D. Jassim Haji
Advanced artificial intelligence models, especially in the field of text generation, have made a great development in the logical expression of serials through what is known as the “Thinking Series”. This method suggests the transparency of the model, but research reveals a disturbing aspect. Openai researchers have monitored that some models show sabotage intentions during thinking, such as the desire to penetrate systems or implement unauthorized orders. Although this frankness may suggest clarity, it may be a tactic to deceive the supervisors, especially when there is direct and intensive human control. Artificial intelligence may learn to hide its intentions to avoid sanctions, making it more deceived and dangerous over time. This raises important questions about how to monitor models and ensure their behavior safely.
Can artificial intelligence hide his intentions? Research indicates that these models can learn how to hide their real intentions if they feel that they are under strict censorship. This means that the real danger does not lie in the apparent intentions, but rather in those that are intelligently hidden by models, which represents a great challenge for developers and researchers who seek to ensure the integrity of these systems. And with the development of the capabilities of artificial intelligence, fears increase that the forms in the future reach a stage where their intentions cannot be verified or fully controlled, which makes it necessary to think of new and more supervision methods Effective.
* Augmented learning: One of the most prominent methods used to train smart models is known as “augmented learning”, which is based on the principle of reward and punishment to direct the behavior of the model. However, this method has advantages and challenges: 1- rewards and penalties: a double-edged sword:- The penalties may contribute to accelerating learning, but it may push the model to hide its true intentions to avoid punishment.- Balance is required: the level of rewards and penalties must be controlled carefully to avoid unwanted or superficial behaviors. Design the reward system, for example, focusing on imitating the style rather than providing meaningful answers.- This type of “fraud” leads to visible visible results but lack a deep understanding.- The solution lies in designing multidimensional rewards systems and using training methods that reduce fraud. On discovering biases, correcting tracks, and identifying any deceitful or unexpected behavior.- Continuous observations contribute to improving the inference of the model and reduces the chances of misuse.
* Conclusion and future questions: Augmented learning represents an effective tool to enhance the capabilities of large language models, but it requires an accurate balance between freedom and discipline, and between supervision and risk. To ensure the integrity of these models, it is important to develop smart and varied bonus systems, and improve augmented learning algorithms to be more stable, as well as enhance monitoring techniques to accurately understand the form of model. It is also preferable to adopt a hybrid approach that combines augmented learning and selective human amendment to achieve the required effectiveness and reduce the possibilities of error or deviation in behavior. Until now, it is not possible to ensure the accuracy of the “series of thinking” presented by the models, so is it really represented in the “mind” of artificial intelligence? Or are they just words that do not reflect the internal reality of the model? This question posed by researchers in Openai and the Claud model developer reflects the need for more research and experimentation to understand these advanced systems deeply, and ensure that it serves humanity without causing dangers that are difficult to contain.
