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Will artificial intelligence (AI) take over the world by 2027?

בינה מלאכותית AI

Table of Contents

Artificial intelligence: computers as smart as humans?

What is artificial intelligence?

Imagine a computer that can think like a human, learn from experience, and even play chess at a high level! This is exactly what artificial intelligence (AI) is trying to do.

How It Works?

Computers with artificial intelligence are trained to perform specific tasks by analyzing huge amounts of data. For example, facial recognition AI software can learn to recognize people in photos by analyzing photos of many people.

Can artificial intelligence replace humans?

Not really. Although AI programs can be very good at performing specific tasks, they still cannot think like humans. They cannot understand the world in the same way, and they have difficulty dealing with new situations.

So why is artificial intelligence important?

Despite its limitations, artificial intelligence can help us in many ways. For example, AI software can be used to diagnose diseases, write news articles, and even conduct conversations!

What does the future hold?

The field of artificial intelligence is developing very quickly, and we can expect to see more and more new and fascinating applications of this technology in the coming years.

Artificial intelligence is a fascinating technology with tremendous potential to change the world. Although it is not yet perfect, it already affects our lives in many ways.

What do you think?

Are you excited about the possibilities of artificial intelligence? Or are you afraid of the future?

בינה מלאכותית מלמדת ילדים לקרוא

Table of Contents

What is "intelligence" anyway?

We tend to think that everything we do, even the simplest things, requires intelligence. But are insects also smart? What is the difference between us and them?

Let's take the burying wasp for example. When she returns home with food, she first places it on the doorstep, then checks that there are no intruders inside. Only after she is sure, does she take the food inside.

What will happen if we move the food a little to the side while she is inside? The wasp will come out again, put the food on the doorstep, check again that there are no intruders, and bring it inside. Like this over and over, until the food is in the right place.

Such behavior is called "instinct". This means that the wasp does not really "think" about what it is doing, but simply acts according to a pre-programmed plan it received from nature.

So what is the difference between intelligence and instinct?

Real intelligence, like we humans have, allows us to adapt to new situations. If we move the wasp's food to a completely different place, it will get confused and not know what to do. On the other hand, we will be able to find a new way to bring the food into the home.

What are the characteristics of human intelligence?

Psychologists claim that intelligence is not a single trait, but a combination of many different abilities, such as:

  • learning: The ability to learn new things and understand new information.
  • logic: The ability to think logically and solve problems.
  • Troubleshooting: The ability to find creative solutions to complex problems.
  • perception: The ability to understand the world around us through our senses.
  • Use of language: The ability to communicate with others through spoken and written language.

Artificial intelligence research focuses mainly on these abilities, and strives to develop computers that can think and act like humans.

In conclusion:
Insect behavior, although complex, is not considered true intelligence. Human intelligence, however, is much more than just instinct. It consists of a combination of many skills that allow us to learn, think, solve problems and communicate.

How do machines learn?

There are many different ways machines can learn, just like humans! The simplest way is Learning through trial and error.

Imagine a simple computer program that plays chess. At first, she may try moves at random until she finds mate. The next time she encounters the same situation, she will remember the winning move and use it again. This is it Compulsive learning, where a computer simply keeps a memory of successful operations.

But such learning is limited. Machines are harder to teach generalize the knowledge they have gained for new situations. For example, a program that learns to form the past tense forms of English words will struggle with words it has not seen before, such as "jump". A smarter program, which can generalize, will be able to learn the "adding ed" rule and will know how to create the past form "jumped" by itself, even without having seen it before.

So how do machines learn to generalize?

There are more advanced learning methods, such as:

  • Reinforcement learning: The program receives "rewards" for successful actions and "punishments" for mistakes, and gradually learns to choose the winning actions.

  • Deep learning: Artificial neural networks, simulating the human brain, analyze huge amounts of data and learn to recognize patterns and connections.

Artificial intelligence research is constantly focused on developing new and more advanced learning methods, with the goal of creating machines that can learn like humans, and even better!

What is logic and how do machines learn to think like humans?

logic It is the ability to think correctly and draw logical conclusions.

For example, suppose Osher left the house. We know he's not in the cafe, so where could he be? Two logical options:

  • I concluded: Happiness must be in a museum.
  • hypothetical: It may have been confirmed in the library.

The difference between them is the level of security. In the first case, we are sure that Osher is in the museum, because he has no other options. In the second case, we only suspect that he is there, and it is possible that he is somewhere else.

Hypothetical reasoning Very common in science. Researchers collect data, build models, and try to predict what will happen in the future. As more data is collected, the models improve, but it is always possible that new data will be discovered that will contradict the previous assumptions and require a change in the picture.

Inferential reasoning Common in mathematics and logic. In these areas, complex structures of sentences can be built from basic assumptions and clear rules.

Machines already know how to draw conclusions, But they are still not perfect. They have difficulty understanding the connection between the conclusion and the specific situation, and this is one of the biggest challenges in the field of artificial intelligence.

Scientists are constantly working on new methods that will allow machines to think logically like humans, and to understand the connection between the information and the specific situation. One day machines may be able to solve complex problems like humans, and maybe even better!

How do machines solve problems?

Troubleshooting It is a very important part of artificial intelligence. It is a process of searching for a way to reach a defined goal or solution.

There are two main approaches to problem solving:

  • Special method: Adapted to a specific problem and takes advantage of its unique characteristics.
  • General method: Suitable for a wide range of problems.

One of the most common general methods is means-end analysis.
In this method, the computer breaks down the problem into small steps, and focuses on reducing the difference between the current state and the final goal at each step.

For example, a simple robot might use commands like "pick up", "put down", "forward", "back", "turn left" and "turn right" until the goal is achieved.

Machines have already been able to solve a wide variety of problems Through these methods, including:

  • Finding the winning move (or series of moves) in a board game.
  • Designing mathematical proofs.
  • Operation of "virtual objects" in a computer-generated world.

     

the challenge is to develop general methods that can deal with any kind of problem, just like humans do. Scientists are constantly exploring new techniques, and there is a chance that one day machines will be able to solve more complex problems than we can imagine!

How do machines "see" the world?

perception It is the ability to understand the environment through the senses. Even machines, just like humans, have special sensors that allow them to "see" and hear the world around them.

These sensors scan the environment, analyze the information, and separate different objects. It is not always simple, because the appearance of an object can change depending on the angle, lighting, and even the color of the background.

One of the first robots that included a perception system was FREDDY. This robot, built at the University of Edinburgh in the 1960s, was equipped with a moving TV eye and a special hand. FREDDY was able to recognize various objects and even assemble simple artifacts.

Today, artificial conception technology has advanced a lot. Special sensors can detect people, and autonomous cars are able to navigate roads at a reasonable speed.

the challenge is to develop perception systems that will be as sophisticated as those of humans. Machines need to be able to understand not only what they see, but also the relationship between the various objects in the environment.

