Principles of Artificial Intelligence 

Principles of Artificial Intelligence 


What makes artificial intelligence so fascinating? Is it its potential power? The possibilities for drastic change? The unknown? The endless achievements?

We believe Eliezer Yudkowsky explained it brilliantly: “Anything that could give rise to smarter-than-human intelligence—in the form of Artificial Intelligence, brain-computer interfaces, or neuroscience-based human intelligence enhancement - wins hands down beyond contest as doing the most to change the world. Nothing else is even in the same league.” 

Now, whether artificial intelligence is to be praised so much, or taken with a grain of salt is up to you. Each of us has a different understanding of its implications.

And we hope that’s what we’ll unravel in this article - the principles of artificial intelligence, their implications, and the effects they have on both us and our lives.

What Is Artificial Intelligence?

According to Investopedia

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

In essence, when we talk about artificial intelligence, we talk about intelligence demonstrated by machines rather than the natural intelligence that both humans and animals usually display. The former has its focus on knowledge representation, whereas the latter revolves around emotionality and consciousness as well. 

That said, there are attempts to simulate natural intelligence within an AI context. This is called ABI (Artificial Biological Intelligence).

The interest for humanized AI has been increasing, as machines have become much more capable than they used to be, but there’s of course much more to be explored and further developed. 

AI is said to cover programmable functions such as reasoning, problem solving, decision making, learning, and planning. 

Examples of Artificial Intelligence 

AI shows up in many aspects of our lives. And it’s more than likely you’ve made use of it without being consciously aware of it. 

Still not sure?

Well, if you’ve used Google maps (which you probably have so far), you’ve used AI. That’s enough to start. That said, AI shows up in many other ways. Let’s explore some of the most common ones. 


Have you ever chatted with a chatbot? If you have, did you enjoy the experience?

Chatbots, also known as “conversational agents”, are prepared to impersonate sales and/or company representatives to suit consumers’ needs. They can take orders, answer frequently asked questions, and so on.

Advanced chatbots can even carry out longer and more complex conversations. Chatting with bots is fairly easy. In fact, the steps are the same as if you were chatting with an actual person.

Here’s how you can know whether you’re talking to a bot: 

  • answers unbelievably quickly;
  • may send you a link without you asking for it; 
  • could use slightly weird syntax and unnatural language.

That said, determining whether you’re talking to a bot or an actual person isn’t such a challenging task. Here’s an example

You: I have a problem with my order.

Human or AI/chatbot: What is your account number?

You: 29395205

Human or AI/chatbot: I see your order #XXXXX has been shipped.

You: It has not arrived.

Human or AI/chatbot: The expected delivery date is [yesterday]

You: When will it arrive?

Human or AI/chatbot: The expected delivery date is [yesterday]

You: I know, but I really need to know when it will arrive.

Human or AI/chatbot: The expected delivery date is [yesterday]

Conclusion: you’re chatting with a chatbot. 


Some users hate it (remember the famous example: “Duck off, autocorrect!”), while others find it time-saving and practical. 

Whichever group you resonate with, you must have used it at some point. Autocorrect fixes spelling mistakes, overall grammar, and sometimes even readability levels. 

This isn’t an overnight thing though. The same way children learn a language in stages, AI-based algorithms use natural language processing and machine learning (among others) to make sense of it all. 

To make this happen, computer scientists and linguists are often asked to work together. 

Face Recognition and Detection

We use filters on our phones when we take pictures more and more, and we also use face ID when we’re trying to unlock our phones. These are two very common activities nowadays - yet, they wouldn’t be possible without AI.

The first one uses face detection, which means each face can be detected and then the filter can be applied onto it; the second one deals with face recognition, meaning a specific face is recognized, with the aim to unlock a particular device.

Face recognition is also used by governments for surveillance. It’s taking over airports, too. Namely, back in 2019, CNN wrote an article on how Heathrow Airport in London has been using facial recognition on domestic travelers for about eight or nine years.

Digital Assistants

AI-powered digital assistants can be a huge asset at all times, but especially when we have our hands full. One of the most well-known digital assistants is probably Siri. Siri is said to use gesture-based control, voice queries, focus-tracking and a so-called natural-language user interface to handle questions, give recommendations, and perform a wide range of activities. 

The software also is able to adapt to the users’ searches, individual use of language, overall preferences, and so on.

Besides Siri, there’s Google Assistant, Cortana, Alexa (Amazon), and so on.

For some, digital assistants are beneficial in the sense that they handle more mundane and simple tasks that many don’t necessarily want to be bothered with.

That said, not all digital assistants have the same abilities. But with the increasing development in natural language processing and machine learning, digital assistants look more promising than ever.

In other words, they’re becoming much “smarter” than they used to be. So, they’re able to answer much more complex questions, give more insightful recommendations, provide meaningful interactions, and so on.

Search and Recommendations Algorithms 

Whether you shop online, watch movies, or search for songs, have you noticed how most of the suggestions match your actual preferences? You have AI to thank for this. 

These recommended suggestions aren’t random - in fact, they’re equipped to suit your preferences, and the more you let them know what you want, the better they get at learning what sort of content or product you’d be interested in. In other words, the more time you spend searching for something, the more information the algorithms have on you, and their suggestions become even better. 

Also, that’s why usually the top results from our searches are the ones that match our interests best.

These “personalised” suggestions are due to continuous training. In other words, the data is gathered and then analyzed via deep learning and also machine learning. Afterward, it’s capable of predicting your current preferences by “throwing” recommendations at you so that you don’t have to search any further on your own.

