Humans are distinguished by their ability to analyze various options before making decisions, both when planning a trip and solving a problem. We simulate multiple scenarios in our mind, weigh the pros and cons, and adjust our decisions accordingly. Researchers are seeking to consolidate this ability in artificial intelligence models, allowing them to conduct structured thought processes. Simulated reasoning in the field of artificial intelligence refers to the ability of linguistic models to make multiple inferences before providing an answer, rather than relying solely on retrieving stored data. It also refers to the ability of intelligent systems to simulate human thinking in making decisions or solving problems. For example, if AI models are asked to solve a mathematical problem, the traditional model will rely on previous patterns and provide a quick answer without checking its correctness. As for the model using simulated thinking, it will analyze and solve the problem step by step, look for errors, verify the correctness of the solution before providing the final answer. The technique of chain thinking.. Teaching artificial intelligence to think step by step
In order for an AI model to become capable of simulated thinking as humans do, it must analyze complex problems in sequential stages. And here the role of the chain-of-Thought – CoT technique appears. How does the thinking chain technique (CoT) work?
CoT is an instructional technique that helps linguistic models solve problems in an orderly manner instead of reaching a quick conclusion, and this technique makes it possible to break down claims into smaller steps and then gradually process them. For example, when solving a mathematical problem, the traditional model tries to find a match with previous examples of training data and gives a similar answer. As for the model using the CoT technique, it identifies each step of the solution, makes calculations logically, and then reaches the final solution. This technique is useful in areas that require logical reasoning, solving multi-step problems, and understanding complex contexts. While traditional models require human input to develop thought chains, advanced models such as O3 and R1 are able to learn and apply this technique automatically. How do linguistic models use simulated thinking?
Different linguistic models rely on multiple strategies to implement simulated thinking processes, and below we will explain the general idea of the methods adopted by some modern models, namely: the O3 model from OpenAI, the R1 from DeepSeek: 1-the O3 model of OpenAI.. He analyzes probabilities like a chess player
The exact details of how the O3 model works have not yet been announced, but it is believed that it uses a technique similar to a technique called (Monte Carlo Tree Search (MCTS), a strategy used in artificial intelligence models intended for games that require analysis and logical thinking, such as chess. This model is similar to a chess player who analyzes several possible moves before making their final decision. The model detects multiple solutions, evaluates their quality, and then chooses the most efficient solution. This method allows him to correct errors in the process of thinking, which makes him accurate in analysis and problem solving, but this requires large computational resources, which makes it slower and more expensive than other models. 2-model R1 from DeepSeek.. Learning from experience like a student
The DeepSeek-R1 model uses an enhanced learning approach (Reinforcement Learning), which allows it to develop its deductive abilities over time, similar to a student who gradually improves by solving exercises and receiving feedback. The future of thinking in artificial intelligence models
Simulated thinking represents an important advance towards the development of more accurate and reliable artificial intelligence models. As these models continue to evolve, it is expected that their abilities to analyze complex problems, correct errors, to ensure the correctness of conclusions will improve. And in the future, it is expected to develop artificial intelligence systems that think as wisely and accurately as human experts.