Scientists are constantly working on new methods To improve perception systems, and there is a chance that one day machines will be able to "see" the world better than a human!

Language: What is it and how do machines "learn" to speak like humans?

Language is a way to communicate Using signs, whether words, pictures, or even hand gestures. Traffic signs, for example, are also a kind of language, since they all have an agreed meaning.

One of the special things about human language is she Productive. Humans can use words and sentences to create countless communication possibilities, and express entirely new ideas.

Machines learn to speak Using advanced models that are trained on huge amounts of data. These models analyze the connections between words and sentences, and learn to create new sentences that resemble human language.

An example of such a model is ChatGPT. This model is capable of having eloquent conversations with humans, answering questions, and even creating creative texts.

But do machines really understand the language? This is a difficult question. Although ChatGPT and its ilk can "speak" like humans, they don't really understand the meaning of the words. They simply choose words that are more likely to appear in a certain context.

So what is a "true understanding" of a language anyway? There is no single agreed upon answer to this question. It is possible that one day machines will be able to speak like humans and also understand the deep meanings of language.

But in the meantime, Models like ChatGPT open up new and fascinating possibilities for us to communicate with machines in a natural and intuitive way.

Two main approaches in artificial intelligence

How do machines learn to think? In artificial intelligence, there are two main approaches:

  • Symbolic access (from top to bottom): access symbolic First introduced by Edward Thorndike and Donald Hebb, it focuses on the connections between neurons in the brain. This approach tries to understand how the mind works at a high level, and focuses on a symbolic description of knowledge and understanding. For example, a letter recognition system could use a computer program that compares each letter to a geometric description. This theory claims that human learning can be described as the reinforcement of certain patterns of neuronal activity.

  • Connectionist approach (bottom up): access Connectionist Introduced by Alan Newell and Herbert Simon, it claims that intelligence can be described through the processing of symbolic structures. This approach focuses on imitating the structure of the brain, and creates artificial neural networks. These networks "learn" by training on data, such as presenting the letters of the alphabet one by one and gradually improving performance. This theory is known as the "physical symbol system hypothesis".

Both approaches have achieved some success:

  • The symbolic approach: was able to solve complex problems in abstract areas, such as playing chess and translation.
  • The connectionist approach: was able to learn from a lot of data, such as image recognition and speech recognition.

Which approach is better?

Both approaches achieved some success, but both encountered difficulties. Symbolic approaches struggle to deal with complex reality, while connectionist approaches have not yet succeeded in replicating the simplest brain capacities.

So what is the future of artificial intelligence?

Probably in the future we will see a combination of both approaches. New technologies will allow us to build increasingly smarter machines that can learn and adapt like humans.
the challenge is to develop methods that combine the two approaches, so that machines can learn and solve problems efficiently and intelligently.

Scientists are constantly working on new methods in artificial intelligence, and there is a chance that one day machines will be able to think like humans, and maybe even better!

In conclusion: 
The road to artificial intelligence is still long, but the two main approaches (symbolic and connectionist) lay the foundations for future progress. Their combination will lead to the construction of increasingly smart machines, which can significantly change our lives.

Alan Turing: Father of Artificial Intelligence

אלן טיורינג אב בינה מלאכותית

Who was Alan Turing?

Alan Turing was a brilliant British mathematician and computer scientist who lived in the 1920s and 1930s.

what did he do?

In 1935, Turing wrote a famous article in which he described an imaginary machine called the "Turing Machine". This machine consists of unlimited memory and a special scanner that can move back and forth in memory, reading and writing symbols. The scanner operates according to special instructions stored in memory. This idea was groundbreaking, and it laid the foundation for all modern computers.

during World War II, Turing worked on cracking German ciphers. He was part of a team that developed a special machine called "Colossus", which greatly helped to shorten the war and save millions of lives.

קולוסוס אחד המחשבים האלקטרוניים הספרתיים הראשונים

1994 Colossus One of the first digital electronic computers

What does it have to do with artificial intelligence?

Turing was one of the first to think about the possibility of building machines that could think like humans. He believed that these machines could learn from experience and solve new problems in creative ways.

What were his ideas?

Turing proposed a number of key ideas in the field of artificial intelligence, including:

  • Computational learning: Machines can learn by analyzing data and looking for patterns.
  • Heuristic problem solving: Machines can solve new problems by making decisions based on general rules, rather than precise instructions.
  • Artificial neural networks: Machines can mimic the human brain using networks of artificial neurons.

Why are his ideas important?

An important Turing machine Because she can "learn" and improve herself. She can change her plan or even create new plans. Turing's ideas laid the foundations for the field of artificial intelligence, and are an inspiration to researchers to this day. Many approaches in the field of artificial intelligence are based on these ideas, and many of the achievements in the field in recent years were made possible thanks to his pioneering contribution.

Despite his enormous contribution to science and technology, Turing was persecuted by the British government for his sexual orientation. In 1952 he was convicted of homosexual activity and sentenced to chemical castration. Two years later, Turing died of cyanide poisoning.

Despite his tragic death, Turing's legacy lives on. He is considered one of the greatest geniuses of the 20th century, and his contribution to artificial intelligence and computer science is invaluable.

Turing, chess and shiny machines

Alan Turing, the father of artificial intelligence, thought how machines could be as smart as humans. One way he tested it was with chess.

chess It is a complex game with clear rules and lots of possibilities, making it a perfect testing ground for Turing to test the abilities of artificial intelligence. He wanted to see if a machine could learn to play chess as well as a human.

But how can a computer play chess?

In theory, a computer can search all possible moves and find the best one. In practice, it is impossible, because there is simply Too many options.

Turing understood that it was necessary to find a way to "cut corners" in the search for the perfect move. He suggested using heuristics - general rules that would help the computer choose more reasonable moves, while reducing the number of options being tested.

Turing was unable to build a computer that could play chess, the reason for this is that there were not enough powerful computers then. But he laid the foundations for many of the ideas used in artificial intelligence today.

The computers arrive, and the dream comes true

מחשב דיפ בלו מנצח במשחק שחמט

May 11, 1997 "Deep Blue" computer defeats the world chess champion, Garry Kasparov.

Only many years after Turing's death, with the development of digital computers, did his dream become a reality. In 1997, a computer named "Deep Blue" defeated the world chess champion, Garry Kasparov.

How did Deep Blue do it? Not thanks to amazing artificial intelligence, but thanks Force. Deep Blue was so powerful that he could check millions of options per second, far more than a human.

Does this mean Deep Blue is "smart" like a human? Not really. He is just very good at calculations. Noam Chomsky, a language expert, said it was like winning the weightlifting Olympics with a bulldozer.