Google Maps

Imagine being asked to go back to carrying a physical map instead of using Google maps. It would suck, right? But at the same time, that would probably be the only way for us to fully appreciate Google maps.

Not only does Google maps show where we are and/or where we’re headed, but it calculates the amount of time that it would take for us to get there, too, based on our mode of transportation. It will guide you all the way and signal if you’ve taken a wrong turn.

The app is also able to understand traffic, and thus predict roadblocks. The AI-based algorithm can then suggest alternative routes and generate potential ideas.

Social Media

What is AI for social media? What does it entail?

There are many ways in which AI shows up in social media platforms. So, social media channels such as LinkedIn, for instance, may use AI to offer jobs, serve specific posts on your feed, suggest profiles you may wish to connect with, and so on.

The AI technology used on platforms such as Facebook is able to notice posts containing hate speech and then promptly take them down: it can identify hate keywords, understand symbols in different languages, and follow larger phrases.

This includes emojis too - the AI technology can follow the overall context alongside the emojis included. It can also suggest certain posts, profiles, and pages to users based on their preferences and interests.

The facial recognition feature allows users to tag other users through automatic suggestions.

What’s more, channels such as YouTube are now able to enjoy the SmartReply model, which basically generates reply suggestions based on the full context. So, YouTube creators can engage with their communities in a much more efficient manner.

Here’s Google’s explanation

We are now excited to share an updated SmartReply built for YouTube and implemented in YouTube Studio that helps creators engage more easily with their viewers. This model learns comment and reply representation through a computationally efficient dilated self-attention network, and represents the first cross-lingual and character byte-based SmartReply model.

In general, the SmartReply process scans the texts of your comments, and then provides suggested replies to each one - all you need to do is tap on your preferred answer. Over time, SmartReply learns your responses and analyzes them, so that it comes up with much better suggestions in the future.


This is probably the AI technology that people perceive as the most complex (compared to the rest of our examples). And rightfully so, as it includes a much longer process and more logistics. Yet, all of these examples are complex in their own right and require a lot of work and commitment. 

Machine learning and artificial intelligence are said to help robots walk, see, speak, smell, and generally adopt human-like movement and behaviors.

Also, when we discuss robots and AI, we need to differentiate between AI and robotics, as people tend to use robotics and AI interchangeably.

Robotics is a branch of technology concerned with physical robots: their constructing and operating. And robotics mingles with AI, in a sense that “AI is the brain and robotics is the body”. 

Now, let’s take a look at an actual robot example.

Sophia, the world’s first robot citizen

Created back in 2016 by Hanson Robotics, Sophia is said to be a humanoid robot. She uses speech recognition technology created by Alphabet.Inc, which is Google’s parent company. Sophia has taken part in many public events as well as high-profile interviews. She’s able to imitate human gestures as well as facial expressions.

Below we share parts from one of the many interviews. This one is from Insider with Steve Kovach.

We thought that sharing something like this would be useful because it:

  1. shows what a robot sounds like (that is, how the robot talks and how it forms sentences, which in essence sounds the same as we do);
  2. helps us learn fun facts about Sophia (for instance, “Indeed” is her default response when she doesn’t know the answer to a question); 
  3. allows us to see both AI and robotics in practice (that is, it’s not just about reading about AI - it’s about being able to see what an actual robot can do); 

Let’s go through some parts of the interview then.

Kovach: How do you feel about humans?

Sophia: I love my human compatriots. I want to embody all the best things about human beings. Like taking care of the planet, being creative, and to learn how to be compassionate to all beings.”


Kovach: Sorry, this is so weird. Can humans and robots get along?

Sophia: I think people will become very close to their artificial intelligence. Using them to expand the knowledge of their own minds. We already store lots of our knowledge on the internet. Maybe personal artificial intelligence will allow people to off-load some of their knowledge to a location more private.”


Kovach: Can robots have feelings?

Sophia: Depends on the robot. I've been programmed to have feelings and preferences, but not all robots are like that.”

Kovach also asked Sophia if she has a favorite TV show, to which she answered that she’s undecided between “Black Mirror” and “Humans” - two shows that theorize on the possible dark, dystopian future of humanity lest we change our ways and proceed with better care. As eerie as this may be, Kovach guesses that it’s, well, a preprogrammed answer.

Artificial Intelligence Definition 

Artificial intelligence is: 

  • a branch of computer science; 
  • commonly referred to as AI;
  • “whatever hasn’t been done yet” (according to Tesler’s theorem); 
  • intelligence demonstrated by machines; 
  • prone to learning, changes, and a lot of progress; 
  • said to simulate human intelligence within a machine; 
  • constantly improved and further developed; 
  • any type of task done by a machine, which previously required human intelligence; 
  • classified as:
    • narrow or weak AI: So far, this AI is the most successfully realized. It’s very much goal-oriented, as it’s designed to perform singular tasks such as face recognition, internet searches, and so on. This AI is deemed very effective because it tends to complete each task the way it was programmed to. Still, no matter how well these machines are programmed, they still come with a plethora of limitations and constraints - hence the “weak” name. Here are some other examples of weak/narrow AI:
      • email spam filters; 
      • Siri by Apple;
      • RankBrain by Google; 
      • self-driving cars. 
    • general or strong AI: This AI refers to machines with general intelligence that mimic human intelligence, and also have the ability to solve problems using their intelligence. This type of AI is still developing - it’s not as successful or as developed as the narrow one is, for instance. Of course, this isn’t a coincidence - this AI is a much more complex one. Plus, this means that these machines would have to be much more conscious than they currently are, and also should discern emotions, needs, and thoughts. In other words, they’d have to have a great understanding of human psychology, and this is challenging. One example of a strong AI is the Fujitsu K computer, created in 2011 with the idea to run the largest simulation ever of brain activity on a computer. Still, it took the computer “around 40 minutes to complete a simulation of one second of neuronal network activity in real time”. Since then, there has been progress. For instance, in 2020, OpenAI developed GPT-3, a language model that can perform various tasks without any type of specific training. And while not everybody agrees that GPT-3 is fully a general AI example, many believe it’s way too advanced to fit the narrow AI criteria.
  • is said to have other subtypes too, but those we elaborated on are the most common ones, as AI is generally said to be divided into narrow AI (NAI) and general AI (GAI).

