Best Introduction To Artificial Intelligence

Best Introduction To Artificial Intelligence – We have seen Machine Learning as an explanation in recent years, the reason for this can be the high amount of data production from applications, the increase in computing power in recent years and the development better algorithms.

Machine learning is used in everything from automating mundane tasks to providing intelligent information, businesses in all sectors are trying to succeed eat You may be using a used device. For example, a fitness tracker like Fitbit, or a smart home assistant like Google Home. But there are many other examples of ML being used.

Best Introduction To Artificial Intelligence

The 1940s saw the creation of the first hand-operated computer, ENIAC (Electronic Numerical Integrator and Computer), was created. At that time, the word “computer” was used as a name for a person with strong digital computing skills, thus, ENIAC was called a digital computer! Well, can you say it has nothing to do with learning?! WRONG, from the beginning the idea was to build a machine that could copy human thought and learning.

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In the 1950s, we see the first computer game program that claims to be able to win the world championship. This app has really helped chess players to improve their skills! At the same time, Frank Rosenblatt created the Perceptron as a very simple classification but when combined in large numbers, in a network, it became a powerful monster. Well, the monster is about time and at that time, it’s a real success. Then we see many years of failure of the neural network because of its difficulties in solving certain problems.

Thanks to data, machine learning became popular in the 1990s. The combination of computer and data analysis has given rise to special approaches to AI. This further shifted the field towards information processing methods. With the large amount of data available, scientists began to develop special systems that can search and learn from large amounts of data. In an impressive feat, IBM’s Deep Blue defeated the reigning world champion, world champion Garry Kasparov. Yes, I know Kasparov accused IBM of cheating, but this is part of history now and Deep Blue rests quietly in a museum.

According to Arthur Samuel, machine learning algorithms allow computers to learn from data, and improve themselves, without being clearly structured.

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Machine learning (ML) is a part of the algorithm that allows the software to be more accurate in predicting the results without the obvious setting. The main purpose of machine learning is to build algorithms that can find information and use data to predict the process by updating the results when new information is received.

In Search learning, an AI system is provided with data to be coded, meaning each data is coded with the code that was ‘zero.

The purpose is to calculate the map function so that you have a new data (x), you can see the output variable (Y) for that data .

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As shown in the example above, we first took some comments and marked them as ‘Spam’ or ‘Not Spam’. This recorded data is used by the supervised learning model, this information is used to train the model.

After learning, we can test our model by searching for new email tests and check the model that can predict the correct answer.

In unsupervised learning, an AI system is presented with unconfirmed, unclassified data and the system’s algorithms are applied to the data without prior training. The operation depends on the coded algorithms. Giving a system to unsupervised learning is one way to test AI.

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In the example above, we have given some letters in our example as ‘Ducks’ and ‘Not Ducks’. In our educational information, we do not give any name to the same information. The unsupervised model can distinguish all characters by looking at the type of data and images. Click the link below or browse the data to learn more.

A forced algorithm, or agent, learns by interacting with its environment. The agent receives rewards for doing it right and punishments for doing it wrong. The agent learns without human intervention by increasing its reward and decreasing its penalty. It is a type of dynamic programming that trains algorithms using a system of rewards and punishments.

In the example above, we can see that the agent is given 2 options. through water or through fire. Forced algorithm works on fabric compensation system. if the agent uses the fire path then the rewards are deducted and the agent tries to know that the fire path should be avoided. If he chooses the tunnel or the safe way, then some points are added to the score, and then the candidate tries to find out which way is safe and the what is not.

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Using the values ​​obtained, the agent improves its environment to choose the next action.

In this blog, I introduced you to the basic concepts of machine learning and I hope this blog helped and inspired you to be interested in the subject. a long way The world, say a hundred years ago, was very dependent on the action of the hand. Simple tasks like arithmetic operations were also time-consuming and tedious. Recognizing this difficulty, various technologies were introduced that can perform difficult calculations.

These technologies are growing rapidly and it is not long before the world realizes its potential. Calculations are faster and more accurate. Such technologies have seen successful implementation in various sectors including research and development, defense, health, business, and others.

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But no matter how good these machines were, there was still a lack of “Intelligence”. Computers can be reliable, accurate and a million times better than humans but they are “stupid machines”.

Artificial Intelligence is an idea that is more advanced than any other and aims to make machines that can learn and respond on their own.

Although the term Artificial Intelligence has been around for more than 5 decades, it has only been 2 decades since the world realized its great potential. Artificial Intelligence has many applications in areas such as Natural Language Processing, Simulations, Robotics and Speech Recognition to name a few.

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Although the potential of Artificial Intelligence and its applications have been realized, but due to the complexity involved, the progress in this field, as of now, is only to maintain the use of the Weak Artificial Intelligence Systems are also called Narrow Artificial Intelligence Systems.

There is still development in the field of Artificial Intelligence and the growth is exponential. Today, Artificial Intelligence is everywhere. From Google to Facebook and Marketing to Learning, Information Technology is at the forefront.

Known as “Language Production” among Psycholinguists, Natural Language Generation is a process that aims to transform any structured data into natural languages. In layman’s terms, natural language processing can be thought of as a process that turns thoughts into words.

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For example, when a child sees a butterfly flying in a garden, he can think about it in different ways. Those ideas can be called ideas. But when the child describes his thought process in his natural language (common language), this process can be called the Generation of Natural Language.

Natural Language Understanding is the opposite of Natural Language Generation. This approach is much easier to interpret than Natural Language.

In the example above, if the child is told the baby instead of being shown, he can interpret the information given to him in different ways. According to this definition, the child will draw a picture of a butterfly flying in a garden. If the interpretation is correct, then it can be concluded that the implementation was successful (Natural Language Understanding).

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As the name suggests, Speech Recognition is a technology that uses Artificial Intelligence to convert human speech into a computer. The process is very helpful and acts as a bridge in human-computer interaction.

Using speech recognition technology, computers can understand human speech in many natural languages. This is why the computer can make interactions with people faster and smoother.

For example, let’s say the child was asked in the first example, “How are you?” at normal times person to person. When a child listens to a person’s speech model, he processes the model according to the data (understanding) that is already in his brain.

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The student writes down relevant information and eventually comes up with an idea of ​​what the model is. Therefore, the child can understand the meaning of the speech and respond accordingly.

Machine Learning is another useful technology in the field of Artificial Intelligence. This technology focuses on teaching a machine (computer) to learn and think on its own. Machine learning usually uses many complex algorithms to train the machine.

During the process, the machine receives a set of classified or unclassified training information that is specific or general.

Lecture 1: Introduction And Scope

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