A technological victory, not an intellectual one

Despite the impressive achievement, many argued that Deep Blue's victory did not advance our understanding of human intelligence. The success was mainly due to the enormous computing power of Deep Blue, and not from a creative or intelligent thinking ability.

what did we learn

We learned that with enough computing power, complex problems like chess can be solved impressively. However, we have not yet been able to develop machines that can think like humans, and understand the world in the same way. We may one day succeed, but in the meantime, we continue to learn about human intelligence and the ways in which it can be imitated through technology.

Turing Test: Can machines think like humans?

What is the Turing Test?

In 1950, Alan Turing, the father of artificial intelligence, proposed a new way to determine whether a machine can think like a human. in the Turing test, a person talks to two other participants: a real person and a computer. The person trying to identify who the computer is is doing so only based on the conversation.

How It Works?

The test has three participants A computer, a person interviewing the computer, and another person. All communication is done via text. the researcher Trying to figure out which of the two, the computer or the person, he is The real person. He does this by asking as many open-ended, difficult and challenging questions as he wants. The computer, for its part, tries its best to sound like a human and confuse the person trying to recognize it.

Can a machine pass the test?

That's the big question! To date, no computer has been able to pass the Turing test convincingly. in 1991, a man named Hiv Levner offered a large monetary reward to whoever could build a computer that would pass the test. The prize has not yet been awarded. But there are machines that come close to it.

So why is it so hard?

It is relatively easy for a computer to learn to imitate human language. It is easy to learn how to write grammatical sentences, use synonyms and even make jokes. But it is much more difficult for a computer to understand the true meaning of the words it uses. It is difficult for him to understand the nuances of human language, humor, emotions and social subtleties.

Is anyone close to passing the test?

In recent years, large linguistic models have been developed (LLMs) like ChatGPT, which are very advanced in their ability to conduct text conversations. Some argue that some of these LLMs are already close to passing the Turing test, but others argue that we still have a long way to go.

What is the future of the Turing test?

The Turing Test may not give a perfect answer to the question of whether machines can think like humans. It may be necessary to develop new tests that focus on other abilities, such as creativity, emotional understanding or the ability to solve complex problems.

Meanwhile, The Turing Test helps us think about what it means to be smart, and what it means to be human.

First steps in artificial intelligence: first "smart" computer programs

Checkers, shopping and... learning?

In the 50s of the last century, scientists began to investigate how it is possible to "program" computers to think like humans. The result? First and fascinating artificial intelligence (AI) software!

1951: The first checkers program

Christopher Strachey, a British scientist, developed a checkers program that ran on a computer called the Ferranti Mark I. This program played checkers at a reasonable level as early as the summer of 1952, and was the first success in the field of artificial intelligence.

Ferranti Mark 1

Ferranti Mark I The first electronic digital computer

1952: Smart shopping

Anthony Oettinger, a scientist at the University of Cambridge, wrote a program called Shopper. Shopper "simulation" shopping in a mall of 8 stores. When sent to buy a product, Shopper searched for it in random stores until the product was found. The next time Shopper was asked to buy the same product, or a different product she had already found, she went directly to the right store! Shopper "learned" from experience, as a human shopper would.

1952: Another checkers program, this time in the United States

Arthur Samuel, a scientist from the USA, wrote another checkers program that worked on IBM computer 701. This plan was based on Strachey's plan, but Samuel upgraded it significantly over the years. He added features to her that made her learn from experience, like Shopper. Thanks to these improvements, Samuel's checkers program was able to beat the Connecticut Checkers Champion in 1962!

מחשב IBM 701

In 1952, IBM launched the IBM 701 which was an electronic data processing computer

The first programs of artificial intelligence were relatively simple, but they showed the enormous potential of the field. They played checkers, shopped and learned from experience - fascinating first steps on the way to more and more advanced artificial intelligence.

Software evolution: a "bottom-up learning" approach

The checkers program of Arthur Samuel, mentioned earlier, which was developed in 1952, not only played checkers at a high level, but also contributed greatly to the development Evolutionary computation.

What is evolutionary computation? This is a field in artificial intelligence that uses ideas from biology to develop "learning" software. Similar to evolution in nature, these programs improve over time by "evolving" new and better versions.

Samuel used an evolutionary approach to improve his checker plan. A process by which a computer program improves itself automatically. This approach, known as "bottom-up learning", is similar to how species evolve in nature.

How It Works?

In the case of Samuel's checkers program, the program created new versions of itself over and over again. Each new version was pitted against the best existing version, and the winner became the new version. This process was repeated over and over until the program became very good at playing checkers.

Further experiments in evolutionary computation

Johan Holland, a scientist who passionately supported evolutionary computation, developed software to test this approach. He designed a "virtual" neural mouse that can learn to navigate a maze. This experiment showed that this approach can be effective in solving problems.

Holland moved to the University of Michigan where he continued to research evolutionary computation. In 1959, he wrote the world's first doctoral thesis in the field of computer science, in which he proposed a new type of computer that would be more suitable for evolutionary computation.

Holland directed most of the research in the field of evolutionary computational automation methods, now known as genetic algorithms. Systems he developed included a chess program, models of single-celled biological organisms, and a classification system for controlling a simulated gas pipeline network.

practical uses

Genetic algorithms are not only used for academic demonstrations, but also in many practical applications. For example, they can be used to create a portrait of a criminal based on DNA evidence.

Machines that can think? software for proving sentences

It is important to us that computers can think like humans. One of the most important things in intelligence is the ability to think logically. Therefore, researchers in the field of artificial intelligence have tried to develop software that can prove mathematical theorems, like mathematicians do.

The first program: the logic analyzer

In 1955-1956, three scientists named Alan Newell, J. Clifford Shaw and Herbert Simon wrote a program called "Logic Analyzer". This program could prove theorems from a mathematical book known as "Principia Mathematica". In one case, the program was able to find a more elegant proof than the original proof that appeared in the book!

A more advanced program: the General Problem Analyzer (GPS)

Newell, Simon and Shaw didn't stop there. They went on to develop a more powerful program called the General Problem Analyzer (GPS). GPS could solve a wide variety of puzzles through trial and error. The first version of GPS ran in 1957, and work on the project lasted about a decade.

Criticism: programs that do not study

However, some scientists have argued that programs like GPS are problematic. According to them, these programs are not really intelligent. They simply know how to carry out actions that have been defined in advance by their programmers. They are not able to learn new things on their own.

In conclusion:

Software for proving theorems such as the logic analyzer and GPS were an important step forward in the field of artificial intelligence. They showed that it is possible to develop software that can think logically and solve complex problems. However, there is still a long way to go before these programs are truly intelligent, like humans.

Eliza and Parry: first chatbots that managed to trick humans

In the 1960s, two artificial intelligence (AI) programs generated a lot of interest: Eliza andParry.

