AI program, but it continued to rapidly develop between 1957 and 1974. The reason behind this was the advancement of computers, which became more powerful and cheaper every year. With AI’s success, government agencies like the Defense Advanced Research Projects Agency (DARPA) began to fund its development in institutions.

In the 1970s, funding and subsequently the technology’s development slowed as computing power failed to keep pace. Interest poured back into AI once again in the 1980s, influenced by the popularization of learning techniques and increasing acceptance of the computer not just as a tool for science and industry but as a component of daily life.

The 1990s and 2000s saw major AI accomplishments, such as when IBM’s Deep Blue chess-playing computer program beat the reigning world champion. Fast-forward to the present and AI is integrated into many aspects of our daily lives. As we continue to produce more and more data, the capabilities of artificial intelligence increase.

5. Can artificial intelligence be hacked?

Artificial intelligence is loaded with positive potential for humankind, but – like any modern technology – malicious actors can misuse it. The ability of AI systems to be hacked is a danger for organizations and institutions that integrate AI into their operations. Financial infrastructure, autonomous vehicles, and even weapons systems have the potential to be hacked as those with harmful intent look to harvest the data within – or worse.

Because of these dangers, there is a substantial effort to confront the different technological vulnerabilities of artificial intelligence. In 2019, DARPA launched the Guaranteeing AI Robustness Against Deception (GARD) program to identify vulnerabilities in AI deployments and build defensive mechanisms to protect these vulnerabilities.

Artificial intelligence is a challenging area because defense systems must be constantly updated. The technology is always evolving, meaning there are new vulnerabilities every day that organizations need to address.

6. AI terminology – What are the most common AI terms you should know?

Artificial intelligence can seem confusing for those who are just diving into it. There’s a lot of technical language and you can spend hours researching specific subfields and categories of AI.

Here is a look at some of the most common AI terms you should know:

  • Algorithm: a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation.[1][2] Algorithms are always unambiguous and are used as specifications for performing calculations, data processing, automated reasoning, and other tasks (Wikipedia)
  • Neural Network: These are designed similarly to the human brain, and they consist of a grid of neurons that allow AI to solve complex problems. While each neuron is a simple unit, receiving and transmitting signals to and from neighboring neurons, the power of the networks arises from the large amount of such units in the grid.
  • Machine Learning: The study of computer algorithms that improve automatically through experience and by the use of data. (Wikipedia)
  • Deep Learning: With minimal human intervention, deep learning enables AI to gain a basic understanding as the layers process data. By eliminating the need to specify an underlying model, deep learning moves from solving a problem to understanding the captured reality.
  • Supervised Learning: A method for training an AI model, supervised learning occurs when the machine is provided with the correct answer. It is the most common training method, and it identifies patterns in the data based on the hints encapsulated in the labelled data provided during training.
  • Unsupervised Learning: Unlike supervised learning, unsupervised learning means the machine can learn from data that is not necessarily labelled. That is, training occurs by looking at examples without a correct answer available. The AI is not provided the answer first, so it identifies patterns by being fed massive amounts of data.
  • Reinforcement Learning: Another paradigm for training an AI model, reinforcement learning means the AI is given a goal to which it aims by taking actions in steps.
  • Natural Language Processing: Also referred to as NLP, this takes place when an AI model is trained to interpret and analyze human communication. NLP is the basis for chatbots, translation services, and personal assistants.

7. Can artificial intelligence be dangerous?

If you have ever turned on a science fiction B-movie, you are probably familiar with the idea that robots are going to take over and wage war on the human race. That might be fun science fiction, but the science fact is that artificial intelligence is a tool we use, not a threat of robotic invasion.

As mentioned previously, AI systems can be hacked, which is especially dangerous if it involves governments or massive amounts of personal data. But another big issue, and perhaps the most significant danger posed by AI, is biased systems.

It only makes sense that AI can be biased. After all, think about how those systems are created: using data fed by humans. As more companies begin implementing complex AI systems, and as governments begin to use the technology, the potential for bias, and the systemic abuse that can come with bias, is powerful.