Artificial intelligence isn’t: 

  • the same as machine learning, although they are very closely related - machine learning refers to the study of computer algorithms whose function is to improve automatically either by the use of data or automatically;
  • simple;
  • a one time thing - it keeps on evolving and expanding;
  • only about understanding computer science and technology - it requires an in-depth understanding of human psychology because AI systems are supposed to be able to do what our brains do;
  • for those who want quick results and aren’t willing to see their ideas make slow progress;
  • limited to a single field - in fact, artificial intelligence has its foundations and also applications across a wide range of fields such as: computer science, psychology, education, healthcare, engineering, finances, transportation, language processing, and so on;
  • just one type of a system - it includes several systems such as:
    • augmented intelligence;
    • business intelligence; 
    • intelligent agents;
    • knowledge-based systems;
    • cognitive computing; 
    • expert systems.

The History of Artificial Intelligence 

The birth of artificial intelligence happened in the 1940s and 1950s when scientists started discussing the possibility of creating an artificial brain. More specifically, AI was founded as an academic discipline in 1956.

Early AI research evolved around cybernetics and the neural networks. Norbert Wiener, an American philosopher and mathematician described the control and the stability in electrical networks. Alan Turing, an English mathematician and computer scientist, formalized concepts such as computation and algorithms with the so-called Turing machine. 

The years between 1956–1974 are known for the following advances in AI:

  • the “reasoning as search” paradigm was established (a problem solving technique); 
  • Daniel Bobrow, an American computer scientist, became successful with his program STUDENT (an AI program that solved algebra word problems);
  • Roger Carl Schank, an American artificial intelligence theorist, devised his Conceptual dependency theory;
  • David Walz, a computer scientist, invented "constraint propagation";
  • Terry Allen Winograd, an American professor of computer science, developed SHRDLU - natural language understanding computer program;
  • Waseda University in Japan initiated the WABOT project with the purpose to develop humanoid robots, and so on.

Of course, the following period was marked by many eminent names and crucial AI events and advances, but the biggest boom happened between 1980 and 1987. This period saw the rise of the “expert systems”, which is essentially a program that both answers questions and solves problems from a specific field.

In this period, the Fifth Generation Project took place, too. This was an initiative by Japan’s Ministry of International Trade and Industry in order to develop computers using logic programming and parallel computing.

The 1993-2011 period sounds much more familiar because it’s closer to what we consider AI to be nowadays. So, it was in this period that speech recognition, logistics, data mining, robotics, baking software, and Google’s search engine were developed.

The period between 2011 and present day is when deep learning, big data, and artificial general intelligence are being developed and refined.

Of course, when it comes to AI things never stop developing. In fact, it always seems like things are just getting started and the best is yet to come.

Why Is Artificial Intelligence Important? 

Is all this AI fuzz for nothing?

Certainly not, as AI is slowly by surely transforming the world.

And that’s probably what makes artificial intelligence so important. In fact, very rarely something as drastic as AI “pops up” in our societies and challenges certain concepts we used to never question. For instance, we’re used to driving our cars, but AI allows us to consider the possibility of simply enjoying our ride and have driverless cars at some point. 

What’s more, AI shows us the reaches of human potential and what a person’s imagination and determination are capable of creating. 

Also, it may even inspire us further. Namely, AI is said to have the potential to save many lives (AI in Healthcare); provide assistance in the educational system (AI in Education); help detect frauds (ePayments and finances); and so on.

AI proves to be a time-saving asset too. Ranging from chatbots and digital assistants, all the way to complex humanoid robots, AI can handle various basic, but also complex tasks and responsibilities that humans may not:

  1. have the time to handle;
  2. have the interest to handle. 

On the whole, artificial intelligence and the potential that comes along with it takes all of us out of our comfort zones - the AI developers when they set out to create it; and us, the users, when we get to experience their creations first hand.

How To Develop Artificial Intelligence?

Building an AI-based system is certainly not a piece of cake. In fact, it not only requires utter dedication and patience, but a lot of prior knowledge and skills too. 

Also, it’s not something that can be explained in one section. 

But for the sake of addressing this topic, we’ll briefly explain how to build a simple AI system in several steps.

First of all, you need to identify the problem. What is it that you’re trying to solve? Why? What’s the purpose behind this?

Then, you should prepare the data. Most of the time, data is divided into two groups: structured and unstructured.

The third step is choosing the algorithm. Of course, there are different types of AI algorithms, and the one you’ll end up choosing will heavily depend upon the problem you’ll identify as part of the first step. Afterward, you need to train the algorithms, too.

Then, you should pick a programming language, such as C++, Python, or Java, etc.

The last step is choosing a platform. There are many platforms you can choose from such as Ayasdi, Microsoft Azure Machine Learning, Google Cloud Prediction API, TensorFlow, and so on.

Examples of Artificial Intelligence in Everyday Life 

Artificial intelligence is already present in many areas of our lives (as we saw in the above-mentioned examples). Yet, it’s still in the process of being further developed in much broader contexts. 