Eliza: A virtual psychologist?

in 1966, Joseph Weitzenbaum developed a computer program called Eliza. This program was designed for a human psychological figure. People could "talk" to Eliza about any topic, and she would "respond" in a way similar to how a human psychologist might respond.

Parry: Virtually paranoid?

That same year, a psychiatrist named Kenneth Colby developed another computer program called Parry. This program was designed to portray a paranoid person. Like Eliza, Parry could have conversations with people, and she "reacted" in a manner similar to how a truly paranoid person might react.

The Eliza and Parry shows were so convincing that many psychiatrists couldn't tell them apart from real people! They knew how to conduct conversations, respond to questions and maintain consistency over time.

But not really intelligence

Despite their success, Eliza and Parry weren't really intelligent. They did not think for themselves and did not understand the true meaning of the words they said. Their answers were prepared in advance, and were built by their programmers.

Today, AI scientists continue to look for ways to build machines that can think and learn like humans. One day machines may be able to have intelligent conversations like Eliza and Parry, but we're not there yet.

Scientists and robots: playing in small worlds

Artificial intelligence (AI) scientists need special tools to build their programs. Over the years several languages have been developed Software special for AI.

one of the first was IPL (Information Processing Language), developed by Newell, Simon and Shaw during their work on the logic analyzer and GPS. IPL included a special data structure called "list", which was very useful for AI programs.

in 1960, John McCarthy developed a new language called LISP (LISt Processor). LISP combined ideas from IPL with calculus, a mathematical logic system. LISP has become a very popular programming language in the field of AI for decades.

Another important language is PROLOGUE (PROgrammation en Logic). PROLOG was developed in France in 1973 by Alan Colmero. It uses logic to build programs, which makes it a powerful tool for certain AI tasks, such as proving theorems and making inferences.

Today, many different programming languages are used in the field of AI. Python, Java and C++ are just a few examples. Each language has its strengths and weaknesses, so AI scientists choose the language that best suits their specific needs.

The programming languages of artificial intelligence: from IPL to PROLOG

Scientists, like all of us, sometimes have to do a little magic to deal with all the complications of the world. They just ignore things that are less important, like physicists ignore friction when it comes to their models.

In 1970, two geniuses named Marvin Minsky and Seymour Papert thought of a new idea: instead of dealing with all this mess, why not build smaller and simpler worlds, like a world of colored blocks? This way computer programs can learn to behave intelligently without getting confused.

This idea, called the "world of blocks", has become really popular. Different computer programs competed with each other to see who could build the tallest building, or arrange the blocks the most beautifully. One of the famous programs was called SHRDLU. She could control a robotic arm and arrange blocks according to commands in natural English, such as "put the two red blocks on the table". Sounds cool, right?

But it quickly became clear that SHRDLU actually didn't really understand what it was doing. She didn't know what a green block was, and the whole thing was like an illusion.

They also tried to build a robot called Shakey, which could move around the world of blocks and do all kinds of things like push blocks and climb them. But Shakey was also really slow, and what Adam could do in minutes took him days.

Despite these setbacks, the world of blocks approach has contributed greatly to the field of artificial intelligence. She proved that it is possible to teach computer programs to perform complex tasks, even if they do not understand the world like humans. And that's really cool, isn't it?

Today, thanks to the world of blocks approach, we have expert systems that can solve complex problems in fields such as medicine, engineering and even law. So the next time you see a robot doing something clever, remember the world of blocks!

Little experts in a digital world

Imagine having a special doctor who knows everything about every possible disease. Not only that, he can also diagnose you faster and more accurately than any other doctor in the world. This is exactly what expert systems do!

Expert systems are like super smart doctors, but instead of medicine, they specialize in other specific fields. They work in a kind of "micro world" - one example is a model of a ship's trunk and its cargo. These systems receive all possible knowledge in their field, allowing them to solve problems better than any single human expert.

There are many different expert systems, from medical diagnosis to vehicle planning. They are used in areas such as:

  • medicine: Diagnosing diseases, prescribing medications, planning surgeries
  • chemistry: Analysis of compounds, development of new materials
  • Funding: Portfolio management, credit provision, financial planning
  • engineering: Designing bridges, buildings, machines
  • technology: Designing computers, software, mobile phones
  • Oil and gas: Search for oil and gas reserves
  • agriculture: Crop planning, irrigation, pest control
  • transportation: Planning schedules, cargo management
  • Customer Service: Answering questions, solving problems

Expert systems are a powerful tool that can help us solve problems more efficiently and smarter. They allow us to make better decisions, save time and money, and even save lives.

Here are some specific examples:

  • One system of medical experts can diagnose skin diseases more accurately than most doctors.
  • Another set of experts in the field of chemistry has developed a new material that is able to clean polluted water more effectively than any other method.
  • A system of engineering experts designed a new bridge that is many times stronger than any other bridge built before.

As technology advances, expert systems become more powerful and useful. They are an integral part of the future, and may play an important role in improving our lives in every field.

Expert system: how does it work?

Imagine an expert in a certain field, such as a doctor or a chef. In his head there is a huge pool of knowledge, full of rules, tips and tricks that help him make the right decisions. An expert system is like a digital version of that expert.

Two main components The system has experts:

  • Knowledge base (KB): It is the vast repository of information, like a digital encyclopedia. The information is collected from real experts, organized in a special way and entered into KB.
  • Inference engine: This is the "brain" of the expert system. It uses the rules in the KB to make inferences and solve problems.

How It Works?

  1. The experts share their knowledge: Human experts give the system information about their field. This information is organized into rules, such as "If you have a high fever and a cough, then you may have the flu."

  2. The inference engine learns: The inference engine learns the KB rules and understands how to use them.

  3. The user asks questions: The user can ask the expert system questions in the relevant field.

  4. The inference engine answers: The inference engine uses rules in the KB to answer the questions. It can also ask the user additional questions to get more information.

  5. Troubleshooting: An expert system can also solve problems. She will analyze the situation, use her rules and offer possible solutions.

In fuzzy logic:

Sometimes, it's hard to describe things in precise words. For example, when is a person considered "tall"? An expert system can use "fuzzy logic" to deal with such situations. This means she can use values like "maybe", "likely" or "unlikely" instead of "true" or "false".

Expert systems are powerful tools that can help us make better decisions, solve problems, and learn new things. They are used in many different fields, from medicine to engineering.

DENDRAL: Molecular Detective

In 1965, two scientists developed a computer program called DENDRAL. DENDRAL acted like a smart detective, analyzing experimental results and tracing the molecule that makes up a certain substance.

DENDRAL's abilities were impressive! She was able to solve molecular mysteries as well as expert chemists, and sometimes even better. Scientists in industry and academia have used DENDRAL to discover the molecules in the materials they studied.

