How to make chat and deepsek causes like AI Chatbots

In September, Openai unveiled a new version of Chatgpt designed for logic through mathematics, science and computer programming. Unlike the previous versions of the chatbot, this new technique can spend “thinking” time through complex problems before settling on one answer.

Soon, the company stated that its new logic technology has improved the major systems of the industry on a series of tests that track the progress of artificial intelligence.

Now other companies, such as Google, Anthropic and China’s lamps, offer similar techniques.

But can AI really be a human -like reason? What does it mean to think for a computer? Are these systems really contact with true intelligence?

There is a guide here.

What does it mean when the AI ​​system is the cause of the AI ​​system?

Argument means that the chatbot spends some extra time to work on a problem.

“The argument is that when the system works additional after the question of questions,” said Dan Klein, Chief Technology Officer of California’s University, Burkeley, and Scalled Cognition, Chief Technology Officer of AI Start-Up.

It can break a problem in individual stages or try to solve it through testing and error.

The original chat immediately answered the questions. The new logic system can work through a problem for several seconds – or even minutes – before responding.

Can you be more specific?

In some cases, a logic system will refine its approach to a question, repeatedly trying to improve the method she has chosen. Other times, it can try many different methods of getting closer to a problem before settling on one of them. Or it can go back and check some tasks that did a few seconds ago, just to see if it was right.

Originally, the system tries to answer your question.

It is like a student of a grade school who is struggling to find a way to solve the math problem and scribes several different options on a sheet of paper.

What kind of questions require an AI system?

This may be a reason about anything. But logic is most effective when you ask questions related to mathematics, science and computer programming.

How is an argument chatbot separate from the first chatbot?

You could first ask chatbots to show how they could reach a particular answer or check their work. Because the original chat had learned from the text on the Internet, where people showed how they found for an answer or checked their own work, it can also do such self-confidence.

But a logic system moves forward. It can do such things without asking. And this can make them more wider and complex ways.

Companies call it an argument system because it seems that it operates more like a person thinking through a difficult problem.

Why is AI logic important now?

Companies like Openai believe that this is the best way to improve their chatbott.

For years, these companies rely on a simple concept: the more internet data they pumped into their chatbots, the better those systems performed.

But in 2024, he used almost all the lessons on the Internet.

This means that they needed a new way to improve their chatbott. So they started building arguments systems.

How do you build an argument system?

Last year, companies like Openi began to overshadow a technique learning reinforcement.

Through this process – which can extend for months – an AI system can learn behavior through extensive testing and error. For example, by working through thousands of mathematics problems, it can learn which methods lead to the correct north and which are not.

Researchers have designed complex reaction mechanisms that show the system when they have done something right and when it has done something wrong.

“This is like training a dog,” said Jerry Tworec, an OpenEE researcher. “If the system does good, you give it a cookie. If it does not do well, you say, ‘Bad dog.”

(New York Times sued Microsoft for copyright violation of news material related to AI system in December.)

Does reinforcement work?

It works very well in some areas, such as mathematics, science and computer programming. These are areas where companies can clearly define good behavior and evil. There are fixed answers to mathematics problems.

Learning reinforcement does not work even in areas such as creative writing, philosophy and morality, where the difference between good and evil is difficult to pinch. Researchers say that this process can usually improve the performance of the AI ​​system, even when it answers questions outside mathematics and science.

“It gradually learns that the patterns of logic take it in the right direction and who do not,” said Jerid Council, Chief Science Officer of Anthropic.

Is learning reinforcement and logic system the same thing?

No. Learning reinforcement is the method that companies use for the creation of logic systems. This is the training phase that eventually allows chatbots to argue.

Do these arguments still make mistakes?

Absolutely. A chatbot is based on everything possibilities. It chooses a path that has learned the most from that data – whether the data has come from the Internet or has originated through learning reinforcement. Sometimes it chooses an option that is wrong or does not understand.

Is this the way to a machine that matches human intelligence?

AI experts are divided on this question. These methods are still relatively new, and researchers are still trying to understand their boundaries. In the AI ​​region, new methods often progress very quickly before it slows down.