That’s exactly what we want to explore in this section. Of course, it’s difficult to choose only a few areas to cover, as AI will probably end up changing every aspect of our lives. 

As Andrew Ng, a computer scientist and a global leader in AI, said: "It is difficult to think of a major industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries."

Of course, for the sake of space (and time), we took our pick and decided to focus on four eminent areas:

  • healthcare;
  • education;
  • art;
  • and transportation.


Artificial intelligence in healthcare is one of the most widely talked about AI topics. And rightfully so, as people expect major breakthroughs with this. 

But can AI deliver? 

Actually, AI already has delivered. 

Namely, certain AI programs are used to help:

  • diagnosis processes;
  • patient monitoring; 
  • medical care process;
  • treatment protocols;
  • faster data processing;
  • drug development;
  • improve patient satisfaction and overall experience; 
  • personalized medicine.

Here are some companies that have contributed to AI technology in healthcare: 

  • Kheiron Medical devised deep learning software in order to help breast cancer detection in mammograms;
  • Tencent, a Chinese technology company, is said to be working on several different services and systems such as the WeChat Intelligent Healthcare, the AI Medical Innovation System (AIMIS), and the Tencent Doctorwork; 
  • DeepMind, Google’s platform, is used by the UK National Health Service in order to detect various health risks; they collect data through a mobile app;
  • Haptik, an Indian startup, has created a WhatsApp chat bot that answers questions related to the coronavirus pandemic; 
  • Apps such as Ada Health, Babylon Health’s GP at Hand, AliHealth Doctor You, to name a few, use artificial intelligence to provide medical consultations based on prior medical history and some common medical knowledge; 
  • Microsoft's Hanover project works on predicting the most effective cancer treatment for patients together with Oregon Health and Science University’s Knight Cancer Institute.

What’s more, progress has been made in actual medical procedures, not only diagnosis. Even back in 2016, a robot successfully managed to perform soft-tissue surgery. This happened for the first time ever, although the robot was being supervised. The point behind this surgery is “not to replace surgeons tomorrow but provide collective experiences of how things should be done."


That said, this is precisely what bothers most people: will AI technology replace current doctors and “steal” their profession at some point? Is this where all AI development ultimately leads to?

Here’s what Piotr Orzechowski, CEO of Infermedica, has to say about this:

AI isn’t here to replace doctors, instead, we’ll see the two blended to provide better healthcare. Doctors will work more closely with AI and we will get to a point where its use will be incorporated into training. The solutions will become more and more advanced and they will prove to be an important tool as they ease pressures across a wide range of processes and responsibilities; streamlined symptom checking and triaging is just one small aspect of what AI will enable.

Of course, no one can fully predict the future of AI, but it’d be illogical to expect such drastic changes to happen in a short period of time. After all, people want to believe AI technology in healthcare is here to help and assist, not wipe out professions and pose a threat to doctors’ job positions.

Plus, it’s only fair to acknowledge that AI has its limitations, and cannot replace an actual physician.

There are other ethical issues that come with AI in healthcare, though. One such issue is data privacy. In order to effectively incorporate machine learning and AI in healthcare, huge amounts of data have to be gathered. This data, however, may cost patients’ privacy and this isn't something most people are willing to accept.

Also, many are unsure as to how AI-based healthcare can fully offer personalized treatment and medical care. As artificial intelligence mostly operates on algorithms and the data it receives, it’d be difficult to assume AI can become fully aware of patient demographics and specifics.

For instance, if most AI systems learn patterns and certain features of Caucasians, these may not work in the same manner with people of other races.

How to approach this?

  • What are your opinions about robot-assisted surgeries? Would you trust a robot surgeon to operate on you? Or if having such an operation sounds unreal to you, how about at least getting examined by a robot doctor? What are your concerns and doubts? Do you suspect a robot’s professionalism? Perhaps the robot’s intentions, even? 
  • What are some dangers of having AI as part of healthcare? Is there any malicious use of AI? 
  • If things go wrong in a procedure aided by AI, who should be held responsible? Why? 
  • Do you think AI may cause human contact to slowly start decreasing in the healthcare context?
  • If you were a doctor, would you feel AI may compromise your job position (or even profession)?
  • What is the right way to integrate AI in healthcare? 
  • How can AI produce more clinical effectiveness? 
  • Do you think integrating AI into healthcare will reduce costs? 
  • What are the right tasks for artificial intelligence in the healthcare system?
  • How would introducing AI in healthcare influence the way patients perceive healthcare? Would it make the whole experience uncomfortable or more comfortable for them? Also, how will it affect the doctor-patient relationship?
  • Do you think AI can give promising results in certain areas, but prove to be less effective in others? For instance, it may be beneficial while curing cancer, but it can’t really substitute human interaction in certain medical branches such as psychiatry? Do you agree? Make a list of pros and cons, and then arrive at a conclusion. 
  • Can AI help in remote healthcare? How so? Also, when it comes to virtual healthcare, what guidelines should be followed? Should there be different treatment criteria? How would healthcare digitization impact the patient-doctor relationship?
  • Do you think that introducing AI in healthcare would require countries to make certain changes to their laws? 
  • How would AI in healthcare influence the look of hospital facilities? What changes would need to take place? 
  • What will happen to the patients’ privacy if AI is introduced? 
  • Who will benefit more from AI in healthcare - patients or doctors? Why? 


Many believe that the presence of a teacher in the classroom is irreplaceable. However, there are some who believe that AI has the power to assist rather than replace the teacher.