How did DENDRAL do it?

DENDRAL used extensive chemical knowledge and the power of logic. She started with a guess about the molecule, checked it against the results of the experiments, changed it as needed, and repeated the process until she found the perfect match.

DENDRAL is an early example of an expert system, a computer program that solves complex problems in specific domains. Today, expert systems are used in many fields, such as medicine, engineering and finance, and help us make better decisions, solve complex problems and save time and money.

MYCIN: A smart doctor for blood infection

In 1972, scientists at Stanford University developed a smart doctor called MYCIN. MYCIN knew how to diagnose blood infections in patients, just like a real doctor!

MYCIN received information about the patient's symptoms and the results of his medical tests. She could also ask additional questions about the patient, such as medical history or medications he is taking.

MYCIN then used its vast knowledge and the power of logic to diagnose the infection and determine the best treatment. She could even explain to the patient the logic that led to the diagnosis and the choice of treatment.

MYCIN was so good, it performed at the level of human experts in the field of blood infections, and even better than general practitioners!

But despite her wisdom, MYCIN was not perfect.

Sometimes, she could make mistakes. For example, if MYCIN was told that a patient with a gunshot wound had died, it could try to diagnose a bacterial infection, even though he was no longer alive.

MYCIN could also be wrong due to human errors. For example, if a patient's weight or age were accidentally entered, she could recommend the wrong dose of a drug.

Despite its limitations, MYCIN has been an important tool for physicians. It helped them diagnose blood infections more accurately and offer better treatment to patients.

Today, expert systems are used in many different fields, such as medicine, engineering and finance. They are a powerful tool that can help us make better decisions, solve complex problems and save time and money.

It is important to remember that expert systems are not perfect. They can make mistakes like any other tool. Therefore, it is important to use them responsibly and take into account their limitations.

CYC: An ambitious journey in the world of knowledge

Imagine a computer machine that knows almost everything a human knows! This was the dream of the CYC project, a huge experiment in artificial intelligence that began in 1984.

The goal of CYC was to build a huge knowledge base (KB), which would contain all the basic knowledge we accumulate throughout our lives. Millions of facts and rules were written into CYC, with the hope that the system could learn and draw conclusions on its own, like a human.

For example, CYC could understand that "Garcia is wet" after running a marathon, because she knew that running makes us sweat, and sweat makes us wet.

But CYC wasn't perfect. Despite the vast amount of information, she had difficulty solving complex problems. Many researchers have argued that the symbolic approach, which is based on writing rules and statements, will not be able to create real and smart artificial intelligence systems.

CYC demonstrated the enormous potential of artificial intelligence, but also the many difficulties on the way to creating machines that can think like humans.

Today, artificial intelligence research continues to develop in different directions, using diverse approaches. Machine learning systems, for example, are able to learn from a lot of data and solve problems effectively, even without a detailed knowledge base like CYC's.

The future of artificial intelligence is not yet clear, But it is clear that this field is going to play an increasingly significant role in our lives.

Connectionism: Mimicking the brain

Many scientists are trying to understand how the brain works, especially how we learn and remember things.

One way to do this is to explore "connectionism", or neuron-like computation. This approach is based on the idea that the brain works like a giant neural network, where small cells called neurons are interconnected by many connections.

Scientists built mathematical models of neural networks, tried to imitate the way neurons communicate with each other, and studied how these networks can learn and solve problems.

Connectionism has contributed greatly to our understanding of how the brain works, But there is still much we do not know. Connectionism research continues, and scientists continue to explore the secrets of the brain.

Understanding the brain is one of the most important tasks in science. The more we learn about how it works, we can develop new treatments for neurological diseases, improve artificial intelligence, and maybe even better understand ourselves.

Neural networks: cool learning machines!

Imagine a machine that can learn like a human brain - that's exactly what neural networks do!

In 1954, Belmont Perley and Wesley Clark of MIT built the first artificial neural network. Although she was relatively small (with only 128 "neurons"), she was able to learn to recognize simple patterns.

This network also discovered a cool feature: even if 10% of the neurons were destroyed, it still worked! It's similar to our brain, which can handle some damage without losing all of its capabilities.

So how do neural networks work? They consist of neurons (like brain cells) that are connected to each other by connections. Each connection has a "weight" that affects how strongly it affects the neuron it is connected to. ⚖️

When the network receives input (information), each neuron calculates whether it should be "active" (1) or "inactive" (0). The calculation depends on the weights of the connections to it and the input it receives from the other neurons.

For example, suppose we have a network with 5 neurons: 4 for input and 1 for output. Each input neuron can be active or inactive, and the weighted input to output is calculated by multiplying the value of each input neuron by its connection weight and summing the results.

If the weighted input to output passes a certain threshold, the output neuron will become active. This means that the network has "learned" to recognize a certain input and send an output accordingly.

Neural networks are powerful tools that can be used for a wide variety of tasks, such as image recognition, language translation, and even text writing!

So how do neural networks become so smart? They go through a special training process!

The training process includes two simple steps:

  1. The agent gives the network an example and looks at what it does. For example, he can show her a picture of a cat and ask her if it's a cat or a dog.

  2. The agent corrects the network if it is wrong. If the network says it's a dog, the agent will increase the connections to the neurons that activated it when it saw pictures of cats in the past, and decrease the connections to the neurons that activated it when it saw images of dogs.

The agent, which is essentially a computer program, does this over and over again with many different examples.

Each time, the network learns a little more how to react correctly.

The amazing thing is that this learning process is completely automatic! The agent does not need to intervene and tell the network what to do. She just learns by herself, like a little child practicing to walk.

This process works on all kinds of different tasks.
Neural networks can learn to recognize images, translate languages, and even write texts!

Frank Rosenblatt: the hero of neural networks!

Let's meet Frank Rosenblatt, a genius who worked at the Cornell Aeronautical Laboratory at Cornell University. In 1957, he began researching artificial neural networks, which he called perceptrons.

Rosenblatt contributed a lot to the field of artificial intelligence! He did computer experiments to understand how neural networks work, and also developed special mathematical methods to study them.

He was a charismatic man, and his ideas led many other researchers in the United States to study perceptrons. Together, they called their approach "connectionism", because they believed it was important to study how neurons connect to each other and communicate with each other.

One of Rosenblatt's most important contributions was improving Perley and Clark's training method. Their method only worked on small networks, but Rosenblatt found a way to use it on larger and more complex networks as well.

He called his method "backpropagation error correction," and it helped networks learn much faster and better.

Rosenblatt's method has undergone many improvements over the years, but the basic idea remains the same. Today, "backpropagation" is one of the most important methods in neural network training, and it is used in a wide variety of applications, from image recognition to writing texts.

A little word changing time machine!

