Supervised, without supervision or reinforcement

Any Android device or AI is only intelligent as long as we use it. This interaction can take the form of construction that human intelligence intends to produce but cannot do. For example, think about your activities and social networks.

The more you interact with them, the brighter they become. If machines can learn or process memories, can they also dream, see illusions, recall involuntarily, or connect the dreams of several people?

Does AI in the 21st century means unforgettable, and if so, is it not the most revolutionary technology we have experienced in our centuries-long media history?

There are many ways to build self-learning programs, but they all work based on three basic types of machine learning: observer learning, supervised learning, and reinforcement learning. Learning without an observer is a good option for analyzing all profiles to find similarities and valuable patterns. For example, certain patients may have similar symptoms in the medical world, or a particular treatment may have several specific side effects.

This model-wide search approach can identify similarities between patient profiles and find emerging patterns without human guidance.

 To clarify the discussion, let’s imagine that doctors are looking for more specific information. Doctors want to develop an algorithm for diagnosing a particular condition. They begin by gathering two sets of data, including medical images and test results from healthy patients and patients with that specific condition.

They then enter the above information into a program designed to identify the characteristics of sick patients rather than healthy patients. The program assigns values ​​to those diagnostic features and creates an algorithm to diagnose future patients based on the number of times these specific features are observed. However, unlike unsupervised learning, physicians and scientists have an active role to play in future events.

 Physicians will make the final diagnosis and check the accuracy of the algorithm predictions. Scientists can then use the updated dataset to adjust program parameters and improve accuracy.

This practical approach is called supervised learning.

 The program assigns values ​​to those diagnostic features and creates an algorithm to diagnose future patients based on the number of times these specific features are observed. However, unlike unsupervised learning, physicians and scientists have an active role to play in future events.

Physicians will make the final diagnosis and check the accuracy of the algorithm predictions. Scientists can then use the updated dataset to adjust program parameters and improve accuracy.

This practical approach is called supervised learning. The program assigns values ​​to those diagnostic features and creates an algorithm to diagnose future patients based on the number of times these specific features are observed.

 However, unlike unsupervised learning, physicians and scientists have an active role to play in future events. Physicians will make the final diagnosis and check the accuracy of the algorithm predictions. Scientists can then use the updated dataset to adjust program parameters and improve accuracy.

This practical approach is called supervised learning.

Now let’s say these doctors plan to design another algorithm to recommend treatment programs. Because these programs run in several stages and may vary depending on each individual’s response to treatment, physicians decide to use reinforcement learning. The program uses an iterative approach to gathering feedback on the most effective drugs, doses, and medicines.

It then compares the data with each patient’s profile to create a specific and optimal treatment plan. As the treatment progresses and the program receives more feedback, it can continuously update the schedule for each patient.

None of these three techniques is inherently more intelligent than the others. While some more or less require human intervention, they all have their strengths and weaknesses that make them more suitable for specific tasks.

However, researchers can build sophisticated artificial intelligence systems with separate programs that can monitor and train each other by using them together. For example, when our unsupervised learning program detects similar groups of patients, it can send that data to a supervised learning program.

The program can then include this information in its forecasts. 

Or perhaps dozens of reinforcement learning programs may simulate patients’ potential outcomes to gather feedback on various treatment plans.

There are countless ways to create these machine learning systems, and perhaps the most promising models are those that mimic the relationship between neurons in the brain. These artificial neural networks can use millions of connections to cope with complex tasks such as image recognition, speech recognition, and even language translation.

However, the more self-guided these models become, the more difficult it will be for computer scientists to figure out how these self-taught algorithms get to their solutions. Researchers are now looking for ways to make machine learning more transparent.

 But as AI becomes more involved in our daily lives, these mysterious decisions have an increasing impact on our work, health, and safety. So as machines continue to learn to study, negotiate, and communicate, we need to consider how to teach each other morality.

Artificial intelligence in the world of painting

Now that we are somewhat familiar with how artificial intelligence works and how it is learned, it is time to see what effect artificial intelligence will have on the art world. The fact is that intelligent algorithms use data as pigments and try to do what artists do with paints and brushes by combining binary numbers. The question now is, can data be turned into a stain?

It is the first question that painters ask. Data can only be transformed into knowledge when experienced, and what is inside and experience can take many forms. When exploring such connections through the vast potential of machine intelligence, we think of the relationship between the human senses and the capacity of machines to simulate nature.