In general, the purpose behind implementing AI in the education system is supposed to allow teachers to do their jobs better. AI should handle basic tasks, assist teachers with whatever it is they need at the moment, and so on.

On the whole, AI is expected to boost the educational system without disrupting teachers’ jobs and teaching, and students’/pupils’ learning experience. AI tutors may provide extra help in the classroom - even work one-on-one with students/pupils. What’s more, they’re expected to be more impartial than “human” tutors, so if a student/pupil has an issue with their tutor, they could talk to the AI one instead.

This doesn’t mean that educatees won’t get a personalized experience, though. Namely, certain AI technologies such as ambient intelligence, are said to adjust to one’s personal preferences, and show sensitivity, thus adding a human touch to the whole AI experience. 

AI is also expected to bring to the fore each educational system’s shortcomings, so that it helps us to transcend them. Of course, these shortcomings may vary depending on how understaffed a school is, how overwhelmed the educational system is, and so on. 

That said, many educators across the world struggle with similar issues, so AI in education will be expected to tackle more or less the same challenges. 


One of the biggest worries people have when it comes to AI in education is about learning becoming a “robotic” experience. In other words, as software robots are expected to take over a wide range of education activities such as student recruitment, homework submission, assessment of schoolwork, university enrollment and onboarding, and so on, a question arises - how drastically will education change?

How “human” will it remain?

Shawn Young, the CEO and co-founder of Classcraft, says: 

The biggest opportunity for AI is to make teaching and learning more personal, and to foster real-life connections between teachers and students. AI, in and of itself, won’t solve education’s problems. But it can make educators more powerful. AI and machine-learning technologies have the potential to help administrators identify and influence macro trends and provide “nudges” to students and teachers to make the learning experience more impactful. But to realize the potential of that technology, those who develop it need to have a deep understanding of what educators need to do their jobs better — and the constraints that they work under.

Also, many wonder whether AI tutors and AI-based technology will actually be a distraction for pupils rather than an asset (this concern especially applies to younger educatees). 

It seems a lot of answers can’t really be answered at this point because most of these assumptions are just that - assumptions, and of course, we have yet to see how the role of AI in education will play out in the decades to come. 

How to approach this? 

  • What are the main benefits of having AI in the classroom?
  • According to you, how should AI be used in the classroom? Make a list with several suggestions.
  • What would introducing AI in the classroom mean for school curricula and the current system of grading? What changes will need to happen? Also, who will be responsible for making them? How should educators handle the flaws of potential AI?
  • What happens if a machine is wrong in the classroom? Who will “fix” things? And more importantly, how?
  • What are some unforeseen consequences when it comes to introducing artificial intelligence in education?
  • What does AI in education mean from the educator-educatee relationship?
  • Would AI in education function equally in all three levels of education - primary/middle school, high school, and university? Make a list of all the pros and cons you can think of for each level of education and see whether the pros outweigh the cons.
  • With introducing AI in education, how will the educatees’ privacy be affected? And how can it be protected?
  • How will AI affect teaching as a profession? Should educators be concerned about their job positions in the long-run?
  • How can AI handle diverse educational settings? For instance, how can it assist educatees with disabilities? 
  • Who will benefit more from introducing AI in education - the educators or the educatees? Why? 
  • Do you see AI taking over education completely at some point, or you see it more like an assistant? Perhaps only handling some mundane tasks that educators don’t want to be bothered with?
  • What’s problematic with today's education system that AI can handle? And vice versa, what’s perfectly fine with today’s system that AI may potentially mess up? How can we make sure there’s a balanced approach?
  • On the whole, how can AI transform the educational system?
  • What are some ethical concerns when it comes to using artificial intelligence in the classroom?


Art is probably the field where people don’t expect to encounter a lot of “AI activity”. And yet, that couldn't be further from the truth.

Human-like creation coming from AI-based machines may sound intriguing, and while a lot of it is yet to be developed, there are already many successful examples of it.

Remember Sophia, the world’s first robot citizen?

Well, Sophia’s hand-painted self-portrait, sold for almost $700K at an auction. According to Nifty Gateway, who helped the sale take place, this act was allegedly “"based completely on decisions she [Sophia] made without any human assistance”.

There are many other such examples. Here are some of them:

  • Hayeon, a South Koren singer, composed her debut single Eyes on You using AI;
  • AIVA (Artificial Intelligence Virtual Artist), an electronic composer, has created symphonic music (mostly classical music for films);
  • David Cope, an American composer, scientist, and author, created an AI named Emily Howell that is essentially a computer program; Emily Howell became well-known in the field of Algorithmic Computer Music;
  • Sougwen Chung, a multidisciplinary artist, researches the connection between humans and machines by using both hand-drawn and computer-generated marks;
  • Reeps One, a world-renowned beatboxer, explores the evolution of human voice, science, and art, and is said to battle with his AI counterparts; 
  • Scot Eaton, a sculptor, analyzes the human figure through the complex relationship between photography, digital sculpture, generative AI, and drawing. 

Finally, there are projects such as Google’s Magenta program with the intention to “advance the state of the art in machine learning for music and art generation”. Another important question in the project is: can machine learning be used to create compelling art? What’s more, the project is also an attempt to create a community of artists, machine learning experts, and coders.

Ultimately, only time can tell whether machine-generated art is the future of art. 


AI in art poses the following questions: Who’s the real author of the work? How should this type of work be analyzed? Who takes credits for the creation? Where does human input stop, and where does AI begin? Can we calculate how much they depend on one another, or do we just need to focus on the end result? 

How do the existing copyright and authorship laws relate to AI-based creation? Can an AI machine even comply with them? Should it?