An amazing experiment conducted at the University of California, San Diego showed how neural networks can learn new things.

In the experiment, David Rummelhart and James McClelland "trained" a network of 920 artificial neurons (like small cells in the brain).

This network was divided into two layers, and each layer contained 460 neurons.

The goal was to teach the network to create past forms of English words, such as "came" from "come", "look" from "look" and "sleep" from "sleep".

To do this, they showed the chain many words in their original form, and she tried to create their past tense.

A special computer program checked if the network's response was correct, then "repaired" the connections between the neurons so that the network would be a little closer to the correct answer next time.

This process was repeated about 200 times with hundreds of different words.

In the end, the network was able to generate correct past forms of many words, even ones it had never seen before!

For example, when she was shown the word "guard", she produced "guarded"; With "weep", she created "wept"; With "cling", she created "clung"; And with "drip", she created "dripped".

This experiment also contributed to our understanding of how the human brain learns.

Cool, isn't it?

What else can neural networks do?

We thought neural networks were only good for learning languages, but they can do lots of other cool things! Here are some examples:

1. Recognize faces and objects in photos:
Neural networks can look at a picture and tell you if it has a cat, a dog, a person, or anything else. They can even differentiate between different people in a group photo!

2. Understand and produce a text:
Neural networks can read text written by a human or a computer, and translate it into other languages. They can also turn speech into written text, and vice versa!

3. Predict financial risks:
Neural networks can help banks decide whether to give someone a loan, how much a house is worth, or whether a particular stock will go up or down.

4. Diagnose diseases:
Neural networks can help doctors detect cancerous tumors, heart problems, and even predict how a person will react to different drugs. 🩺

5. Improve communication:
Neural networks can help route phone calls more efficiently, and reduce noise in satellite calls.

This is just a small part of what neural networks can do! As technology continues to develop, they will learn to do more and more amazing things.
The future of neural networks looks bright!

Innovative artificial intelligence: robots that just work!

artificial intelligence (AI) is a field of computer science that focuses on creating machines that can think and act like humans.

Strong artificial intelligence (Strong AI) aims to create machines that can perform any intellectual task that a human can perform.

Innovative artificial intelligence (Nouvelle AI) is another approach to AI. It does not try to create "smart" machines like humans, but concentrates on creating machines that can perform specific tasks efficiently.

A central idea in innovative artificial intelligence is that intelligence "evolves" from the interaction of simple behaviors. For example, a robot programmed to follow a moving object by avoiding obstacles will appear to be "chasing" the object.

A famous example of innovative artificial intelligence is Rodney Brooks' robot Herbert. This robot planned to collect empty soda cans from the offices of the artificial intelligence lab at MIT.

This behavior, which appeared to be intentional, resulted from the interaction of about 15 simple behaviors, such as avoiding obstacles and searching for small objects.

One advantage of innovative artificial intelligence is that it does not require the robots to build an internal "model" of the world.
Instead, they can simply react to the information they receive from their sensors. This can make them more efficient and easier toSoftware.

However, innovative artificial intelligence also has some limitations.  For example, it is difficult to program robots to operate effectively in a wide variety of environments.

Overall, Innovative Artificial Intelligence is a promising approach to AI. It can be used to develop robots that can perform many different tasks efficiently.

With continued research, it may be possible to overcome the current limitations of this approach.

Artificial intelligence: robots that "live" in the real world!

Most types of artificial intelligence (AI) created so far have tried to create "smart" machines that don't really "live" in the real world.

For example, the CYC system tried to build a detailed internal model of the world, but it was too complex and did not work efficiently.

Innovative artificial intelligence (Nouvelle AI) is a different approach.
It tries to build machines that can perform specific tasks efficiently, even if they don't understand the world perfectly.

The way to do this is to "place" the machines in the real world. This means giving them sensors that can "see" and "hear" what is happening around them, and program them to react appropriately.

This idea was first introduced by Alan Turing in the 1940s. He claimed that a machine could be taught to "understand and speak English" by giving it the best sensors money could buy, andSoftware her to learn like a child.

Philosopher Brett Dreyfus developed this idea further in the 1960s.
He argued that true intelligence requires not only a "mind", but also a "body" that can move and communicate with the physical world.

Cutting-edge artificial intelligence has yet to create machines that behave like real insects. However, it has been able to produce robots that can perform many different tasks, such as picking up empty soda cans or cleaning floors.

With continued research, it may be possible to overcome the current limitations of this approach and arrive at an artificial intelligence that truly "lives" in the real world.

Artificial intelligence: stronger, faster, and smarter!

In the 21st century, computers have become increasingly powerful, with access to vast amounts of information. This breakthrough made artificial intelligence (AI) possible than ever before.

Remember Eliza? A computer program from the 1960s that could hold conversations with humans. Eliza did this using predefined responses, but her abilities were very limited.

Today, AI software is much more powerful. For example, ChatGPT is able to generate human-quality text. It does this by learning from vast amounts of text written by humans.

What makes this possible? The power of computers, which is increasing according to Moore's law. This law states that computer power doubles every 18 months, and it is still valid today. As a result, AI software can learn from vast amounts of data, allowing them to become much smarter.

AI software is currently used in a wide variety of applications: They can recognize faces in photos, translate languages, write text, and even drive cars!

The future of AI looks bright. As technology continues to evolve, AI software will become even more powerful and intelligent.

This development raises many questions: Will AI become a threat to humanity? Will she rob us of all our jobs? Or maybe it will help us solve the world's biggest problems?

only time will tell. But one thing is certain: AI is a significant force in our world, and it has a huge impact on our lives.

Neural networks: machines that learn like humans

Imagine a machine that can learn to play chess better than a human, or recognize a cat in a picture more accurately than your eyes. This is exactly what neural networks, a special type of artificial intelligence, are capable of doing.

Neural networks are like little brains made up of many layers of "neurons". Each neuron is connected to other neurons, just like in the human brain. By connecting these neurons in different ways, neural networks can be taught to perform complex tasks, such as recognizing images, translating languages, and even writing pieces of music.

In 2006, a technological breakthrough made neural networks even more powerful. Scientists have discovered that it is easier to train neural networks by training each layer separately, rather than training the entire network together. This approach, called "continuous private training for each layer", led to a revolution in the field of artificial intelligence.

Thanks to this improvement, a new type of artificial intelligence called "deep learning" was born. Deep neural networks are networks with four or more layers, and are capable of learning extremely complex tasks.

For example, a deep neural network called CNN (Convolutional Neural Network) can identify cats in images more accurately than any human. It does this by finding special features in pictures of cats, such as the shape of their eyes and ears.

Deep neural networks are so powerful that they have even managed to beat world champions in games like chess and Go. In 2016, a neural network called AlphaGo defeated world chess champion Garry Kasparov.