Digital drawings take the form of pictorial sequences based on hidden data sets collected from sensors.

 It is then possible to use intelligent algorithms to convert wind speed, intensity, and direction into a spatial data pigment. The result of an imaginary experience is also based on speculation. This dynamic visual art form of data is an attempt to mimic the ability of humans to re-imagine natural events.

Radar sets from the seas are using high-frequency, it is possible to collect accurate data from the sea surface, and its dynamic movement is possible with predictive machine intelligence.

Research on artificial intelligence evolves every day, feeling connected to a more extensive and innovative system than ourselves. This dynamic visual art form of data is an attempt to mimic the ability of humans to re-imagine natural events. Radar sets from the seas are using high-frequency, it is possible to collect accurate data from the sea surface, and its dynamic movement is possible with predictive machine intelligence.

Research on artificial intelligence evolves every day, feeling connected to a more extensive and innovative system than ourselves. This dynamic visual art form of data is an attempt to mimic the ability of humans to re-imagine natural events.

 Radar sets from the seas are using high-frequency, it is possible to collect accurate data from the sea surface, and its dynamic movement is possible with predictive machine intelligence.

Research on artificial intelligence evolves every day, feeling connected to a more extensive and innovative system than ourselves.

For example, in a 2017 research project in Turkey, researchers began digitizing a free source library of cultural documents in Istanbul called the Arzu Archive, one of the world’s first public layouts based on artificial intelligence.

In this project, artificial intelligence explored about 7.1 million documents that are 270 years old. During this process, one of the research group’s inspirations was a short story called The Library of Babel by Argentine author Jorge Luis Borges.

In the story, the author imagines the world as a vast library of all possible 410-page books with a specific format and character set. Through this inspiring image, the research team devised a way to physically explore the vast archives of knowledge in the age of machine intelligence.

The result was a user-centered immersive space. The Arezoo Archive was able to profoundly transform the library experience in the age of machine intelligence.

An important point that AI professionals should pay attention to is that innovative projects focus more on recall and knowledge transfer. In the field of visual arts, you should pay attention to the fact that memories are not static but changing interpretations of past events.

 So you have to look at how machines can mimic subconscious events like dreaming, remembering, and hallucinations.

Any Android device or AI is only intelligent as long as we use it. This interaction can take the form of construction that human intelligence intends to produce but cannot do. For example, think about your activities and social networks.

The more you interact with them, the brighter they become. If machines can learn or process memories, can they also dream, see illusions, recall involuntarily, or connect the dreams of several people?

 Does AI in the 21st century means unforgettable, and if so, is it not the most revolutionary technology we have experienced in our centuries-long media history?

There are many ways to build self-learning programs, but they all work based on three basic types of machine learning: observer learning, supervised learning, and reinforcement learning. Learning without an observer is a good option for analyzing all profiles to find similarities and valuable patterns.

 For example, certain patients may have similar symptoms in the medical world, or a particular treatment may have several specific side effects.

This model-wide search approach can identify similarities between patient profiles and find emerging patterns without human guidance. To clarify the discussion, let’s imagine that doctors are looking for more specific information.

Doctors want to develop an algorithm for diagnosing a specific condition.

 They begin by gathering two sets of information, including medical images and test results from healthy patients and patients with that particular condition. They then enter the above information into a program designed to identify the characteristics of sick patients rather than healthy patients.

The program assigns values ​​to those diagnostic features and creates an algorithm to diagnose future patients based on the number of times these specific features are observed. However, unlike unsupervised learning, physicians and scientists have an active role to play in future events.

Physicians will make the final diagnosis and check the accuracy of the algorithm predictions. Scientists can then use the updated dataset to adjust program parameters and improve accuracy. This practical approach is called supervised learning.

 The program assigns values ​​to those diagnostic features and creates an algorithm to diagnose future patients based on the number of times these specific features are observed.

However, unlike unsupervised learning, physicians and scientists have an active role to play in future events.

Physicians will make the final diagnosis and check the accuracy of the algorithm predictions. 

Scientists can then use the updated dataset to adjust program parameters and improve accuracy. This practical approach is called supervised learning. The program assigns values ​​to those diagnostic features and creates an algorithm to diagnose future patients based on the number of times these specific features are observed. However, unlike unsupervised learning, physicians and scientists have an active role to play in future events.