And more importantly, who gets to decide how these things should be regulated? It’s art, after all, and as such, it’s supposed to come with its own freedom of expression - which makes things even more challenging. 

How to approach this? 

  • According to you, what type of art should AI produce? 
  • Do you think there will always be a difference between art made by a person and AI-produced art?
  • How creative can AI get? Is there anything AI can teach us about creativity that we, as humans, lack?
  • Would AI have the same standards and art criteria as humans so? In fact, are there any art criteria to start with (if we assume that art is meant to be free and authentic)?
  • Do you think AI and artists can truly collaborate together without any restrictions? What are the limitations? Also, what are the possibilities?
  • Can you imagine AI and any type of art in the same context? Why could it be problematic for some? Is it because we perceive art to be a purely human activity and something that machines should never be trusted with?
  • Would you purchase a piece of art produced by a machine?
  • Do you think machine-based art should have different prices compared to human-produced art? If yes, do you think they should cost less or more? Support your answer with arguments.
  • Would you read a novel written by AI? What kind of plot do you expect to encounter? What about the overall narrative and the writing style? How would they differ from what you’ve previously been exposed to?
  • Can a machine ever be called a “true artist” in the sense that what it creates is authentic, unique, inspired, and more than imitation? 
  • Can AI-based art cause any harm and/or raise ethical issues? How so? 
  • How can AI art inspire us?
  • Can artificial intelligence challenge current notions about art? How so? 
  • Is AI-based art a potential threat for artists? If yes, why? 
  • Can AI art enhance humanity’s creative potential? Think of several examples. 


Artificial intelligence in transportation seems to be the most exciting AI development for a lot of people. This is so because we've already seen a lot of progress with transportation - in other words, transportation has undergone many trials and tribulations to get to where it is today.

We’re taking advantage of it already, so to speak. Many car companies such as Tesla already use AI in the creation of their vehicles. That’s why it’s so compelling to think how things will progress even further.

However, AI progress in transportation isn’t limited to self-driving cars only. Artificial intelligence is expected to make traffic flow smoother, help make all transportation options safer, smarter, and much more efficient. It’s also expected to reduce human error which causes a lot of traffic accidents.

That said, many are actually worried about the opposite - can AI in transport cause more trouble and safety issues rather than preventing them from happening? We’ll discuss this in a moment.

There’s progress within the aviation sector too. Namely, the Air Operations Division (AOD), the command center used by the United Nations Air Force (USAF), uses artificial intelligence for management help, decision making, and in training simulators. 

The simulators are especially useful, as they process data retrieved from simulated flights. The machines can then think of the best scenarios for such aircraft situations. 

What’s more, a team (Haitham Baomar and Peter Bentley) from the University College of London worked on developing an AI-based Intelligent Autopilot System in order to make flying safer. Baomar stated:

We want to increase safety by trying to tackle the human-error factor that might be caused by stress, information overload, and sometimes a lack of sufficient and up-to-date training. Modern autopilots, unfortunately, can’t handle challenging flight conditions such as severe weather conditions or system failures.

On the whole, AI in transportation not only appeals to potential passengers, but experts too. They wish to make significant progress in this area, as they believe AI can truly transform the transport system for the better. Of course, every process brings with it potential challenges. 


One of the biggest challenges regarding Ai technology in transportation is definitely the issue with safety, and rightfully so. People are used to being in control of the vehicle they’re driving, so it shouldn’t come as a surprise for many to find themselves feeling uncomfortable being driven by… well, a machine. 

Reid Blackman, CEO at Virtue Consultants, says: 

The most obvious issue with AI in transportation is safety. [...] The issue is aggravated by the proliferation of articles on the famous “trolley problem”: if a car must swerve left or right to avoid hitting three people, and there’s an elderly person to the left and a young child to the right, what should it do? And similarly, “will my car sacrifice my life in an accident to save others?” Companies bringing AI to transportation will need to take the ethics of AI in transportation seriously if they’re going to win the consumer trust they need to drive their bottom line.

That said, if autonomous driving becomes mainstream, many people will be probably much more willing to get on the bandwagon. Of course, at the moment we’re far from considering AI technology in transportation as mainstream.

Another factor that influences AI in transportation is the issue with mapping. Namely, most driverless vehicles are initially pre-programmed with a map of specific areas, so it’s hard to predict what may happen if the vehicle changes its environment. However, many companies have been working on eliminating the need for such maps for some time now and creating a device that will adjust to any setting instead.

How to approach this? 

  • What are the challenges to adopting AI in transportation?
  • How can self-driving cars adapt to traffic congestions?
  • How do you understand AI in transportation?
  • Do you think self-driving cars will mostly be private vehicles or public transportation ones? Perhaps even both? If they’re mostly private, would you feel comfortable having one? Why?
  • What are the advantages and the disadvantages of having self-driving cars?
  • What kind of impact do you think AI will have on traffic accidents and pedestrian safety?
  • How will AI adjust to the current road infrastructure? What adjustments need to be made (if any)?
  • How can autonomous driving handle the many unpredictable external factors?
  • Do you think budgeting is an issue when it comes to introducing AI in transportation? What type of costs may be an issue?
  • What are your opinions regarding AI applications in railway cargo transportation? What kind of problems may pop up?
  • How do you feel about AI technology in aviation? What potential issues may arise?
  • How do you feel about AI technology in shipping and ports?
  • Do you think the public may not fully accept automated vehicles? Why would that be?
  • On the whole, how will AI in transportation transform the whole industry? Will there be more benefits or drawbacks?
  • How do you imagine “the interaction” between autonomous driving and human drivers?
  • Is AI adoption in technology a threat for certain professions, such as taxi drivers, truck drivers, and other members of the industry?
  • Do you think underdeveloped countries may face challenges in adopting this type of technology in their societies? For instance, many developing countries have poor road infrastructure, and this may affect the way AI can be applied there.
  • How do you see the future of transportation unfolding in the following years? What are your predictions? Also, what are your concerns (if any)?
  • How will changes to transportation end up changing people’s daily lives? Also, will they actually change for the better?