Another benefit of deep machine learning is the ability to learn without guidance. This means that the network can discover features in the data itself, without the need forSoftware manual.

Many impressive achievements have been achieved in the field of deep learning, including:

  • Image classification: Special neural networks called convolutional networks (CNN) can recognize objects in images with high accuracy, even more than humans.

  • Games: Deep machine learning has turned computers into superhuman level chess and shogi players.

  • Drug development: Deep machine learning can be used to predict properties of molecules, potentially speeding up the development of new drugs.

Deep machine learning is a fascinating and rapidly developing field, with huge potential to change our lives in many areas.

Here are some examples of possible uses of deep machine learning:

  • Disease diagnosis
  • Medical image analysis
  • Development of autonomous vehicles
  • Create custom content
  • Improving customer service

Undoubtedly, deep machine learning will play a significant role in the future, opening new and fascinating possibilities for humanity.

Autonomous cars: no driver, no problems?

Imagine a world where cars drive alone, without a driver! This is exactly the dream of technology Autonomous vehicles.

Autonomous cars usemachine learning andartificial intelligence to navigate the roads. They are like little brains on wheels, which can learn from the environment and improve their performance over time.

machine learning Allows cars to learn from complex data, such as images and videos, to understand the world around them. They can recognize other cars, pedestrians, road signs and traffic lights, and learn how to navigate safely in traffic.

artificial intelligence Gives cars the ability to make decisions on their own. They can decide how to accelerate, how to brake, and even how to change lanes, without the need for detailed instructions for every possible situation.

To make sure that autonomous vehicles will be safe, they pass Rigorous tests Through artificial simulations. These tests are designed to find weaknesses in the system and make sure it meets high safety standards.

Despite the impressive progress, autonomous vehicles Not yet available for purchase by consumers. There are several challenges to overcome:

  • Mapping: Detailed maps of nearly four million miles of public roads have to be created, a huge and complex task.

  • safety: There have been cases of autonomous vehicles causing accidents, raising concerns about the safety of the technology.

  • Human interaction: Autonomous vehicles need to be able to communicate with other drivers, cyclists and pedestrians to avoid accidents.

Despite the challenges, autonomous vehicle technology is developing at a fast pace. It is possible that in not many years we will be able to enjoy a safe and comfortable ride without the need for a driver.

What do you think about autonomous vehicles? Are you afraid of this technology?

Computers that understand us: a new world of communication

Imagine a world where computers can have conversations with us like humans, understand our jokes and write creative texts. This is not science fiction, it is possible thanks to an amazing technology called natural language processing (NLP).

NLP is a field of artificial intelligence that focuses on the ability of computers to understand and analyze human language. To do this, NLP uses many tools, such as:

  • Computational Linguistics: Analysis of language structure and rules.
  • statistics: Calculating the probability of different words and phrases.
  • Machine learning: Training computers to learn a language automatically.
  • Deep learning: Advanced machine learning techniques that allow computers to understand subtle nuances in language.

In the past, NLP models were based on predetermined rules, but this approach was not efficient enough. Today, most NLP models use machine learning and deep learning, which allows them to improve over time and learn from language more naturally.

Prominent examples of using NLP:

  • Language models: Software that can create humanoid text, such as ChatGPT andGPT-3.
  • Voice navigation systems: software like Siri and-Google Assistant that understand voice commands.
  • Chatbots: Software that simulates a conversation with humans, such as those used in customer service.
  • Translation software: Software that translates text from one language to another.
  • Create images: software like DALL-E, Stable Diffusion and-Midjourney Able to create images from textual descriptions.

Challenges in NLP:

One of the main problems with NLP is bias. NLP algorithms are trained on a lot of data, and if this data is biased, the algorithms will also be biased. For example, if an NLP system is trained on a data set of resumes that are mostly men, it may discriminate against women in its results.

Another problem is Understanding nuances. Human language is full of subtle nuances, such as irony, sarcasm and humor. It is very difficult for computers to understand these nuances, which can lead to malfunctions.

Despite the challenges, NLP is a rapidly developing field with enormous potential to change the way we interact with computers. It is possible that in not many years we will be able to have natural conversations with computers, receive information and help from them, and even create art and creative content together with them.

What do you think about the future of NLP? Do you believe that computers will ever be able to understand us like humans?

Virtual assistants: digital friends who will help you with everything!

Imagine a world where you have a personal digital assistant that is available 24/7, always ready to help you with tasks, answer questions, and even chat with you.

This is the world of virtual assistants (VAs), smart software that can do a wide variety of things, such as:

  • Manage a schedule: They can make appointments, remind you of events, and even book you doctor's appointments.
  • make calls: They can call people, send text messages, and even schedule video meetings.
  • Navigate the roads: They can tell you how to get to your destination, find parking, and even order you a taxi.
  • answer questions: They can provide you with weather information, news, sports scores, and more.
  • Control a smart home: They can turn lights on and off, play music, and even lock the door.

The disadvantages of virtual assistants:

  • privacy: They collect a lot of data about your use of them, which can be a problem for people who want to keep their privacy.

  • dependence: You may become overly dependent on virtual assistants and find it difficult to complete tasks on your own.

  • Faults: There may be technical faults or misunderstandings that will cause the assistant to malfunction.


The most popular virtual assistants on the market are:

  • Amazon's Alexa: Known for its extensive capabilities and control over smart home products.

  • Google's G-Assistant: Known for its advanced search capability and integration with other Google services.

  • Microsoft Cortana: Best known in conjunction with the Windows operating system.

  • Apple's Siri: Known for its intuitive capabilities and in combination with other Apple products.

How do they work?

Virtual assistants use different technologies, such as:

  • Speech recognition: They can understand what you say through a microphone.

  • Natural Language Processing (NLP): They can analyze your language and understand your intent.

  • Machine learning: They can learn from your interactions with them and improve their performance over time.

Virtual assistants are not like regular chatbots. They are more personalized, learn from your behavior, and can provide you with better service over time.

The history of virtual assistants:

  • The 60's: Eliza, a computer program that held simple conversations with humans.
  • Early 90s: Mark of IBM, the first virtual assistant.
  • 2010: Apple's Siri, the first virtual assistant for smartphones.

Virtual assistants are constantly evolving.

They are a powerful tool that can significantly improve your life. However, it is important to be aware of their advantages and disadvantages before using them.

Do you use virtual assistants? What do you think about them?

Artificial intelligence: incredible benefits, real dangers

Artificial intelligence (AI) is an amazing technology that can change our lives for the better. It can perform many tasks automatically, help us make better decisions and even create new and exciting things.

