Education scientist have embraced the change of focus form a front loaded teaching format to a focus on the learning process of pupils or students. Computer scientists have adopted this strategy in shifting from just knowledge data bases and predictions of likely next words in a sentence or paragraph to learning models. Deep Seek has surprised most large language models by its successful strategy to focus on learning and reasoning. So-called reinforcement learning is key to the programming of next generation AI models. Reasoning in most cases builds on multiple step sequences in answering a more complex question. The model then returns the answer and the steps (reasoning) applied. There is a debate whether summaries or translations of texts would need the reasoning function of AI models. Most of the time reasoning might not be necessary or even counterproductive, if the translation would try to correct an obviously faulty reasoning in a text.
Maybe, imagine also that an ordinary LLM would translate a text containing fake news. A correction loop which involves a cross-checking with reliable external sources like any encyclopedia or wikipedia would complicate the answering procedure of any text. However, this is a bit like, how the process of reinforcement learning with human feedback (RLHF) works. Reinforcement learning applies a form of accuracy reward, which guides the learning or answering process with checks against mathematical or programming accuracy. Just think of basic logic to be respected in the answer.
Similarly, a formal accuracy control checks against mathematical models and ensures the answer is returning a text with a normal sentence structure or numbering of reasoning steps, an intro and concluding phrase, like we were all asked to do in school or universities. The amount of corrections from humans is reduced quite a lot and the computing resources are also only a fraction of the previous LLMs, which are retrieving answers from enormous databases or gigantic data factories consuming lots of energy in the processing of requests. Remember the movie on Kasparov, the world chess champion, who got beaten by a computer from IBM that did not only have a huge stock of previous games and tournaments, but could make judgments on positions and promising strategies to pursue. Don’t be surprised if a DeepSeek answer is superior to what our own mind and reasoning is capable of. Reinforcement learning is a learning tool, which we also may apply, if we deem it appropriate or just as one way of coming to an answer. (useful reference: Sebastian Raschka, Building a LLM from scratch, Manning).
(Image, ChatGPT, 2 humanoid robots are thinking and discussing how to repair a notebook which is sitting on a workbench). 
(See image). ChatGPT provides a more careful definition as the “crowd” or networked intelligence of Wikipedia. AI only “refers to the simulation” of HI processes by machines”. Examples of such HI processes include the solving of problems and understanding of language. In doing this AI creates systems and performs tasks that usually or until now required HI. There seems to be a technological openness embedded in the definition of AI by AI that is not bound to legal restrictions of its use. The learning systems approach might or might not allow to respect the restrictions set to the systems by HI. Or, do such systems also learn how to circumvent the restrictions set by HI systems to limit AI systems? For the time being we test the boundaries of such systems in multiple fields of application from autonomous driving systems, video surveillance, marketing tools or public services. Potentials as well as risks will be defined in more detail in this process of technological development. Society has to accompany this process with high priority since fundamental human rights are at issue. Potentials for assistance of humans are equally large. The balance will be crucial.
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