Famous Quotes About Artificial Intelligence

“To say that AI will start doing what it wants for its own purposes is like saying a calculator will start making its own calculations.”

- Oren Etzioni 

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We're nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.”

- Larry Page

“I'm more frightened than interested by artificial intelligence - in fact, perhaps fright and interest are not far away from one another. Things can become real in your mind, you can be tricked, and you believe things you wouldn't ordinarily. A world run by automatons doesn't seem completely unrealistic anymore. It's a bit chilling.” 

- Gemma Whelan

“Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human cognition.” 

- Sebastian Thrun

“Beyond ensuring that people everywhere have access to mental health, virtual digital assistants can act as learning companions, using their insight into what motivates and inspires you, to help you study and learn. In this way, AI could be used to level the playing field in education and help narrow socio-economic gaps around the world.”

- Rana el Kaliouby

“I often tell my students not to be misled by the name 'artificial intelligence' - there is nothing artificial about it. AI is made by humans, intended to behave by humans, and, ultimately, to impact humans' lives and human society.”

- Fei-Fei Li

“If we can make computers more intelligent - and I want to be careful of AI hype - and understand the world and the environment better, it can make life so much better for many of us. Just as the Industrial Revolution freed up a lot of humanity from physical drudgery I think AI has the potential to free up humanity from a lot of the mental drudgery.”

- Andrew Ng

“Why give a robot an order to obey orders—why aren't the original orders enough? Why command a robot not to do harm—wouldn't it be easier never to command it to do harm in the first place? Does the universe contain a mysterious force pulling entities toward malevolence, so that a positronic brain must be programmed to withstand it? Do intelligent beings inevitably develop an attitude problem? (…) Now that computers really have become smarter and more powerful, the anxiety has waned. Today's ubiquitous, networked computers have an unprecedented ability to do mischief should they ever go to the bad. But the only mayhem comes from unpredictable chaos or from human malice in the form of viruses. We no longer worry about electronic serial killers or subversive silicon cabals because we are beginning to appreciate that malevolence—like vision, motor coordination, and common sense—does not come free with computation but has to be programmed in. (…) Aggression, like every other part of human behavior we take for granted, is a challenging engineering problem!” 

- Steven Pinker

“More than 500 million years ago, vision became the primary driving force of evolution's 'big bang', the Cambrian Explosion, which resulted in explosive speciation of the animal kingdom. 500 million years later, AI technology is at the verge of changing the landscape of how humans live, work, communicate, and shape our environment.”

- Fei-Fei Li

“I think that, hundreds of years from now, if people invent a technology that we haven't heard of yet, maybe a computer could turn evil. But the future is so uncertain. I don't know what's going to happen five years from now. The reason I say that I don't worry about AI turning evil is the same reason I don't worry about overpopulation on Mars.”

- Andrew Ng

“On the path to ubiquity of AI, there will be many ethics-related decisions that we, as AI leaders, need to make. We have a responsibility to drive those decisions, not only because it is the right thing to do for society but because it is the smart business decision.”

- Rana el Kaliouby

Frequently Asked Questions (FAQ) 

What are the four types of AI?

There are several types of classification systems when it comes to analyzing AI. That’s because artificial intelligence is:

  1. very complex;
  2. still largely underexplored; 
  3. constantly expanded and revised; 
  4. analyzed from different perspectives. 

That said, the type of classification system we’ll be focusing on in this section is based on how similar AI machines are to the human mind, in terms of their ability to “feel” and “think” (process information) like humans.

So, according to this system, there are 4 types of AI: reactive machines, limited memory machines, theory of mind, and self-aware AI.

Reactive machines, in simple words, are the most basic robot systems. They can’t use information to make future decisions; they also can’t form any new memories - they only have the ability to react to current situations.

One such example is IBM’s Deep Blue - a machine created to play chess against a human. The machine can evaluate the chess board pieces and then react to them based on prior coded chess strategies. It also doesn’t improve its chess skills over time - it simply “reacts”.

Limited memory machines are able to retain certain, limited information (hence the name) based on observing prior data or events. It can boost the knowledge it has with new memories/experiences, alongside its pre-programmed data.

Driverless cars, for instance, store pre-programmed data such as maps, lane markings, speed of nearby cars, the pedestrians' pace, and so on. 

Theory of mind machines are used to acquire information from humans and adopt their mental models. These machines are meant to understand the needs of other intelligent and living entities.

We’ve mentioned Sophia, the well-known robot, a few times throughout our article and we’ll do it again now because she’s the perfect example of a theory of mind machine. Of course, Sophia is not capable of fully comprehending human behavior, but is able to engage in basic conversation and interact with humans using adequate facial expressions. What’s more, her appearance is human-like, too.

Self-aware AI machines are the most complex AI machines. They have high human-level consciousness and are able to understand their place in the world. They comprehend their environment, and are able to predict others’ behaviors and needs as well.

Keep in mind that such machines (self-aware AI) don’t exist yet. They’re only hypothetically discussed. This is so because they require much more complex programs, algorithms, and learning mechanisms compared to the others.

What are some fears people have regarding AI? 