Physicians will make the final diagnosis and check the accuracy of the algorithm predictions. Scientists can then use the updated dataset to adjust program parameters and improve accuracy. This practical approach is called supervised learning.

Now let’s say these doctors plan to design another algorithm to recommend treatment programs. Because these programs run in several stages and may vary depending on each individual’s response to treatment, physicians decide to use reinforcement learning.

The program uses an iterative approach to gathering feedback on the most effective drugs, doses, and treatments.

It then compares the data with each patient’s profile to create a specific and optimal treatment plan.

 As the treatment progresses and the program receives more feedback, it can continuously update the schedule for each patient. None of these three techniques is inherently more intelligent than the others. While some more or less require human intervention, they all have their strengths and weaknesses that make them more suitable for specific tasks.

 However, researchers can build sophisticated artificial intelligence systems with separate programs that can monitor and train each other by using them together.

For example, when our unsupervised learning program detects similar groups of patients, it can send that data to a supervised learning program. The program can then include this information in its forecasts. Or perhaps dozens of reinforcement learning programs may simulate patients’ potential outcomes to gather feedback on various treatment plans.

There are countless ways to create these machine learning systems, and perhaps the most promising models are those that mimic the relationship between neurons in the brain. 

These artificial neural networks can use millions of connections to cope with complex tasks such as image recognition, speech recognition, and even language translation.

However, the more self-guided these models become, the more difficult it will be for computer scientists to figure out how these self-taught algorithms get to their solutions. Researchers are now looking for ways to make machine learning more transparent.

 But as AI becomes more involved in our daily lives, these mysterious decisions have an increasing impact on our work, health, and safety. So as machines continue to learn to study, negotiate, and communicate, we need to consider how to teach each other morality.

Artificial intelligence in the world of painting

Now that we are somewhat familiar with how artificial intelligence works and how it is learned, it is time to see what effect artificial intelligence will have on the art world. The fact is that intelligent algorithms use data as pigments and try to do what artists do with paints and brushes by combining binary numbers. The question now is, can data be turned into a stain?

It is the first question that painters ask. Data can only be transformed into knowledge when experienced, and what is inside and experience can take many forms. When exploring such connections through the vast potential of machine intelligence, we think of the relationship between the human senses and the capacity of machines to simulate nature.

Digital drawings take the form of pictorial sequences based on hidden data sets collected from sensors. It is then possible to use intelligent algorithms to convert wind speed, intensity, and direction into a spatial data pigment.

 The result of an imaginary experience is also based on speculation. 

This dynamic visual art form of data is an attempt to mimic the ability of humans to re-imagine natural events. Radar sets from the seas are using high-frequency, it is possible to collect accurate data from the sea surface, and its dynamic movement is possible with predictive machine intelligence.

Research on artificial intelligence evolves every day, feeling connected to a more extensive and innovative system than ourselves. This dynamic visual art form of data is an attempt to mimic the ability of humans to re-imagine natural events.

 Radar sets from the seas are using high-frequency, it is possible to collect accurate data from the sea surface, and its dynamic movement is possible with predictive machine intelligence.

Research on artificial intelligence evolves every day, feeling connected to a more extensive and innovative system than ourselves. This dynamic visual art form of data is an attempt to mimic the ability of humans to re-imagine natural events.

  Radar sets from the seas are using high-frequency, it is possible to collect accurate data from the sea surface, and its dynamic movement is possible with predictive machine intelligence.

 Research on artificial intelligence evolves every day, feeling connected to a more extensive and innovative system than ourselves.

For example, in a 2017 research project in Turkey, researchers began digitizing a free source library of cultural documents in Istanbul called the Arzu Archive, one of the world’s first public layouts based on artificial intelligence. In this project, artificial intelligence explored about 7.1 million documents that are 270 years old.

 During this process, one of the research group’s inspirations was a short story called The Library of Babel by Argentine author Jorge Luis Borges. In the story, the author imagines the world as a vast library of all possible 410-page books with a specific format and character set.

Through this inspiring image, the research team devised a way to physically explore the vast archives of knowledge in the age of machine intelligence. The result was a user-centered immersive space.

The Arezoo Archive was able to profoundly transform the library experience in the age of machine intelligence.

An important point that AI professionals should pay attention to is that innovative projects focus more on recall and knowledge transfer. In the field of visual arts, you should pay attention to the fact that memories are not static but changing interpretations of past events. So you have to look at how machines can mimic subconscious events like dreaming, remembering, and hallucinations.