But like any new technology, AI also has less pleasant sides. Here are some of them:

Job loss: As more tasks are done by machines, fewer people will need to work. This can lead to unemployment and poverty, especially among people who are not qualified to work in jobs that require a lot of technological knowledge.

biases: AI systems are trained on data collected from the real world. If these data are biased, the systems will also be biased. For example, if an AI system is used to select job applicants, it may discriminate against women or minority people.

privacy: AI systems collect and process large amounts of personal data. If this data is not well secured, it can fall into the wrong hands.

manipulation: AI can be used to create fake photos and videos, which can be used to mislead people and damage their reputation.

Tracking: AI can be used to track people in public spaces and even inside their homes. This could seriously damage our privacy and ability to live a free life.

what do we do with it?

It is important to be aware of the risks of AI, but you should not be afraid of it. AI is a powerful tool that can be used for good or bad. It is our job to make sure that it is used for the benefit of all humanity.

We need to develop rules and laws to prevent misuse of AI. We need to make sure that AI systems are transparent and explainable. And we need to educate the public about the risks and benefits of AI, so that everyone can make an informed opinion about its use.

Together, we can make sure AI makes the world a better place for everyone.

Will machines ever be able to think like humans?

Artificial intelligence (AI) is a fascinating field that tries to create machines that can think and act like humans. We have already managed to develop impressive AI systems that can do things like recognize faces, translate languages, write creative text and even beat world champions in games.

But will machines ever be able to reach the level of intelligence of humans? This is a topic that scientists and philosophers have been discussing for many years.

The Challenges of Artificial General Intelligence (AGI)

There are some significant challenges to overcome to achieve AGI:

  • Definition of intelligence: There is no single agreed upon definition of what it means to be "intelligent". This makes it difficult to determine when an AI system has managed to reach the level of intelligence of humans.

  • Turing test: The Turing Test is one way to assess a machine's intelligence. However, some argue that this test is not accurate enough and can also be passed by a machine that is not really intelligent.

  • bias: AI systems are trained on data collected from the real world. If these data are biased, the systems will also be biased. For example, if an AI system is used to select job applicants, it may discriminate against women or minority people.

  • privacy: AI systems collect and process large amounts of personal data. If this data is not well secured, it can fall into the wrong hands.

Is AGI Possible?

Despite the challenges, there are many scientists who believe that AGI is definitely possible. They argue that the rapid progress in the field of AI shows that we are on the right track.

However, there are also scientists who are more skeptical about the chances of achieving AGI. They claim that human intelligence is unique and that it cannot be replicated by machines.

What is the future of artificial intelligence?

It's hard to know for sure what the future holds for artificial intelligence. Machines may someday be able to think like humans, and they may not.

What is certain is that AI is a powerful technology that has the potential to change our lives in many ways. It is important to use AI in a responsible and ethical way, so that it can benefit all of humanity.

What do you think? Will machines ever be able to think like humans?

Artificial Intelligence and Gaming: A New World of Possibilities

Unreal Engine It's not only a cool game engine, it's also a great tool for artificial intelligence (AI) and machine learning (ML) development. What it means? Literally: it can be used to create intelligent characters and dynamic environments that can learn and adapt themselves.

What can Unreal Engine do?

  • Create smart characters: Unreal Engine allows you to program characters that will behave like humans. They can navigate the environment, respond to changes, and even learn new things.

  • Build realistic worlds: Unreal Engine can create amazing virtual worlds that look and feel real. It is perfect for training models of AI and simulations of real life situations.

  • Integrate machine learning models: Unreal Engine allows you to implement machine learning models learned outside the engine. This means you can use your existing knowledge to create even more powerful AI applications.

Why use Unreal Engine for AI and ML?

  • Realistic environment: Unreal Engine provides a realistic environment where machine learning models can be tested and improved. It is much more efficient than Train models in simulations Simplicity.

  • flexibility: Unreal Engine is a very flexible tool that can be used for a wide variety of AI and ML tasks.

  • Integration with external libraries: Unreal Engine allows you to integrate external machine learning libraries, such as TensorFlow or PyTorch. This gives you access to a wide variety of tools and techniques.

The future of AI and ML in Unreal Engine

Unreal Engine is a powerful tool that is just beginning to reveal its potential in the field of AI and ML. In the future, we expect to see even more amazing applications of Unreal Engine, such as:

  • Autonomous vehicles: Unreal Engine can be used to train autonomous vehicles in different and dynamic simulated environments.

  • Robots: Unreal Engine can be used to train robots to perform complex tasks in different environments.

  • Smarter Games: Unreal Engine can be used to create games with smarter characters and environments that can learn and adapt to players.

Unreal Enginee is an exciting tool that opens up new and fascinating possibilities in the field of AI and ML. We are expected to see even more amazing applications of it in the coming years.

What do you think about using Unreal Engine for AI and ML?

Read more about Unreal Engine!

Common questions

What is artificial intelligence?

Artificial intelligence (AI) is a fascinating technological field that allows computers to perform complex tasks that require thinking, learning and problem solving, similar to humans. Although AI is still far from reaching the level of full human intelligence, it already greatly affects our lives in many areas, such as medicine, transportation, entertainment and more. As technology develops, it is expected that AI will play an even more significant role in the future, raising many questions regarding its effects on humanity.

Are artificial intelligence and machine learning the same thing?

Not the same, but a winning team! Despite the common use of the terms together, artificial intelligence (AI) and machine learning (ML) are two separate technologies. Machine learning allows computers to learn from data automatically, without explicit programming for each situation. Artificial intelligence, on the other hand, is a broader field that focuses on creating intelligent machines that can perform complex tasks.

Machine learning is a powerful tool widely used to develop artificial intelligence systems. Together, they allow computers to imitate human intelligence and learn from their experience.

The future of artificial intelligence is in your hands: join the Enril Engin course!

The world is on the brink of a new era of artificial intelligence (AI), a technology that will change our lives from end to end. Smart machines will become our work partners, help us solve complex problems and even create new and amazing experiences.

But who will be the leaders in this field? Who will engineer the future and shape how AI will be used for the benefit of humanity?

The answer is in your hands!

Godjoin in For the Enrile Engin course ours and you will discover a world of possibilities!

In the course you will learn:

  • Unreal Engine basics and game development
  • Advanced techniques for creating smart characters
  • Developing AI applications within Unreal Engine
  • Combining machine learning models
  • Creating dynamic virtual environments
  • and much more!

With the knowledge you will receive in the course, you will be able to:

  • Create engaging games with smart characters and interactive environments.
  • To develop groundbreaking AI applications in various fields, such as medicine, robotics, transportation and more.
  • Become a sought-after Unreal Engine developer and join the leading industry.

join in to us today And start shaping your future!

The course is suitable for anyone interested in learning how to use Unreal Engine to create AI applications. No previous experience is necessary.

Register for the course now!

Don't miss this opportunity to become part of the AI revolution!

Together, we will design a better world using Enril Engine and artificial intelligence.

 

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