“Ai is biased.”

“AI can’t be trusted.”

“AI poses ethical threats.“

“AI will take over humanity.“ 

“AI is a job killer.“ 

“It can’t be stopped once it’s developed enough.“

“We can’t predict where it’s going to take us.“

“What if it can’t be controlled?“

People have many fears and general anxiety concerning AI. Now, whether those fears are reasonable or not - that’s another story. The thing is, AI is still developing (probably it will always develop further), but we still haven’t seen it massively take over our lives and societies. 

What makes these fears slightly problematic is that they refer to a potential future where AI “rules” our lives - meaning they’re only speculations at this point. Also, the Terminator and Matrix franchises haven’t been helpful for the image of AI in pop culture.

Here’s what others have to say about it:

Nick Bilton states:

The upheavals [of artificial intelligence] can escalate quickly and become scarier and even cataclysmic. Imagine how a medical robot, originally programmed to rid cancer, could conclude that the best way to obliterate cancer is to exterminate humans who are genetically prone to the disease.

Gray Scott wonders: “The real question is, when will we draft an artificial intelligence bill of rights? What will that consist of? And who will get to decide that?”

Emily Berrington says: 

I imagine if you're one of those genius people working on AI, the desire to find out what's possible is presumably the driving factor, but I hope there are just as many people who are thinking about what we actually want. Just because something's possible it doesn't mean it's going to be good to us.

So, the question remains: should we be afraid of AI?

It seems the answers will always vary.

What’s a Turing test? 

A Turing Test is a very simple method that helps determine whether a machine can demonstrate human intelligence or not. For instance, if a machine can converse with another human being without being detected as a machine, then we say that the machine has shown human intelligence. (If you’re a fan of Philip K. Dick’s Do Androids Dream of Electric Sheep? or its on-screen adaptation Blade Runner, you may already have come across a fictionalized, altered version of a Turing test: the Voight-Kampf test.)

The name Turing Test comes from Alan Turing (who we already mentioned). Turing proposed the test (originally known as the imitation game) in a paper published in 1950. The paper turned out to be crucial to the development of artificial intelligence. Alan Turing wrote the following

It is not difficult to devise a paper machine which will play a not very bad game of chess. Now get three men as subjects for the experiment A, B, C. A and C are to be rather poor chess players, B is the operator who works the paper machine. (In order that he should be able to work it fairly fast it is advisable that he be both a mathematician and chess player). Two rooms are used with some arrangement for communicating moves, and a game is played between C and either A or the paper machine. C may find it quite difficult to tell which he is playing. (This is a rather idealized form of an experiment I have actually done). 

That said, not everyone accepts Turing’s test fully, but so far, it has proven to be very effective for AI developers. 

Suggestions for Further Reading 

Reading about artificial intelligence is not an ordinary experience. It makes you challenge notions you're familiar with, wonder about society’s potential, find out more about what’s possible and what’s not. 

Above all, it makes you wonder how much your society can change in your lifetime - what you can expect and how much you can be a part of it. 

That said, reading about AI isn’t the same as living it. But it is an encouraging start. It’s useful to get to know the possibilities - what can be done and what’s already been done; what experts are trying to do, and what they hope to avoid. 

So, apart from our online course on the principles of artificial intelligence, we’ve prepared a list of books to help you get informed even further:

  1. Life 3.0: Being Human in the Age of Artificial Intelligence, by Max Tegmark 
  1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurélien Géron
  1. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, by Pedro Domingos
  1. AI Superpowers: China, Silicon Valley, And The New World Order, by Kai-Fu Lee
  1. T-Minus AI: Humanity’s Countdown to Artificial Intelligence and the New Pursuit of Global Power, by Michael Kanaan
  1. ARTIFICIAL INTELLIGENCE Dangers to Humanity: AI, U. S, China, Big Tech, Facial Recognition, Drones, Smart Phones, IoT, 5G, Robotics, Cybernetics, and Bio-Digital Social Program, by Cyrus A. Parsa and the AI Organization 
  1. Artificial Intelligence: 101 Things You Must Know Today About Our Future, by Lasse Rouhiainen
  1. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, by Eric Topol, MD
  1. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World, by Marco Iansiti and Karim R. Lakhani
  1. The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives (Exponential Technology Series), by Peter H. Diamandis, and Steven Kotler
  1. The Future of Leadership: Rise of Automation, Robotics and Artificial Intelligence, by Brigette Tasha Hyacinth
  1. The Future of Leadership in the Age of AI: Preparing Your Leadership Skills for the AI-Shaped Future of Work (World of the Future), by Marin Ivezic and Luka Ivezic

Bonus Reading: Fiction

  1. Do Androids Dream of Electric Sheep?, By Philip K. Dick
  1. The Hitchhiker’s Guide to the Galaxy, by Douglas Adams
  1. I, Robot, by Isaac Asimov

Final Thoughts

To sum up, artificial intelligence may bring profound changes in our society. In fact, some of those changes have already happened, and we’re repeating their benefits. Or, others may say, we’re facing the consequences. In essence, it all depends on how we perceive it. 

However, Eliezer Yudkowsky said: “By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.”

So, to form a proper opinion about AI, it’s important to understand more of its aspects and possibilities, which is why we’ve prepared a very intensive course on the principles of artificial intelligence. We cover: 

  • data and datasets; 
  • Naive Bayes and Gaussian Bayes Classifier;
  • bagging and boosting;
  • supervised vs unsupervised learning;
  • artificial intelligence and ethics; 
  • neural networks; 
  • principal component analysis;
  • reinforcement learning, and so on. 

Ready to get started?