Some of these systems demonstrate bias because experts train them on data that reflects inequities, which in turn causes the AI to learn those inequalities and perpetuate them. Another issue is flawed data sampling, which means certain groups are either over- or underrepresented in the training data.

One area where AI poses perhaps the most danger is facial recognition technology. There have already been instances of high error rates for women and people in underprivileged or minority groups in facial recognition technologies, along with the use of bias systems in law enforcement. Facial recognition technology is also often used for tracking purposes, which can infringe on privacy rights.

8. How will artificial intelligence change the future?

Artificial intelligence is one of the greatest technological advancements in human history, and it is going to have a massive impact on the future. We already see incredible changes in sectors like healthcare, where new vaccines, cures, and therapeutic remedies are developed at a fast rate thanks to AI.

One of the most immediate impacts will involve the future of work automation, will replace many jobs in various sectors, especially those that involve manual, repetitive tasks. Besides taking these tasks over, AI will increasingly augment human decision-making in organizations.

AI changes substantial portion of the workforce. Many of the current positions become redundant and new opportunities arise. For example, data annotators, AI platform management, and more.
Governments started to implement initiatives to facilitate the evolution of the new workforce. At the same time, AI has the potential to create many new jobs. Individuals in the workforce will begin to undertake retraining and upskilling initiatives, which will provide them with the skills needed to become part of the future economy. Business leaders will implement these initiatives within their organizations to better prepare their workforces.

9. Will artificial intelligence surpass human intelligence?

The question of whether AI will surpass human intelligence, or if a super intelligent system is possible, was brought up back in 1951 by Alan Turing. According to Turing, we should be worried about how AI and its applications could one day surpass and “humble” the human species.

However, there are many different views on this subject by AI specialists. Some believe this is not possible, and even if so, we could turn the machines off at any time. Others believe that AI will undoubtedly surpass humans, and our goal should be to implement our own ethical and moral systems into the machines.

Those who believe this is a possibility say that a computer with general intelligence can analyze all existing books and documents at an incredible rate. It would then go on to make discoveries humans have never even considered.

This type of machine would not have the same human limitations as we have; no slow thinking, no emotions, no irrationality, and no need for sleep. The consensus among experts is that this is not an urgent or immediate risk but that it is a remote possibility that should be prepared for. Fortunately, many of those experts are doing just that, working to create limitations or incorporate ethics into the way AI-powered machines think.

10. How can we apply artificial intelligence?

Artificial intelligence is applied in many different ways depending on the industry. AI can supplement, or completely take over nearly every task.

One of the most common applications seen by individuals in their everyday lives involves AI-powered chatbots. These chatbots work to deliver answers to questions through conversations on mobile devices and voice-activated interfaces. They are also becoming commonplace in homes everywhere, putting us in a position to interact with AI each day.

While AI is present in our everyday lives, it is also applied in more dramatic ways that we don’t often see. For example, AI played a crucial role in analyzing the COVID-19 pandemic, assisting in everything from contact tracing and the progression of the virus across the world to vaccine development and distribution. Besides the COVID-19 pandemic, AI was already responsible for major medical breakthroughs. Smarter medicine, powered by AI, has the potential to drastically change the way we approach human health and wellness.

11. Will artificial intelligence replace humans?

Many people worry that AI will one day replace humans. While it is true that AI will replace humans in many tasks throughout many industries, we are far from a point at which it can replace us in every industry.

A lot of this has to do with the creative mind, or in other words, our ability to apply creativity in everything we do. Whether it be creative business solutions or the arts, AI cannot yet replicate us in that way.

People will benefit in the AI-driven world of the future by learning new skills that rely more on creativity and less repetition and other actions easily replicated by machines. By refocusing on creativity, there is a far greater chance that you will thrive in the future AI-driven world.

12. Which are the most powerful artificial intelligence companies?

All of these incredible AI advancements are the work of the world’s most powerful and effective AI companies. You might be surprised – or not – to find out that they are often the same big companies whose products we use daily.

Here are some of the top AI companies:

  • Amazon Web Services: AWS is the most powerful company when it comes to cloud computing. It offers consumer and business AI products and services, including products like the Amazon Echo and services involving text-to-speech and image recognition.
  • Google: Another one of the major companies is Google, which has its Google Cloud platform. Google has been a major AI player for many years, with the company investing heavily in the technology and acquiring many AI startups. Google Cloud sells various AI and machine learning services to businesses, and it has industry-leading software projects.
  • IBM: Since the 1950s, IBM has played a role in the development of AI. Its more recent contributions involve IBM Watson, which includes an AI-based cognitive service, AI software as a service, and systems that deliver cloud based-analytics and AI services.

13. What are some common benefits of artificial intelligence technology?

AI technologies have many unique benefits, but the most notable is the great degree of precision there is, due to the fact that AI bases its decisions on large amounts of optimization data and improved algorithms.
Another benefit is the development of AI-based robots that operate in dangerous environments. Technology can be used for search-and-rescue missions, to defuse a bomb, explore the oceans, or operate on Mars, all by minimizing or even eliminating the risk to human scientists, researchers, and explorers.

By assisting humans in repetitive tasks, AI gives us time to pursue activities that are more rewarding. This can have a significant impact on society. For example, AI can change the shopping experience and automatically renew the house inventory, without any human interaction.
It can also improve safety, for example, by shifting to autonomous cars that interact with each-other, or automated warehouse control system.
AI innovations can open up opportunities in every field, freeing up time once spent on mundane responsibilities to be reallocated to creative expression and human connection. This could even lead to a more vibrant artistic community as workers have more time to do the things they love.

Some of the other common benefits of AI include its ability to operate 24/7, to provide deep insights into nearly every sector, and to make decisions faster and more accurately.

14. How can AI be implemented in different industries?

As mentioned previously, AI will impact nearly every industry in one way or another.

Here is a look at how it can be implemented in different industries:

  • Healthcare: AI is applied in various healthcare services to identify patterns that help experts achieve more accurate diagnosis and treatment. Medical imaging, medication management, drug discovery, and robotic surgery are other potential uses.
  • E-Commerce: Retail and e-commerce companies use AI to identify consumer behavior patterns, helping them develop more effective strategies. It can also help improve customer experience.
  • Banking and Finance: AI applications are having a dramatic impact on the financial sector. It is used to process loan applications, for investment recommendations, fraud detection, and much more.
  • Entertainment: Game developers use AI to create highly realistic gaming experiences. It can also create more personal experiences, as games use AI to adjust for the player’s skill level or play style. In other sectors of the entertainment industry, such as film, AI is used to create digital effects
  • Manufacturing: One of the most impacted sectors is manufacturing, which is undergoing automation and the high deployment of robotics. AI is present in nearly every layer of operation, such as workforce planning, product design, product quality testing, and employee safety.


15. What are the Pros & Cons of using AI:

As we have seen, there are a lot of benefits to artificial intelligence, and you have probably noticed there are some downsides, too. What is critical to remember is that the way we implement artificial intelligence is what determines if AI is beneficial or harmful. The responsible implementation of AI has already unlocked so many doors and will continue to do so.

Let’s take a look at the pros and cons of this world-changing technology:


  • Reduces human errors
  • Doesn’t require rest
  • Can assist humans in daily tasks
  • Better and more rational decision making, influenced by data rather than emotion
  • Can automate repetitive tasks
  • Major medical advancements
  • Can be used in dangerous scenarios without risking human lives
  • Powers new innovations


  • Could be expensive
  • Cannot replicate some forms of human thinking
  • Needs to be constantly updated
  • Not creative
  • Requires large-scale workforce retraining

Artificial intelligence is the main technology that is driving the fourth industrial revolution, the very one we are living through today. It will continue to dramatically impact every sector of society, from business to healthcare to education to space exploration. AI carries massive risks, though, and the way it is implemented will largely depend on how governments and private organizations approach the technology and will, in turn, determine the way we interact with it and each other in the coming years. It will become one of the biggest issues of the future as we create more data, leading to concerns surrounding data ownership, security, and privacy.

While artificial intelligence seems like a highly complex topic, these 15 questions and answers provide you with everything needed to prepare for the future of AI.



The History of Artificial Intelligence